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Glycans play important roles in host-microbe interactions . Tissue-specific expression patterns of the blood group glycosyltransferase β-1 , 4-N-acetylgalactosaminyltransferase 2 ( B4galnt2 ) are variable in wild mouse populations , and loss of B4galnt2 expression is associated with altered intestinal microbiota . We hypothesized that variation in B4galnt2 expression alters susceptibility to intestinal pathogens . To test this , we challenged mice genetically engineered to express different B4galnt2 tissue-specific patterns with a Salmonella Typhimurium infection model . We found B4galnt2 intestinal expression was strongly associated with bacterial community composition and increased Salmonella susceptibility as evidenced by increased intestinal inflammatory cytokines and infiltrating immune cells . Fecal transfer experiments demonstrated a crucial role of the B4galnt2-dependent microbiota in conferring susceptibility to intestinal inflammation , while epithelial B4galnt2 expression facilitated epithelial invasion of S . Typhimurium . These data support a critical role for B4galnt2 in gastrointestinal infections . We speculate that B4galnt2-specific differences in host susceptibility to intestinal pathogens underlie the strong signatures of balancing selection observed at the B4galnt2 locus in wild mouse populations .
The luminal surface of the intestinal mucosa is covered by distinct layers of highly glycosylated mucus that form a physical barrier between the intestinal microbial community and the host’s tissues . In addition to their important roles in host metabolism and signaling , glycans are known to contribute to the composition and physiology of the intestinal microbiota , thereby playing an important role in regulating microbe-host interactions [1] . Host glycans can contribute to a beneficial microenvironment for symbiotic microbes by providing carbohydrate sources or by serving as attachment sites [1–3] , but glycans can in the same way also mediate pathogenic interactions [4 , 5] . The patterns of intestinal carbohydrate structures , which vary along sites of the gastrointestinal tract , are the product of a combination of host glycosyltransferase expression programs as well as microbial influences [6 , 7] . The genes responsible for synthesizing carbohydrate blood group antigens frequently display signatures of balancing selection and are implicated in the co-evolution of hosts and their pathogens [8] . A well-described example is the FUT2 gene , which encodes an α-1 , 2-fucosyltransferase that directs the expression of the H antigen in mucosal tissues and bodily secretions . Homozygosity for loss-of-function FUT2 mutations leads to loss of expression of ABO and H blood group glycans in secretions and is known as the “nonsecretor” phenotype , which is common in human populations [9] . Nonsecretor status has been implicated as a detrimental genetic risk factor for inflammatory disorders such as Crohn’s disease [10] and primary sclerosing cholangitis [11] , while being positively associated with resistance to intestinal pathogens [12–14] . Glycosylation of the epithelium has recently been recognized as a direct immune cell mediated response to infection as a means to restore the protective functions of the microbial community and to ensure tissue homeostasis [15–17] . Glycans also mediate species specificity of pathogens . For example , the different associations of Helicobacter species to Lewis antigens in the canine gastric mucosa [18] . Gastrointestinal ( GI ) expression of the blood group glycosyltransferase β-1 , 4-N-acetylgalactosaminyltransferase 2 ( B4galnt2 ) , which directs biosynthesis of a carbohydrate antigen similar to blood group A termed the Sd ( a ) [19] is conserved across vertebrates [20] . However , in mice there is a common allele which confers a tissue specific switch in B4galnt2 expression from gut to blood vessels [21] . This allele is termed “Modifier of von Willebrand Factor-1” ( Mvwf1 ) [22] because B4galnt2 vascular expression leads to aberrant glycosylation of the vascular-derived blood coagulation factor von Willebrand factor ( VWF ) , resulting in accelerated VWF clearance from circulation [23] . Mvwf1 was first described in the RIIIS/J inbred mouse strain [22] , and subsequent studies revealed RIIIS/J-like B4galnt2 alleles , which confer the B4galnt2 tissue-specific switch from gut ( epithelial ) to blood vessel ( endothelial ) expression , to be common in wild mouse populations [24] . Further , this variation appears to have been maintained in the mouse lineage for several million years despite the presumed detrimental effect of prolonged bleeding time , possibly due to a protective role in host-pathogen interactions [25] . A role for B4galnt2-glycans in intestinal host-microbe interactions is supported by the observation of significant alterations in the intestinal microbiota in B4galnt2-deficient mice [26] . Taken together , the prevalence of alleles conferring the tissue-specific switch in B4galnt2 expression in mice , the strong signatures of selection observed at the B4galnt2 locus in wild mouse populations and the altered resident microbiota found in B4galnt2-deficient mice support the hypothesis that variant tissue-specific B4galnt2 expression alters susceptibility to enteric infections in mice . To investigate the role of variant host B4galnt2 expression in the context of intestinal infection , we challenged mice engineered to express B4galnt2 in various tissue-specific patterns with a mouse model of the intestinal pathogen Salmonella enterica serovar Typhimurium ( S . Typhimurium ) . Prior to- and during the course of infection , we examined histological and molecular markers of inflammation along with bacterial community profiles . We found that the composition of the intestinal microbiota was consistently influenced by the expression of B4galnt2-glycans , and that B4galnt2-associated intestinal microbial community profiles were predictive of- and responsible for susceptibility to S . Typhimurium infection . We demonstrate that mice deficient in intestinal B4galnt2 expression developed significantly less pathology after S . Typhimurium infection , in concert with attenuated induction of pro-inflammatory cytokines and infiltration of immune cells . Furthermore , we find that vascular B4galnt2 expression leads to decreased Salmonella colonization and increased inflammatory cytokine expression . Overall , our study elucidates a new role for this key host carbohydrate blood group antigen in the interplay between the host , commensals , and susceptibility to pathogen infections .
To test the hypothesis that expression of intestinal B4galnt2 glycans influences host susceptibility to enteric pathogens , we used an established model for S . Typhimurium induced colitis [27] . Mice were bred to carry the desired combinations of alleles which express B4galnt2 in the intestinal epithelium ( “B6”: referring to the endogenous C57BL6/J allele ) , vascular endothelium ( “RIII”: referring to the RIIIS/J-derived Mvwf1 bacterial artificial chromosome transgene [21] ) , or lack a functional B4galnt2 gene due to a targeted knock-out allele ( “B6 -/-”: referring to the B4galnt2 knock-out [23] ) . Twenty-four hours after streptomycin pre-treatment , mice were orally infected with S . Typhimurium SL1344 ( “acute” infection , examined after 24 hours [28] ) or the attenuated ΔaroA mutant ( “chronic” infection , examined after 14 days [29] ) . None of the animals showed signs of inflammation or other pathology prior to infection . After infection in both the acute and chronic Salmonella models , mice expressing B4galnt2 in the intestinal epithelium ( B6 +/- / RIII - and B6 +/- / RIII + ) exhibited higher numbers of detached epithelial cells and neutrophils within the cecal lumen , increased inflammatory cell infiltration [29 , 30] within the intestinal mucosa , and worsened submucosal edema in the ceca ( Fig 1A ) . The dramatic reduction of cecum weight in infected B6 +/- mice compared to B6 -/- mice in acute Salmonella infection one day post infection ( p . i . ) indicated more severe disease [27] ( Fig 1B ) . Accordingly , mice that did not express B4galnt2 in the intestinal epithelium ( B6 -/- ) developed significantly less cecal inflammation in both the acute and chronic infection model ( Fig 1C ) . In order to evaluate Salmonella colonization , colony forming units ( CFUs ) were quantified from homogenized ceca . While Salmonella burdens were comparable between different B4galnt2 intestinal epithelial-expressing genotypes ( B6 ) , RIII + ( B4galnt2-endothelial expressing ) animals exhibited lower Salmonella colonization in the acute Salmonella infections ( Fig 1D ) . These results demonstrate a significant influence of intestinal epithelial B4galnt2 expression on susceptibility to Salmonella-induced colitis , and an independent effect of vessel-specific B4galnt2 expression on Salmonella burden . In contrast , infection of mice without prior streptomycin treatment resulted in equal bacterial organ colonization , organ weights , and elicited no intestinal inflammation regardless of the genotype of mice ( S1 Fig ) . Due to the marked differences between mouse B4galnt2 genotypes in the acute infection model , we performed further studies only in this model . B4galnt2-GalNAc residues have been shown to be detectable on the apical surface of intestinal epithelial cells [23 , 26] . Immunohistochemical co-staining with Dolichos biflorus agglutinin ( DBA ) specifically detecting B4galnt2-derived β-1 , 4 linked GalNAc residues [21 , 23] and MUCIN 2 ( MUC2 ) , the major secreted mucus protein in the large intestine , demonstrated a partial co-localization in goblet cells ( Figs 2A and S2A ) . While MUC2 is considered to be glycosylated by B4GALNT2 [31] , GalNAc residues were also detected in the intestinal mucosa of Muc2-deficient mice ( S2B Fig ) , indicating the presence of other B4GALNT2-glycosylated substrates such as glycolipids [32 , 33] and other glycoproteins [34–36] . To determine if B4galnt2-mediated glycosylation altered overall mucus thickness , which could make it easier for bacteria to cross the mucus layer and reach the epithelium , intestinal tissue of uninfected mice were fixed with Carnoy’s fixative , stained with alcian blue and the thickness of the dense inner mucus layer was determined . Although mucus thickness was not significantly affected by the lack of intestinal B4galnt2 expression ( B6 -/- ) , it did show slight differences between RIII + and RIII - ( Fig 2B and 2C ) . Furthermore , less DBA lectin staining was observed in the cecal mucosa of S . Typhimurium infected mice on day one p . i . compared to uninfected mice ( Fig 2D ) . In contrast to the DBA staining ( GalNAc ) , the detection of N-Acetylglucosamine ( GlcNAc ) residues recognized by Wheat Germ Agglutinin ( WGA ) showed no clear difference after infection , suggesting the alteration of mucosal DBA lectin-reactive carbohydrate profiles that occur in response to S . Typhimurium infection did not affect substrates glycosylated by WGA-reactive GlcNAc ( Fig 2D ) . B4galnt2 gene expression was also down regulated upon infection ( Fig 2E ) which further corroborates the lectin staining results . To test the direct effect of B4galnt2 expression on Salmonella’s interaction with the cecal epithelium , we performed both FISH staining of cecal sections 1 day p . i . as well as in vitro experiments with the intestinal epithelial Mode-K cell line and siRNA-mediated knockdown of B4galnt2 expression . Bacteria were stained by FISH using the Gam42a probe , which stains γ-Proteobacteria . In our experience virtually all Gam42a positive bacteria reaching the tissue in the streptomycin model at day 1 p . i . are Salmonella . Bacteria were counted if they were adherent to epithelial cells or invaded into the tissue in ten high power fields per cecal section . While adherent Salmonella were not significantly different in B6 +/- mice compared to B6 -/- mice , significantly more Salmonella were found to have invaded into the tissue of B6 +/- mice ( Fig 3A ) . To further investigate whether B4galnt2 expression influences the interaction of Salmonella with epithelial cells , we used the intestinal epithelial Mode-K cell line and siRNA-mediated knockdown of B4galnt2 ( knockdown efficiency: 96%; Fig 3B ) . Adhesion and invasion assays showed that knockdown of B4galnt2 expression does not significantly influence adhesion of Salmonella to epithelial cells ( Fig 3C ) . However , invasion of S . Typhimurium into B4galnt2-expressing cells is slightly , but significantly increased relative to B4galnt2-knockdown cells ( Fig 3C ) . This data shows that epithelial expression of B4galnt2- both in vitro and in vivo- directly facilitates invasion by Salmonella . We analyzed the transcript levels of pro-inflammatory cytokine genes in cecal tissues both prior to and after S . Typhimurium infection , focusing on those cytokines known to be induced early in Salmonella-triggered inflammation and associated with control of infection [37 , 38] . The transcripts for the cytokines Tumor necrosis factor-α ( Tnf-α ) , Interleukin-6 ( Il-6 ) , Interferon-γ ( Ifn-γ ) and Monocyte chemotactic protein-1 ( Mcp-1 ) were elevated in all mice after infection , but to a significantly higher degree in B6 +/- mice compared to B6 -/- mice one day p . i . ( Fig 4A–4D; Tnf-α: Z = -2 . 123 , P = 0 . 0336; Il-6: Z = -2 . 458 , P = 0 . 0138; Ifn-γ: Z = -2 . 417 , P = 0 . 0147; Mcp-1: Z = -2 . 219 , P = 0 . 0261; Wilcoxon test via Monte-Carlo resampling ) . Protein levels of Lipocalin-2 ( LCN-2 ) , a molecule implicated in antimicrobial defense and innate immunity [39 , 40] , were also increased in cecal tissue homogenates in B6 +/- mice compared to B6 -/- mice after infection ( Fig 4E , S1 Table ) . Furthermore , vascular endothelial B4galnt2 expressing animals ( RIII + ) exhibited increased Il-6 expression ( Z = -1 . 932 , P = 0 . 0528 ) , but decreased LCN-2 production ( S1 Table ) , suggesting a role for vascular B4galnt2 expression in the host immune response to intestinal infection ( Fig 3 ) . We also analyzed cecal tissue sections for the presence of cells positive for CD68 , which is strongly expressed by monocytes and macrophages , and CD3 , which is expressed on mature T cells . Immunohistochemical staining and subsequent quantification of cell numbers revealed no difference in cell numbers according to endothelial B4galnt2 expression ( RIII ) , but significantly fewer CD68 + and CD3 + cells were observed in the cecal tissues of B6 -/- mice ( Figs 5A , 5B and S3A and S1 Table ) after infection . The presence of neutrophils was further investigated by myeloperoxidase ( MPO ) staining . In line with our previous results , B6 -/- had fewer MPO positive cells in the intestinal mucosa ( lumen and edema ) compared to B6 +/- mice ( Figs 4C and S3B ) one day p . i . , which was further quantified by the relative fluorescence signal intensity ( P = 0 . 0001; Fig 4D , S1 Table ) . Overall , we detected significant differences in the abundance of CD68 + and CD3 + cells after infection with respect to the expression of B4galnt2 in the intestinal epithelium , but almost no differences with respect to vascular endothelial expression . To examine the effect of B4galnt2 genotype on the intestinal microbiota in the context of infection , pyrosequencing of the 16S rRNA gene in fecal samples was performed for each individual before and after streptomycin treatment , and after S . Typhimurium infection . This resulted in a total of 122 , 818 sequences , with an average of 998 . 52 ± 13 . 49 SD reads per sample after normalization ( Good’s coverage of OTUs: 92 . 46 ± 9 . 05% SD ) . To obtain a detailed picture of the interaction of microbial communities with host factors , we first assessed within-sample ( alpha ) diversity at multiple complementary levels including species richness ( Chao1 ) , distribution ( Shannon H ) , and two phylogenetic measures including Nearest Taxon Distance ( NTI ) and the Net Relatedness Index ( NRI ) [41] . Species diversity within and between bacterial communities was strongly influenced by the administration of streptomycin and S . Typhimurium ( S4 Fig ) . Prior to streptomycin treatment and infection , the richness and evenness of operational taxonomic units ( OTUs ) show no significant differences according to B4galnt2 genotype ( Fig 6A and 6B , and Table 1 ) in concordance with the results of Staubach et al . [26] . Phylogenetic clustering among close relatives ( i . e . NTI ) is significantly increased in animals with B4galnt2 expression in the endothelium ( RIII + ) , while clustering of large phylogenetic groups ( i . e . NRI ) shows no discernable patterns ( S5 Fig , Table 1 ) . After S . Typhimurium infection , the number of species and the evenness of their distribution showed a clear decrease with inflammation ( Figs 6C and S5C ) . Phylogenetic clustering of deep branches , on the other hand , is only weakly influenced by genotype and inflammation after S . Typhimurium infection ( S5E Fig , Table 1 ) , while terminal phylogenetic clustering ( NTI ) shows a strong negative correlation to inflammation ( S5 Fig , Table 1 ) . In addition , the abundance of S . Typhimurium detected by 16S rRNA gene sequencing is influenced by B6- and RIII genotype , especially the low abundance observed in the RIII +/B6 -/- genotype ( S6 Fig ) , which is consistent with the observations based on colony forming units ( Fig 1D; see above ) . Next , we attempted to determine which aspects of microbial communities may be associated with infection susceptibility by correlating diversity measurements prior to antibiotic treatment to the outcome of infection ( inflammation score , S . Typhimurium load ) . Species richness , distribution , and the amount of phylogenetic clustering displayed a significant relationship to the severity of infection outcome , whereby pathology is predicted with relatively high power ( Table 2 ) . Furthermore , epithelial B4galnt2 expression ( i . e . B6 ) significantly increases predictive power ( Figs 6D and S7 ) and may therefore be an important factor modifying the involvement of the microbiota during pathogenesis . Specifically , species loss ( ΔChao1 ) caused by the streptomycin and S . Typhimurium infection , which is higher in phylogenetically clustered and species rich communities ( ΔChao1~NTI before infection , ρ = -0 . 4216 , P = 0 . 006435 , ΔChao1~Chao1 before infection , ρ = -0 . 9854 , P < 2 . 2 × 10−16; Spearman rank correlation ) may explain why high species diversity before treatment is correlated to a high inflammatory response ( Table 2 ) . Community resistance , measured here as the community turnover ( Δunweighted UniFrac ) between the pre- and post-infection time points , is higher in B6 -/- mice ( i . e . lower Δunweighted UniFrac; Figs 6E , 6F , S8D , S8H and S8L ) and shows a strong positive correlation with inflammation and species diversity ( Figs 6F , 6G and S8 and S2 Table ) . Interestingly , the community turnover between the untreated and streptomycin treated communities ( before infection ) is not associated to the final Salmonella load or severity of inflammation . Thus , B4galnt2 expression in the gut epithelium influences the diversity and resistance of bacterial communities , which in turn is associated with the outcome of infection . Furthermore , these results also underscore the metastable character of highly diverse communities , as was already implied by May in 1972 [42] . To infer whether differences between the bacterial communities of mice with different B4galnt2 expression patterns may contribute to susceptibility , we performed beta diversity analyses . Accordingly , diversity between communities was measured based on different characteristics in untreated animals , including OTU- presence/absence ( Jaccard/JA ) , -abundance ( Bray-Curtis/BC ) and-distribution ( Redundancy Analysis/RDA ) , in addition to the presence/absence- ( unweighted UniFrac/UW-UF ) and abundance of phylogenetic branches ( weighted UniFrac/W-UF ) . This yielded similar community differences with respect to B6 genotype in nearly all measures ( Figs 7A and S9 and S3 Table ) and importantly , confirms the previous findings of Staubach et al . 2012 [26] with the current cohort of mice , which were re-derived and housed in a different animal facility . In addition , the bacterial communities among B6 +/- animals displayed far less inter-individual variation in their community composition than B6 -/- animals ( S9 and S10 Figs ) . Differences in community structure after S . Typhimurium infection were also evaluated and correlated with inflammation score as an additional variable . This showed that differences in communities with respect to B4galnt2 genotype are also present after infection . Furthermore , the communities changed their species composition with increasing inflammation , which appeared to be most prominent in the microbiota of B6 +/- animals ( RDA: B6-F1 , 38 = 3 . 4908 , P = 0 . 0022 , inflammation- F1 , 38 = 5 . 0547 , P = 0 . 0002 , adjusted R2 = 0 . 1406; Figs 7B and S8 and S3 Table ) . Lastly , the inter-individual distance among B6 -/- also remained higher after S . Typhimurium infection ( S9 and S10 Figs ) . To investigate the drivers of community differentiation between B4galnt2 genotypes , we employed indicator species analysis . Before treatment and subsequent infection , several genera and species were associated with B4galnt2 expression ( B6 +/- ) in the gut , including members of the Bacteroidales ( Bacteroides , Prevotella , Prevotellaceae ) and Parasutterella ( Proteobacteria ) , while Turicibacter ( Firmicutes ) and other members of the Bacteroidales ( Barnesiella , Porphyromonas , Porphyromonadaceae ) were indicative of mice lacking B4galnt2 expression in the gut ( B6 -/-; Fig 7C and 7D and S4 and S5 Tables ) . In addition , Turicibacter , Erysipelotrichaceae , and Marvinbryantia ( Firmicutes ) are associated to endothelial expression of B4galnt2 glycans ( RIII +; Fig 7C and 7D and S4 Table ) . To further understand the nature of potential interactions among indicator taxa , we performed a targeted correlation network analysis using Spearman rank correlations of the indicator genera to the remaining community members . Interestingly , the genera displaying differential preferences with respect to B4galnt2 genotype were also negatively correlated with one another , suggesting competitive exclusion mediated by the presence/absence of B4galnt2 glycans ( Turicibacter-Bacteroides: ρ = -0 . 485 , P = 0 . 0013; Turicibacter-uncl . Prevotellaceae: ρ = -0 . 447 , P = 0 . 0034 ) . Further , only Turicibacter , which is an indicator for the lack of B4galnt2 expression in the gut , is directly correlated to the indicators of B6 +/- genotype while uncl . Porphyromonadaceae ( B6 -/- indicator ) are only associated to Turicibacter abundance ( Fig 8A ) . Through this analysis we additionally found Parabacteroides as negatively associated to Bacteroides and Prevotellaceae , suggesting either competition for B4galnt2 glycans or a secondary indicator for their absence ( Fig 8A and S6 Table ) . Furthermore , we detected associations of taxa post infection , such as an overabundance of Salmonella and Cyanobacteria in B6 +/- , and uncl . Bacteroidales and uncl . Firmicutes in B6 -/- mice . Interestingly , we found taxa associated to B4galnt2 expression in the gut overlapping with a previous study by Staubach et al . ( 2012 ) , such as Barnesiella and Porphyromonadaceae ( S4 and S5 Tables ) [26] , which further strengthens the evidence for interactions with B4galnt2 given the independence of these cohorts of mice ( see above ) . Lastly , we explored the dataset for individual taxon associations with inflammation , revealing Turicibacter and Salmonella to be positively associated to inflammation , potentially benefiting from the inflammatory reactions at the epithelial barrier . Other indicators for the absence of B4galnt2 glycans like Parabacteroides or Prophyromonadaceae , however , decline with increasing inflammation ( S7 Table ) . Only the unclassified Erysipelotrichaceae , which are secondary indicators for the absence of B4galnt2 glycans in the epithelium ( see Fig 8A and S6 and S7 Tables ) , are potential probiotic bacteria whose abundance prior to treatment decreases with inflammation ( ρ = -0 . 320 , P = 0 . 0417 ) . The analysis of the complete co-occurrence network revealed strong dependencies among community members before treatment ( Fig 8B ) . Specifically , we found a higher incidence of weak negative interactions ( competition ) , and a low number of very strong positive interactions ( Fig 8B ) . The co-occurrence network after S . Typhimurium infection shows a comparable distribution of positive and negative interactions , as observed before infection ( S11A Fig ) . Further , it reveals the widespread impact of Salmonella ( indicator of B6 +/- ) on the microbial community , as its position is highly central and strongly influences several other highly integrated parts of the community ( S11B Fig ) . In order to determine whether the microbiota composition contributes to the elevated susceptibility of B6 +/- mice to inflammation , we transplanted feces from B6 +/- and B6 -/- donor mice into germfree C57BL/6J ( B6 +/+ ) recipient mice . 21 days post fecal transplantation , mice were treated with streptomycin and 24 hours later infected with S . Typhimurium . Cecum weight and S . Typhimurium colonization ( CFU count ) do not differ significantly between the fecal donor genotypes ( Fig 9A , 9B and 9C ) . However , the extent of tissue inflammation caused by S . Typhimurium infection was significantly lower in mice transplanted with microbiota from B6 -/- mice due to decreased mucosal damage and decreased submucosal edema ( Fig 9A and 9D ) . These results demonstrate that the differences in microbiota composition from B6 +/- and B6 -/- mice are responsible for the lower susceptibility of B6 -/- mice to Salmonella induced inflammation .
Infectious diseases are one of the strongest selective forces on many levels of biological complexity . Over time , a steady cycle of adaptation and counter-adaptation has left molecular traces in the genomes of many organisms including humans [43] . The most prominently affected members are genes associated with the immune system , e . g . MHC [44] , however , others including blood-group-related genes display similar signatures of selection [3 , 8 , 45–47] . In this study , we investigated intestinal infection as a potential driver of selection at B4galnt2 observed in the wild by studying the effect of variant tissue-specific expression of B4galnt2 on host-microbiota interactions and susceptibility to intestinal infection with Salmonella . This revealed strong evidence for the influence of B4galnt2-specific host glycosylation on microbial community composition and a role in pathogen resistance . Our experiments revealed less intestinal pathology , lower inflammatory responses , and changes in microbial community structure and composition in animals lacking B4galnt2 expression in their intestinal epithelium . Host mucosal glycans can directly interact with the microbiota by serving as specific attachment sites or as nutrient sources for some microorganisms . Thus , host mucosal carbohydrates can influence , directly and indirectly , the establishment of overlapping competitive niches , which serve as a barrier against potential pathogens ( i . e . “colonization resistance” ) [48 , 49] . We found B4galnt2-expression-dependent characteristics of the intestinal microbiota , such as species and phylogenetic diversity , which predict the colonization success of S . Typhimurium and the severity of the accompanying intestinal inflammation . In our experiment , species-rich and phylogenetically clustered microbial communities appear to be more vulnerable to Salmonella infection , and ultimately inflammation . Before the seminal works of May and others [42 , 50 , 51] , high diversity habitats were synonymous with high stability and productivity [52 , 53] . However , the diversity-stability debate remains unresolved [54–56] . High diversity only has a stabilizing effect if reactions of community members are asynchronous , which balances the reduction of one species by the complementary increase of other community members [57–59] . This “portfolio”- [60] or “insurance” effect [61] dampens perturbations by a release of inter-species competition , or by differential susceptibility to the environmental stressors [54] . Diverse communities also exhibit an intrinsically higher tendency of community change , as a large number of species ( especially rare species ) are prone to becoming lost through environmental perturbation and stochastic events due to their limited relative abundance [62] . This has been observed in grassland communities , where compositional instability increases with community diversity [63] . Thus , the comparably high number of strong positive interactions in the bacterial communities of this study ( see Fig 8B ) may therefore explain the tendency of exacerbated species loss and inflammation after disturbance , as the stabilizing effects of competitive release are lower [57 , 60 , 64 , 65] . Furthermore , evolutionary relatedness among community members has a strong influence on community reactions and productivity . Closely related species ( e . g . phylogenetically clustered ) presumably overlap in their niches and functional capacities [66 , 67] and react in similar ways to environmental stressors , which dampens the insurance effect ( i . e . “negative insurance effect” ) as observed in the investigated microbial communities [68 , 69] . Antibiotic treatment usually has long lasting effects , but previous studies show that a certain degree of resilience occurs through short-term repopulation of dormant bacteria [49 , 70] . The disturbance in microbial communities appears to be buffered in mice not expressing B4galnt2 glycans in the epithelium , possibly by conferring “colonization resistance” via a higher potential to compete with invading Salmonella and by dampening the effects of community disturbance [67 , 71 , 72] . Thus , in the context of a diminished and disturbed microbial community after streptomycin treatment [73] , it is likely that the more resilient/resistant communities in B6 -/- mice maintain a greater potential for rapid recovery [48 , 70 , 74] . We further postulate that B4galnt2 genotype-dependent host-microbe interactions modulate the host’s immune response , contributing to less severe pathology and increased pathogen clearance in mice lacking intestinal epithelial B4galnt2 expression . Commensal gut bacteria benefit from the intestinal mucus and its diverse glycan residues , as they offer a complex repertoire of binding sites and carbohydrate sources independent of the host diet [1 , 3 , 75 , 76] . The indicator species identified for mucosal B4galnt2 expression , Prevotella and Bacteroides , are known to digest and bind a large spectrum of glycans [77] . These bacteria of high metabolic potential show signs of niche competition with the genus Turicibacter , an indicator for B4galnt2-deficient mice . Turicibacter , e . g . Turicibacter sanguinis , is a known member of the human and murine gut microbiome , but can only utilize a narrow range of carbohydrates [78] . As suggested by Dimitriu et al . ( 2013 ) [79] , the trade-off between low metabolic capacity and competitive abilities [78 , 80] with the potential for fast colonization might explain the association of Turicibacter with B6 -/- mice and the co-increase with S . Typhimurium [81–83] . It was also suggested that Turicibacter possesses immune modulatory characteristics ( increasing iNK T cell , and marginal zone B cell abundance [84] ) , and may thus help to lower the susceptibility to gut inflammation in B6 -/- compared to B6 +/- mice in the face of equivalent Salmonella burdens [79] . However , Turicibacter could also benefit from existing tissue inflammation , as several genomic features such as laminin , internalin , or a collagen binding pilus allow this genus to act as an opportunistic pathogen , and thus explain its association with tissue inflammation [78 , 80] . Similarly , Barnesiella shows repeated association to the absence of B4galnt2 glycans [26] . This genus has the potential to counteract inflammatory responses and thus appears to play a central role in the gut microbiome [85] . Co-staining of MUC2 and DBA lectin demonstrated a partial co-localization in goblet cells , suggesting that MUC2 is glycosylated by B4galnt2 in agreement with previously published data [31] . However , B4galnt2 glycans were also detectable in the cecal mucosa of Muc2-deficient mice ( Figs 2A and S2A ) , indicating additional intestinal targets of B4GALNT2 glycosylation . Other glycosylation targets for B4galnt2 are Sd ( a ) /Cad antigens , which have been shown to be present in colonic mucins [34 , 36] , glycolipids and glycoproteins [32 , 33 , 35 , 86] . Intestinal mucin glycans , including blood group α-1 , 2 fucosylated receptors , have been proposed as attachment sites for Salmonella [87 , 88] , but Salmonella does not appear to directly bind B4galnt2-GalNAc residues in vitro [4] . The glycan profile may also change in animals not expressing B4galnt2 in addition to the lack of β1 , 4-GalNac residues/Sd ( a ) , whereby the increase or decrease of other residues may offer new nutrient sources or attachment sites for bacteria or immune cells [35 , 89] . Nevertheless , we found slightly increased invasion into epithelial cells in vivo and in vitro when B4galnt2 is expressed . However , our fecal transfer experiments demonstrate that the altered bacterial community of B6 -/- mice confers resistance towards Salmonella induced inflammation . Thus , it is likely that indirect mechanisms , such as the microbial community and its capability of glycan liberation , subsequent changes in nutrient or microbe abundances [90] and the type of interactions [72] , are responsible for the higher susceptibility of mice expressing B4galnt2 in the intestinal epithelium to S . Typhimurium infection . Our study reveals an increased production of pro-inflammatory mediators , higher numbers of immune/inflammatory cells , and more severe colitis after S . Typhimurium infection in the ceca of mice expressing B4galnt2 in the intestinal epithelium . Although endothelial B4galnt2 expression did not impact the development of colitis as judged by histology , RIII + mice had lower pathogen burden in the cecum and lower levels of Mcp-1 and LCN-2 compared to RIII - mice , supporting a role for vascular B4galnt2 in host immune defense in the face of intestinal pathogens . Functionally , carbohydrate differentiation antigens play an important role in the homing and differentiation of intraepithelial lymphocytes in the small intestine , indicating a plausible phenotype that may result from the expression of B4galnt2 in endothelial cells [91–94] . The recruitment of neutrophils and CD3 + cells [35] , as well as leukocyte infiltration , were reported to be influenced through the glycosylation of selectin receptors [95] and could be associated with the elimination of carbohydrate ligands for selectins . B4galnt2 expression in gastrointestinal cancers has been shown to reduce metastatic dissemination , adding to the role of the Sd ( a ) antigen in cell motility [96 , 97] . Further studies focusing on the role of endothelial B4galnt2 expression are needed to understand the impact of B4galnt2-GalNAc residues in host immune responses and its potential role for homing of immune cells to the intestine . In summary , we demonstrate that different patterns of tissue-specific B4galnt2 expression not only influence intestinal microbial communities , but also change host susceptibility and immunological responses to S . Typhimurium infection [45 , 98] . Thus , a complex scenario including B4galnt2-dependent changes in microbial communities , vascular immune phenotypes , bleeding tendencies and susceptibility to intestinal infections likely contributes to the maintenance of variation at B4galnt2 in wild mouse populations .
All genetically engineered mouse lines used in the study were backcrossed >20 generations to a C57BL/6J background prior to breeding of the experimental animals . C57BL/6J ( B6 +/+ ) mice were purchased from The Jackson Laboratory . Mice heterozygous for the B4galnt2 knock-out allele ( B6 +/- ) [23] and RIIIS/J-B4galnt2 BAC transgenic ( RIII + ) mice which exhibit the Mvwf1 phenotype [21] were re-derived at the University Clinic Eppendorf , Hamburg , Germany . Intercross of B6 +/- × B6 +/-RIII + generated heterozygous B6 +/-/RIII + , B6 -/-/RIII + , B6 +/-/RIII - and B6 -/-/RIII - offspring , which were raised and housed together as littermates under specific pathogen-free conditions in individually ventilated cages at the animal facility of the University of Kiel , Germany . Standard chow ( ssniff , Soest , Germany ) and water were provided ad libitum . Germ-free C57BL/6J mice were produced at the gnotobiotic facility of the Hannover Medical School . Experiments were conducted in the animal facility of the Leibniz Research Center Borstel , Germany and at the animal facility of University Hospital Schleswig-Holstein Kiel . All experiments were conducted consistent with the ethical requirements of the Animal Care Committee of the Ministry of Energy , Agriculture , the Environment and Rural Areas of Schleswig-Holstein , Germany and in direct accordance with the German Animal Protection Law . The protocols were approved by the Ministry of Energy , Agriculture , the Environment and Rural Areas of Schleswig-Holstein , Germany ( Protocol: V312-72241 . 123–3 and V312-7224 . 123–3 ) . Streptomycin ( 20mg per mouse ) ( Sigma-Aldrich , Hamburg , Germany ) was given by oral gavage to mice aged 10–14 weeks . 24 hours after antibiotic administration , mice were infected with either S . Typhimurium SL1344 ( acute infection; [28] ) or the attenuated S . Typhimurium ΔaroA ( chronic infection; [29] ) at a dose of 3 × 106 bacteria in 100 μL HEPES buffer ( 100 mM , pH 8 . 0; PAA , Cölbe , Germany ) . Control mice ( mock-infection ) were given 100 μL HEPES buffer . Bacterial loads were determined by plating serial dilutions of homogenized organs on Luria Bertani agar ( Roth , Karlsruhe , Germany ) containing streptomycin ( 100 μg/mL ) . Mouse intestinal epithelial Mode-K cells were grown in DMEM supplemented with 5% fetal bovine serum ( Biochrom , Berlin , Germany ) and 1% HEPES ( GE Healthcare , Frankfurt , Germany ) . For the siRNA knockdown of B4galnt2 1 × 105 cells per well were seeded in a 24 well plate containing 10nM siRNA and lipofectamine ( Life Technologies , Darmstadt , Germany ) according to manufacturer’s instructions for reverse transfection . As a negative control cells were treated with scrambled siRNA . 24h post transfection cells were infected with an MOI 50 of wildtype S . Typhimurium grown to late-logarithmic phase . 30 min p . i . , cells were washed and extracellular bacteria were killed by addition of medium containing gentamicin ( 100 μg/ml ) . Cells were lysed at various timepoints ( 30 min , 1 h and 4 h ) and the number of adherent and invaded bacteria was determined by plating serial dilutions . Cecal tissues were fixed in Carnoy’s fixative overnight , embedded in paraffin , and then cut in 5 μm sections on glass slides . Sections were deparaffinized and incubated with a Texas red-conjugated EUB338 general bacterial probe ( 5’-GCTGCCTCCCGTAGGAGT-3’ ) and an AlexaFluor 488 conjugated Gam42a probe ( 5’-GCCTTCCCACATCGTTT-3’ ) that recognizes bacteria that belong to the γ-Proteobacteria class ( 37°C , O/N , dark ) . Tissue samples were washed with hybridization buffer ( 0 . 9 M NaCl , 0 . 1 M Tris pH 7 . 2 , 0 . 1% SDS ) . This step was repeated with FISH Washing Buffer ( 0 . 9 M NaCl , 0 . 1 M Tris pH 7 . 2 ) with gentle shaking for 15 minutes . Sections were washed with water and mounted using Prolong GOLD with DAPI ( Molecular Probes ) and imaged using an AxioImager microscope equipped with an AxioCam HRm camera operating through AxioVision software . High power field ( HPF ) ( 630X ) was used for enumerating intracellular and extracellular S . Typhimurium . Carnoy’s-fixed paraffin-embedded tissues were sectioned ( 5 μm ) , deparaffinized , and stained with 1% Alcian Blue ( Sigma-Aldrich , Hamburg , Germany ) solution ( in 1% acetic acid ) for 10 min , counterstained in nuclear fast red solution ( 1% ) , dehydrated , and mounted for examination . Photographs were taken at an original magnification of 100x and mucus thickness was measured at six random locations per section using NIS-Element Software ( Nikon , Dusseldorf , Germany ) . Fresh feces from B6 +/- or B6 -/- mice was sampled and immediately homogenized ( 1:10 w/v ) in transfer buffer ( sterile phosphate buffered saline containing 0 . 05% cysteine HCl ( Sigma-Aldrich ) ) . After centrifugation , the supernatant was collected and 200 μL were orally gavaged into germ-free adult C57BL/6J recipient mice . 21 days post transplantation mice were treated with streptomycin and 24 hours later infected with S . Typhimurium . Tissues were fixed in 10% neutral buffered formalin overnight and embedded in paraffin . 5 μm sections were deparaffinized and stained with haematoxylin and eosine ( H&E ) . Histological scores in the ceca of infected mice were determined as previously described [30] . Briefly , pathological changes were assessed by evaluating various parameters such as presence of luminal cells , infiltrating immune cells , crypt abscesses and the formation of edema in the respective layer of the intestinal bowel wall including the surface epithelium , mucosa and submucosa . Formalin fixed tissue sections ( 5 μm ) were deparaffinized and rehydrated . After antigen retrieval with 10 mM sodium citrate buffer ( pH 6 . 0 ) and blocking with 2% normal goat serum , specimens were incubated with antibodies specific for S . Typhimurium ( Clone B395M , Dunn Laboratories , Asbach , Germany ) , CD3 ( Abcam , Cambridge , UK ) , CD68 ( Abcam , Cambridge , UK ) , myeloperoxidase ( MPO ) ( Thermo Fisher Scientific , Schwerte , Germany ) , and MUC2 ( Santa Cruz , Dallas , TX , USA ) followed by fluorescently labeled secondary antibodies ( Molecular Probes , Invitrogen , Carlsbad , CA , USA ) or with fluorescently labelled DBA ( dolichus biflorus agglutinin ) and WGA ( wheat germ agglutinin ) lectins ( Vector laboratories , Burlingame , CA , USA ) . Counterstaining of nuclei was performed using 4 , 6-Diamidin-2-phenylindol ( DAPI ) ( Invitrogen , Carlsbad , CA , USA ) . Images were obtained using a Leica SP5 confocal microscope ( Leica , Wetzlar , Germany ) . Lipocalin-2 concentrations in the supernatant of tissue homogenates were determined with a mouse specific ELISA Development Kit by R&D Systems ( R&D Systems , Wiesbaden , Germany ) according to the manufacturer’s instructions . RNA was extracted from cecal tips by using the High Pure RNA Tissue Kit ( Roche Diagnostics , Mannheim , Germany ) and reverse transcription was conducted with the Transcriptor High Fidelity cDNA Synthesis Kit ( Roche Diagnostics , Mannheim , Germany ) according to the manufacturer’s instructions . RT-qPCR was performed with Quantitect SYBR-Green Mastermix ( QIAGEN , Hilden , Germany ) for the following genes: Ifn-γ , fw TCAAGTGGCATAGATGTGGAAGAA , rev TGGCTCTGCAGGATTTTCATG; Tnf-α , fw CCACCACGCTCTTCTGTCTAC , rev AGGGTCTGGGCCATAGAACT; Il-6 , fw GAGGATACCACTCCCAACAGACC , rev AAGTGCATCATCGTTGTTCATACA; Mcp-1 , fw CCTGCTGTTCACAGTTGCC , rev ATTGGGATCATCTTGCTGGT; B4galnt2 , fw TGGCAAGTCCTACCATGAGG , rev GTCTGCAGAAGTGGCTGGA; Gapdh , fw ATTGTCAGCAATGCATCCTG , rev ATGGACTGTGGTCATGAGCC; Hprt , fw AGTGTTGGATACAGGCCAGAC , rev CGTGATTCAAATCCCTGAAGT . Relative gene expression was calculated using geNORM and the 2-∆∆Ct method , with Gapdh and Hprt as housekeeping genes [99] . DNA was extracted from fecal samples ( stored at -80°C ) using the PowerSoil DNA Isolation Kit ( MO Bio Laboratories , Carlsbad , CA ) following the manufacturer’s protocol . The 16S rRNA gene was amplified using barcoded primers flanking the V1 and V2 hypervariable regions ( 27F-338R ) and were sequenced following the methods describe in Rausch et al . 2011 [100] . Raw sequences were trimmed by mothur 1 . 31 . 2 requiring no ambiguous bases , a mean quality score within a window of 50 base pairs of ≥ 35 and a minimum length of 200 nucleotides for the coupled V1-V2 region [101] . Chimeric sequences were determined using USEARCH 4 . 25 ( database informed UCHIME algorithm ) [102] . Sequences were confirmed as bacterial using the RDP classifier with ≥ 60% bootstrap threshold [103] . For all downstream analyses of diversity and habitat association , we took a random subset of 1000 sequences per sample to normalize the read distribution ( Good’s Coverage; no treatment: 85 . 67 ± 6 . 61% SD; Streptomycin: 97 . 38 ± 3 . 13% SD; S . Typhimurium infection: 98 . 36 ± 1 . 74% SD ) . These sequences were aligned to the curated SILVA seed database using the NAST alignment procedure as implemented in mothur and subsequently OTU binning was carried out via average distance clustering [104] . Phylogenetic tree construction on representative OTU sequences ( average distant sequence of the OTU ) was done by FastTree 2 . 1 using the CAT substitution model with gamma correction [105] . Raw sequence data can be accessed online under the accession number PRJEB5269 at the European Nucleotide Archive . Species diversity indices ( Chao1 species richness , Shannon-Weaver index ) , as well as the phylogenetic distance at the tips of the phylogenetic tree ( Nearest Taxon Index , NTI ) and its deep branches ( Net Relatedness Index , NRI ) were calculated in R [106–108] . The phylogenetic measures of beta diversity ( unweighted- and weighted UniFrac ) and metrics based on shared OTU presence ( Jaccard ) or abundance ( Bray-Curtis ) were calculated in “vegan” [109–111] . Statistical analysis of community composition based on different beta diversity metrics was performed with Principal Coordinate Analysis ( PCoA ) and non-parametric multivariate analysis of variance and multivariate dispersion as implemented in the “vegan” package for R with 105 permutations . For constrained ordination ( Redundancy Analysis ) the OTU table was Hellinger-transformed and RDA was carried out following Legendre and Legendre [112] . Significance of factors and axes was ascertained using a permutative ANOVA approach ( 5000 permutations ) . Linear mixed models ( LMM , cage as random factor ) were applied to alpha diversity measures and optimized with model selection by AIC criterion , normality of model residuals and refitting of the final model under Restricted Maximum Likelihood ( REML ) [113] . The R2LR values of the final mixed model were calculated using the MuMIN package for R [114 , 115] . Lipocalin-2 levels , fluorescence signals , inflammation scores , CFU counts , and cecum weights were analyzed in a linear model framework with parameter selection to minimize the AIC value and no significant reduction of fit . For the comparison of expression values among genotypes we employed a Wilcoxon test with Monte-Carlo resampling [116] . Salmonella counts ( Gam24a + cells ) in Mode-K cell cultures were analyzed using an LMM with the independent rounds of experiments as random factor to incorporate experimental variation . Indicator species analysis was based on 105 permutations using the indicator value to assess the association for each taxon [117] . All P-values of the genera and OTU associations were adjusted by the Benjamini-Hochberg procedure . Taxon co-occurence networks were calculated by SPARCC based on 105 permutations and significant associations ( P < 0 . 05 ) were included in the network construction [118] .
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Human blood groups are among the oldest known genetic polymorphisms . It has been proposed that blood group variation is a byproduct of pathogen-driven selection , including in the gastrointestinal tract where blood-group-related genes are often variably expressed . The B4galnt2 gene is responsible for the synthesis of the Sd ( a ) /Cad carbohydrate blood group antigen and displays variable tissue-specific expression patterns in wild mouse populations . Using an established model for Salmonella Typhimurium induced colitis , we found that loss of B4galnt2 expression in the intestinal epithelium decreases susceptibility to infection . These effects were strongly associated with the influence of B4galnt2 expression on the intestinal microbiota , whereby microbial diversity prior to infection was highly predictive of reduced inflammation and resistance to Salmonella Typhimurium infection . Additionally , B4galnt2 expression in blood vessels also distinctly influenced intestinal phenotypes and Salmonella susceptibility . These data lend new insights into bacterial community diversity as an “extended phenotype” that can be mediated by host genetic variation at blood-group-related genes . This work further provides strong experimental evidence in support of a scenario of complex selection on the B4galnt2 tissue-specific expression variants via host-microbe relationships and susceptibility to infectious disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[] |
2015
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Expression of the Blood-Group-Related Gene B4galnt2 Alters Susceptibility to Salmonella Infection
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DNA double-strand breaks ( DSB ) are very harmful lesions that can generate genome rearrangements . In this study , we used intrachromosomal reporters to compare both the efficiency and accuracy of end-joining occurring with close ( 34 bp apart ) vs . distant DSBs ( 3200 bp apart ) in human fibroblasts . We showed that a few kb between two intrachromosomal I-SceI-induced DSBs are sufficient to foster deletions and capture/insertions at the junction scar . Captured sequences are mostly coupled to deletions and can be partial duplications of the reporter ( i . e . , sequences adjacent to the DSB ) or insertions of ectopic chromosomal sequences ( ECS ) . Interestingly , silencing 53BP1 stimulates capture/insertions with distant but not with close double-strand ends ( DSEs ) , although deletions were stimulated in both case . This shows that 53BP1 protects both close and distant DSEs from degradation and that the association of unprotection with distance between DSEs favors ECS capture . Reciprocally , silencing CtIP lessens ECS capture both in control and 53BP1-depleted cells . We propose that close ends are immediately/rapidly tethered and ligated , whereas distant ends first require synapsis of the distant DSEs prior to ligation . This "spatio-temporal" gap gives time and space for CtIP to initiate DNA resection , suggesting an involvement of single-stranded DNA tails for ECS capture . We therefore speculate that the resulting single-stranded DNA copies ECS through microhomology-mediated template switching .
DNA double-strand breaks ( DSBs ) are highly toxic lesion that can cause profound genome rearrangements and/or cell death . Faithful DSB repair is vital for cell survival and the maintenance of genome stability , but it should also allow for genetic diversity in essential physiological processes such as , for instance , the establishment of the immune repertoire . Thus , DSB repair should be tightly controlled . There are two levels of genetic modification through DSB repair: 1- the rearrangement/joining of distant DNA sequences and 2- mutagenesis at the sealed junction . In this latter case , deletions , DNA capture or complex events , associating different processes can alter the structure of the repair junction . DSBs are repaired by two general processes: the first uses an intact homologous sequence and is referred to as homologous recombination ( HR ) , and the second process joins the two DNA double-strand ends ( DSE ) in a sequence homology-independent manner [1] . In mammalian cells , the end-joining ( EJ ) of DSEs is a prominent DSB repair pathway . Canonical non-homologous end-joining ( C-NHEJ ) , which is KU-Ligase 4 dependent , is able to join DSEs in a conservative way at the repair junction , although it is adaptable to imperfectly cohesive ends [2–4] . More recently , an alternative end-joining ( A-EJ ) pathway has been described that does not require sequence homology , but is initiated by CtIP-dependent single-strand DNA resection . Therefore , A-EJ is highly mutagenic at the repair junction , typically generating deletions resulting from the initial resection and frequently using microhomologies distant from the DSB to join the resected DNA ends [2–10] . Several mechanisms generate DSBs: DNA-damaging agents , such as ionizing radiation or reactive oxygen species , and nucleases generate DSBs with two proximal DSEs . Prolonged replication stress also generates DSBs [11] . Importantly , the arrest of replication forks generates single-ended DSEs [12 , 13] , and the joining of such structures , which are distant , inevitably generates rearrangements . Importantly , we have recently reported that the cohesin complex prevents the joining of distant double-strand ends but not of close ends , specifically in the S phase [14] . Alternatively , single-ended DSEs can initiate DNA copy through template-switching . Indeed , a model of genetic rearrangements accounting for copy number variation upon replication stress , which is initiated by microhomology annealing , has been proposed: MMBIR/FoSTeS ( microhomology-mediated break-induced replication/ Fork Stalling and Template Switching ) [15–17] . In yeast , chromosome rearrangements occurring via template switching between diverged repeated sequences have also been described [18] . On one DSB with two proximal DSEs , it has been proposed that C-NHEJ components tether the two ends , allowing their immediate ligation [19–22] . Remarkably , C-NHEJ-defective cells exhibit strong chromosome instability , underlining the fact that C-NHEJ is essential for the maintenance of genome stability . Consistently , C-NHEJ protects against the mobility of DNA ends , thus preventing unscheduled rearrangements [23–25] . Conversely , A-EJ is involved in chromosome translocation in mouse , drosophila and yeast cells [26–28] . However , the mechanisms leading to genome rearrangements appear to be more complex because C-NHEJ has also been shown to be involved in genome rearrangement events such as capture of excised chromosomal sequences and translocation , in the mammalian genome [2 , 29] . Moreover , analysis of the junctions repaired by EJ reveals the occurrence of complex events in addition to the direct joining of two DSEs . Indeed , these events frequently associate deletions with capture of DNA sequences . Moreover , while the genetic control of the end-joining processes per se has been extensively studied [1] , the mechanisms resulting in rearranged end-joining junctions are poorly documented . Here , we address the question of the impact of the distance between two DSEs on both the efficiency and the accuracy of end joining . To analyze these processes at a precise molecular level in living human cells , and in the chromosomal context , we used several intrachromosomal substrates monitoring the end joining of DSBs targeted into the substrates by the meganuclease I-SceI . These substrates have been derived from previously extensively characterized , validated and discussed substrates monitoring end-joining [2–4 , 8 , 10 , 30–32] . We show that a distance of only a few kb between the two DSEs , which is short at the nucleus scale , is sufficient not only to significantly reduce joining efficiency but also to induce error-prone DSB repair associated with complexly rearranged end-joining junctions . Particularly , a distance between the DSEs favors the capture of chromosome sequences that can be partial duplications of the EJ reporter or ectopic chromosomal sequences ( ECS ) . We show here that these captures are promoted by CtIP and counteracted by 53BP1 , suggesting the involvement of single-strand resection at the initiation of such events . Therefore , according to these data , the junction patterns analyzed here , the MMBIR/Fostes model [15–17] , and analysis of chromosome rearrangement in yeast [18] , we speculate that the chromosomal captures at the end-joining junctions of two distant DSEs also result from micro-homology-mediated template switching . These complex events only arise with distant DSEs , thereby indicating a requirement for a "spatio-temporal-gap" that allows the coupling of the resection with chromosomal insertions . These data reveal mechanisms resulting in DNA capture at the joining of two distant DSBs , underlining the complex possibilities for DNA end processing to alter the accuracy of DSB repair . Importantly , even a distance of a few kb between two DSBs is sufficient to induce such complex processing , adding an additional level of risk for genome integrity .
We designed several intrachromosomal reporter substrates monitoring non-homologous EJ , between which the key difference was the distance between the two DSEs ( I-SceI sites ) , 34 bp versus 3200 bp ( Fig 1A and S1 supplementary information ) . A 34-bp-gap should allow for more direct or rapid tethering and ligation of the two DSEs . In contrast , a 3200-bp-gap absolutely requires a synapsis step to bring together the two DSEs prior to ligation . End-joining events were monitored by the expression of GFP or CD4 reporters ( Fig 1B ) . We established several independent clones bearing one or two substrates in SV40-transformed human fibroblasts ( Fig 2 ) . Note that , for a given type of substrate , the frequency of I-SceI–induced EJ did not significantly vary between different clones with the same reporter type , suggesting the absence of position effect ( Fig 2 ) . Remarkably , the efficiency of EJ was consistently 3 . 5-fold higher in reporters containing a 34-bp-gap than in those containing a 3200-bp-gap ( Fig 2 ) . This shows that even a few kilobases of separation between DSEs , which is short at the genome-scale level , reduce EJ efficiency; this effect is therefore not restricted to large-scale genomic rearrangements [33] . In previous studies , we defined two classes of EJ repair events: conservative EJ ( C-NHEJ ) , which is KU/XRCC4-dependant and uses the annealing of at least one of the 3’ protruding nucleotides ( 3’Pnt ) generated by I-SceI cleavage , which are eventually associated with insertions at the repair junctions; and non-conservative alternative EJ ( A-EJ ) , which is KU/XRCC4-independant and deletes at least all four 3’-Pnt ( see S1 supplementary information and [4 , 10 , 34–36] ) . Again , deletions in A-EJ can be associated with insertions at the repair junction . DSEs separated by 34 bp produced a higher proportion of conservative rejoining events ( 64% conservative repair: 57% HiFi ( High Fidelity events , i . e . error-free ) + 7% insertions in GCK20 cells ) than in each of the two cell lines ( GC92 and GC49 ) with DSEs separated by 3200 bp ( 40% conservative repair: 36% HiFi+4% insertions in GC92 cells , and 33% conservative repair: 23% HiFi+ 10% insertions in GC49 cells ) ( GC92 vs . GCK20 , p = 0 . 007; GC49 vs . GCK20 , p = 0 . 005 by t-test; Table 1 and S2 Supplementary information ) , suggesting that distance between DSEs fosters error-prone repair , likely by A-EJ . We previously showed that CtIP promotes non-conservative rejoining of DSEs separated by 3200 bp and that 53BP1 antagonizes CtIP in this process [36] . Here , we reproduced these results ( Table 2 ) , and in addition , we showed that silencing CtIP ( Fig 3A ) also increases conservative events ( HiFi ) with DSEs separated by 34 bp ( Table 2 ) . Silencing 53BP1 ( Fig 3A ) , which protects against CtIP-induced resection [37] , increased the percentage of non-conservative events with distant ends ( Table 2 ) and significantly increased the size of deletions ( Fig 3B ) . With close ends , silencing 53BP1 ( Fig 3A ) also impaired conservative events ( 64%: 57% HiFi + 7% insertions in control cells vs . 56%: 52% HiFi + 4% insertions in 53BP1-depleted cells , Table 2 ) ; however , the size of deletions was not affected by 53BP1 depletion ( Fig 3B ) . Therefore , 53BP1 is necessary to protect distant DSEs from extensive degradation . For close DSEs , unprotection by 53BP1 silencing is compensated by the tethering and rapid ligation of the two close ends , thus avoiding the attack of the DSE by nucleases and generation of long resections . Remarkably , rejoining 3200-bp-distant DSEs seems to significantly favor long insertions ( ≥ 45 bp and even >200 bp ) compared to rejoining close 34-bp-distant ends ( Fig 4A and 4B , and S2 supplementary information ) . Strikingly , silencing 53BP1 2 . 5-fold increased the frequency of these long insertions at the rejoining junction of 3200-bp-distant DSEs ( Fig 4B , Tables 3 and 4 , and S2 and S3 supplementary information ) . Interestingly , CtIP depletion decreased the frequency of long insertions in control cells but , more specifically , abolished the increased stimulation of long insertions resulting from 53BP1 depletion ( Fig 4A and 4B and Table 3 , S2 and S3 Supplementary information ) . In contrast , in the repair of 34-bp-separated DSEs , 53BP1 depletion had no impact on insertion size and frequency ( Fig 4A and 4B and S3 Supplementary information ) . Collectively , the data show that deprotection of DSEs is not sufficient to efficiently promote insertions and that the distance between DSEs also matters . Sequencing of the insertional rejoining events of distant DSEs revealed that insertions could be classified into two main categories ( Tables 3 and 4 , S4 Supplementary information ) . The first category entailed partial duplication of sequences adjacent ( either in 5’ or in 3’ ) to the I-SceI cleavage site ( 31 sequences over 63 insertions ≥45 bp total ) . Among these events , four sequences implicated sequence homology at one border and copying of a part of the intervening sequence between the two I-SceI sites . Therefore , these rare events might be attributed to an HR-dependent process involving the sister chromatid . However , the vast majority ( 27/31 events of partial duplication of the EJ reporter ) did not exhibit sequence homology at the borders nor copy of the intervening sequence immediately downstream from the I-SceI site . Therefore , for these latter cases , we exclude a process initiated by HR , and we propose that they occur through microhomologies-mediated unequal sister chromatid exchange , involving non-homologous sequences ( see below ) . These events are similar to some of the translocation junctions observed in Ewing sarcoma [38] . The second category entailed capture of ectopic chromosomal sequences ( ECS ) . In this latter category , there were also no sequence homologies between the donor and recipient DNA molecules observed , excluding the involvement of homologous recombination in the promotion of such events . Note that DNA capture has been described at translocation junctions involving two different chromosomes [39] . Importantly , for 3200-bp-separated DSEs , CtIP depletion abolished ECS capture ( Table 3 ) . Silencing 53BP1 stimulated the occurrence of long insertions ( ≥45 bp ) in the two different cell lines we used here to monitor rejoining of distant ends , but the pattern differed between them . Indeed , silencing 53BP1 in the GC92 cell line stimulated both the partial duplication of the EJ reporter and ECS capture , whereas silencing 53BP1 only stimulated partial duplication of the reporter sequence in the GC49 cell line ( Tables 3 and 4 and S4 Supplementary information ) . These differences may reflect a position effect and differences in chromatin conformation between the two different cell lines . However , the data conclude that ablation of 53BP1 leads to insertions at the seal junction of distant DSEs . For 34-bp-separated DSEs , the number of long insertions was very small in spite of the large number of repair events sequenced ( only 2 and 3 insertions ≥45 bp among 135 and 120 sequences in control or 53BP1-depleted cells , respectively ) , but importantly , depletion of 53BP1 did not stimulate ECS capture , in contrast with 3200-bp-separated DSEs ( Fig 4B , S5 Supplementary information ) . Intriguingly , captured ECS and partial duplications of the EJ reporter were frequently flanked by stretches of unidentified sequences ( N-additions ) . Because these sequences are unidentified it is not possible to determine whether micro-homologies are involved; however , among the remaining events , which do not present unidentified sequences at the borders of the inserted sequence , approximately two-thirds exhibited micro-homologies ( ≥2 bp ) at the junction borders ( S4 Supplementary information ) .
The present data can be unified in the model shown in Fig 5A . Partial duplication of the EJ reporter and ECS capture requires the association of both DSE resection and distance . Indeed , distance creates a “spatiotemporal gap , " giving time and space for CtIP to initiate DNA end resection . Both 53BP1 and KU have been proposed to protect DNA ends from degradation . Indeed , with the substrate used here , the absence of KU also increased non-conservative end-joining and long deletions [2 , 3] . In addition , ablation of the KU70-KU80 heterodimer , which is involved in the tethering of the two DSEs of one DSB , consistently leads to increased mobility of the DSE and genome rearrangement [43] . Therefore , both 53BP1 and KU should protect DSEs . However , for close ends , the absence of 53BP1 should be compensated by the proximity of the two ends , which should permit rapid joining . In addition , close ends favor the tethering of the two ends by KU . This situation does not provide enough time and space for nucleases to attack the DNA extremities , even in the absence of 53BP1 . With distant DSEs , the synapsis of the two ends is first required , even with such a short distance as 3 . 2 kb . In this situation , the tethering of the two ends prior to ligation cannot pre-exist , even in the presence of KU . Therefore , the synapsis step provides space and time for CtIP to initiate resection , resulting in complex sealing patterns . The absence of 53BP1 , which counteracts CtIP , increases such events . Upon replication stress , micro-homology-mediated rearrangements have been proposed in a process called MMBIR/FoSTeS [16] . In yeast , template switching between repeat sequences also generates rearrangements [18] . Associating the present data with the above models , an additional attractive hypothetical model speculates that single-stranded DNA tails generated by CtIP favor microhomology-mediated template switching ( MMts ) , initiating copying of ECSs ( Fig 5B ) . 53BP1 protects against such events . Unidentified N-additions , by increasing ssDNA tail length , can enhance the probability of finding micro-homology to anneal . Different polymerases are able to promote non-template N-additions; for example , in the course of V ( D ) J recombination [44] . Alternatively , unidentified sequences might result from several successive rounds of annealing/copying of few nucleotides on different templates . In addition , the two aforementioned processes might also cooperate to generate unidentified sequences at the DNA capture borders . Then , similarly to SDSA ( synthesis dependent strand annealing ) ( see [1] ) ) , flip back to the recipient molecule by micro-homology annealing with the acceptor molecule , results in ECS capture ( Fig 5B ) . In one particular case of this model , MMts with a misaligned sister chromatid should result in partial duplication of the reporter sequence through sequence homology-independent microhomologies-mediated unequal sister-chromatid exchanges ( Fig 5C ) . Physiological joining of distant DSEs occurs in V ( D ) J and class switch recombination and therefore should be highly controlled . For instance , in V ( D ) J recombination , the cleavage happens after the synapsis has brought the involved sequences close together , thus protecting against potential genome instability generated by the synapsis of distant broken ends . In contrast , replication stress generates unscheduled single-ended DSBs . Consequently , EJ of replication-stress-induced DSEs necessarily involves distant DSEs . Importantly , replication stress has been proposed to act during early stages of malignancy [45 , 46] . Interestingly , using the substrates described here , we have reported that the cohesin complex protects against the joining of distant ends ( but does not inhibit the joining of close ends ) , specifically in the S phase , thus preventing genetic rearrangements generated by the joining of replication stress-induced double-strand ends [14] . Consistent with the present data , we have previously reported that DNA end mobility generated by cohesin complex ablation also increases the occurrence of long insertions at sites of distant DSE rejoining ( 3 . 1% in control cells vs . 9 . 7% in RAD21-depleted cells ) [14] . Thus , these data suggest that the substrate used here with distant I-SceI-induced DSBs mimics , at least in part , some features of the joining of distant single-ended DSBs generated by replication stress . The present data show that unscheduled EJ of distant DSEs can yield potentially deleterious genome rearrangements at the repair junction , involving a complex mix/cooperation of different DNA processes . CtIP is required at the initiation of homologous recombination and sister-chromatid exchanges [36] , which allows for the restart of arrested replication forks , thereby promoting genome stability maintenance . However , we show here that CtIP is a two-edge sword , jeopardizing genome stability at the joining of distant DSEs . Therefore , by counteracting CtIP , 53BP1 plays a pivotal role in genome stability maintenance of unscheduled DSBs .
All DNA manipulations were performed as previously described [47] . The cell lines were derived from SV40-transformed GM639 human fibroblasts and were cultured in DMEM supplemented with 10% fetal calf serum ( FCS ) and 2 mM glutamine and were incubated at 37°C in 5% CO2 . Linearized NHEJ reporters were electroporated into the cells , and individual clones were selected using blasticidin ( 5 μg/mL ) or neomycin ( 500 μg/ml ) . The meganuclease I-SceI was expressed by transient transfection of the expression plasmid pCMV-HA-I-SceI ( 47 ) with Jet-PEI , following the manufacturer’s instructions ( Polyplus transfection , Illkirch , France ) . The expression of HA-tagged I-SceI was verified by Western blotting . For silencing experiments , 50 , 000 cells were seeded 1 day before transfection , which was carried out using 20 nmol of onTarget plus SMARTpool for human TP53BP1 ( Dharmacon , Chicago , IL , USA ) , CtIP siRNA ( 5'- GCUAAAACAGGAACGAAUC -3' ) and/or control siRNA ( SR-CL000-005 , Eurogentec , Angers , France; 5’ AUGAACGUGAAUUGCUCAA -3’ , #019317273 , Eurofins , Ebersberg , Germany ) and INTERFERin following the manufacturer’s instructions ( Polyplus Transfection , Illkirch , France ) . Forty-eight hours later , the cells were transfected with the pCMV-HA-I-SceI expression plasmid . After transfection with the pCMV-HA-I-SceI plasmid and incubation for 72 hours , the cells were collected in PBS and 50 mM EDTA , pelleted and fixed with 2% paraformaldehyde for 20 minutes . The percentage of GFP-expressing cells was scored by FACS analysis using a BD Accuri C6 flow cytometer ( BD , Franklin Lakes , NJ , USA ) . The percentage of CD4-expressing cells was measured after incubation for 10 minutes with 1 μl of anti-CD4 antibody coupled to Alexa 647 ( rat isotype , RM4-5 , Pharmingen , San Diego , CA , USA ) . For each cell line , at least 3 independent experiments were performed , and HA-I-SceI expression and efficiency of silencing was verified each time by Western blot . For western blot analysis , the cells were lysed in buffer containing 20 mM Tris HCl ( pH 7 . 5 ) , 1 mM Na2EDTA , 1 mM EGTA , 150 mM NaCl , 1% ( w/v ) NP40 , 1% sodium deoxycholate , 2 . 5 sodium pyrophosphate , 1 mM β-glycerophosphate , 1 mM NA3VO4 and 1 μg/ml leupeptin supplemented with complete mini protease inhibitor ( Roche , Mannheim , Germany ) . Denatured proteins ( 20–40 μg ) were electrophoresed in 9% SDS-PAGE gels , transferred onto a nitrocellulose membrane and probed with the specific antibodies anti-HA ( MMS-101R , Covance , Berkeley , CA ) , anti-53BP1 ( #4937 , Cell Signaling , Danvers , MA , USA ) , anti-CtIP ( rabbit , courtesy of Dr . R . Baer ) , and anti αTubulin ( #T5168 , Sigma Aldrich , Munich , Germany ) . Immunoreactivity was visualized using an enhanced chemiluminescence detection kit ( ECL , Pierce ) . We amplified the junction sequences by PCR of genomic DNA using the primers CMV-6 ( 5'-TGGTGATGCGGTTTTGGC-3’ ) and CD4-int ( 5'-GCTGCCCCAGAATCTTCCTCT-3' ) . The PCR products were cloned with a TOPO PCR cloning kit ( Invitrogen Life Technologies ) and sequenced ( GATC Biotech , Konstanz , Germany and Eurofins , Ebersberg , Germany ) . For each sample , 2 to 5 experiments were pooled in the sequencing data . In each of these experiments , HA-I-SceI expression , and efficiency of silencing were verified by Western blot . Insertions were blasted using the BLAST program of the National Centre of Biotechnology Information ( National Institutes of Health , Bethesda MD , USA ) . Insertions were blasted to the end joining reporter , the I-SceI expression plasmid , the mitochondrion genome , the human genome ( Homo sapiens , taxid: 9606 ) , human ALU repeat elements and the nucleotide collection using megablast and discontinuous megablast . Sequences identified as “non-templated nucleotides” were identified by neither of these searches . Statistical analyses ( Mann-Whitney tests for the size of insertions and t-test for the frequency of insertions and the ratio of conservative vs . non-conservative repair ) were performed using GraphPad Prism 3 . 0 ( GraphPad Software ) .
|
A DNA double-strand break is a very toxic lesion that can be repaired by rejoining DNA ends . This repair process can have deleterious consequences on the genome by joining DNA ends that were not originally fused ( translocations ) or modifying the DNA sequence with deletions or insertions . Here , we show that rejoining distant ends ( only a few kb apart ) favors error-prone repair characterized by deletion of the original sequences and also favors insertions of ectopic chromosomal sequences . These insertions are coupled to error-prone repair , i . e . , deletion of the original sequence , initiated by the nuclease CtIP . Interestingly , favoring deletions by removal of the protection factor 53BP1 is not sufficient to efficiently promote insertions of ectopic sequences when the DNA ends are close . Therefore , the association of both unprotection and distance between DNA ends favors insertion of ectopic chromosomal sequences . The requirement of CtIP that generates single-strand DNA suggests that the generation of single-strand DNA favors insertions of ectopic sequences by microhomology-mediated template switching .
|
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2016
|
53BP1 Protects against CtIP-Dependent Capture of Ectopic Chromosomal Sequences at the Junction of Distant Double-Strand Breaks
|
A developing plant organ exhibits complex spatiotemporal patterns of growth , cell division , cell size , cell shape , and organ shape . Explaining these patterns presents a challenge because of their dynamics and cross-correlations , which can make it difficult to disentangle causes from effects . To address these problems , we used live imaging to determine the spatiotemporal patterns of leaf growth and division in different genetic and tissue contexts . In the simplifying background of the speechless ( spch ) mutant , which lacks stomatal lineages , the epidermal cell layer exhibits defined patterns of division , cell size , cell shape , and growth along the proximodistal and mediolateral axes . The patterns and correlations are distinctive from those observed in the connected subepidermal layer and also different from the epidermal layer of wild type . Through computational modelling we show that the results can be accounted for by a dual control model in which spatiotemporal control operates on both growth and cell division , with cross-connections between them . The interactions between resulting growth and division patterns lead to a dynamic distributions of cell sizes and shapes within a deforming leaf . By modulating parameters of the model , we illustrate how phenotypes with correlated changes in cell size , cell number , and organ size may be generated . The model thus provides an integrated view of growth and division that can act as a framework for further experimental study .
The development of an organ from a primordium typically involves two types of processes: increase in cell number through division , and change in tissue shape and size through growth . However , how these processes are coordinated in space and time is unclear . It is possible that spatiotemporal regulation operates through a single control point: either on growth with downstream effects on division , or on division with downstream effects on growth . Alternatively , spatiotemporal regulation could act on both growth and division ( dual control ) , with cross talk between them . Distinguishing between these possibilities is challenging because growth and division typically occur in a context in which the tissue is continually deforming . Moreover , because of the correlations between growth and division it can be hard to distinguish cause from effect [1] . Plant development presents a tractable system for addressing such problems because cell rearrangements make little or no contribution to morphogenesis , simplifying analysis [2] . A growing plant organ can be considered as a deforming mesh of cell walls that yields continuously to cellular turgor pressure [3 , 4] . In addition to this continuous process of mesh deformation , new walls are introduced through cell division , allowing mesh strength to be maintained and limiting cell size . It is thus convenient to distinguish between the continuous expansion and deformation of the mesh , referred to here as growth , and the more discrete process of introducing new walls causing increasing cell number , cell division [5–8] . The developing Arabidopsis leaf has been used as a system for studying cell division control within a growing and deforming tissue . Developmental snapshots of epidermal cells taken at various stages of leaf development reveal a complex pattern of cell sizes and shapes across the leaf , comprising both stomatal and non-stomatal lineages [9] . Cell shape analysis suggests that there is a proximal zone of primary proliferative divisions that is established and then abolished abruptly . Expression analysis of the cell cycle reporter construct cyclin1 Arabidopsis thaliana β-glucuronidase ( cyc1At-GUS ) [10] shows that the proximal proliferative zone extends more distally in the subepidermal as compared with the epidermal layer . Analysis of the intensity of cyc1At-GUS , which combines both epidermal and subepidermal layers , led to a one-dimensional model in which cell division is restricted to a corridor of fixed length in the proximal region of the leaf [11] . The division corridor is specified by a diffusible factor generated at the leaf base , termed mobile growth factor , controlled by expression of Arabidopsis cytochrome P450/CYP78A5 ( KLUH ) . Two-dimensional models have been proposed based on growth and cell division being regulated in parallel by a morphogen generated at the leaf base [12 , 13] . These models assume either a constant cell area at division , or constant cell cycle duration . The above models represent important advances in understanding the relationships between growth and division , but leave open many questions , such as the relations of divisions to anisotropic growth , variations along both mediolateral and proximodistal axes , variation between cell layers , variation between genotypes with different division patterns , and predictions in relation to mutants that modify organ size , cell numbers , and cell sizes [14] . Addressing these issues can be greatly assisted through the use of live confocal imaging to directly quantify growth and division [15–22] . Local rates and orientations of growth can be estimated by the rate that landmarks , such as cell vertices , are displaced away from each other . Cell division can be monitored by the appearance of new walls within cells . This approach has been used to measure growth rates and orientations for developing Arabidopsis leaves and has led to a tissue-level model for its spatiotemporal control [16] . Live tracking has also been used to follow stomatal lineages and inform hypotheses for stomatal division control [23] . It has also been applied during a late stage of wild-type leaf development after most divisions have ceased [24] . However , this approach has yet to be applied across an entire leaf for extended periods to compare different cell layers and genotypes . Here , we combine tracking and modelling of 2D growth in different layers of the growing Arabidopsis leaf to study how growth and division are integrated during organ morphogenesis . We exploit the speechless ( spch ) mutant to allow divisions to be followed in the absence of stomatal lineages , and show how the distribution and rates of growth and cell division vary in the epidermal and subepidermal layers along the proximodistal and mediolateral axes and in time . We further compare these findings to those of wild-type leaves grown under similar conditions . Our results reveal spatiotemporal variation in both growth rates and cell properties , including cell sizes , shapes , and patterns of division . By developing an integrated model of growth and division , we show how these observations can be accounted for by a model in which core components of both growth and division are under spatiotemporal control . Varying parameters of this model illustrates how changes in organ size , cell size , and cell number are likely interdependent , providing a framework for evaluating growth and division mutants .
Tracking cell vertices on the abaxial epidermis of spch seedlings imaged at about 12-h intervals allowed cells at a given developmental stage to be classified into those that would undergo division ( competent to divide , green , Fig 1A ) , and those that did not divide for the remainder of the tracking period ( black , Fig 1A ) . During the first time interval imaged ( Fig 1A , 0–14 h ) , division competence was restricted to the basal half of the leaf , with a distal limit of about 150 μm ( all distances are measured relative to the petiole-lamina boundary , Fig 1 ) . To visualise the fate of cells at the distal limit , we identified the first row of nondividing cells ( orange ) and displayed them in all subsequent images . During the following time intervals , the zone of competence extended together with growth of the tissue to a distance of about 300 μm , after which it remained at this position , while orange boundary cells continued to extend further through growth . Fewer competent cells were observed in the midline region at later stages . Thus , the competence zone shows variation along the proximodistal and mediolateral axes of the leaf , initially extending through growth to a distal limit of about 300 μm and disappearing earlier in the midline region . To monitor execution of division , we imaged spch leaves at shorter intervals ( every 2 h ) . At early stages , cells executed division when they reached an area of about 150 μm2 ( Fig 2A , 0–24 h ) . At later stages , cells in the proximal lamina ( within 150 μm ) continued to execute division at about this cell area ( mean = 151 ± 6 . 5 μm2 , Fig 2B ) , while those in the more distal lamina or in the midline region executed divisions at larger cell areas ( mean = 203 ± 9 . 7 μm2 or 243 . 0 ± 22 . 4 μm2 , respectively , Fig 2A , 2B and 2D ) . Cell cycle duration showed a similar pattern , being lowest within the proximal 150 μm of the lamina ( mean = 13 . 9 ± 0 . 8 h ) and higher distally ( mean = 19 . 4 ± 1 . 8 h ) or in the midline region ( 18 . 9 ± 2 . 1 h , Fig 2C and 2E ) . Within any given region , there was variation around both the area at time of division execution and the cell cycle duration ( Fig 2F and 2G ) . For example , the area at execution of division within the proximal 150 μm of the lamina had a mean of about 150 μm2 , with standard deviation of about 40 μm2 ( Fig 2F ) . The same region had a cell cycle duration with a mean of about 14 h and a standard deviation of about 3 h . Thus , both the area at which cells execute division and cycle duration show variation around a mean , and the mean varies along the proximodistal and mediolateral axes of the leaf . These findings suggest that models in which either cell area at the time of division or cell cycle duration are fixed would be unable to account for the observed data . To determine how cell division competence and execution are related to leaf growth , we measured areal growth rates ( relative elemental growth rates [25] ) for the different time intervals , using cell vertices as landmarks ( Fig 1B ) . Areal growth rates varied along both the mediolateral and proximodistal axis of the leaf , similar to variations observed for competence and execution of division . The spatiotemporal variation in areal growth rate could be decomposed into growth rates in different orientations . Growth rates parallel to the midline showed a proximodistal gradient , decreasing towards the distal leaf tip ( Fig 1C and S1A Fig ) . By contrast , mediolateral growth was highest in the lateral lamina and declined towards the midline , becoming very low there in later stages ( Fig 1D and S1B Fig ) . The region of higher mediolateral growth may correspond to the marginal meristem [26] . Regions of low mediolateral growth ( i . e . , the proximal midline ) showed elongated cell shapes . Models for leaf growth therefore need to account not only for the spatiotemporal pattern of areal growth rates but also the pattern of anisotropy ( differential growth in different orientations ) and correlated patterns of cell shape . Cell size should reflect both growth and division: growth increases cell size while division reduces cell size . Cell periclinal areas were estimated from tracked vertices ( Fig 1E ) . Segmenting a sample of cells in 3D showed that these cell areas were a good proxy for cell size , although factors such as leaf curvature introduced some errors ( for quantifications see S5 Fig , and ‘Analysis of cell size using 3D segmentation’ in Materials and methods ) . At the first time point imaged , cell areas were about 100–200 μm2 throughout most of the leaf primordium ( Fig 1E , left ) . Cells within the proximal 150 μm of the lamina remained small at later stages , reflecting continued divisions . In the proximal 150–300 μm of the lamina , cells were slightly larger , reflecting larger cell areas at division execution . Lamina cells distal to 300 μm progressively enlarged , reflecting the continued growth of these nondividing cells ( Fig 1E and Fig 3A ) . Cells in the midline region were larger on average than those in the proximal lamina , reflecting execution of division at larger cell areas ( Fig 1E and Fig 3C ) . Thus , noncompetent cells increase in area through growth , while those in the competence zone retain a smaller size , with the smallest cells being found in the most proximal 150 μm of the lateral lamina . Visual comparison between areal growth rates ( Fig 2B ) with cell sizes ( Fig 2E ) suggested that regions with higher growth rates had smaller cell sizes . Plotting areal growth rates against log cell area confirmed this impression , revealing a negative correlation between growth rate and cell size ( Fig 4B ) . Thus , rapidly growing regions tend to undergo more divisions . This relationship is reflected in the pattern of division competence: mean areal growth rates of competent cells in the lamina were higher than noncompetent cells , particularly at early stages ( Fig 3I ) . However , there was no fixed threshold growth rate above which cells were competent , and for the midline region there was no clear difference between growth rates of competent and noncompetent cells ( Fig 3I ) . Plotting areal growth rates for competent and noncompetent cells showed considerable overlap ( S6 Fig ) , with no obvious switch in growth rate when cells no longer divide ( become noncompetent ) . Thus , high growth rate broadly correlates with division competence , but the relationship is not constant for different regions or times . To determine how the patterns and correlations observed for the epidermis compared to those in other tissues , we analysed growth and divisions in the subepidermis . The advantage of analysing an adjacent connected cell layer is that unless intercellular spaces become very large , the planar cellular growth rates will be very similar to those of the attached epidermis ( because of tissue connectivity and lack of cell movement ) . Comparing the epidermal and subepidermal layers therefore provides a useful system for analysing division behaviours in a similar spatiotemporal growth context . Moreover , by using the spch mutant , one of the major distinctions in division properties between these layers ( the presence of stomatal lineages in the epidermis ) is eliminated . Divisions in the abaxial subepidermis were tracked by digitally removing the overlying epidermal signal ( the distalmost subepidermal cells could not be clearly resolved ) . As with the epidermis , 3D segmentation showed that cell areas were a good proxy for cell size , although average cell thickness was greater ( S11 Fig , see also ‘Analysis of cell size using 3D segmentation’ in Materials and methods ) . Unlike the epidermis , intercellular spaces were observed for the subepidermis . As the tissue grew , subepidermal spaces grew and new spaces formed ( Fig 5A–5D ) . Similar intercellular spaces were observed in subepidermal layers of wild-type leaves , showing they were not specific to spch mutants ( S8 Fig ) . Vertices and intercellular spaces in the subepidermis broadly maintained their spatial relationships with the epidermal vertices ( Fig 5C , 5E and 5F ) . Comparing the cellular growth rates in the plane for a patch of subepidermis with the adjacent epidermis showed that they were similar ( S9 Fig ) , although the subepidermal rates were slightly lower because of the intercellular spaces . This correlation is expected , because unless the intercellular spaces become very large , the areal growth rates of the epidermal and subepidermal layers are necessarily similar . The most striking difference between subepidermal and epidermal datasets was the smaller size of the distal lamina cells of the subepidermis ( compare Fig 6A with Fig 1E , and Fig 3A with Fig 3B ) . For the epidermis , these cells attain areas of about 1 , 000 μm2 at later stages , while for the subepidermis they remain below 500 μm2 . This finding was consistent with the subepidermal division competence zone extending more distally ( Fig 6B ) , reaching a distal limit of about 400 μm compared with 300 μm for the epidermis . A more distal limit for the subepidermis has also been observed for cell cycle gene expression in wild type [10] . Moreover , at early stages , divisions occurred throughout the subepidermis rather than being largely proximal , as observed in the epidermis , further contributing to the smaller size of distal subepidermal cells ( S10 Fig ) . Despite these differences in cell size between layers , subepidermal cell areal growth rates showed similar spatiotemporal patterns to those of the overlying epidermis , as expected because of tissue connectivity ( compare Fig 6C with Fig 1B ) . Consequently , correlations between growth rate and cell size were much lower for the subepidermis than for the epidermis ( Fig 4B and 4C ) . This difference in the relationship between growth and cell size in different cell layers was confirmed through analysis of cell division competence . In the subepidermis , at early stages there was no clear difference between mean growth rates for competent and noncompetent cells ( Fig 3J cyan , green ) , in contrast to what is observed in the epidermis ( Fig 3I cyan , green ) , while at later stages noncompetent cells had a slightly lower growth rate ( Fig 3J yellow , red ) . To determine how the patterns of growth and division observed in spch related to those in wild type , we imaged a line generated by crossing a spch mutant rescued by a functional SPCH protein fusion ( pSPCH:SPCH-GFP ) to wild type expressing the PIN3 auxin transporter ( PIN3:PIN3-GFP ) , which marks cell membranes in the epidermis [23] . The resulting line allows stomatal lineage divisions to be discriminated from non-stomatal divisions ( see below ) in a SPCH context . At early stages , wild-type and spch leaves were not readily distinguishable based on cell size ( S12 Fig ) . However , by the time leaf primordia attained a width of about 150 μm , the number and size of cells differed dramatically . Cell areas in wild type were smaller in regions outside the midline region , compared with corresponding cells in spch ( Fig 7A ) . Moreover , cell divisions in wild type were observed throughout the lamina that was amenable to tracking ( Fig 7B , 0–12 h ) , rather than being largely proximal . Divisions were observed over the entire lamina for subsequent time intervals , including regions distal to 300 μm ( Fig 7B , 12–57 h ) . These results indicate that SPCH can confer division competence in epidermal cells outside the proximal zone observed in spch mutants . To further clarify how SPCH influences cell division , we used SPCH-GFP signal to classify wild-type cells into two types: ( 1 ) Stomatal lineage divisions , which include both amplifying divisions ( cells express SPCH strongly around the time of division and retain expression in one of the daughter cells ) ( S1 Video , orange/yellow in Fig 7C ) and guard mother cell divisions ( SPCH expression is bright and diffuse during the first hours of the cycle , transiently switched on around time of division , and then switched off in both daughters ) . ( 2 ) Non-stomatal divisions , in which SPCH expression is much weaker , or only lasts <2 h , and switches off in both daughter cells ( S2 Video , light/dark green in Fig 7C ) . If cells with inactive SPCH behave in a similar way in wild-type or spch mutant contexts , we would expect non-stomatal divisions to show similar properties to divisions in the spch mutant . In the first time interval , non-stomatal divisions ( green ) were observed within the proximal 150 μm ( Fig 7C , 0–12 h ) , similar to the extent of the competence zone in spch ( Fig 1A , 0–14h ) . The zone of non-stomatal divisions then extended to about 250 μm and became restricted to the midline region . After leaf width was greater than 0 . 45 mm , we did not observe further non-stomatal divisions in the midline region , similar to the situation in spch leaves at a comparable width ( Fig 1A , 58-74h , 0 . 48 mm ) . These results suggest that similar dynamics occur in the non-stomatal lineages of wild type and the spch mutant . To determine how SPCH modulates division , we analysed stomatal and non-stomatal divisions in the lamina . Considerable variation was observed for both the area at which cells divide ( 25–400 μm2 ) and cell cycle duration ( 8–50 h ) ( S13 Fig ) . The mean area at which cells execute division was greater for non-stomatal divisions ( about 165 ± 28 μm2 [1 . 96 × standard error] ) than stomatal divisions ( about 80 ± 6 μm2 ) ( S13 Fig ) . Similarly , cell cycle durations were longer for non-stomatal divisions ( about 25 ± 3 h ) compared with stomatal divisions ( about 18 ± 1 h ) . These results suggest that in addition to conferring division competence , SPCH acts cell autonomously to promote division at smaller cell sizes and/or for shorter cell cycle durations . Given the alteration in cell sizes and division patterns in wild type compared to spch , we wondered if these may reflect alterations in growth rates . When grown on agar plates , spch mutant leaves grow more slowly than wild-type leaves ( S14A Fig ) . The slower growth of spch could reflect physiological limitations caused by the lack of stomata , or an effect of cell size on growth—larger cells in spch cause a slowing of growth . However , the tracking data and cell size analysis of spch and wild type described above were carried out on plants grown in a bio-imaging chamber in which nutrients were continually circulated around the leaves . Growth rates for wild type and spch leaves grown in these conditions were comparable for much of early development , and similar to those observed for wild type on plates ( compare Fig 7D with Fig 1B , S14 Fig ) . These results suggest that the reduced growth rates of spch compared with wild type at early stages on plates likely reflect physiological impairment caused by a lack of stomata rather than differences in cell size . As a further test of this hypothesis , we grew fama ( basic helix-loop-helix transcription factor bHLH097 ) mutants , as these lack stomata but still undergo many stomatal lineage divisions [27] . We found that fama mutants attained a similar size to spch mutants on plates , consistent with the lack of stomata being the cause of reduced growth in these conditions ( S14 Fig ) . Plots of cell area against growth rates of tracked leaves grown in the chamber showed that , for similar growth rates , cells were about three times smaller in wild type compared with spch ( compare Fig 4A with Fig 4B ) . Thus , the effects of SPCH on division can be uncoupled from effects on growth rate , at least at early stages of development . At later stages ( after leaves were about 1 mm wide ) , spch growth in the bio-imaging chamber slowed down compared with wild type , and leaves attained a smaller final size . This later difference in growth rate might be explained by physiological impairment of spch because of the lack of stomata , and/or by feedback of cell size on growth rates . This change in later behaviour may reflect the major developmental and transcriptional transition that occurs after cell proliferation ceases [9] . The above results reveal that patterns of growth rate , cell division , and cell size and shape exhibit several features in spch: ( 1 ) a proximal corridor of cell division competence , with an approximately fixed distal limit relative to the petiole-lamina boundary; ( 2 ) the distal limit is greater for subepidermal ( 400 μm ) than epidermal tissue ( 300 μm ) ; ( 3 ) a further proximal restriction of division competence in the epidermis at early stages that extends with growth until the distal limit of the corridor ( 300 μm ) is reached; ( 4 ) larger and narrower cells in the proximal midline region of the epidermis; ( 5 ) a proximodistal gradient in cell size in the epidermal lamina; ( 6 ) a negative correlation between cell size and growth rate that is stronger in the epidermis than subepidermis; ( 7 ) variation in both the size at which cells divide and cell cycle duration along both the proximodistal and mediolateral axes; and ( 8 ) variation in growth rates parallel or perpendicular to the leaf midline . In wild-type plants , these patterns are further modulated by the expression of SPCH , which leads to division execution at smaller cell sizes and extension of competence , without affecting growth rates at early stages . Thus , growth and division rates exhibit different relations in adjacent cell layers , even in spch , in which epidermal-specific stomatal lineages are eliminated , and division patterns can differ between genotypes ( wild type and spch ) without an associated change in growth rates . These observations argue against spatiotemporal regulators acting solely on the execution of division , which then influences growth , as this would be expected to give conserved relations between division and growth . For the same reason , they argue against a single-point-of-control model in which spatiotemporal regulators act solely on growth , which then secondarily influences division . Instead , they suggest dual control , with spatiotemporal regulators acting on both growth and division components . With dual control , growth and division may still interact through cross-dependencies , but spatiotemporal regulation does not operate exclusively on one or the other . To determine how a hypothesis based on dual control may account for all the observations , we used computational modelling . We focussed on the epidermal and subepidermal layers of the spch mutant , as these lack the complications of stomatal lineages . For simplicity and clarity , spatiotemporal control was channelled through a limited set of components for growth and division ( Fig 8A ) . There were two components for growth under spatiotemporal control: specified growth rates parallel and perpendicular to a proximodistal polarity field ( Kpar and Kper , respectively ) [16] . Together with mechanical constraints of tissue connectivity , these specified growth components lead to a pattern of resultant growth and organ shape change [28] . There were two components for cell division under spatiotemporal control: competence to divide ( CDIV ) , and a threshold area for division execution that varies around a mean ( Ā ) . Controlling division execution by a threshold cell size ( Ā ) introduces a cross-dependency between growth and division , as cells need to grow to attain the local threshold size before they can divide . The cross-dependency is indicated by the cyan arrow in Fig 8A , feeding information back from cell size ( which depends on both growth and division ) to division . An alternative to using Ā as a component of division-control might be to use a mean cell cycle duration threshold . However , this would bring in an expected correlation between high growth rates and large cell sizes ( for a given cell cycle duration , a faster-growing cell will become larger before cycle completion ) , which is the opposite trend of what is observed . Spatiotemporal regulators of growth and division components can be of two types: those that become deformed together with the tissue as it grows ( fixed to the tissue ) and those that maintain their pattern to some extent despite deformation of the tissue by growth ( requiring mobile or diffusible factors ) [28] . In the previously published growth model , regulatory factors were assumed , for simplicity , to deform with the tissue as it grows [16] . These factors comprised a graded proximodistal factor ( PGRAD ) , a mediolateral factor ( MID ) , a factor distinguishing lamina from petiole ( LAM ) , and a timing factor ( LATE ) ( S15A and S15B Fig ) . However , such factors cannot readily account for domains with limits that remain at a constant distance from the petiole-lamina boundary , such as the observed corridors for division competence . This is because the boundary of a domain that is fixed to the tissue will extend with the tissue as it grows . We therefore introduced a mobile factor , proximal mobile factor ( PMF ) , that was not fixed to the tissue to account for these behaviours . This motivation is similar to that employed by others [11–13] . PMF was generated at the petiole-lamina boundary and with appropriate diffusion and decay coefficients such that PMF initially filled the primordium and then showed a graded distribution as the primordium grew larger , maintaining a high concentration in the proximal region and decreasing towards the leaf tip ( S15C and S15D Fig ) . This profile was maintained despite further growth , allowing thresholds to be used to define domains with relatively invariant distal limits . Further details of the growth model are given in Materials and methods , and the resultant growth rates are shown in S16 Fig ( compare with Fig 1B and 1D ) . Cells were incorporated by superimposing polygons on the initial tissue or canvas ( S15A Fig , right ) . The sizes and geometries of these virtual cells ( v-cells ) were based on cells observed at corresponding stages in confocal images of leaf primordia [16] . The vertices of the v-cells were anchored to the canvas and displaced with it during growth . Cells divided according to Errera’s rule: the shortest wall passing through the centre of the v-cell [29] , with noise in positioning of this wall incorporated to capture variability . V-cells were competent to divide if they expressed factor CDIV , and executed division when reaching a mean cell target area , Ā . As the observed area at time of division was not invariant ( Fig 2F ) , we assumed the threshold area for division varied according to a standard deviation of σ = 0 . 2Ā around the mean . CDIV and Ā are the two core components of division that are under the control of spatiotemporal regulators in the model ( Fig 8A , 8C and 8D ) . Variation between epidermal and subepidermal patterns reflects different interactions controlling cell division ( interactions colour coded red and blue , respectively , in Fig 8C and 8D ) . We first modelled cell divisions in the subepidermis , as this layer shows a more uniform pattern of cell sizes ( Fig 3B and Fig 6A ) . Formation of intercellular spaces was simulated by replacing a random selection of cell vertices with small empty equilateral triangles , which grew at a rate of 2 . 5% h−1 , an average estimated from the tracking data . To account for the distribution of divisions and cell sizes , we assumed that v-cells were competent to divide ( express CDIV ) where PMF was above a threshold value . This value resulted in the competence zone extending to a distal limit of about 400 μm . To account for the proximodistal pattern of cell areas in the lamina ( Fig 3B and Fig 6A ) and larger cells in the midline ( Fig 3D and Fig 6A ) , we assumed that Ā was modulated by the levels of PMF , PGRAD , and MID ( Fig 8D , black and blue ) . These interactions gave a pattern of average v-cell areas and division competence that broadly matched those observed ( compare Fig 8E and 8F with Fig 6A and 6B , and Fig 3F and 3H with 3B and 3D , S3 Video ) . For the epidermis , the zone of division competence was initially in the proximal region of the primordium and then extended with the tissue as it grew ( Fig 1A ) . We therefore hypothesised that in addition to division being promoted by PMF , there was a further requirement for a proximal factor that extended with the tissue as it grew . We used PGRAD to achieve this additional level of control , assuming CDIV expression requires PGRAD to be above a threshold level ( Fig 8C , red and black ) . V-cells with PGRAD below this threshold were not competent to divide , even in the presence of high PMF . Thus , at early stages , when PMF was high throughout the primordium , the PGRAD requirement restricted competence to the proximal region of the leaf ( Fig 8H ) . At later stages , as the PGRAD domain above the threshold extended beyond 300 μm , PMF became limiting , preventing CDIV from extending beyond about 300 μm . To account for the earlier arrest of divisions in the midline region ( Fig 1A ) , CDIV was inhibited by MID when LATE reached a threshold value ( Fig 8C , red ) . As well as CDIV being regulated , the spatiotemporal pattern of Ā was modulated by factors MID and PMF ( Fig 8D black ) . With these assumptions , the resulting pattern of epidermal divisions and v-cell sizes broadly matched those observed experimentally for the epidermis ( compare Fig 8G with Fig 1E , S4 Video ) . In particular , the model accounted for the observed increases in cells sizes with distance from the petiole-lamina boundary , which arise because of the proximal restrictions in competence ( compare Fig 3E and 3G with Fig 3A and 3C ) . The model also accounted for the elongated cell shapes observed in the midline region , which arise through the arrest of division combined with low specified growth rate perpendicular to the polarity . Moreover , the negative correlations between growth rates and cell size , not used in developing the model , were similar to those observed experimentally ( Fig 4B and 4D ) . These correlations arise because both growth and division are promoted in proximal regions . We also measured the cell topology generated by the epidermal model . It has previously been shown that the frequency of six-sided neighbours observed experimentally for the spch leaf epidermis is very low compared with that for other plant and animal tissues and also with that generated by a previous implementation of Errera’s rule ( S17 Fig ) [30] . The topological distribution generated by the epidermal leaf model gave a six-sided frequency similar to that observed experimentally , falling two standard deviations away from the mean and thus close to a reasonable fit ( S17 Fig ) . The increased similarity of the model output to the spch leaf epidermal topology , compared with a previous implementation of Errera’s rule [31] , may reflect the incorporation of anisotropic growth in our model . If polarity is removed from our model to render specified growth as isotropic ( while preserving local areal growth rates ) , the frequency of six-sided neighbours increases , becoming more like the empirical data for the shoot apical meristem ( S17 Fig ) . A further likely contribution to the lowering of six-sided neighbour frequency generated by our model is the use of random noise to displace the positioning of new walls , rather than positioning them always to pass precisely through the cell centre . Thus , our analysis shows how incorporating more realistic growth patterns can be valuable in evaluating division rules . Taken together , the simulations show that the pattern of growth and division can be broadly accounted for by factors modulating specified growth rates ( Kpar and Kper ) and cell division components ( CDIV and Ā ) . Variation between epidermal and subepidermal patterns generated by the models reflects different interactions controlling cell division ( Fig 8C and 8D ) . Many mutants have been described that influence cell division and/or leaf size [32 , 33] . To gain a better understanding of such mutants , we explored how changes in key parameters in our model may alter leaf size , cell size , and cell number . As leaf size is normally measured at maturity , we first extended our analysis to later stages of development . Tracking spch to later stages of development showed that overall growth rates declined , on average , while remaining relatively high towards the proximal region of the lamina ( S4B Fig ) , consistent with a previous study [18] . Cell divisions were not observed after the leaf reached a width of about 0 . 9 mm ( S4A Fig , 96h ) . To capture arrest of division , we assumed that CDIV was switched off throughout the leaf after LATE reached a threshold value . In the previously published growth model [16] , the decline of growth rates with developmental time was captured through an inhibitory effect of LATE on growth . To extend the model to later stages and bring about eventual arrest of growth , we assumed that LATE increased exponentially after 189 h and inhibited both Kper and Kpar thereafter . Parameters for growth inhibition were adjusted to give a final leaf width of about 3 mm , which was the final size attained for leaf 1 in spch mutants in the bio-imaging chamber . The v-cell sizes generated by the model broadly matched the patterns observed ( Fig 9A and 9B , S5 Video ) . As epidermal divisions have ceased by the time the spch leaf is about 1 mm wide , all the growth depicted in Fig 9A and 9B occurs in the absence of division ( i . e . , cell expansion ) . However , a notable discrepancy between the model output and the experimental data was the generation of distal v-cells that exceeded the values observed ( about 20 , 000 μm2 compared with about 10 , 000 μm2 ) . A similar result was obtained if the model was tuned to match not only the final leaf width but also the reduced growth rate of spch in the growth chamber at later stages ( S14B and S14C Fig ) . A better fit was obtained by inhibiting specified growth rates in distal regions at later stages . This inhibition was implemented by introducing inhibitory factors with levels that increased distally . The result was that distal v-cells remained at or below about 10 , 000 μm2 ( Fig 9C and S6 Video ) . We refer to this as the limit-free model . Another way of limiting the size of distal v-cells was to introduce feedback from cell size to growth , so that the specified growth rate decreased as v-cells approached upper size limits ( Fig 9J and S7 Video ) . This feedback corresponds to introducing a further interaction in the regulatory pathway ( Fig 8A , magenta ) . We refer to this as the limiting cell size model . We varied parameters in both the limit-free model ( Fig 9C ) and the limiting cell size model ( Fig 9J ) to see how the parameters influence cell number , cell size , and final leaf size . Increasing Ā by a constant amount did not change leaf size with the limit-free model but resulted in fewer , larger v-cells ( Fig 9D ) . Reducing Ā resulted in a leaf with more v-cells that were , on average , smaller but did not change leaf size ( Fig 9E ) . With the limiting cell size model , increasing or decreasing Ā had similar effects as with the limit-free model but also slightly reduced or increased leaf size ( Fig 9K and 9L ) . Thus , it is possible to affect cell number and size without a major effect on organ size or growth . To investigate how changing growth parameters influences cell numbers and areas , we reduced the specified growth rates ( values for Kpar and Kper ) by 5% . For the limit-free model this resulted in a smaller leaf with both smaller and fewer v-cells ( Fig 9F ) . There were fewer cells because they grew more slowly and thus took longer to reach Ā , and cells were smaller because they grew at a slower rate after they had ceased dividing . Conversely , increasing specified growth rate by 5% led to larger leaves , with more v-cells that were , on average , larger ( Fig 9G ) . The model with limiting cell size gave similar results ( Fig 9M and 9N ) . Thus , modulating growth rates has consequences on organ size , cell size , and cell number . This may account for why many mutants with smaller organs have both fewer cells and smaller cells [34] . To examine the effect of changes in developmental timing , we altered the onset of LATE . Moving the onset earlier for the limit-free model led to smaller leaves because of the earlier decline in growth rate ( Fig 9H ) . There were fewer v-cells because of the earlier arrest of division , and there was also a slight reduction in v-cell size . Delaying the onset of LATE had the opposite effect of increasing leaf size , cell number , and cell size ( Fig 9I ) . The limiting cell size model gave similar results ( Fig 9O and 9P ) . Thus , changes in developmental timing affected organ size and cell number , with a lesser effect on cell size . This is because changing LATE shifts both the onset of the growth rate decline and the time of division arrest ( inactivation of CDIV ) . A further application of the model is to explore the effects of the environment on leaf growth and division . To illustrate this possibility , we analysed data for the spch mutant grown on plates , which exhibits a greatly reduced growth rate compared with growth in the chamber ( S14A and S14B Fig ) . A prediction of the model is that cell divisions should cease when the leaf is at a smaller size ( i . e . , the leaf will have grown less by the time the threshold value of LATE for division arrest is reached ) . In addition , as spch plants grown on plates have impaired general physiology , the rate of developmental progression ( physiological time ) may also be slowed down . We simulated these effects by modifying the model parameters such that the overall growth rate was reduced by 40% and physiological time reduced by 45% . This gave a growth curve matching that observed for spch grown on plates ( blue line , S14A Fig ) . As expected , this model takes longer to attain a given leaf width ( e . g . , 0 . 5 mm ) than the original model . The resulting cell areas are larger at the 0 . 5-mm leaf-width stage , particularly in proximal regions , because divisions arrest when the leaf is at a smaller size , so all subsequent cell growth occurs in the absence of division ( Fig 10A and 10B and S18 Fig ) . To test this prediction of enlarged cell size , we compared leaves when they had attained a width of about 0 . 5 mm ( Fig 10C and 10D ) , which is just before divisions cease for spch grown in the chamber ( Fig 1 ) . Cells in the proximal lamina of the chamber-grown leaves were relatively small ( mean = 123 . 3 ± 6 . 4 μm2 for region shown in Fig 10I ) , typical of dividing cells ( Fig 10C and 10G ) ; whereas those of the plate-grown leaves were larger ( mean = 199 . 8 ± 17 . 3 μm2 for region shown in Fig 10J ) , indicating division arrest ( Fig 10D and 10H and S18 Fig ) . Proximal lamina cells in plate-grown leaves also showed greater shape complexity , typical of pavement cells that have ceased division ( Fig 10K–10N and S18 Fig ) . These results suggest that cell divisions in much of the lamina cease when the leaf is smaller for plate-grown compared to chamber-grown leaves , as predicted by the model . The sizes of midline cells for plate-grown leaves predicted by the model are larger than those observed ( compare Fig 10B with Fig 10H ) , indicating that withdrawal of competence from this region , as implemented in the model , may be activated too early . Conversely , the most proximal lamina cells in the plate-grown leaves ( dark blue cells , Fig 10H ) are smaller than predicted ( Fig 10B ) , suggesting that the uniform arrest of division when LATE reaches a threshold value is an oversimplification .
Execution of leaf cell division does not occur at an unvarying cell size , even within a given region and developmental stage . Similar variability has been observed for cell divisions in apical meristems [21 , 38] . Variability may reflect experimental errors in estimation of cell size , stochasticity in the process of division , and/or mechanisms other than geometric size sensing that influence division execution ( e . g . , factors such as vacuole size , which is not monitored in our analysis ) . We model such variability by explicitly adding variation around a mean threshold size needed for division , Ā . Controlling division execution by a threshold cell size ( Ā ) introduces a cross-dependency between growth and division , as cells need to grow to attain the local threshold size before they can divide . An alternative to using Ā would be to use a mean cell cycle duration threshold . However , this would bring in an expected correlation between high growth rates and large cell sizes ( for a given cell cycle duration , faster growing cells will become larger before cycle completion ) , which is the opposite of the correlation observed . In contrast to the epidermal layer , intercellular spaces are observed in the subepidermis of wild-type and spch from early stages . The spaces may originate , in part , from a reduction in adhesion between subepidermal cells , allowing cell walls to become detached from each other . In addition to reduced adhesion , a further requirement for intercellular spaces is that cells are not too tightly packed against each other . Packing may be reduced if subepidermal cells have lower specified growth rates than the epidermis . Subepidermal cells could move away or be pulled apart from each other , as epidermal growth creates more space than they can fill through their own expansive growth . According to this view , the epidermis rather than the subepidermis provides the expansive force driving planar growth , in contrast to what has been described for other tissues , such as the stem [39] . A primary role for the epidermis in driving planar growth is also consistent with the observed developmental effects of epidermal gene activity [40] . However , it is possible that the subepidermis provides a restraint on growth , which could account for the effect of subepidermal tissue on leaf shape in some chimeras [41] . Spatiotemporal control of growth and division in the model of spch is established through combinatorial interactions between five factors: PGRAD , MID , LAM , LATE , and a mobile factor that allows proximal corridors with fixed distal limits to be established ( PMF ) . PMF is similar to the previously proposed mobile growth factor [11] , except that the effect of PMF on division does not have a consequential effect on growth . To account for the difference in distal limits of the division corridor between cell layers , PMF action extends more distally in the subepidermis compared with the epidermis , either because the competence threshold requirement for PMF is lower in the subepidermal layer , or because PMF levels are higher . A candidate factor for coordinating proliferation between layers is the transcriptional coactivator ANGUSTIFOLIA3 [42 , 43] . Candidates for LAM are LEAFY PETIOLE [44] and members of the YABBY gene family [45] , which are expressed in the lamina and promote lateral outgrowth . A fixed corridor for division has also been described for other systems such as the root , where a division zone is maintiained at a distance of about 300–500 μm from the quiescent centre in Arabidopsis [46] . In contrast to the leaf , regions of highest growth rate in the root are outside the cell division zone , providing further support for a dual control mechanism . The spatial extent of the division zone in roots is maintained through auxin-cytokinin interactions [47] . Auxin-cytokinin interactions also influence leaf growth and division: temporal arrest of leaf growth depends on auxin-induced cytokinin breakdown [48]; increased cytokinin degradation in leaf primordia can accelerate termination of cell proliferation [49]; and accumulation of specific cytokinins may promote indeterminate leaf growth [50] . However , it is currently unclear whether auxin , cytokinin , and/or other molecular players underlie PMF . A limitation of our model is that it does not consider modulation of growth or division near the leaf margin , creating serrations [51 , 52] . Serrations have previously been modelled by displacement of the leaf outline without modelling the tissue growth explicitly [52 , 53] . In terms of the modelling framework described here , they may reflect alterations in polarity and/or growth rates of tissue , and accounting for these behaviours would require the introduction of additional factors into the model , as illustrated by generation of winglike outgrowths in barley lemma mutants [54] . To account for the further proximal restriction of competence in the epidermis at early stages , PGRAD limits divisions in the epidermis until the distal limit set by PMF is reached . PMF also interacts with MID in the epidermis , accounting for larger cells in the midline region . The elongated shape of proximal midline cells is a result of early arrest of division combined with low specified growth rates perpendicular to the proximodistal polarity . Divisions in the wild-type epidermis are also influenced by SPCH . We show that SPCH acts autonomously in the epidermis to confer competence , and has little impact in the proximal midline region , where its activity has previously been shown to be low [55] . The autonomous effect of SPCH on division competence contrasts with its nonautonomous effects at later stages of development , with regard to layer thickness and photosynthetic capacity [56] . This difference in autonomy may reflect primary and secondary consequences of SPCH activity . SPCH also promotes asymmetric divisions and divisions at smaller cell sizes or shorter cell cycle durations . The complex pattern of divisions in wild type epidermis observed here and elsewhere [9] would thus reflect the combined effect of PMF , PGRAD , MID , and SPCH , although the molecular basis of these interactions remains to be established . In agreement with [24] , we observed that mean cell cycle duration is relatively constant for wild type ( about 20 h ) . However , cell cycle duration varies from 8 h to 50 h around the mean . Some of this variation depends on whether SPCH is active: epidermal cells that do not show high SPCH activity divide at a larger cell size and longer cell cycle duration . Moreover , the size at which cells with active SPCH divide is not fixed but becomes progressively smaller with successive divisions [23] , indicating that cell cycle duration likely becomes shorter as well . Thus , the spatiotemporal variation in cell cycle duration may be the consequence of variation in growth rates ( for a given threshold division size , cell cycle duration depends on growth rate ) and/or direct control of cell cycle length . Most small-leaf mutants have both fewer and smaller cells [34] . Such outcomes can be generated with the model by reducing specified growth rates . The leaves end up smaller because of the lower growth rate , cells are smaller because they grow less after divisions have arrested , and there are fewer cells because they grow more slowly and thus take longer to reach Ā . Thus , the observation that organ size , cell size , and cell number are commonly reduced together in mutants is a natural outcome of the model . Change in developmental timing through factor LATE also leads to changes in leaf size , although this is mainly reflected in changes in cell number rather than cell size . This is because changing LATE shifts both the onset of growth rate decline and the time of division arrest ( loss of division competence ) . Such variation in developmental timing could underlie mutants that change organ size with little or no effect on cell size , such as kluh and big brother [57 , 58] . Loss of expression of D-type cyclins leads to premature termination of cell division and fewer cells autonomously in each layer , without a major change in leaf size [59 , 60] . Such features can be captured by changing model parameters that are specific to cell division , such as the value of Ā , in one or more layers ( Fig 9D , 9E , 9K and 9L ) . This situation corresponds to compensation [61–63] , as change in cell number is counterbalanced by a change in cell area ( organ size is preserved ) . However , no dedicated mechanism for counterbalancing is needed , as division is under separate spatiotemporal control from growth in our model . Although execution of division does not have an immediate effect on growth rates in our model , we explore the possibility of feedback from division on growth at later developmental stages . If growth slows down when cells approach an upper size limit , then cell division could postpone the slowing down of growth by reducing cell size . Such a mechanism would lead to cell division extending the duration of growth , thus increasing leaf size . Mature leaves display an array of final cell sizes that correlate with levels of endoreduplication [64 , 65] , suggesting that as cells approach a size limit , endocycles are induced that allow them to surpass the limit . If endoreduplication is impaired , these cell size limits may not be so easily overcome , leading to smaller leaves with smaller cells [66 , 67] . However , the extent to which endoreduplication is limited in wild type and thus may constrain final cell size and growth is unclear . Through modelling , we show that it is possible to account for the data with or without feedback from cell size on growth . If endocycles are promoted as cells enlarge , then promoting division ( e . g . , by reducing Ā ) should lead to lower levels of endoreduplication ( as cells will be smaller ) . This prediction is in accord with the effect of overexpressing D-type cyclins , which leads to smaller cells with lower levels of endoreduplication [68] . Conversely , inhibiting cell division ( e . g . , by increasing Ā ) should give larger cells and higher levels of endoreduplication , as observed with cycd3 mutants [7] . However , if both division and the ability to endoreduplicate are impaired , cell size may eventually feedback to inhibit growth rate , giving smaller leaves and perhaps accounting for the phenotype of ant mutants , which have smaller organs with larger cells that do not endoreduplicate more than wild type [7 , 69] . A further application of the model is to explore the effects of different environments on leaf growth and division . As an illustration , we compared leaves of spch mutants grown in a bio-imaging chamber ( in which nutrients were continually circulated around the leaves ) with those grown on agar plates ( in which growth rate is greatly reduced ) . Cell divisions arrested when leaves were at a smaller size in the slow-growing conditions , as predicted by the model in which division arrest depends on a timing mechanism ( LATE ) . However , the growth and cells sizes observed suggests that the timing mechanisms are not based on external time but passage of physiological time , which may also be affected by altered growth conditions . The model presented here identifies core components of growth and division that may be regulated and interact to generate the spatiotemporal patterns observed . Further integrative studies on growth and division at the subcellular , cellular , and tissue level in different genotypes and environments should help provide a deeper understanding of the mechanisms by which regulatory factors are established and control these core components .
For tracking growth of the speechless mutant , we used the previously published Arabidopsis line , spch-1 , containing a fluorescently labelled plasma membrane marker [70] . To more precisely determine division execution times , we crossed the spch mutant to an Arabidopsis line containing fluorescently labelled nuclei , HTA11-GFP [71] , and PIN3:PIN3-GFP [72] , which labels plasma membranes in the epidermal layer only . For tracking growth in the wild-type background and to distinguish cells in the stomatal lineage , we used the previously published Arabidopsis line containing pSPCH:SPCH-GFP and PIN3:PIN3-GFP [23] . For measuring leaf widths in the fama mutant we used the previously published line fama-1 ( Ohashi-Ito and Bergmann , 2006 ) . Seeds were surface sterilised with 70% ethanol containing 0 . 05% Sodium Dodecyl Sulfate ( SDS ) for 10 min and then rinsed with 100% ethanol . Sterilised seeds were sown on petri dishes containing 25 mL of MS growth media {1× Murashige and Skoog salt mixture , 1% ( w/v ) sucrose , 100 mg/mL inositol , 1 mg/mL thiamine , 0 . 5 mg/mL pyridoxin , 0 . 5 mg/mL nicotinic acid , 0 . 5 mg/mL MES , 0 . 8% ( w/v ) agar , pH 5 . 7} and kept at 4 oC in the dark for 72 h ( stratification ) . Plates were then transferred to a controlled environment room ( CER ) at 20 oC in long-day conditions ( 16-h light/8-h dark cycles ) for 5–8 d . At 5–8 d after stratification , seedlings were transferred under sterile conditions into an autoclaved optical live-imaging chamber [16 , 73] and continuously supplied with 1/4 strength MS liquid growth medium , including sucrose to support growth . Time-lapse imaging was carried out at regular intervals using a Leica SP5 Confocal microscope , a Zeiss LSM 5 EXCITER confocal microscope , or a Zeiss LSM 780 confocal microscope . For experiments imaged with a high temporal resolution ( intervals of 1–2 h ) , the chamber remained mounted on the microscope stage for the duration of the experiment , with room temperature and photoperiod set to be similar to that of the CER in which seedlings were germinated . For experiments with a longer interval between imaging ( 12–24 h ) , the chamber was returned to the CER between confocal imaging . Experiments were carried out on leaf 1 within the range of 0 . 15–2 . 75 mm width . Seedlings were positioned in the chamber such that the abaxial epidermis of the leaf was oriented approximately parallel and adjacent to the coverslip , although it curved away to some extent at the leaf margins . This curvature affected the leaf outline produced when projected images were made from confocal image stacks . Leaf outlines ( indicated by dotted lines in Fig 1 , Fig 2 , Fig 6 , Fig 7 , Fig 9 , S2 Fig , S3 Fig , S4 Fig , S6 Fig , and S9 Fig ) reflect projections onto the imaging plane rather than being corrected for curvature and thus convey a shape that appears narrower than the actual leaf outline . Some regions could not be tracked because of occlusion by overlapping leaves ( at early developmental stages ) or because movement in the z-dimension caused parts of the leaf to go out of focus . Thus , some cell lineages could not be traced all the way back to the initial time point . Images are available from https://figshare . com/s/b14c8e6cb1fc5135dd87 . To facilitate cell tracking , confocal image stacks were converted into 2D projections using either Volviewer [74] ( http://cmpdartsvr3 . cmp . uea . ac . uk/wiki/BanghamLab/index . php/VolViewer ) or Fiji [75] . For early stages , when the leaf could be captured in a single scan , VolViewer was used to create a projection of the leaf surface . At later stages , when leaves were larger , multiple overlapping tiled scans were required to capture the entire leaf . In such cases , Fiji was used to create multiple 2D projections , which were merged together using Photoshop to create a single composite image . Leaf width was measured in 3D , when possible , using VolViewer . For later stages , leaf width was measured in 2D from merged projections using Fiji . Projections of the subepidermal layer were created in VolViewer using the ‘Depth-Peal Shader’ lighting editor . Several projections were created for each z-stack ( using different parameters to reveal as many cells as possible in approximately the middle of the cell layer ) and merged together using Photoshop to create a composite image . Projected confocal images were used to calculate growth rates and cell areas and monitor cell division dynamics in 2D by placing points around the vertices of individual cells using PointTracker , as described in [16] . A toolset ( Track ‘n’ R ) was created for ImageJ ( https://imagej . nih . gov/ij/ ) to facilitate access to ImageJ macros and offer improved visualisation of PointTracker data using R [76] . Track ‘n’ R was used to create leaf outlines , visualise the zone of cell division , and analyse cell lineages and to display cell cycle duration , cell area at division execution , and growth rates ( source code and detailed instructions for Track’n’R and PointTracker have been deposited at https://github . com/fpantin/Track-n-R and https://figshare . com/s/b14c8e6cb1fc5135dd87 respectively ) . Graphical outputs from Track ‘n’ R were reoriented so that the leaf tip pointed upwards . Cellular growth rates over a time interval t1–t2 were calculated according to ln ( At2−At1 ) / ( t2−t1 ) , where At1 is cell area at t1 and At2 is cell area at t2 . If a cell divided in this interval , At2 was the area of the clone it gave rise to at time t2 . For each tracking experiment , the first row of nondividing cells was identified in the first time point and coloured orange by hand using Photoshop . These cells were identified in each subsequent image and also coloured orange ( Fig 1A and S3 Fig and S4 Fig ) . The approximate location of the petiole-lamina boundary was identified based on the shape of the leaf outline in the last image available for each dataset . A cell was identified in the midline of this image , in line with the base of the leaf lamina . This cell was then traced back to through each image to its earliest ancestor in the first time point , thus identifying the location of the petiole-lamina boundary even when the leaf shape was less developed . Cells were identified as part of the midline region based on appearance ( shape and location ) in the last image of each tracking experiment . The lineage of these cells were traced back to the beginning of the experiment ( S2 Fig ) . Cells that did not form part of the midline region were classed as lamina cells . For 3D segmentation and volume measurements , confocal image stacks were processed using Python scripts , as described [17] , with additional scripts added to measure the external surfaces of epidermal cells in 3D and the corresponding 2D projections ( source code and detailed instructions have been deposited at https://figshare . com/s/b14c8e6cb1fc5135dd87 ) . Fiji macros [75] using the 3D Viewer and Point Picker plugins were used to visualise images and select cells during manual quality control . For the epidermis , plotting projected segmentation-based area against vertex-based area gave a good linear fit ( R2 = 0 . 87 ) with a gradient of about 1 , showing that vertex-based cell area is a good proxy for projected cell area ( S5A Fig ) . Areas extracted from the cell surface plotted against vertex-based cell area also gave a good linear fit ( R2 = 0 . 77 ) with a gradient of about 1 . 2 ( S5B Fig ) . The higher value for the gradient likely reflects curvature of the cell surface and the leaf , both of which increase area compared to projected values . Nevertheless , segmented surface area remains linearly related to vertex-based area . Plotting cell volume against segmentation-based cell surface area gave a linear fit , with R2 = 0 . 91 and a gradient suggesting an approximately constant cell thickness of about 9 μm ( S5C Fig ) . Variation in cell thickness is displayed by plotting cell volume divided by surface area as a heat map . Although a slight increase in cell thickness was observed in the proximal midline ( about 15 μm ) , cell thickness showed relatively little spatial variation for much of the lamina ( S5D Fig ) , compared with the striking spatiotemporal variation in cell area ( S5E Fig ) and cell volume ( S5F Fig ) . Thus , the major contribution to cell size variation derives from cell area rather than cell thickness . These results are also consistent with fixed leaf sections shown in [10] , which have epidermal cells in the range of 8–15 μm thick ( measured according to the scale in the published images ) . For the subepidermal cell layer , fewer cells could be segmented in 3D because the bases of the cells were too deep within the tissue to be captured clearly by confocal imaging . However , around 13 cells could be segmented and plotting projected segmentation-based area against vertex-based cell area gave a good linear fit ( R2 = 0 . 98 ) with a gradient of about 1 ( S11A Fig ) . Plotting the volume of these cells against projected segmentation-based cell areas showed that they had similar thickness to epidermal cells of the same area , except for cells in the proximal midline region , where subepidermal cells have a greater volume because of increased thickness ( S11B and S11C Fig ) . These results are also consistent with fixed leaf sections shown in [10] , which have subepidermal cells in the range of 9–14 μm thick . To quantify cell shape complexity , we employed Lobe-Contribution Elliptic Fourier Analysis ( LOCO-EFA ) , a method to decompose the outline of each cell into a list of biologically meaningful descriptors [77] . The LOCO-EFA decomposition was used to estimate a measure of cell shape complexity , coined the ‘cumulative difference’ ( CD ) , which is the integral over all LOCO-EFA modes larger than 2 of the mismatch ( Exclusive OR or XOR ) between the original and reconstituted shape , yielding a scalar value representing the degree of shape complexity of each cell [77] . We used this measure normalised to cell area; hence , a small or large cell with the same shape will yield the same cell complexity measure ( CD ) . It ranged from zero ( low complexity , which describes perfectly circular or elongated cells ) to higher values , as more LOCO-EFA harmonics are required to accurately describe the shape . The computer code was written in C and is available on a remote repository ( Git repository ) , which is publicly available on Bitbucket ( https://bitbucket . org/mareelab/LOCO_EFA ) . The expression pattern of pSPCH:SPCH-GFP was analysed from time-lapse images to distinguish cells in the stomatal lineages from non-stomatal lineages . For each cell division , the duration of SPCH expression was determined from the time when SPCH first became visible in the nucleus of the mother cell to when it could no longer be seen in each daughter cell . S1 Video shows an example of cell division in the stomatal lineage . S2 Video shows an example cell division in a non-stomatal lineage . To facilitate imaging of the subepidermal cell layer in wild-type leaves , seedlings grown on plates were stained by the modified pseudo-Schiff propidium iodide ( mPS-PI ) method , as previously described [78] . After approximately 1 wk ( for the mounting solution to set ) , leaf primordia were imaged using a Leica SP5 Confocal microscope . Projections of the subepidermal layer were created in VolViewer using the ‘Depth-Peal Shader’ lighting editor , as described above for spch in “Image processing” . All models and GFT-box software used for modelling can be downloaded from http://cmpdartsvr3 . cmp . uea . ac . uk/wiki/BanghamLab/index . php/Software . Models are also downloadable from https://figshare . com/s/b14c8e6cb1fc5135dd87 . To implement an integrated model of division and growth , we built on a previously published tissue-level model for wild-type leaf growth at early stages of development [16] . This model has two interconnected networks: the Polarity Regulatory Network specifies tissue polarity and hence specified orientations of growth , and the Growth Regulatory Network ( KRN ) determines how factors influence specified growth rates . Specified growth orientations are established in relation to a polarity field , determined by the local gradient of a factor determining polarity field ( POL ) that propagates through the tissue , termed the canvas . The resultant growth and shape depend on the specified growth rates parallel ( Kpar ) and perpendicular ( Kper ) to the polarity , and the mechanical constraints arising from the connectedness of the tissue . In the equations , factors are denoted by i subscripted with the factor name . For instance , the factor PGRAD is described by ipgrad in the equations . Factors may promote growth rates through the linear function pro , defined as follows: pro ( pf , if ) =1+pfif where if is a factor , F , and pf is a promotion coefficient for that factor . Factors may inhibit growth through the function inh , defined as follows: inh ( hf , if ) =1/ ( 1+hfif ) where hf is a inhibition coefficient for factor F . All multiplications and divisions are elementwise . The previously proposed tissue-level growth model [16] was based on tracking only a subset of cell vertices and therefore had a lower cellular resolution than the data presented in this paper . Based on the higher resolution the cell fate map of the midline region of wild type and spch ( S2 Fig ) , we widened the initial MID domain ( S15 Fig ) so that it gave a better match to the cellular data . Running the model with this change produced a narrower leaf , as MID inhibits Kper . To compensate for this effect and to account for the regions with high growth rate perpendicular to the midline ( Fig 1D and S1B Fig ) , we promoted Kper with PMF . The initial starting canvas for all models consists of 3 , 000 finite elements , which are not subdivided during the simulations , and model time is aligned with days after initiation ( DAI ) , which is defined based on growth curves of leaf width [16] . To give finer resolution , times are given in hours ( hours after initiation [HAI] ) . A list of growth parameter values is given in Table 1 . Specified growth rates are modulated by a set of overlapping regional factors , PGRAD , MID , and LAM , the concentrations of which are fixed to the canvas and deform with it during growth ( S15A Fig ) . PGRAD declines distally and accounts for the proximodistal variation in growth rate parallel to the polarity . LAM is expressed highest in the presumptive lamina and at lower levels in the distal regions that will form the petiole . LAM promotes growth perpendicular to polarity . MID is expressed in the midline region , as shown in S15A Fig , and inhibits growth perpendicular to the polarity . The maximum value of these factors is 1 . Specified growth rates are also modulated by diffusible factor PMF , which is fixed to a value of 1 at the approximate position of the lamina-petiole boundary and allowed to diffuse through growth with a diffusion rate of dpmf and a decay rate of μpmf , giving the distribution shown in S15 Fig . A temporally varying factor , LATE , is activated throughout the canvas to decrease growth at later stages . The value of LATE is initially 0 but rises linearly with time after 149 h: ilate{0ift<148hglate ( t−148h ) ift≥148h where glate defines the increase of LATE with time . LATE inhibits specified growth rates with an inhibition coefficient of hlate . Polarity is established using factor PROXORG , which is set to 1 at the base of the canvas and 0 elsewhere ( S15A Fig ) . The value of POL is fixed at a value of bpol , where PROXORG is greater than zero . POL diffuses throughout the canvas with a diffusion rate of Dpol and a decay rate of μpol . POL distribution is allowed to establish during the setup phase for 20 time steps before the commencement of growth . Polarity is initially proximodistal and then deforms with the canvas as it grows to its final shape . This is a model for spch subepidermis during early stages of development . To incorporate cell divisions within our tissue-level model , we superimposed polygons on the initial canvas to represent cells ( S15A Fig , right ) . The sizes and geometries of these v-cells are based on cells observed at corresponding stages in confocal images of leaf primordia [16] . The vertices of the v-cells are anchored to the canvas and displaced with it during growth . New vertices are introduced as v-cells divide , according to the shortest wall passing through the centre of the v-cell [29] . Calling this the nominal new wall , the actual new wall is chosen to be parallel to this , through a point that is randomly displaced from the midpoint of the nominal new wall . The displacement is a vector chosen uniformly at random from a disc centred on the midpoint . The radius of this disc is 0 . 25 times the length of the nominal new wall . The length of the new wall is shortened slightly to give more realistic wall angles [79] . Cell divisions are determined through controlling competence and Ā . This is a model for spch epidermis during early stages of development . This model extends the Fig 8G and 8H—spch epidermis model to later stages of development . A new factor , EARLYGROWTH , was introduced in the model setup and set to a value of 1 throughout the canvas . After 189 h , EARLYGROWTH decreases linearly by a value of 0 . 0417 h−1 until it reaches a minimum value of 0 . To arrest growth , we modified factor LATE to increase exponentially after 189 h: ilate{0ift<148hglate ( t−148h ) if189h>t≥148hAeBtift≥189h where A = glate ( 189–148 ) e−B 189 and B = 1 / ( 189–148 ) . This ensures ilate evaluates to glate ( t– 148 ) at 189 h . This model inhibits distal growth during later stages in order to reduce the size of distal cells . It does this by using the existing factors PGRAD and LAM . In this model , cell size can affect growth . This is an alternative way to limit the size of distal cells without having to use the factors PGRAD and LAM , as in the Fig 9C—Later stage spch limit-free epidermis model . Cell division threshold models were developed for the Fig 9C—later stage spch limit-free epidermis model and Fig 9J—later stage spch limiting cell size epidermis model . Each of the cell division models was identical to its parent model , but the cell target area for division , Ā , was increased by a constant a' for t ≥ 114 h . In Fig 9D and 9K , a' = 85 μm2 , while in Fig 9E and 9L , a' = −85 μm2 . Growth rate mutant models were developed for the Fig 9C—later stage spch limit-free epidermis model and the Fig 9J—later stage spch limiting cell size epidermis model . Each of the growth rate mutant models was identical to its parent model , but Kper and Kpar were globally scaled by a factor k' . In Fig 9F and 9M , k' = 0 . 95 , while in Fig 9G and 9N , k' = 1 . 05 . For the Fig 9C—later stage spch limit-free epidermis model: Kpar=k'_ . ppgrad¡pgrad . inh ( hlate , ¡late . inh ( 1 . 5 , ( 1‑¡earlygrowth ) ) . inh ( 2 , ( 1‑¡lam ) . ( 1‑¡earlygrowth ) ) . inh ( 4 , ( 1‑¡pgrad ) . ( 1‑¡earlygrowth ) ) Kper=k'_ . plam¡lam . inh ( hmid , ¡mid ) . pro ( ppmf , ¡pmftk ) . pro ( plate , ¡late . ¡earlygrowth ) . inh ( 1 . 2 , ¡late . ( 1‑¡earlygrowth ) ) . inh ( 4 , ( 1‑¡pgrad ) . ( 1‑¡earlygrowth ) ) For the Fig 9J—later stage spch limiting cell size epidermis model: Kpar=k'_ . ω . Ppgrad¡pgrad . inh ( hlate , ¡late ) . inh ( 0 . 24 , ¡late . ( 1‑¡earlygrowth ) ) Kper=k'_ . ω . Plam¡lam . inh ( hmid , ¡mid ) . pro ( Ppmf , ¡pmftk ) . pro ( Plate , ¡late . ¡earlygrowth ) . inh ( 2 . 8 , ¡late . ( 1‑¡earlygrowth ) ) Changes to models relative to those used to generate Fig 9C and Fig 9J are shown underlined . Models were developed for the Fig 9C—later stage spch limit-free epidermis model and Fig 9J—later stage spch limiting cell size epidermis model . Each of the LATE mutant models was identical to its parent model , but the activation of LATE and EARLYGROWTH was shifted by a constant number of hours , t′ . In Fig 9H and 9O , t' = −6 h , while in Fig 9I and 9P , t' = 6 h . ilate{0ift<148h+t′_glate ( t−148h+t′_ ) if189h+t′_>t≥148h+t′_AeBtift≥189h+t′_ where A = glate . ( 189+t′ – 148+t′ ) . e-B 189+t' and B = 1 / ( 189+t' – 148+t' ) . Changes to models relative to those used to generate Fig 9C and Fig 9J are shown underlined . In the above models , t refers to actual time . We modified the late stage spch epidermis model ( as used for Fig 9B ) by setting physiological time to be a constant fraction ( physiological ratio ) of duration since the start of the simulation ( when t = 87 h ) . Key transitions and growth rates were then set in relation to physiological time . Parameters describing physical processes such as diffusion were left unchanged . For the model for spch grown on plates , the physiological ratio was 0 . 55 . In addition , the growth rates were globally scaled by a factor k' = 0 . 6 The net result of these two changes is that growth in actual time is slowed to 0 . 33 of normal . If this overall growth rate was matched purely by changing physiological time , the leaf on the plate would end up larger than observed at maturity . Conversely , if the growth rate was matched purely through changes in k' , the leaf would end up much smaller than observed for spch on plates at maturity . Thus , changes in both physiological time and k' are needed to match the observed growth curve . The only change in relation to the late stage spch epidermis model ( as used for Fig 9B ) was that the physiological ratio ( as defined above ) was set to 0 . 75 .
|
Organ morphogenesis involves two coordinated processes: growth of tissue and increase in cell number through cell division . Both processes have been analysed individually in many systems and shown to exhibit complex patterns in space and time . However , it is unclear how these patterns of growth and cell division are coordinated in a growing leaf that is undergoing shape changes . We have addressed this problem using live imaging to track growth and cell division in the developing leaf of the mustard plant Arabidopsis thaliana . Using subsequent computational modelling , we propose an integrated model of leaf growth and cell division , which generates dynamic distributions of cell size and shape in different tissue layers , closely matching those observed experimentally . A key aspect of the model is dual control of spatiotemporal patterns of growth and cell division parameters . By modulating parameters in the model , we illustrate how phenotypes may correlate with changes in cell size , cell number , and organ size .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"cell",
"division",
"analysis",
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"cycle",
"and",
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"processes",
"brassica",
"cell",
"polarity",
"plant",
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] |
2018
|
Spatiotemporal coordination of cell division and growth during organ morphogenesis
|
The allosteric mechanism plays a key role in cellular functions of several PDZ domain proteins ( PDZs ) and is directly linked to pharmaceutical applications; however , it is a challenge to elaborate the nature and extent of these allosteric interactions . One solution to this problem is to explore the dynamics of PDZs , which may provide insights about how intramolecular communication occurs within a single domain . Here , we develop an advancement of perturbation response scanning ( PRS ) that couples elastic network models with linear response theory ( LRT ) to predict key residues in allosteric transitions of the two most studied PDZs ( PSD-95 PDZ3 domain and hPTP1E PDZ2 domain ) . With PRS , we first identify the residues that give the highest mean square fluctuation response upon perturbing the binding sites . Strikingly , we observe that the residues with the highest mean square fluctuation response agree with experimentally determined residues involved in allosteric transitions . Second , we construct the allosteric pathways by linking the residues giving the same directional response upon perturbation of the binding sites . The predicted intramolecular communication pathways reveal that PSD-95 and hPTP1E have different pathways through the dynamic coupling of different residue pairs . Moreover , our analysis provides a molecular understanding of experimentally observed hidden allostery of PSD-95 . We show that removing the distal third alpha helix from the binding site alters the allosteric pathway and decreases the binding affinity . Overall , these results indicate that ( i ) dynamics plays a key role in allosteric regulations of PDZs , ( ii ) the local changes in the residue interactions can lead to significant changes in the dynamics of allosteric regulations , and ( iii ) this might be the mechanism that each PDZ uses to tailor their binding specificities regulation .
Allosteric regulation orchestrates functional behaviors in biological networks through appropriate switches . From a biochemical perspective , allostery can be described as a perturbation at one place in a protein structure , such as the binding of a ligand that alters the binding affinity of a distant site or enzymatic activity [1] . Several models have been suggested for explaining the ‘allosteric mechanism’ . Models of conformational transition between co-existing states such as the MWC model of Monod [2] , and the ‘induced fit’ KNF model of Koshland [3] were the first views among them . They described allostery as a binding event that causes conformational change via a single propagation pathway [4] . A new view of allosteric transitions supported from NMR studies , referred to as the ‘population shift’ model , has replaced the MWC and KNF models [5]–[8] . The population shift models claim that a protein in the unliganded form exhibits an ensemble of conformational states and ligand binding leads to a redistribution of the population of these states . In this view , it is important to explore how protein dynamics might contribute to allostery and make communication possible within a protein . Unlike the classical allostery models , the population shift-models also suggest that allostery can be mediated without any significant conformational change [9]–[15] but rather from changes in dynamics . Moreover , recent experimental and theoretical evidences indicate that allostery is not limited to multi-domain proteins or complexes [5] and it may even be a fundamental property of all proteins , even single domain proteins . In single domain proteins , it is evident that residues that are energetically connected through structural rearrangements and dynamics lead to allosteric regulation [6] , [11] , [15]–[17] . More importantly , studies on single domain protein PDZ ( post-synaptic density-95/discs large/zonula occludens-1 ) have indicated that allostery can arise not only from large conformational changes , but also from changes in dynamics [12] , [14] . Indeed , PDZ domain proteins ( PDZs ) are the most studied system for understanding single domain allostery [11] , [16] , [18]–[25] . PDZs are small protein-protein interaction modules and typically recognize specific amino acids in the C-terminal end of peptide motifs or proteins [26]–[28] . Various studies on several PDZs , including statistical coupling analysis ( i . e . sites that have correlated mutation based on evolutionary information ) [16] , [29] , [30] , molecular dynamics [11] , [22] , [31]–[33] , normal mode analysis [34] , [35] , NMR relaxation methods and site directed mutational analysis [12] , [18]–[20] , [25] , [36] have shown that several PDZs exhibit allosteric behavior that appears to connect incoming signals , notably binding to recognition motifs present on an upstream partner , to downstream partners [11] , [16] , [18]–[25] . In many different cellular contexts , PDZs function to transduce these binding events into favorable domain-domain assembly of complexes [14] . Thus , it is critical to understand the residues involved in these allosteric pathways in order to modulate the PDZ mediated interaction in cell regulation especially those in disease pathways . Moreover , a recent experimental study by Petit et al . [12] , has confirmed yet another strong allosteric power of one of the PDZs: the hidden dynamic allostery . The removal of the non-canonical third helix ( α3 ) in PSD-95 ( PDZ3 ) , which lies outside of the binding pocket , reduces the binding affinity drastically due to a change in side chain dynamics upon truncation , indicating the role of entropy and dynamics in allosteric regulation . More interestingly , further investigation has shown that the removal of this distal α3 disrupts the communication between PDZ3 and SH3-GK , which modulates the binding of Disc large protein ( Dlg ) to the localization protein GukHolder [37] . Therefore , the hidden dynamic allostery related with α3 is indeed a regulatory module within the context of larger interdomain interactions . In summary , PDZs do not solely act as simple scaffold proteins . On the contrary through dynamics , they propagate signals to functionally important distant sites for intramolecular and intermolecular interactions [16] . They all have the same conserved structure and similar sequences [16] , yet different PDZs have evolved different dynamics properties tailored to mediate different functions in the cell [14] . Thus , it would be very important to understand how signals are passed from one residue to another within the network of PDZs and how the sequential and structural variations alter the allosteric pathways for those allosteric PDZs [11] , [18] , [20]–[24] . Here we would like to tackle this problem with our new method called perturbation response scanning ( PRS ) [38] , [39] . PRS treats the protein as an elastic network and uses linear response theory ( LRT ) to obtain residue fluctuations upon external perturbation . By sequentially exerting directed random forces on single residues along the chain of the unbound form and recording the resulting relative changes in the residue coordinates using LRT , we can successfully reproduce the residue displacements from the experimental structures of bound and unbound forms . The method is well established and tested for 25 proteins that display a variety of conformational motions upon ligand binding , including shear , hinge , allosteric , and partial refolding as well as more complex protein motions [39] . In the present study , we investigate the allosteric transitions by analyzing response fluctuation profiles upon perturbation on binding site residues by PRS . We focus on two widely studied PDZs: the third PDZ from the post-synaptic-density-95 ( PSD-95 PDZ3 ) and the second PDZ from the human tyrosine phosphates 1E ( hPTP1E PDZ2 ) . The results from our computationally inexpensive and effective approach successfully identify the dynamically linked allosteric residues obtained from experiments ( NMR or mutagenesis techniques ) [12] , [18]–[20] , [25] , [36] as well as evolutionarily coupled residues from sequence-based statistical approaches [16] , [29] , [30] and key residues predicted from molecular dynamics , normal mode analysis and protein energy-based networks [11] , [22] , [31]–[35] , [40] . As a further test , we construct the communication pathway between these residues that might be responsible in transmitting allosteric signals . We achieve this through linking residues that show similar directionality of motion upon perturbation of binding sites . Interestingly , the constructed allosteric pathway indicates a strong structural residue coupling network . Moreover , we observe that the two PDZs , PSD-95 and hPTP1E , have distinct allosteric pathways despite their structural similarity , indicating the role of dynamic coupling in these domains [14] , [35] , [41] . The residues in the allosteric pathway of PSD-95 are homogenously distributed along the secondary structural motifs while the allosteric pathway of hPTP1E shows more localization around in regions of β1–β2 loop , β2 and β3 strands and the region of β5 strand and the α2 helix , missing the region of the α1 helix . The differences in the allosteric pathways of these two PDZs indicate the critical of role of dynamic coupling in PDZ domains and that differences in residue sequences within the same fold can lead to different dynamic coupling . Indeed , PDZs master this to mediate different cellular functions in different parts of the cell [14] . In addition to that , our PRS analysis indicates that the allosteric pathway of PSD-95 significantly alters upon removal of the distal third helix ( α3 helix ) . This indicates that local changes in the network alter the directionalities of correlated motion , which may lead to a change in binding affinity [35] , [42] . Strikingly , when we incorporate the change in backbone dynamics into the docking computation through generating multiple conformations by PRS , we also observe an increase in binding energies upon removal of the third helix .
Mutagenesis and NMR relaxation methods demonstrated that a network of residues exists that has a dynamic response upon ligand binding in both hPTP1E PDZ2 and PSD-95 PDZ3 [12] , [19] , [20] , [25] , [36] . Thus , we applied our approach to the unbound structures of two PDZ domain proteins: hPTP1E ( PDB entry: 3LNX ) and PSD-95 ( PDB entry: 1BFE ) and computed the allosteric response ratio χj for each residue , which is the normalized average mean square fluctuation response of residue j upon perturbing only the binding site residues over the mean square average response of the same residue j obtained by perturbations on all residues . Thus , the index of allosteric response ratio χ enables us to identify residues that are more sensitive to perturbation around the binding pocket . Figure 1 presents the allosteric response ratio profiles of ( A ) hPTP1E and ( C ) PSD-95 and the corresponding color-coded ribbon diagrams of these two proteins . Experimentally identified residues are marked with red dots . The ribbon diagrams of ( B ) hPTP1E and ( D ) PSD-95 are colored based on the allosteric response ratio , χj , using a spectrum of red ( the highest mean square fluctuation response ) to orange , yellow , green , cyan and blue ( the lowest response ) . The residues with the highest allosteric response ratio ( χj>1 . 00 ) are shown as stick representations . Particularly , those in agreement with the experimental analysis are labeled . Overall , there is a good agreement with experimentally identified allosteric residues and those predicted by our approach . Using χj>1 . 00 as a threshold value for the allosteric response ratio , we predicted 6 out of 10 experimentally identified allosteric residues for hPTP1E [25] and similarly 8 out of 11 for PSD-95 [19] ( i . e . the predicted residues correspond to the peaks in the allosteric response ratio profiles ) . We would like to note that we also tested our approach in another allosteric PDZ domain , SAP97 ( PDB entry: 2AWX ) which shows slight conformational change upon binding [18] . Using the same threshold value for χj>1 . 00 , we were able to distinguish not only the residues near canonical binding sites but also those distant from the binding site ( Table S1 ) , indicating the predictive power of PRS in identifying allosteric residues . To our knowledge , all of previous computational studies including all-atom molecular dynamics [31] , [32] and the rotamerically induced perturbation method ( RIP ) [11] identified certain critical residues using the previous NMR structure of hPTP1E ( See Table S2 for predictions based on the previous NMR structure by different methods ) . Here , we apply our computational approach to the recently reported high-resolution crystal structure of hPTP1E PDZ2 [25] , indicating that new bound and unbound structures deviate from previously determined NMR structures of hPTP1E and there are very minor structural changes in PDZ2 upon peptide binding . The previous study of the RA-GEF2 peptide binding to hPTP1E PDZ2 using NMR relaxation technique identified residues that have significant changes in their side-chain dynamics upon peptide binding [20] , [36] . This study also revealed that there are two distal surfaces physically linked to the peptide-binding site: ( i ) “distal surface 1 ( DS1 ) ” , which contains residues in the N terminal of β6 and the anti-parallel β strand formed by β4 and β5 ( Val61 , Val64 , Leu66 , Ala69 , Thr81 , and Val85 ) , and ( ii ) ”distal surface 2 ( DS2 ) ” , located next to helix α1 , consisting of residues Ala39 and Val40 . In the recent study Zhang et al . [25] identified 10 residues ( Ile6 , Ile20 , Val22 , Val26 , Val30 , Ile41 , Val61 , Val64 , Val78 , Val85 ) that have significant changes in side-chain dynamics upon binding both RA-GEF2 and APC peptides to PDZ2 . These identified residues overlap with the findings of their previous study and they are located in the region of the binding site ( Ile20 , Val22 , Val26 in the β2 strand , and Leu78 in helix α2 ) , DS1 ( Val61 , Val64 and Val85 ) , and in DS2 ( Ile41 ) . The highest allosteric response ratios obtained by PRS are also observed for the same residues except Val26 and Val64 ( Figure 1A ) . Other residues that give high mean square fluctuation response ( χj>1 . 0 ) are summarized in more detail in Table 1 , and those which agree with the experimentally identified ones [25] are highlighted in boldface . We also construct a two-way contingency table that presents the pattern matching between the experimentally identified residues and our prediction by PRS using a Fisher's exact test . The resulting p-value of hPTP1E , 2 . 9E-2 , from the test indicates that there is a statistically significant matching between experiment and our method ( Table S4 ) . In addition , the residues critical in allosteric pathways are characterized via statistical coupling analysis ( SCA ) of an evolutionary network using a large and diverse multiple sequence alignment of the PDZ domain family . Using the SCA method , Lockless and Ranganathan [16] predicted a set of residues within the family of PDZ domains that communicate signals through the protein core . When we compare our predictions with those obtained from SCA , nine residues ( Ser17 , Ile20 , Gly24 , Gly25 , Gly34 , Ala46 , Val61 , His71 and Val85 ) emerge as the residues with high allosteric response ratio ( χi ) that are in agreement with the evolutionary network residues of hPTP1E [16] , [30] , [54] . The Fisher' exact test based on our method and SCA provides a p-value of 5 . 0E-4 , indicating a high level of agreement . ( Table S4 ) . The residues identified with high allosteric response ratios for PSD-95 PDZ3 are also in good agreement with double mutant cycle analysis [19] . The two-way contingency table based on experiment and method resulted in a high level of pattern matching , with a Fisher's exact test p-value of 1 . 5E-3 ( Table S4 ) . The mutational study of Chi et al . [19] indicates that the three positions Gly329 , Val362 , and Ala376 yield significant energetic coupling interactions with His372 . In fact , among these coupling interactions the interaction between His372 and Val362 show long-range energetic coupling in the PSD-95 PDZ3 domain . As shown in Figure 1B , PRS analysis also captures the importance of the long-range energetic coupling interaction between His372 and Val362 of the PSD-95 PDZ3 domain . In this context , it is worth noting that studies based on a non-equilibrium perturbation-based molecular dynamics technique , called anisotropic thermal diffusion ( ATD ) [22] , and the rotamerically induced perturbation method ( RIP ) [11] , [41] , also reported a complete signaling pathway of PDZs including PSD-95 . ATD analysis proposed a signaling pathway between His372 and Ile335 that passed through Ile327 and Phe325 [22] . RIP analysis has also shown that some PDZs have more dynamic responses than the others and this was highly coupled with evolutionary SCA analysis [11] . The general pattern derived from both perturbation based MD analyses agreed with that obtained from PRS ( See details for Table S4 ) . The list of residues identified as allosteric residues with these different methods for these two PDZs is presented in Tables S2 and S3 . Furthermore , the energetic coupling residues ( Gly329 , Leu323 , Ile327 , His372 , Ala376 , Gln384 ) in PSD-95 were also successfully identified using an ENM-based structural perturbation ( SPM ) method [33] , [47] , [49] , [55] based on exploring the propagation of the response of a local perturbation at a given residue to all other residues in a given structure . As we mentioned earlier , the basic premise behind SPM and PRS methods is similar except the harmonic springs connected to residues are changed by a small amount in SPM whereas the force is directly applied to residues in PRS . In addition to that , SPM focuses on changes in the single mode upon perturbation . It is usually the 1st slowest mode in large proteins [52] . However , in the case of the small domain protein of PSD-95 , rather than the 1st mode , the 13th and 20th slowest modes significantly overlap with binding induced fluctuations [33] . On the other hand , PRS does not use the bound structure . PRS uses the Hessian of the whole unbound conformation and it automatically includes the modes that induce a response vector upon exerting forces on the binding site residues . By linking the residues involved in allosteric regulations with respect to their response behavior , we can construct the allosteric pathways with PRS . PRS enables us to measure the relative directionality between the responses of a pair of neighboring residues to a perturbation . ( i . e . the alignment of their response vectors ) . If the residues collectively move in line , their directionality should be parallel . After obtaining the directionality of different pairs of residues , we carry out a systematic analysis of the residues with the highest allosteric response ratio . For these residues , we search all possible interactions with a window size of 3 and identify residue pairs that collectively move in line together . A pathway is constructed by linking the sequential pairs showing similar directional response upon perturbation . Each constructed pathway is weighted based on alignment angles ( i . e . directional similarity ) between linking residues . Then we select the pathway with maximum total weight . By this analysis , the allosteric pathway constructed for hPTP1E PDZ2 follows through the connections Ser 17 → Val22 → Gly25 → Arg31 → Ile35 → Val61 → Leu64 → Thr70 → Ala74 → Leu78 → Thr81 → Leu88 ( Figure 2A ) . Interestingly , the residues Val22 , Val61 , and Leu78 are located at the critical regions determined by the mutational analysis [25] . Since the model in the present study is low-resolution , we identify the residue Val22 that is near residue Ile20 . The experimental mutational analysis showed that a change at Ile20 resulted in extensive changes in side chain dynamics while mutations at residues Ile35 and His 71 had a limited response in dynamics . Thus it is concluded that Ile20 might act as a hub that is energetically and dynamically important for transmitting changes in dynamics throughout the PDZ domain [36] . When we analyze the directionality preference of this residue with each residue identified for the most highly weighted pathway , we find that Ile20 collectively moves together with each of them , indeed acting as a hub in our dynamic network analysis . Moreover , the PRS pathway shows a remarkably high similarity ( Ser17 , Gly25 , Ile35 , Val61 , His71 , and Val75 ) with the statistical coupling analysis obtained by Lockless and Ranganathan [16] . As shown in Figure 2B , the most highly weighted pathway for PSD-95 is obtained through connections Ile314 → Ile327 → Ile338 → Ala347 → Leu353 → Val362→ Leu367 → His372 → Lys380 → Val386 → Glu396 . Interestingly , Val362 [16] , [19] , Lys380 , and Val386 [16] yield significant energetic coupling interactions with His372 which are confirmed by mutagenesis studies . While the general pattern of signal propagation predicted from our method agrees with that inferred from the SCA analysis [16] there are some differences . The discrepancy between our model and the two proposed pathways by SCA may result because SCA analysis investigates the signaling pathway originating from a single residue , His372 . However other residues at the binding pocket may be important for intramolecular signaling . Our analysis uses response profiles obtained by sequentially exerting a random force at a single residue along all the residues at the binding site . Thus , our approach might lead to the prediction of extra residues , such as Lys380 , that interacts with the peptide and is near His372 . Our model does not include Phe325 in the allosteric pathway , yet it finds Ile327 , which is near residue 325 . Moreover , MD analysis has shown that the mutation of Ile327 to Val leads to a dramatic signal reduction of Phe325 , showing that position 327 is involved in mediating the signal pathway and highly linked with Phe325 [22] . Overall , when we compare the allosteric pathways of the two different PDZs , PSD-95 and hPTP1E , we see a clear difference ( Figure 2C ) . There are some overlap regions between the two PDZ domains including residues in the β2 and β3 strands , the loop between β4 and β5 strands , and the C-terminal of the α2 helix . However , the predicted allosteric pathway of PSD-95 has a more homogeneous distribution through N-terminal to C-terminal , whereas the pathway of hPTP1E seems more localized , especially in regions of β1-β2 loop , β2 and β3 strands and the region of β5 strand and the α2 helix , missing the regions around the α1 helix . Indeed , the allosteric behavior of Ala347 in the α1 helix has also been found by SCA [16] and other MD analysis [22] . This comparison indicates that these two PDZs with similar sequences and structures have different allosteric behavior , indicating the role of dynamic coupling in single domain allostery . Thus , slight changes in the residue network changes dynamic coupling , which can lead to distinct allosteric paths . A recent experimental study [12] provided further support that allosteric communication can be driven by the network of residue interactions of PSD-95 without any conformational change . To investigate this phenomenon , they removed the non-canonical C-terminal third helix ( α3 , residues 394-399 ) . Strikingly , removal lowers the binding affinity 21-fold and has a significant effect on the internal dynamics of PDZ3 , even though it lies outside of the binding site and does not make direct interactions with the binding C-terminal peptide ( CRIPT ) residues . Using PRS , we also analyzed the truncated PSD-95 structure and investigated the impact of removal of helix α3 in the allosteric communication pathway . The most highly weighted pathway of the truncated structure is presented in Figure 3 . Comparison of the pathway of PSD-95 ( Figure 2B ) and the truncated one ( Figure 3 ) computed by PRS remarkably shows that the removal of the α3 helix significantly alters the allosteric pathway , indicating that the interactions responsible in transmitting intramolecular signals are being lost upon truncation of helix α3 . For the truncated PSD-95 structure , the most highly weighted pathway has been identified through connections Ile314 → Ile 327 → Glu334 → His372 → Lys380 → Ile388 , which is shown in Figure 3 . Some of the interactions specifically located in the α1 helix and the loop between the β4 strand and the α2 helix predicted for the full PSD-95 were lost after removal of the α3 helix . Qian and Prehoda [37] showed that truncation of a portion of the α3 helix modulates and initiates the binding of Dlg to the localization protein GukHolder . Therefore , it is reasonable to say that this non-canonical α3 helix has a significant biological role in this allosteric regulation and the fact that the α3 helix is involved in the allosteric pathway obtained by PRS supports this . In our recent work , we analyzed the dynamics of PDZs showing different binding specificities and showed that we can discriminate the binding specificity of PDZs based on their dynamics [35] . Within this picture , it is not surprising to see a change in binding affinity of PSD-95 upon truncation of the distal helix α3 , because this leads to a change in dynamics . In order to investigate this any further , we also investigate the changes in the binding affinity upon removal of the α helix using docking techniques where we incorporate the changes in dynamics of PSD-95 into docking . Computational docking methods are commonly used to identify the correct conformation of ligand-bound proteins along with their binding energy . However , docking algorithms predict incorrect binding modes or energies for about 50–70% of all ligands when the receptor is kept in a single conformation [56] . This is especially critical for PDZ whose dynamics play a key role in peptide binding specificity [35] . Some docking methods also incorporate the side chain flexibility of the receptor around binding pockets [57]–[60] . In our previous study [42] , we incorporated the backbone flexibility of PDZs by generating multiple receptor conformations through restrained-replica exchange molecular dynamics ( REMD ) runs where the restraints are obtained by binding-induced elastic network modes . In this present study , we first generate multiple receptor conformations using the response vectors obtained upon perturbation of each residue via PRS . This provides us more computational efficiency in exploring conformational space . Then , we dock these multiple receptor conformations of PSD-95 and the truncated one against its native peptide ( CRIPT ) using RosettaLigand [58] , [60] . RosettaLigand is docking software that computes the best-docked pose through a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side-chain conformations are optimized simultaneously . The lowest binding energy scores and corresponding peptide RMSDs of PSD-95 and the truncated third alpha helix of PSD-95 structures interacting with the CRIPT peptide are summarized in Table 2 for two different docking cases , ( i ) using only bound crystal structure ( PDB code:1BE9 ) and ( ii ) using ensemble of structures obtained by applying PRS to the crystal structure . We cannot see this difference in binding affinities when we perform single receptor docking by using only the full and α3 helix truncated forms of the crystal structure . When we use PRS generated multiple receptor conformations to predict binding energies of PSD-95 and the truncated one , we find that the binding energy increases upon truncation of the C-terminal third alpha helix ( α3 helix ) as also observed experimentally [12] . This analysis indicates that the residue networks and their related dynamics indeed play a key role in binding affinities of PDZ . Our PRS analysis suggests that the significant change in the dynamics pathway of residue communication , caused by truncation of the α3 helix , leads to a change in binding affinity of its native peptide . Allosteric responses in PDZs usually arise , because a perturbation at one site is transferred to the distal part of the protein through a network of residue communications . Here we investigate how the perturbation of a residue at the binding site is transferred through the dynamics of the residue network interactions . Thus we investigate the allosteric response of the two most investigated PDZs , PSD-95 and hPTP1E using our low resolution dynamics approach PRS . PRS is based on ENM where it uses only the topology of the given structure , and then using linear response theory , it computes the response fluctuation vector of each residue in the chain upon exerting a random force on a single residue . Using PRS , we compute the allosteric response ratio for each residue , which is the normalized average mean square fluctuation response upon perturbation . Most of the residues that are identified experimentally as residues in allosteric pathways indeed show high allosteric response ratios , indicating the consistency and usefulness of the PRS method for extracting the residues in the signaling pathway . Since PRS not only gives the mean square fluctuation of the response but also its directionality , we construct the allosteric pathway by linking the residues aligning in the same direction upon perturbations . Interestingly , our analysis has shown that the allosteric pathways of PSD-95 and hPTP1E are distinctively different from each other , despite the fact that they have similar structures . Likewise , we also observe a significant change in the allosteric pathway upon truncation of the distal α3 helix of PSD-95 . Moreover , our flexible docking analysis where we generate an ensemble of multiple receptor conformations by PRS shows an increase in binding energy upon truncation . Overall , these results strongly suggest that local changes in residue network interactions can lead to changes in dynamics in allosteric regulations and various PDZs grasp to mediate different functions in the cell .
We analyze unbound structures of hPTP1E ( 3LNX ) [25] and PSD-95 ( 1BFE ) [61] in this study . The backbone root mean square deviation ( RMSD ) between hPTP1E and PSD-95 structures is 1 . 89 Å , while the sequence identity between pairs is only 36% . The all-atom RMSD between unbound and bound structures of PSD-95 is 1 . 13 Å ( backbone RMSD = 0 . 73 Å ) while that of hPTP1E is 1 . 03 Å ( backbone RMSD = 0 . 46 Å ) . PRS is based on sequentially exerting directed random forces on single-residues along the chain of the structure and recording the resulting relative displacements of all the residues using LRT . The model views a protein structure as a three-dimensional elastic network . The nodes of the elastic network are Cα atoms of each residue where identical springs connect the interacting α-carbons in their native fold . In all elastic network models ( ENMs ) , all residue pairs are subject to a uniform , single-parameter harmonic potential if they are located within an interaction range , or cutoff distance , rc . The major drawbacks of using cutoff distances are: ( i ) they are generally taken arbitrarily and ( ii ) their optimal values vary for different proteins [62] , [63] . Instead of using any arbitrary cutoff distance , the interaction strength between all residue pairs can be weighted by the inverse of the square distance of their separation [63] , [64] . We modify PRS by applying the concept of inverse square dependence for the interactions between residue pairs [63] , [64] and introducing specificity between bonded and non-bonded interactions [35] . We tested the modified version on previously analyzed [39] 25 unbound protein structures that make various conformational changes upon bindings , and the results showed that the modified version successfully captures these conformational changes . The free-body diagram of the central Cα atom of each sphere exhibits all of the pairwise interaction forces generated by the coordinating Cα atoms as schematically illustrated in Figure 4A . Each Cα atom must be in equilibrium under the action of interaction forces in the absence of external forces . The sum of forces on residue i along the x- , y- , and z-directions must be equal to zero under native state conditions , ( 1 ) where fij is the internal force on site i due to its interaction site j , , . is the angle between the x axis and the line of action of fij , rij is the instantaneous separation vector between sites i and j and Xi , Yi and Zi are the components of the instantaneous position , Ri . The force balance can be generalized to the complete set of N sites ( i . e . sites are Cα atoms of a protein ) and M interactions ( i . e . an interaction between any two Cα atoms is determined if the distance between two Cα atoms is less than the cut-off distance ) as ( 2 ) where B is the directional cosine matrix . If there are external forces acting on a set of residues of the folded structure as shown in Figure 4B , the force balance of the complete set of N sites and M interactions takes the following form: ( DOC ) ( 3 ) where Δf is the residual interaction forces and ΔF is a 3Nx1 vector containing the external force components at each residue . The native structure may undergo conformational changes about the equilibrium state under the action of these forces . During this process , the positional displacements ΔR and the bond deformations Δr are geometrically compatible . The relation between the positional displacement vector and the bond distance is given by ( 4 ) where [B]T is the transpose of B . Within the scope of an elastic network of residues that are connected to their neighbors with springs , the interaction forces , Δf , are related to the bond distance through Hooke's law by ( 5 ) where the coefficient matrix K is diagonal . Although the entries of K are taken to be equivalent in the original method [38] , we introduce two different spring constants for the residue interaction network for bonded and non-bonded interactions , γb and γnb . The spring constant of the bonded part ( γb ) is taken as 1 . For the non-bonded part ( γnb ) , the interactions between residue pairs i and j are weighted by the inverse square of the distances , rij ( as 8/rij2 ) . Moreover , the work done by the external forces ΔF is equal to the work done by the internal forces Δf so substituting Equations ( 4 ) and ( 5 ) into Eq . ( 3 ) , we obtain ( 6 ) Let's note that the term in Eq . ( 6 ) is also equivalent to the Hessian ( H ) [65] . On the other hand , one may choose to perturb a single residue or a set of residues , and calculate the response of the residue network through , ( 7 ) orwhere the ΔF vector contains the components of the externally applied force vectors on the selected residues . In this study , first we apply a force as a unit vector on residue i along 7 directions ( i . e . in x- , y- , z- , both x- and y- , both x- and z- , both y- and z- , all x , y , z directions . Then , we build a perturbation response matrix that includes average displacement ΔR for each residue j due to a force applied on residue i , ( 8 ) where the magnitude of positional displacements for residue j in response to a perturbation at residue i is defined as , ( 9 ) In order to predict which residues are critical in allosteric pathways , we distinguish the residues exhibiting significant fluctuation upon perturbation on binding site residues . Therefore , we define an index called the allosteric response ratio , χj for each residue , which is the ratio of average fluctuation response of the residue j upon perturbations placed on binding site residues to average response of residue j upon perturbations on all residues , shown as: ( 10 ) where Aij is the response fluctuation profile of residue j upon perturbation of residue i . The numerator is the average mean square fluctuation response obtained over the perturbation of the binding pocket ( BP ) residues , whereas denominator is the average mean square fluctuation response over all residue perturbation . Thus , NBP is the number of residues in the binding pocket and NBP1 and NBPm correspond to residue indexes in the binding pocket ( residues 320-328 and 371-380 for PSD-95 and residues 16-23 and 70-79 for hPTP1E ) . To identify the critical residues in the allosteric pathway , for each residues we compute χj in each perturbed direction and take into account of the maximum value of χj . Then , we sort out all χj and select the residue positions by setting a threshold of 1 . 0 or better . To understand how the sensitivity and specificity change , we predict the allosteric residues by varying the threshold of response ratio lower or higher than 1 . 00 . We found that taking a threshold value lower than 1 . 0 gives same experimentally identified allosteric residues to ones obtained by using χj>1 . 00 as a threshold value ( Table S5 ) . We should note that the procedure has been also repeated using several random directions , rather than the 7 directions and we observed that our predictions do not change significantly . The schematic representation showing how we identify allosteric binding sites can be found in Figure 4C . While PRS is a residue-based low-resolution approach , the essential dynamics analysis [66] is carried out on all-atom molecular dynamics ( MD ) trajectories to support the validity of the methodology . The details of the analysis are explained in Text S1 . The comparison of residues that give the highest mean square fluctuation response ( χj>1 . 00 for PSD-95 ) upon perturbation with respect to the coarse-grained approach and the essential dynamics analysis is presented in Table S6 . Overall , 82% of predicted residues from the essential dynamics analysis of all-atom MD trajectories are the same as those obtained by our low-resolution model ( see Text S1 for more details ) . Moreover , the residues found by the coarse-grained approach that do not overlap with those of the all-atom approach are sequentially in close proximity to the residues identified by both approaches . PRS can be used to measure the degree of collectivity of the response of a group of neighboring residues to a perturbation on any residue . This enables us to construct an allosteric pathway through linking those residues showing similar response upon perturbations of the binding site . To understand the nature of the response , the submatrix of residue k in response to perturbations in i from the inverse of the Hessian ( See Equation 7 ) matrix can be decomposed into its eigenvalues and eigenvectors: ( 11 ) If the residues collectively move in line they have a single dominant eigenvalue and their corresponding eigenvectors should be parallel , indicating that they move cooperatively in the same direction . Therefore , to compare if the responses of two residues are same , we check the dot product of their corresponding eigenvectors , ( 12 ) where θ is the angle between the two eigenvectors . After obtaining the directionality of different pairs of residues upon perturbations on the binding site , we carry out a systematic network analysis using only the residues that give the highest fluctuation response upon perturbation . For these identified residues , we use a window size of 3 ( i . e . if the residue 320 shows the highest mean square fluctuation response , the residues 319 , 320 , and 321 are taken into account ) , and search extensively to find residue pairs in sequence that move collectively upon perturbation . To this aim , we first calculate the overlap coefficients of the residue pairs by using the dot product of response vectors ( Eq . 12 ) . Using a cut off value of 0 . 98 , we find the residue pairs that move in the same direction . Importantly , this means we identify the residue pairs showing also a high allosteric response ratio . We then perform an extensive search by generating all possible pathways through connecting these identified residue pairs and weight each pathway with the product of overlap coefficients . As an example , the predicted allosteric residue containing 314 in PSD-95 has the highest overlap coefficient with residue 327 with a value of 0 . 99 . Then residue 327 has also very high overlap coefficient ( with a value of 0 . 98 ) with residue 338 . We then construct a pathway Ile314→Ile327→Ile338 which gives a total weight of 0 . 99x0 . 98 = 0 . 97 . After exhaustive construction of all possible pathways we select the pathway with maximum total weight .
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PDZ domain proteins ( PDZs ) act as adapters in organizing functional protein complexes . Through dynamic interactions , PDZs play a key role in mediating key cellular functions in the cell , and they are linked to currently challenging diseases including Alzheimer's , Parkinson's and cancer . Moreover , they are associated with allosteric regulations in mediating signaling . Therefore , it is critical to have knowledge of how the allosteric transition occurs in PDZs . We investigate the allosteric response of the two most studied PDZs , PSD-95 and hPTP1E , using the perturbation response scanning ( PRS ) approach . The method treats the protein as an elastic network and uses linear response theory ( LRT ) to obtain residue fluctuations upon exerting directed random forces on selected residues . With this efficient and fast approach , we identify the key residues that mediate long-range communication and find the allosteric pathways . Although the structures of PSD-95 and hPTP1E are very similar , our analysis predicts that their allosteric pathways are different . We also observe a significant change in allosteric pathways and a decrease in binding affinity upon removal of the distal α3 helix of PSD-95 . This approach enables us to understand how dynamic interactions play an important role in allosteric regulations .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"computational",
"biology"
] |
2011
|
Change in Allosteric Network Affects Binding Affinities of PDZ Domains: Analysis through Perturbation Response Scanning
|
Polyketides , a diverse group of heteropolymers with antibiotic and antitumor properties , are assembled in bacteria by multiprotein chains of modular polyketide synthase ( PKS ) proteins . Specific protein–protein interactions determine the order of proteins within a multiprotein chain , and thereby the order in which chemically distinct monomers are added to the growing polyketide product . Here we investigate the evolutionary and molecular origins of protein interaction specificity . We focus on the short , conserved N- and C-terminal docking domains that mediate interactions between modular PKS proteins . Our computational analysis , which combines protein sequence data with experimental protein interaction data , reveals a hierarchical interaction specificity code . PKS docking domains are descended from a single ancestral interacting pair , but have split into three phylogenetic classes that are mutually noninteracting . Specificity within one such compatibility class is determined by a few key residues , which can be used to define compatibility subclasses . We identify these residues using a novel , highly sensitive co-evolution detection algorithm called CRoSS ( correlated residues of statistical significance ) . The residue pairs selected by CRoSS are involved in direct physical interactions in a docked-domain NMR structure . A single PKS system can use docking domain pairs from multiple classes , as well as domain pairs from multiple subclasses of any given class . The termini of individual proteins are frequently shuffled , but docking domain pairs straddling two interacting proteins are linked as an evolutionary module . The hierarchical and modular organization of the specificity code is intimately related to the processes by which bacteria generate new PKS pathways .
The extraordinary biosynthetic capabilities of polyketide synthases ( PKS ) , some of the largest known bacterial multi-enzyme complexes , have been extensively investigated over the past decade . Using a combination of biochemical and genetic techniques , researchers have developed a detailed understanding of the organization and structure of these complex enzymes , as well as of the biochemical reactions they catalyze [1] . The assembly of a polyketide proceeds by the successive addition of acyl extender groups to a growing biochemical polymer . Each module of a modular PKS is a multidomain catalytic unit responsible for a single step of polyketide chain extension . PKS proteins can each contain one or more modules . Different modules add different basic or modified extender units , so the order of proteins and their modules in the multiprotein chain determines the chemical structure of the final polyketide product . Crucially , PKS catalytic domains exhibit broad substrate tolerance , and are able to extend “unnatural” polyketide substrates [2 , 3] . In particular , PKS modules are catalytically active even when their order is changed , enabling a combinatorial diversity of possible polyketide products . This property , which suggests that modules are frequently shuffled during natural selection , has generated enormous interest in using PKS pathways to achieve combinatorial biochemistry in the laboratory [2–6] . Biochemical studies of PKS systems have recently been complemented by powerful computational tools . These tools allow results from well-characterized PKS systems to be extended to the rapidly growing set of putative PKS gene clusters in fully sequenced bacterial genomes . Computational analysis of sequence data has been used to identify catalytic domains and to predict their substrate specificity , both for PKS systems [7 , 8] as well as for the closely related nonribosomal peptide synthase ( NRPS ) systems [8 , 9] . In a recent analysis , Minowa et al . [10] were able to effectively predict the catalytic function of individual proteins , as well as the order in which multiple proteins act to produce the final polyketide product , by combining a variety of data including the chromosomal context of genes , and the sequences and phylogeny of catalytic and linker regions . While these predictive tools can be extremely accurate , their output cannot necessarily be interpreted in ways that provide biological insight . It is therefore important , in parallel with predictive approaches , to investigate underlying evolutionary and molecular mechanisms . For example , a new generation of sequence-based classification algorithms promises not only to have predictive power , but also to reveal sequence features that are important for discrimination , thus providing insight at the molecular level [11] . Comparative sequence analysis can also be used to shed light on the evolutionary history of a system , as shown in a recent study of PKS catalytic-domain duplication [12] . Here we use sequence data to investigate PKS protein interactions . For the PKS proteins to line up in the correct order , protein interactions must be specific [13 , 14]: certain protein pairs must be allowed to bind , while others must be prevented from doing so . It is remarkable that these proteins are able to correctly discriminate between various possible binding partners , given that all the docking domains which mediate their interactions are homologous . We uncover key sequence features that govern PKS protein interactions , and thereby construct a predictive specificity code . We show that , by studying how the code is organized , we can learn a great deal about the evolutionary origins as well as the molecular basis of this elegant specificity .
Biochemical investigations have shown that interactions between PKS proteins map to docking domains at their N- and C-termini [13–15] . Starting with a seed alignment of terminal fragments from well-characterized PKS pathways [7] , we used PSI-BLAST to assemble a dataset of proteins with regions homologous to these ( Text S1 , Section 1 ) . Homologous regions were only detected at protein termini , never in the interior . Most of the proteins we pulled up belonged to biochemically characterized modular PKS pathways , and the rest to putative modular PKS pathways in fully sequenced genomes . Almost all known modular PKS proteins contained homologous termini , and we found no significant hits to non-PKS proteins . This suggests that all modular PKS proteins employ the same mechanism to mediate interactions . We next confined our attention to 42 biochemically characterized modular PKS pathways in which the order of proteins within the multiprotein chain has been determined ( Dataset S1 ) . An alignment of the protein termini revealed short , conserved regions at the very ends of the proteins ( Figure 1A ) : a 19 aa C-terminal “head” region , and a 27 aa N-terminal “tail” region ( Figure 1B ) . Throughout our discussion , we shall refer to these regions as docking domains . ( The docking domains originally defined by Broadhurst et al . [15] are slightly larger protein interaction regions , of which the heads and tails defined here form only a part: PKS protein termini typically contain one conserved N-terminal helix , which corresponds to our tail domain , and three conserved C-terminal helices , of which the most C-terminal helix corresponds to our head domain . ) When these domains were clustered according to sequence similarity [16] ( Text S1 , Section 2 ) , the heads and tails each independently assorted into three phylogenetic groups ( Figure 2A ) . When the docking domains of the proteins in each multiprotein chain were labeled according to group membership ( Figure 2B ) , a striking pattern emerged: phylogenetic clustering coincided precisely with head–tail interactions ( Figure 2C ) . Heads from one group could only interact with tails from a corresponding group , but were incompatible with other tails , and vice versa . From this one-to-one pairing , we were able to assign common labels to the head and tail clusters , thus defining three mutually incompatible classes of docking domain pairs: H1–T1 , H2–T2 , and H3–T3 . However , membership within a compatibility class was only a necessary , not a sufficient , condition for head–tail interaction: there were still numerous cases in which domain pairs belonging to the same compatibility class did not interact ( Figures 2B and 2C ) . This implies that , within each class , there are additional rules that determine the set of allowed interactions . In essence , there is another layer to the PKS specificity code . To gain further insight into the rules that governed specificity , we sought to identify residue pairs that co-evolved between interacting partners [17 , 18] . This task is complicated by the fact that our dataset is small and nonuniformly sampled , therefore dominated by spurious correlations . To overcome this problem , we developed a new algorithm called CRoSS ( correlated residues of statistical significance ) which uses both interaction and noninteraction data to identify significant pairings between head and tail residues ( Methods ) . For each site pair , the algorithm first calculates , separately for interactors and noninteractors , the joint distribution of amino acids summed over pathways . It then assigns a score to that site pair , which is essentially a p-value reporting the significance of the difference between these distributions . CRoSS has numerous advantages over existing co-evolution algorithms [17 , 18] ( Figure S1D ) . Because it is based on comparisons within rather than between pathways , it is less susceptible to errors from nonuniform sampling; because it reports a significance rather than a correlation , it can be applied to datasets of any size; and because it identifies sites but averages over amino acids , it is more sensitive given smaller datasets . CRoSS can generally be used to investigate protein specificity whenever data are available about which protein pairs do or do not interact , such as for bacterial two-component systems [19] . We used CRoSS to investigate the key residues that determine specificity within each compatibility class . We were able to detect significant residue pairs only for class H1–T1 ( Figures 3A and 3B ) . ( This does not mean there are no correlations in the other cases , only that we cannot detect them with confidence given the smaller sizes of those datasets . ) We found that the H1–T1 correlation matrix was extremely sparse ( Figure 3B ) , showing that a small number of site pairs co-evolved independently , uncorrelated to any broad phylogenetic patterns . CRoSS identified only seven significant correlated residue pairs for H1–T1 interactors , involving three head residues and five tail residues ( Figure S1C ) . Note that while residues involved in protein secondary structure are expected to be highly conserved , those that determine specificity should show moderate sequence conservation but strong co-evolution . Such residues cannot be identified from structure alone , but can only be identified from the type of correlation analysis presented here , or from detailed domain swapping and mutagenesis experiments [20 , 21] . Having identified the sites predominantly responsible for H1–T1 specificity , we next asked whether we could construct a specificity code for interactions within this class . We focussed on the three most significant CRoSS pairs , which involved three residues each on the head and tail domains ( indicated by asterisks in Figure 3C , and by arrows in Figure 4A ) . For any given head or tail , these residues define a short amino acid code word . We used a Monte Carlo technique to cluster these words into cliques [22] , such that interactions were enriched within cliques but suppressed between them ( Figures 4B and 4C; Text S1 , Section 3 ) . The code words broke up into clearly distinguishable sets of synonyms ( Figure 4D ) , with sequences much more similar than expected by chance ( p-value < 0 . 02 , estimated by using datasets with randomly permuted interactions; Figure 4E ) . These clusters essentially correspond to a refinement of H1–T1 into interaction compatibility subclasses ( Figure 3C ) . The organization of docking domains into compatibility classes and subclasses is striking , but perhaps circumstantial . Are there falsifiable predictions that can be tested against independent sources of data , other than those used during the original analysis ? In this section , we present three tests of the specificity code . The first test is structural: by mapping our results onto an NMR structure of PKS docking domains , we ask whether the correlated residue pairs picked out by CRoSS involve actual physical interactions . The second test is statistical: we break up our data into training and test sets , using the former to make predictions , and the latter to validate them . The third test is functional: we use published experimental data involving hybrid PKS pathways to validate our classification of docking domains into compatibility classes .
One of our central findings is that the PKS specificity code is hierarchical . At the highest level , there are phylogenetically diverged , extremely distinct compatibility classes of docking domains; at the next level , there are subclasses of domains that essentially differ from one another at just a few residues . As such , it is possible to achieve interactions between any pair of docking domains in a given phylogenetic class by a handful of mutations , but docking domains from different phylogenetic classes are likely to remain forever incompatible . This hierarchical organization provides important clues about the selective pressures that operate on PKS pathways . If it were possible to switch the class of any docking domain to any other by mutation , undesirable interactions in PKS multiprotein chains would arise at high frequency , reducing the overall fitness of a bacterial population . At the opposite extreme , if docking domains were all extremely distinct from one another , it would be prohibitively difficult to “reprogram” the order of a PKS multiprotein chain , so bacteria would not be sufficiently nimble in response to rapidly changing ecological conditions . The observed hierarchy of classes and subclasses might represent an optimal intermediate strategy , balancing the competing requirements of robustness and flexibility . If we examine how PKS pathways are encoded at the genetic level , we uncover two further puzzles . First , why are the PKS docking domains positioned so close to protein termini ( Figure 1A ) , when it is not uncommon to find protein–protein interaction domains deep within protein coding regions ? Second , why does gene order tend to match protein order in PKS pathways , when experiments involving hybrid PKSs [23 , 24] have shown that gene order is not essential for function ? The key to both these puzzles lies in the patterns of PKS pathway inheritance . If proteins were inherited in their entirety over evolutionary timescales , we would expect their N- and C-termini to have similar phylogenetic trees . Instead , we find that pairs of proteins with closely related tails can have distantly related heads and vice versa ( Figure 6B ) , implying that domain shuffling occurs frequently . More remarkably , we find that the interacting domain pairs , the head of one protein and the tail of its partner , have similar phylogenies ( Figure 6C ) . Interacting docking domains , straddling two proteins , constitute the unit of inheritance . This is presumably because the two also represent a unit of function , one being useless without the other . Their combined inheritance is ensured if these two protein fragments are encoded contiguously on the genome . Indeed , we find that most of the docking domain pairs in our dataset are adjacently transcribed ( Dataset S2 ) . Of course , this would require that docking domains map to gene termini , and that gene order match protein order . In a world of rampant recombination and gene transfer , domain and gene order are functionally irrelevant , but evolutionarily important . The high-level structure of the PKS protein interaction code is striking . The hierarchical organization of docking domains into compatibility classes and subclasses creates multiprotein chains that are simultaneously robust and reprogrammable . A pair of interacting docking domains form a single module , a genetic unit straddling protein termini , so they are always inherited together . And the correspondence between gene order and protein order is a beautiful example of abstraction , allowing new protein configurations to be efficiently sampled through the underlying process of DNA recombination . All this amounts to a common standard for information exchange , allowing microbes to access a shared pool of biosynthetic capabilities . It would appear that PKS pathways should not simply be regarded as machines , evolved to produce this or that polyketide product . Rather , they represent a sort of genetic sketchpad , allowing biosynthesis to be abstractly represented , shared , and shuffled , in a process of continual biochemical innovation .
Head and tail domains of interest were grouped by PKS pathway . For each pathway , known interactors ( I ) and known noninteractors ( NI ) were coupled , and appropriately weighted . For example , consider a hypothetical pathway which involves five proteins , with mI = 4 head–tail interfaces . Let the heads be labeled hi and the tails ti , numbered by interface i = 1 , . . . , mI . The I dataset will contain the mI = 4 interacting pairs {h1 , t1} through {h4 , t4} , each with weight wI = 1 . The NI dataset will contain the mNI = 12 noninteracting pairs {h1 , t2} , {h1 , t3} , . . . , {h4 , t2} , {h4 , t3} , each with a weight wNI = mI/mNI = 1/3 . The total contribution of this pathway to both the I and the NI dataset will therefore be: We carried out this procedure for all pathways , leaving out those with mNI = 0 . The datasets I and NI each contain an ordered list of head–tail pairs indexed by r , with corresponding weights wr . The sequences of the rth domain pair are represented by binary variables as follows: , , where i runs over head sites and j over tail sites , and α and β run over the 20 amino acids . We next calculated the joint distribution of amino acids at some site pair {i , j}: For any given site pair {i , j} , it follows from Equation 1 ) that the marginal distribution of amino acids are identical: Therefore , the only way the joint distributions can be different is if they display different correlations . However , we must be careful not to take the observed correlations too seriously , given the small size of the dataset . We therefore calculated the significance of the difference between the joint distributions using a chi-squared test [28]: where Here , νij is the number of bins for which either or is nonzero , Q is the incomplete gamma function , and Γ ( a ) is the Euler gamma function . If this were a true chi-squared test , the final CRoSS matrix ρij would give the probability that the interaction and noninteraction distributions were drawn from the same underlying joint-distribution . The lower this value , the more significant the site pair {i , j} as a predictor of specificity . In fact , the fractional weighting used in Equation 1 ) means that the values ρij overestimate the probability of the null hypothesis . Moreover , we are testing multiple hypotheses , one for each site pair under consideration . In choosing a threshold to classify a particular subset of site pairs as being significant , it is useful to compare the distribution of values ρij against a background distribution ρij , generated by applying CRoSS to compare random pairings with noninteractors . This is how the seven significant pairs in Figure 3B were identified ( Figure S1C ) . CRoSS was implemented in MATLAB ( The MathWorks ) .
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Biomolecular interactions can be extraordinarily specific . In many instances , a protein can select its single correct binding partner from among a large array of closely related candidates . For polyketide synthases ( PKSs ) , a family of bacterial enzymes , such specificity is essential . Like workers on an assembly line , PKSs function as multiprotein chains , each enzyme modifying its substrate before passing it along to the next . And like a well-designed jigsaw puzzle , the overall multiprotein chain is correctly ordered precisely because each component protein can only bind to specific nearest neighbors . A PKS multiprotein chain is held together by sticky “head” and “tail” domains found at either end of each protein , the head of one protein binding to the tail of the next . We looked for patterns in the amino-acid sequences of these domains that could explain why certain head–tail pairs bind , while others do not . We discovered that heads and tails each come in three very different varieties . Mismatched head–tail pairs do not bind at all , while the binding of a matching head–tail pair is governed by the amino acids found at a few key positions on the physical interface between these domains .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"evolutionary",
"biology",
"eubacteria",
"computational",
"biology"
] |
2007
|
The Origins of Specificity in Polyketide Synthase Protein Interactions
|
Depression is characterized by a marked decrease in social interactions and blunted sensitivity to rewards . Surprisingly , despite the importance of social deficits in depression , non-social aspects have been disproportionally investigated . As a consequence , the cognitive mechanisms underlying atypical decision-making in social contexts in depression are poorly understood . In the present study , we investigate whether deficits in reward processing interact with the social context and how this interaction is affected by self-reported depression and anxiety symptoms in the general population . Two cohorts of subjects ( discovery and replication sample: N = 50 each ) took part in an experiment involving reward learning in contexts with different levels of social information ( absent , partial and complete ) . Behavioral analyses revealed a specific detrimental effect of depressive symptoms–but not anxiety–on behavioral performance in the presence of social information , i . e . when participants were informed about the choices of another player . Model-based analyses further characterized the computational nature of this deficit as a negative audience effect , rather than a deficit in the way others’ choices and rewards are integrated in decision making . To conclude , our results shed light on the cognitive and computational mechanisms underlying the interaction between social cognition , reward learning and decision-making in depressive disorders .
One of the core clinical symptoms of depression is anhedonia , which refers to a reduced motivation to engage in daily life activities ( motivational anhedonia ) and a reduced enjoyment of usually enjoyable activities ( consummatory anhedonia ) [1 , 2] . In principle , this clinical manifestation could be explained by reduced reward sensitivity , both in terms of incentive motivation and in terms of reinforcement processes [3–5] . A direct prediction of this hypothesis is that depressive symptoms should be associated with reduced reward sensitivity in learning contexts both at the behavioral and neural level . However , while some studies do find evidence that depressive symptoms in the general population and in clinical depression are associated with blunted reward learning and reward-related signals in the brain [6 , 7] , others indicate no [8 , 9] or mixed effects [5] . As a consequence , there is no strong consensus about which components of reward processing are most predictive of depressive symptoms in both the general population and clinical depression [5] . Another striking clinical manifestation of depressive symptoms is a marked decrease in social interactions . Depression is indeed associated with social risk factors , social impairments and poor social functioning [10] . Surprisingly , despite the importance of the socio-cognitive impairments that are often associated with elevated depressive symptoms , non-social aspects have received disproportionate attention . Furthermore , when social aspects are investigated the focus is often on emotional processing and theory of mind but not on how social information is integrated to produce efficient goal-directed behavior [11] . In the present study , our goal was to investigate whether the reward-learning deficit that is often associated with elevated depressive symptoms interacts with the social context [12] . According to social learning theory , a sizable amount of decisions are not directly shaped by people’s personal history of reward and punishments , but are rather acquired through social observation [13] . More specifically , this framework posits that human learning occurs mostly in social contexts , where subjects can be influenced by social cues ( i . e . others’ choices and outcomes ) [13 , 14] . In order to test how depressive symptoms affect the integration of social cues during reinforcement learning , we administered a variant of a previously validated observational learning task on two independent samples of participants [14 , 15] . Subjects also completed psychometric questionnaires assessing depression and anxiety ( a co-morbid trait ) symptoms . The task included a ‘Private’ learning condition , in which participants only had access to the outcome of their own choice , and two social conditions: the ‘Social-Choice’ condition in which participants had access to the demonstrator’s choice , and the ‘Social-Choice+Outcome’ condition in which participants had access to the demonstrator’s actions and their outcome ( Fig 1A and 1B ) . Our design allowed us to test several hypotheses concerning the relation between depressive symptoms and learning performance in private and social contexts . First , our design allowed us to test whether or not depressive symptoms degrade reward learning per se , as assumed by the standard account of depression as a reward sensitivity deficit . Second , by comparing the ‘Private’ and the ‘Social’ learning contexts , we could assess whether or not depressive symptoms are associated with a learning deficit in ‘Social’ contexts , as predicted by evidence of socio-cognitive impairments in depressive patients . Finally , thanks to computational analyses , we could precisely characterize the learning deficit in the ‘Social’ context either as a primary social learning deficit ( i . e . impaired imitation ) or as a secondary social learning ( i . e . a negative audience effect ) .
An online experiment was particularly suited to test our hypothesis because—compared to laboratory-based experiments—it provides a more diversified pool of subjects , in terms of psychiatric traits and cognitive performance [16–19] . Specifically , we tested 50 participants in the general population and then ran a direct replication of the experiment on a second independent sample of 50 participants . In the main text , we report the meta-analytical p-values computed using a mixed effect meta-analysis . In the tables we present the results separately for each experiment and highlight the replication criteria proposed by the open science framework [20] . Levels of depressive and anxiety symptoms spanned a large range ( Table 1 ) [21] , with good internal consistency ( Hospital Anxiety Depression scale—depression subscale: Cronbach’s alpha 85%; anxiety subscale: Cronbach’s alpha 84% ) . Participants were paired with a virtual demonstrator and performed a probabilistic reinforcement learning task in three contexts: a ‘Private’ condition , in which participants performed the task individually with no access to the demonstrator’s choices and outcomes , and two social conditions: the ‘Social-Choice’ condition in which participants had access to the demonstrator’s choices , and the ‘Social-Choice+Outcome’ condition in which participants had access to the demonstrator’s choices and their outcome . Overall , participants displayed robust instrumental learning and chose the most rewarded symbol above chance in all conditions ( meta-analysis ‘Private’: MMETA = 0 . 65 ± 0 . 03 , zMETA = 11 . 37 , p < . 001; ‘Social-Choice’: MMETA = 0 . 65 ± 0 . 03 , zMETA = 11 . 83 , p < . 001; ‘Social-Choice+Outcome’: MMETA = 0 . 67 ± 0 . 03 , zMETA = 12 . 45 , p < . 001; ± corresponds to the 95% confidence intervals; Fig 1C; See S1 Table for the results on the two samples separately ) . Contrary to previous studies [14 , 15] , we used an online adaptive learning algorithm that determined the demonstrator’s behavior ( Q-learning with learning rate = 0 . 5 and choice temperature = 10 ) . As a consequence , the virtual demonstrators displayed realistic learning curves with some variability of performance ( Fig 1C ) . We predicted that observational learning would result in a correlation between the participants’ and the demonstrator’s correct choice rate in a given learning session . As predicted , a higher correct choice rate for the demonstrator was associated with a higher correct choice rate for participants in both social conditions ( ‘Social-Choice’ condition: rMETA = . 20 ± 0 . 07 , zMETA = 2 . 89 , p = . 004; ‘Social-Choice+Outcome’ condition: rMETA = . 20 ± 0 . 07 , zMETA = 2 . 87 , p = . 004 ) but not in the private condition ( rMETA = - . 01 ± 0 . 11 , zMETA = -0 . 05 , p > . 250; Fig 2A; see Table 2 for the results on the two samples separately ) . In order to confirm that participants actually integrated the virtual demonstrator as a social partner , we measured the influence of participants’ rating of trustworthiness of the demonstrator’s face on social learning . An effect of perceived trustworthiness evaluations was found , such that participants who perceived the demonstrator’s avatar as more trustworthy had higher correct choice rates in the ‘Social-Choice’ ( rMETA = . 32 ± 0 . 13 , zMETA = 2 . 54 , p = . 011 ) and in the ‘Social-Choice+Outcome’ conditions ( rMETA = . 29 ± 0 . 10 , zMETA = 2 . 96 , p = . 003 ) but not in the ‘Private’ condition ( rMETA = . 11 ± 0 . 10 , zMETA = 1 . 09 , p > . 250; Fig 2B ) . This effect of the social evaluation of the demonstrator’s avatar confirms that participants processed the information in a social context . A significant effect of depressive symptoms was found such that the higher the depressive symptoms , the lower the rate of correct choices in the ‘Social-Choice’ condition only ( rMETA = - . 33 ± 0 . 10 , zMETA = -3 . 47 , p < . 001; ‘Private’ condition: rMETA = . 04 ± 0 . 16 , zMETA = 0 . 16 , p > . 250; ‘Social-Choice+Outcome’ condition: rMETA = - . 05 ± 0 . 10 , zMETA = -0 . 48 , p > . 250; Fig 3A ) . However , a similar effect of anxiety , which is a comorbid trait of depression [22 , 23] , was found as a trend ( rMETA = -0 . 18 ± 0 . 10 , zMETA = -1 . 85 , p = . 065; Fig 3B ) . In order to better understand the effect of depressive symptoms on learning in social contexts , we ran a mixed linear logistic regression that included depressive and anxiety scores , taken as continuous between-subject variables ( the regression also included a range of controls listed in Table 3 ) . The analysis revealed a significant effect of depression scores such that the higher the depressive scores , the lower the rate of correct choices in the ‘Social-Choice’ condition compared to the ‘Private’ condition ( zMETA = -2 . 85 , p = . 004; no other significant effect of depression and anxiety scores was evidenced: all ps > . 250; Fig 3A ) . Importantly , the negative effect of depressive symptoms in the ‘Social-Choice’ condition was particularly robust , because it was found in both the discovery and the replication sample and in the blocks with stable and reversal contingencies ( within-subject ) ( S2 Fig ) . Finally , we tested whether the correct choice rates in the ‘Social-Choice’ condition identified participants with difficulties linked to depressive symptoms ( i . e . scoring ≥ 8 on the HAD depression subscale [21] ) from participants in whom these difficulties are absent . The classification analysis revealed that the performance in the ‘Social-Choice’ condition identified participants with depressive symptoms with good accuracy of 73 ± 1% and with good sensitivity , or True Positive Rate ( 82 ± 2% ) but low specificity , or True Negative Rate ( 53 ± 3% ) of the classifier ( Fig 4A ) . Although model-free analyses reveal a robust negative effect of depressive symptoms on learning in the ‘Social-Choice’ condition , they do not elucidate the cognitive mechanisms underlying this effect . Indeed , the effect of depressive symptoms could either be due to differences in social information processing , such as the demonstrator’s choices and outcomes ( i . e . a primary social learning deficit ) or to differences in the weighting of the information generated by participants’ own choices when social information is also available ( i . e . a secondary social learning deficit or audience effect ) . These two hypotheses are hard to tease apart based on raw behavioral analyses , because both predict a reduced correct choice rate in the ‘Social’ conditions . Thus , to arbitrate between these two possibilities , we fitted a previously validated social reinforcement learning model [14 , 24] . This model allows for biasing participants’ choice depending on the demonstrator’s choice in the ‘Social-Choice’ condition ( i . e . imitation ) and to update the value attributed to each symbol depending on the demonstrator’s outcome in the ‘Social-Choice+Outcome’ condition ( i . e . vicarious trial-and-error ) . To directly assess the ‘socially induced individual learning deficit’ hypothesis [14] , we allowed participants to have different individual learning parameters in the ‘Private’ ( learning rate: αP , temperature parameter: βP ) and in the two social conditions ( ‘Social-Choice’ and ‘Social-Choice+Outcome’ conditions: αS , βS; Fig 5A ) . More precisely , individual learning and decision-making were modeled with classical softmax ( Eq 1 ) and delta-rule ( Eq 2 ) functions , respectively governed by learning rate and choice randomness ( or temperature ) parameters: Pt ( st , at ) =1/ ( 1+e ( ΔQt ( st ) ) *β ) ( 1 ) Qt+1 ( st , at ) =Qt ( st , at ) +αP*RPEt ( 2 ) Where RPEt is the reward prediction error calculated as follows ( Eq 3 ) : RPEt=Rt−Qt ( st , at ) ( 3 ) During the ‘Social-Choice’ condition , the model assumes that the Demonstrator’s choice induces an ‘action’ prediction error ( APEt; ( Eq 4 ) ) , which measures how surprising the Demonstrator’s choice is , given the subject’s current estimate of the probability of selecting this option: APEt=1−Pt ( st , at ) ( 4 ) The APEt is then used to bias choice probability ( Eq 5 ) in the subsequent trial and the effect is scaled by a parameter κ ∈ {0–1}: Pt+1 ( st , at ) =Pt ( st , at ) +κ*APEt ( 5 ) Finally , in the ‘Social-Choice+Outcome’ trials , the model assumes that the demonstrator’s outcome induces an ‘observational’ reward prediction error ( Eq 6 ) , which is scaled by observational learning rate αO ∈ {0–1} ( Eq 7 ) : OPEt=R ( demonstrator ) t−Qt ( st , at ) ( 6 ) Qt+1 ( st , at ) =Qt ( st , at ) +αO*OPEt ( 7 ) To sum up , this computational model allowed us to address both primary social learning deficits ( i . e . learning deficits captured by the parameters κ and αO , which are specific to social information ) and secondary social learning deficits ( i . e . learning deficits captured by the parameters βS and αS , which are specific to individual learning in contexts where social information is available ) . As previously , we analyzed the model parameters fitted on participants’ actual behavior using correlations . Higher depression scores were specifically associated with lower learning rates in the ‘Social’ conditions ( rMETA = - . 25 ± 0 . 10 , zMETA = -2 . 55 , p = . 011; all others , including anxiety: |zMETA| < 1 . 30 , all ps > . 190; Fig 5B–5D ) . These results where further confirmed by with structural equation modeling accounting for the correlation between the parameters ( depression scores: zMETA = -2 . 61 , p = . 009; other ps > . 188; Fig 4C ) . Interestingly , high depression scores were not solely associated with decreased learning rates in the ‘Social’ conditions , but also with decreased learning rates in the ‘Social’ conditions when controlling for the learning rates in the ‘Private’ condition ( zMETA = -3 . 08 , p = . 002 ) , which indicates that the presence of social information decreased the learning rate of the most depressed participants . To assess the complementary utility of computational measures , we tested whether the learning rate in the ‘Social’ conditions could identify participants with symptoms of depression ( i . e . HAD depression subscale score equal or above 8 [21] ) . The difference in learning rates detected participants with depressive symptoms ( score ≥ 8 ) with good accuracy ( 64 ± 1% ) , good sensitivity ( 64 ± 2% ) and good specificity ( 65 ± 3% ) . A comparison between a classifier based on the model parameters and a classifier based on correct choice rates revealed that the model-based classifier was more specific to detect participants with higher symptoms of depression ( t ( 198 ) = 5 . 86 , p < . 001 ) , but was less sensitive ( t ( 198 ) = -12 . 03 , p < . 001; Fig 4C ) than the classifier based on correct choice rates . Model-based analyses indicated that the severity of depressive symptoms specifically reduced individuals’ learning rate in ‘Social’ conditions ( αS ) : a parameter that is used both in the ‘Social-Choice’ and in the ‘Social-Choice+Outcome’ condition . Model-free behavioral analyses showed that the learning deficit associated with depressive symptoms was specific to the ‘Social-Choice’ condition . To ascertain that this computational result was compatible with our model-free observation , we ran the same statistical analysis on simulated data [25] . Crucially , data simulated using the fitted parameters accurately recovered the decrease in performance associated with depression scores in the ‘Social-Choice’ condition compared to the ‘Private’ condition using the same mixed linear regression as on behavioral data ( zMETA = -2 . 72 , p = . 007 ) as well as the blunted effect of depression scores in the ‘Social-Choice+Outcome’ condition compared to the ‘Private’ condition ( zMETA = -1 . 74 , p = . 082 ) . Therefore , it appears that , although depressive symptoms are associated with decreased learning rates in both social conditions , its detrimental effect is manifest only in the ‘Social-Choice’ condition . This is probably due to showing the demonstrator’s outcomes in the ‘Social-Choice+Outcome’ condition . This additional outcome information may compensate for the decreases learning rates with depressive symptoms . Confirming this intuition , our simulation analyses accurately recovered the absence of significant effect of depressive symptoms in the ‘Private’ condition ( zMETA = -0 . 29 , p > . 250; S6 Fig ) . Thus , the simulations captured the specificity of the behavioral effect of depression scores and illustrate that our model provides an accurate description of the data . As we were interested in the modulation of specific parameters by depression scores we tested whether our task allowed us to successfully retrieve a correlation between parameters in simulated datasets , an important quality check often referred to as ‘parameter recovery’ [25] . To do so , we ran 100 sets of simulations for each parameter , each simulating 100 participants , with the parameter of interest correlating with an arbitrary variable ( defined as the depression scores ) and the other parameters being randomly set for each participant in the range obtained by optimization on the total sample . The simulated data were then fitted using our social reinforcement-learning model . Overall parameter recovery was very good , especially for the parameters of the social conditions , with significant correlations were found in the 100% of the simulated datasets ( average correlation coefficient of the parameters: r = 0 . 73 ± 0 . 01 ) . Importantly , the recovery of the correlations was specific to the manipulated parameter with false alarms detected in less than 10% of the cases except for learning rate and choice temperature in the ‘Private’ condition ( which was not our condition of interest ) ( Fig 5B ) . This result indicates that it is very unlikely that a correlation of one of our parameters with participants’ HAD depression scores is due to an effect of depression scores on another parameter .
In the present study we assessed reinforcement learning with a behavioral paradigm involving both private and social contexts , while concomitantly assessing depressive and anxiety symptoms in the general population . First , we replicate previous findings showing that participants integrate the demonstrator’s choices and outcomes , which is consistent with the idea that social learning processes ( both in terms of imitation and vicarious trial-and-error ) play a role in human reinforcement learning [14 , 15 , 26–28] . Second , we show that the severity of depressive symptoms is associated with a learning impairment that is specific to the learning context where participants are informed about the demonstrator’s choices ( social context ) . This negative effect was robust to the inclusion of anxiety , and robust across experiments and outcome contingencies . Finally , computational analyses allowed us to characterize the effect of depressive symptoms as a secondary social learning deficit , i . e . a reduction of the learning rate in social contexts . We found that depressive symptoms had a specific effect on imitation in the ‘Social-Choice’ condition . Crucially , the effect was robust to the inclusion of anxiety , which did not modulate performance in our task . That anxiety had no effect may come as a surprise given that previous studies have found that anxiety is associated with deficits in social and non-social reinforcement learning [29] . One possible explanation is that anxiety might be more strongly linked to classical fear conditioning than reward-based instrumental learning [30] . Depressive symptoms might thus undermine social reinforcement learning in instrumental and reward-maximization contexts , while anxiety might affect the same processes when outcomes are independent from the participants’ choices ( i . e . Pavlovian learning ) and when outcomes have a negative valence ( aversive contexts ) . Model-free analyses per se do not allow us to pinpoint the psychological mechanisms underlying the negative effect of depressive scores on correct choice rates in the ‘Social-Choice’ context . The absence of interaction between the demonstrator’s performance and depressive symptoms suggests that depressive symptoms did not lead participants to disproportionally follow ‘bad examples’ or to be insensitive to ‘good examples’ . However , interpretations based on negative results are , at best , unsafe . To formally characterize the psychological mechanisms of the detrimental effects of depressive symptoms we thus turned to model-based analyses . We fitted subjects’ choice with a slightly modified version of a previously validated social reinforcement-learning model [14] . As in standard algorithms , the model assumes that subjects learn option values via the calculation of a reward prediction error , that the values are moderated by a learning rate ( αP ) and that choices are generated via a soft-maximization process whose stochasticity is governed by a temperature ( βP ) [31] . In addition to this ‘private’ learning module , the model also displays sensitivity to social information: in the ‘Social-Choice’ condition the demonstrator’s choice biases the subsequent subject’s choice ( the magnitude of this effect is governed by an imitation rate κ ) and in the ‘Social-Choice+Outcome’ condition the demonstrator’s outcome is integrated into the subject’s value function with a vicarious learning rate ( αO ) . Finally , we also allowed for different private learning rates and temperatures in the ‘Social’ contexts ( αS and βS ) . This precise model parameterization allowed us to disentangle two different hypotheses concerning the drop in performance associated with depressive symptoms in the ‘Social-Choice’ condition . A correlation between depressive scores and imitation rates and/or vicarious learning rates would imply what we define a ‘primary’ social learning impairment ( i . e . an impairment of the social learning processes per se ) . On the contrary , a correlation between the ‘Social’ context-specific learning rate and/or temperature would imply a ‘secondary’ social learning impairment ( i . e . an impairment of the private learning processes in presence of social information ) . We found that depressive scores negatively correlated with the private learning rate in the social context ( αS ) , thus indicating that the effect was consistent with a secondary impairment and was specific to the learning ( as opposed to the decision ) process . In other words , our computational results suggest that one possible way in which depressive symptoms affect learning in social contexts is conceptually similar to a negative audience effect [32 , 33] , where the presence of social signals ( the demonstrator’s choices ) induces a reduction of subjects’ instrumental performance . From a methodological point of view , our study exemplifies how computational approaches can provide new insights on the way in which cognitive processes vary with clinical symptoms . Indeed , computational modeling demonstrated that the effect of depressive symptoms was selective of the way individual information was processed [34 , 35] . It is worth noting that these conclusions were only allowed after a careful testing of the ability of our task to precisely identify which model parameter was influenced by depressive symptoms [25] . The exact cognitive and psychological mechanisms that mediate the negative effect of social signals in instrumental performance remain to be characterized . One possibility given that depressive symptoms are associated with lower cognitive functioning in general [36] is that the mere presence of others exacerbates these difficulties by capturing already scarcer attentional resources . Alternatively , negative perception of self and negative comparison to others are core symptoms of depressive symptoms [37] . Therefore , it is possible that the most depressed participants perceived their demonstrator’s behavior as more reliable , thus underweighting the information they acquired through their own experience . Our results provide new evidence that depression-related reward learning deficits are highly context-dependent [3–5] , and suggest that the difference in learning rates associated with depressive symptoms may only arise in social contexts [5 , 9] . Crucially , our results suggest that supposedly neutral aspects of the experimental setup ( such as whether or not the task is done in the presence or absence of an experimenter ) , may affect the results and explain inconsistent findings [38] . In line with recent propositions , our results also suggest that a deeper investigation of socio-cognitive impairments in depressive symptoms may provide important new insights [10 , 11] . Following this idea , it would be particularly interesting to contrast the effect of depressive symptoms on learning when the information is socially ( as in the current study ) compared to asocially provided . Finally , we suggest that developing tools assessing reward learning outside and inside social contexts ( characterized either by the presence of another player or by the social nature of the outcomes [39] ) may prove useful to improve diagnosis and personalize treatments of depressive syndromes in the long term . An obvious limitation of our study , is that we did not control for participants’ actual diagnosis and treatment , which may be problematic since medication interacts with decision-making in depression [40] . Therefore , our results would benefit from being replicated in carefully characterized population , while controlling for medication status and medical history . This replication would allow us to further measure the diagnostic value of our behavioral task and associated computational model-based analyses . Indeed , in the present study , we only tested its ability to detect participants with depressive symptoms as identified by a self-rated scale [21] . It would be particularly interesting to test whether our behavioral and computational measures improve existing self-assessments that detect clinically diagnosed cases of depression [41] . Finally , longitudinal designs will be required to assess whether or not our behavioral and computational measures present good test-retest reliability and reflect states or traits , and whether or not they predict the evolution of depressive symptoms to clinical diagnosis . Our results have implications beyond their clinical relevance . Consistent with the ‘social learning theory’ participants imitated demonstrators’ choices ( ‘Social-Choice’ condition ) and learned from their outcomes ( ‘Social-Choice+Outcome’ condition ) [13 , 14] . At the behavioral level , these two psychological processes were manifest in the fact that participants’ performance was modulated by the demonstrators’ performance . In particular , we found that participants observing a demonstrator performing ‘well’ performed better in the social compared to the private learning context . Importantly , the opposite was also true: participants observing low performing demonstrators displayed lower performance in the social compared to the private context . This latter result is in apparent contrast with the normative view that imitation should be biased toward successful individuals in order to be evolutionary adaptive [42–44] . This is also in contrast with recent empirical evidence using a very similar paradigm and showing that imitation rate is modulated by the actual performance of the demonstrator , so that demonstrators making random ( i . e . , non reward-maximizing ) decisions are less imitated [15] . Two differences between the previous design and ours may explain this discrepancy . First , the previous study involved mild electric shocks ( primary reinforcer ) , while our study involved abstract points to be converted into money ( secondary reinforcer ) . More importantly perhaps , the previous design involved a between-subjects design with two groups of participants paired either with a consistently good or with a consistently bad participant , while in our experiments the performance of the demonstrator was allowed to fluctuate in a within-subject manner around an optimal behavior . Therefore , it could also be argued that our experiment is not well-suited for measuring demonstrators’ performance effects on participants’ imitation behavior as such effects require a relatively long and stable reputation building process [45 , 46] . The question remains whether or not social learning in our task ( imitation and vicarious trial-and-error ) engaged domain-specific social cognitive module or domain-general information processing modules . In the absence of additional data ( such as neuroimaging ) we cannot provide a definitive answer . However , evidence from post-learning face ratings provides some clues [47] . We found a positive correlation between performance in the social contexts and the demonstrator’s judgment of trustworthiness . Even if we cannot infer a causal link and its direction from the post-learning face evaluation , these results suggest that a specific socio-cognitive module ( face evaluation ) correlated with instrumental performance , thus demonstrating the engagement of social information-specific processing and our reinforcement learning task .
Two independent cohorts of 100 American participants , similar in terms of reported age ( mean reported age across the two cohorts: 33 . 39 ± 2 . 03 ) and of reported male/female ratio ( mean reported male/female ratio across the two cohorts: 35%; see Table 1 ) were recruited via Amazon Mechanical Turk to participate in this online study . Each participant received a fixed 4$ amount for completing the 40-minute task to which a bonus earned during the experiment was then added ( average bonus: 0 . 49$ ) . Participant received a description of the study and signed an informed consent before starting the experiment . The study was approved by the the local Ethical Committee ( Conseil d’évaluation éthique pour les recherches en santé–CERES n°201659 ) and is in accordance with the Declaration of Helsinki ( World Medical Association , 2008 ) . The first cohort corresponded to a ‘discovery experiment’ where we explored the relation between instrumental performance and clinical scores; the second cohort corresponded to a ‘replication experiment’ where we tested the robustness and replicability of the effect identified in the first experiment . Participants performed the probabilistic instrumental learning task described in the Results section ( Fig 1A and 1B ) . The task was programmed on Qualtrics and was composed of six learning blocks of 20 trials each . In each block , participants had to choose between two cues . Cues were characters of the agathodaimon font and were always presented in pair and only in one block per subject . The cue-to-condition attribution was randomized across subjects . Participants made their choice by pressing the E or P keys to choose the leftmost or rightmost symbol . Participants were given no explicit information on reward probabilities , which they had to learn through trial and error . In addition , they were encouraged to accumulate as many points as possible , with their final amount of points being translated into bonus money at the end of the experiment ( conversion rate: 40 points equals 1$ bonus ) . In each pair , cues were associated with reciprocal reward probabilities ( 20/80% or 30/70% ) . For instance , in a 30/70% pair , the most rewarded cue provided a positive outcome ( +1 point ) 70% of the times and a negative outcome ( -1 point ) 30% of the time , while the less rewarded cue provided a negative outcome 70% of the time and a positive outcome 30% of the time . Participants had unlimited time to make their choice ( Mean reaction time: 2 . 47 ± 0 . 88 s , no significant effect of depressive symptoms were found on the reaction times , all ps > . 250 ) . Participants were told they had been paired with another player at the beginning of the experiment with whom they played in turn in each trial . In addition , it was indicated that there was no competition between them and the other player and that each player played for her/himself . As in previous studies [48] , the behavior of the demonstrators was determined by a reinforcement learning algorithm ( Q-learning ) with a reasonable set of free parameters ( 𝛼 = 0 . 5 , ß = 10; see below for a description for the Q-learning and its parameters ) . To avoid social perceptual biases , the other player was represented by a neutral avatar , chosen to be generally perceived as neither dominant or submissive nor trustworthy or untrustworthy [49] . Participants had to choose their own avatars in a set of other 16 identities ( 8 female , 8 male ) at the beginning of the task . Participants performed this task in three different contexts with different amounts of social information: a ‘Private’ condition in which they did not have access to the demonstrator’s behavior , a ‘Social-Choice’ condition in which participants could see the demonstrator’s behavior but not their outcomes and a ‘Social-Choice+Observation’ in which participants could observe the demonstrator’s decisions and outcomes . Importantly , participants performed each condition ( ‘Private’ , ‘Social-Choice’ and ‘Social-Choice+Outcome’ ) in separate blocks and each block was repeated twice . In the ‘Stable’ type of contingency , outcome probabilities were set at 30/70% and did not change during the block . In the ‘Reversal’ type of contingency , outcome probabilities were set at 20/80% and was inverted across cue after 10 trials ( in average ) . Finally , at the end of the experiment , participants rated their demonstrator’s avatar on three personality traits ( trustworthiness , dominance and competence ) and completed the Hospital Anxiety and Depression Scale [21] as well as the Peters et al . Delusions Inventory , that was included in the exploratory analysis of the Discovery sample and then discarded in absence of any significant effect and its inclusion did not affect the effect of depression . The total procedure lasts approximatively 45 minutes . The analyses were performed on all participants and trials . No exclusion criteria was applied .
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Blunted sensitivity to rewards is at the core of depression . However , studies that investigated the influence of depression on decision-making have often done so in asocial contexts , thereby providing only partial insights into the way depressive disorders impact the underlying cognitive processes . Indeed , atypical social functioning is also a central characteristic of depression . Here , we aimed at integrating the social component of depressive disorders into the study of decision-making in depression . To do so , we measured the influence of self-reported depressive symptoms on social learning in participants performing an online experiment . Our study shows that depressive symptoms are associated with decreased performance only when participants are informed about the actions of another player . Computational characterizations of this effect reveal that participants with more severe depressive symptoms differ only in the way they learn from their own actions in a social context . In other words , our results indicate that depressive symptoms are associated with a negative audience effect and thus provide new insights into the way social cognition and decision-making processes interact in depression .
|
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2019
|
Depressive symptoms are associated with blunted reward learning in social contexts
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The ascending modulatory systems of the brain stem are powerful regulators of global brain state . Disturbances of these systems are implicated in several major neuropsychiatric disorders . Yet , how these systems interact with specific neural computations in the cerebral cortex to shape perception , cognition , and behavior remains poorly understood . Here , we probed into the effect of two such systems , the catecholaminergic ( dopaminergic and noradrenergic ) and cholinergic systems , on an important aspect of cortical computation: its intrinsic variability . To this end , we combined placebo-controlled pharmacological intervention in humans , recordings of cortical population activity using magnetoencephalography ( MEG ) , and psychophysical measurements of the perception of ambiguous visual input . A low-dose catecholaminergic , but not cholinergic , manipulation altered the rate of spontaneous perceptual fluctuations as well as the temporal structure of “scale-free” population activity of large swaths of the visual and parietal cortices . Computational analyses indicate that both effects were consistent with an increase in excitatory relative to inhibitory activity in the cortical areas underlying visual perceptual inference . We propose that catecholamines regulate the variability of perception and cognition through dynamically changing the cortical excitation–inhibition ratio . The combined readout of fluctuations in perception and cortical activity we established here may prove useful as an efficient and easily accessible marker of altered cortical computation in neuropsychiatric disorders .
The modulatory systems of the brain stem send widespread , ascending projections to the specialized circuits of the cerebral cortex that mediate perception , cognition , and goal-directed behavior . These systems regulate ongoing changes in brain state , even during periods of wakefulness [1–4] . They are recruited during , and in turn , shape cognitive processes such as perceptual inference , learning , and decision-making [5–8] . Because these systems are implicated in most neuropsychiatric disorders , they are also major targets of the pharmacological therapy of brain disorders [5 , 9 , 10] . Taken together , neuromodulatory systems have remarkably specific effects on cognition , despite the widespread nature of their projections to the cortex . An important challenge for neuroscience is to uncover the mechanistic principles by which neuromodulatory systems interact with the cortical computations underlying cognition . One key parameter of cortical computation that might be under neuromodulatory control is the intrinsic variability—i . e . , fluctuations that occur during constant ( or absent ) sensory input [11 , 12] . Specifically , it has been proposed that the catecholaminergic neuromodulators noradrenaline and dopamine may shift the cortical computations underlying decision-making from a stable ( “exploitative” ) to a variable ( “exploratory” ) mode [5 , 13] . A context-dependent adjustment of the variability of cortical computations may also be adaptive for perceptual inference in the face of ambiguous sensory input [14] . Animal work has shown that catecholamines and acetylcholine , another important neuromodulator , alter the intrinsic variability of neural activity [2 , 15–18] through highly selective interactions with specific elements ( pyramidal cells and/or inhibitory interneurons ) of cortical microcircuits [19 , 20] . However , it is unknown how these changes at the level of cortical microcircuits relate to the intrinsic variability of perception and cognition . At the larger scale of cortical mass action that is assessable with noninvasive recordings in humans , activity also fluctuates intrinsically in a spatially and temporally structured manner [21 , 22] . The temporal structure of these fluctuations is characteristic of so-called “scale-free” behavior: power spectra that scale as a function of frequency according to a power law , P ( f ) ∝ fβ[23 , 24] , indicating long-range temporal autocorrelations [25–28] . Some studies have linked the spatiotemporal structure of the fluctuations in cortical population activity to specific perceptual and cognitive processes [27 , 29–31] . However , it is unknown if and how these fluctuations in cortical population activity are dynamically regulated by neuromodulatory systems . We aimed to close these gaps by systematically quantifying the effects of catecholaminergic and cholinergic neuromodulation on the intrinsic variability in perception and large-scale cortical activity in the healthy human brain . To this end , we combined placebo-controlled , selective pharmacological interventions , psychophysical measurements of fluctuations in perception in the face of a continuously presented and ambiguous visual stimulus , and recordings of fluctuations in cortical population activity using magnetoencephalography ( MEG ) . Catecholamines , but not acetylcholine , increased both the variability of perception as well as long-range temporal correlations of intrinsic cortical activity in the visual and parietal cortices . Based on previous theoretical and experimental work [32–35] , we interpreted the increase in perceptual variability in terms of an increase in the net ratio between cortical excitation and inhibition in those cortical regions . Simulating a recurrent neural network under synaptic gain modulation enabled us to show that an analogous mechanism may account for the increase of long-range temporal correlations of cortical activity under catecholamines .
The ambiguous visual stimulus that was continuously presented during both Task-counting and Task-pressing induced ongoing fluctuations in perception , i . e . , spontaneous alternations between two apparent rotation directions of 3D motion ( Fig 1B; see S1 Movie ) , a phenomenon that is referred to as multistable perception . The rate of the perceptual alternations reported by the participants provided a readout of visual cortex circuit state . Current models explain bistable perceptual fluctuations in terms of the interplay between the feed-forward , excitatory drive of stimulus-selective neural populations in the visual cortex; mutual inhibition between them; stimulus-selective adaptation; and neural “noise” [32 , 33] . Increases in the ratio between feed-forward , excitatory input to and mutual inhibition within the cortical circuit give rise to faster perceptual alternations . This idea is supported by convergent evidence from functional magnetic resonance imaging , magnetic resonance spectroscopy , and pharmacological manipulation of GABAergic transmission [30 , 35] . We reasoned that neuromodulators such as noradrenaline might dynamically change these parameters [36 , 37] and thereby alter the rate of perceptual fluctuations . Atomoxetine increased the rate of perceptual fluctuations compared to both Placebo and Donepezil conditions ( Fig 3A; atomoxetine versus placebo: p = 0 . 007 , t = 2 . 913; atomoxetine versus donepezil: p = 0 . 001 , t = 3 . 632; donepezil versus placebo: p = 0 . 966 , t = −0 . 043; all paired t tests , pooled across Task-counting and Task-pressing ) . The perceptual alternation rates were highly consistent across Task-counting and Task-pressing ( S1C Fig ) , supporting the validity of the counting condition as behavioral readout of bistable perceptual fluctuations . Likewise , the atomoxetine effect on the perceptual fluctuation rate was evident for Task-counting ( p = 0 . 045 , t = 2 . 103; paired t test; S1A Fig ) and Task-pressing ( p = 0 . 018 , t = 2 . 540; paired t test; N = 26 subjects , see Materials and methods; S1B Fig ) individually . These changes in perceptual fluctuations were not explained by an increase in the rates of eye blinks or fixational eye movements . First , there was no significant increase during atomoxetine compared to placebo in any of five different eye movement parameters measured here ( S2 Fig ) . Second , none of these parameters correlated with the perceptual alternation rate ( S2 Fig ) . Third and most importantly , the effect of atomoxetine on the perceptual dynamics was also significant after removing ( via linear regression ) the individual eye movement parameters ( Fig 3B ) . In sum , atomoxetine had an effect on bistable perceptual fluctuations that was both robust and specific and evident when compared with either placebo or donepezil . This effect was in line with an increase in the strength of the excitatory feed-forward drive of the visual cortex , relative to the strength of mutual inhibition between the neural subpopulations encoding the competing perceptual interpretations of the ambiguous stimulus . Such an effect should have occurred in the motion-sensitive visual cortical areas , in which the visual competition induced by the ambiguous structure-from-motion stimulus is implemented [38 , 39] . We estimated long-range temporal correlations of band-limited amplitude fluctuations ( indicated by the scaling exponent α; see Materials and methods for details ) to quantify intrinsic fluctuations in cortical population activity . Our analyses focused on amplitude envelope fluctuations in the 8–12-Hz frequency range ( “alpha band” ) for two reasons . First , as expected from previous work [40] , the cortical power spectra exhibited a clearly discernible peak in this frequency range , which robustly modulated with sensory or task drive ( suppressed under Task-counting , Fig 1C ) . Second , previous studies reported robust long-range temporal correlations with peaks in the same frequency range [28] . We first replicated two previously established observations pertaining to the scaling exponent α . First , the average across cortical patches and participants was α = 0 . 67 ( SD = 0 . 09 ) during Fixation ( Placebo condition only ) and α = 0 . 64 ( SD = 0 . 07 ) during Task-counting ( Placebo condition only ) , indicative of long-range temporal correlations similar to the ones found in previous work [25 , 28 , 41] . Second , the sensory and task drive during Task-counting reliably reduced α compared to Fixation , again as shown in previous work [26 , 42] . Across all voxels , α was significantly larger during Fixation than during Task-counting ( p = 0 . 0062 , t = 2 . 97 , paired t test , Placebo condition only ) . This difference was significant across pharmacological conditions in large parts of the cortex , including the occipital and parietal regions , that were driven by the motion stimulus ( p < 0 . 05 , cluster-based permutation test; Fig 4D ) . Having verified the validity of our measurements of α , we then tested for changes in α under the pharmacological conditions ( Figs 4B–4E and 5 ) . There was a highly significant increase in α for atomoxetine compared to placebo when collapsing across voxels as well as across Fixation and Task-counting ( p = 0 . 0068 , t = 2 . 93; paired t test , Fig 4B and 4C ) . This effect was widespread but not homogenous across the cortex , comprising occipital and posterior parietal as well as a number of midline regions , including the thalamus ( Fig 4D , p = 0 . 0022; cluster-based permutation test ) . Because it is unclear to what extent intrinsic activations from deep sources can be recovered using MEG , we focus our description and conclusions on the effects in cortical regions . Importantly , the atomoxetine effect on α was also present at the level of MEG sensors ( S4 Fig ) and hence did not depend on the source reconstruction method applied here ( see Materials and methods ) . The effect of atomoxetine on α was subtle , likely due to the low dosage . However , importantly , the effect was highly reproducible across repeated measurements . We assessed reproducibility with two complementary approaches . The first was a region-of-interest ( ROI ) analysis . We defined an ROI in terms of a significant cluster for Atomoxetine > Placebo ( one-sided paired t test , p < 0 . 05 , uncorrected ) during the first run collected in each session ( collapsed across Fixation and Task-counting ) and extracted this ROI’s mean α from the second run . We then reversed the procedure and so extracted a second , independent ROI-based α and averaged the α-estimates . This approach revealed a strong increase under atomoxetine ( p = 0 . 0023 , t = 3 . 365 ) . The second approach assessed the reproducibility of the spatial pattern of effects across both runs . To this end , we correlated the ( nonthresholded ) individual maps for the atomoxetine versus placebo difference computed from the first and second run in each session ( again pooling across Task-counting and Fixation ) and tested the resulting correlation coefficients across participants . The average correlation was significantly different from 0 ( mean r = 0 . 29 , p < 0 . 0001; permutation test against 0 ) . The atomoxetine-related increases in scaling exponent α were evident during both Fixation and Task-counting ( Fig 5A , Fixation: p = 0 . 0245; Fig 5B , Task-counting: p = 0 . 0035; cluster-based permutation test ) . The effects occurred in largely overlapping regions of the occipital and parietal cortices ( Fig 5C ) . There was no interaction between the effects of atomoxetine and Task-counting anywhere in the cortex: a direct comparison of the two atomoxetine versus placebo difference maps , from Fixation and from Task-counting , yielded no significant clusters ( p > 0 . 081 for all clusters; cluster-based permutation test ) . The same cortical regions in which α increased during atomoxetine exhibited decreases during Task-counting: when testing for the task-dependent change in α ( Fig 4A ) , specifically in the regions comprising the conjunction cluster of the atomoxetine effect ( Fig 5C ) , the reduction during Task-counting was also highly significant ( Fig 5D ) in all pharmacological conditions . In contrast to the robust effect of atomoxetine on α , there was no evidence for an effect of donepezil at the dosage used here . The difference between donepezil and placebo ( collapsed across Fixation and Task-counting ) did not reach significance , neither when pooling across voxels ( p = 0 . 50 , t = 0 . 68; Bayes factor [BF] = 0 . 68; paired t test; Fig 4B ) nor when testing all voxels individually ( p > 0 . 22 for all clusters; two-sided cluster-based permutation test; Fig 4E; S5 Fig ) . atomoxetine also increased the scaling exponents when directly compared to donepezil during Task-counting ( S6A Fig; p < 0 . 05; two-sided cluster-based permutation test ) but not during Fixation ( S6B Fig ) . Taken together , the rich experimental design gave rise to a highly specific and consistent pattern of changes in α under the different experimental conditions , which helped constrain the mechanistic interpretation of the results . The atomoxetine effects were specific and not just due to the application of any drug targeting neurotransmitter systems . It is possible that the absence of detectable donepezil effects on α was due to the low dosage or short administration period used here . However , the control analyses presented in the next section revealed clear effects of donepezil on both cortical activity as well as markers of peripheral nervous system activity . During Fixation , atomoxetine and donepezil both reduced posterior cortical alpha-band power relative to placebo in both the 8–12-Hz ( Fig 6A; p < 0 . 05 for all clusters; two-sided cluster-based permutation test ) as well as the 2–8-Hz frequency ranges ( S7A Fig ) . This suppression in low-frequency power under cholinergic boost is consistent with previous work in rodents [16 , 17] and humans [43] . The atomoxetine-induced changes on 8–12-Hz power exhibited a different spatial pattern from the one of corresponding change in the scaling exponent α: within the cluster of the significant main effect of atomoxetine on α ( Fig 4D ) , power did not correlate with the changes in α ( group average spatial correlation between pooled difference maps within the cluster; r = 0 . 073 , p = 0 . 129 , BF = 1 . 065 ) . During Task-counting , neither drug altered MEG power in the low frequencies ( 8–12 Hz: Fig 6B , p > 0 . 05 for all clusters; two-sided cluster-based permutation test; 2–8 Hz: S7B Fig ) , presumably due to the already suppressed power in the 8–12-Hz range in that condition ( Fig 1C ) . S3 Fig shows the global drug-related changes in power , averaged across all MEG sensors . Together with the findings reported in the previous section , the analyses of the mean MEG power indicate that ( i ) both drugs reduced the amplitude of cortical low-frequency oscillations and ( ii ) MEG power and the scaling exponent α reflected at least partially distinct aspects of intrinsic cortical dynamics . We also controlled for changes in peripheral physiological signals under the drugs as potential confounds of the effect on cortical scaling behavior ( Fig 7 ) . As expected , atomoxetine increased average heart rate ( Fig 7A and 7B ) . Donepezil had no detectable effect on average heart rate during either Fixation ( p = 0 . 8676 , t = 0 . 16; paired t test; BF = 0 . 8676; Fig 7A ) or Task-counting ( p = 0 . 3274 , t = 1 . 0; paired t test; BF = 0 . 3139; Fig 7B ) . Both drugs altered heart rate variability , increasing α computed on the time series of inter-heartbeat intervals ( see Materials and methods ) in both behavioral contexts relative to placebo ( Fixation: p = 0 . 0012 , t = 3 . 62; Task-counting: p = 0 . 0167 , t = 2 . 55; Fig 7C; Fixation/Donepezil: p = 0 . 0076 , t = 2 . 88; Task-counting/Donepezil: p = 0 . 0049 , t = 3 . 06; Fig 7D; all paired t tests ) . Critically , the atomoxetine-induced changes in heart rate showed no ( Task-counting: r = 0 . 00 , p = 0 . 99; Pearson correlation; BF = 0 . 15 ) or only weak and statistically nonsignificant ( Fixation: r = 0 . 24 , p = 0 . 21; Pearson correlation; BF = 0 . 31 ) correlations with the changes in cortical activity ( Fig 7A/7B , right ) . Similarly , the atomoxetine-related changes in the scaling behavior of inter-heartbeat intervals were not correlated with the changes in cortical scaling behavior ( Fixation: r = 0 . 22 , p = 0 . 26 , BF = 0 . 27; Task-counting: r = 0 . 26 , p = 0 . 19 , BF = 0 . 35; Fig 7C/7D , right ) . Atomoxetine also decreased spontaneous blink rate during Fixation ( p = 0 . 034 , t = 2 . 24; paired t test ) but not during Task-counting ( p = 0 . 112 , t = 1 . 645; BF = 1 . 130; paired t test; S2B Fig ) . However , again there was no significant correlation between changes in blink rate and changes in cortical scaling behavior due to atomoxetine ( Fixation: r = −0 . 26 , p = 0 . 19 , BF = 0 . 35; Task-counting: r = −0 . 09 , p = 0 . 64 , BF = 0 . 16 ) . In sum , drug-induced changes in peripheral physiological signals under the drugs , if present , did not account for the atomoxetine-induced changes in the scaling behavior of the fluctuations in cortical activity ( Figs 4 and 5 ) . These controls support our interpretation in terms of a specific effect of atomoxetine on cortical variability rather than nonspecific secondary effects due to the systemic drug effects or changes in retinal input due to blinks . Atomoxetine had an effect on perceptual fluctuations that was in line with a relative increase in excitation in the cortical circuits of the occipital and posterior parietal cortices that processed the ambiguous visual motion stimulus . We reasoned that this change in circuit state might have also produced the observed change in the scaling behavior of intrinsic cortical activity fluctuations under atomoxetine . In order to solidify this intuition , we simulated the activity of a neural network model made up of recurrently connected excitatory and inhibitory integrate-and-fire units ( Fig 8 ) . In what follows , we use the term “excitation–inhibition [E/I] ratio” to refer to the ratio of excitatory and inhibitory activity across the circuit [44] and “E/I balance” to refer to a specific regime of E/I ratios , in which excitation and inhibition change in a proportional way [45–48] . We started from a network ( Fig 8A ) that was similar to the one developed and analyzed in a previous study [49] . The basic features of the model were as follows . The model was built to generate oscillations of neural mass activity ( summed across all units ) in the alpha band ( 8–12 Hz; Fig 8B ) . The amplitude envelope of these oscillations fluctuated over time , with scale-free long-range temporal correlations . Those scale-free intrinsic fluctuations in cortical activity were sensitive to variations in the percentage of excitatory and inhibitory connections in the circuit ( i . e . , microstructure ) . Our previous work [49] , which we reproduced here ( Fig 8D–8F ) , showed that such a model accounts for the joint emergence of two scale-free phenomena at different spatial scales ( single unit activity versus mass activity ) and temporal scales ( tens of milliseconds versus hundreds of seconds ) : ( i ) neuronal avalanches with an event size distribution following a power law and ( ii ) long-range temporal correlations of the amplitude envelope fluctuations of the circuits’ mass activity . Both phenomena have been established in empirical measurements of cortical population activity [25 , 50] . Neuronal avalanches are activity deflections ( i . e . , exceeding a certain threshold ) that propagate through the cortical network [50] , with an “event size” corresponding to the number of activated units . In line with [51] , we quantified the power-law scaling of the size distributions of avalanches in the model with the kappa index ( κ ) : the similarity between the actual event size distribution and a power-law distribution with an exponent of −1 . 5; a κ of 1 indicates perfect match between the two . We extended this model by means of a multiplicative modulation of synaptic gain [36 , 52] ( Fig 8B ) . This allowed us to explore how catecholaminergic effects on neural circuits might change the two phenomena of scale-free neural population activity described above . We first determined the structural connectivity ( small squares in Fig 8D–8F ) and the timescale parameters of the model such that the network generated intrinsic alpha-band oscillations ( Fig 8C ) with amplitude fluctuations that exhibited neuronal avalanches with scale-free event size distributions ( Fig 8D ) as well as long-range temporal correlations ( with α ~ 0 . 85 ) . We then independently modulated specific excitatory or inhibitory connections through the multiplicative scaling of the corresponding synaptic weights in two ways . In the version shown in Fig 8 , we modulated only excitatory synapses , but independently on excitatory as well as inhibitory neurons ( EE and IE ) , thus producing asymmetries in the circuits’ net E/I ratio , similar to recent modeling work on a cortical circuit for perceptual decision-making [44] . In the second version ( S8A Fig ) , we co-modulated EE and IE and independently modulated inhibitory synapses on excitatory neurons ( EI ) . This was intended to specifically simulate glutamate receptors ( AMPA or NMDA ) in the former two cases ( mediating the effects of excitatory neurons ) as opposed to modulations of GABA receptors in the latter case ( mediating the effects of inhibitory neurons on others ) . NEE and NIE were co-modulated by the same factor for simplicity , because we did not assume that excitatory ( glutamatergic ) synapses would be differentially modulated depending on whether they were situated on excitatory or inhibitory target neurons . Both types of changes in net E/I ratio robustly altered κ ( Fig 8G and S8B Fig ) , α ( Fig 8H and S8C Fig ) , and the mean firing rate ( Fig 8I ) . The effect of changes in E/I ratio on the scaling exponent α were nonmonotonic and dependent on the starting point: increases in excitation led to increases in α when starting from an inhibition-dominant point but to decreases in α when starting from an excitation-dominant point ( Fig 8H , white line ) . The effects of excitatory and inhibitory gain modulation on the temporal correlation structure of the simulated population activity were qualitatively similar to the effects of changes in the fraction of excitatory and inhibitory synapses simulated ( as shown in Fig 8D–8F ) . The latter simulated individual differences in cortical anatomical microstructure , and the former simulated state-dependent changes in cortical circuit interaction , which occur within an individual brain . In the model , the scaling exponent α exhibited a nonmonotonic dependence on E/I ratio ( see the white diagonal line in Fig 8G–8I and schematic depiction in Fig 9 ) . Consequently , without knowing the baseline state , any change in α was ambiguous with respect to the direction of the change in E/I ratio ( i . e . , towards excitation or inhibition dominance ) . Thus , the observed increase in α under atomoxetine during Fixation could have been due to either an increase or a decrease in E/I ratio . Importantly , insights from animal physiology helped constrain the baseline state during Task-counting: in the awake state , visual drive decreases E/I ratio in primary visual cortex ( V1 ) , due to the recruitment of inhibitory mechanisms that outweigh the excitatory sensory drive [53 , 54] . We assumed that the same held for the Task-counting condition ( constant visual stimulation ) of our study . This condition enabled us to infer the change in net E/I ratio under atomoxetine . The rationale is illustrated in Fig 9 . The animal physiology results referred to above indicate that the observed decrease in α during Task-counting was due to a shift towards inhibition dominance ( yellow point in Fig 9A ) . Under this assumption , the atomoxetine-induced increase in α was due to an increase in net E/I ratio ( Fig 9B ) . Because the effects of atomoxetine on α were the same during Task-counting and Fixation , it is likely that the same mechanism was at play during Fixation . In sum , under certain conditions , the simulations provided a mechanistic explanation for the observed MEG effects: effective changes in the cortical E/I ratio due to multiplicative changes of synaptic gain [36] or other mechanisms [19 , 37]—the same conclusion inferred from the increase in the rate of perceptual alternations above .
The atomoxetine effects on the scaling exponent were widespread across the cortex , but not entirely homogenous . They were pronounced across the occipital and parietal cortex but not robust in the frontal cortex ( see Fig 5B ) . This distribution might point to a noradrenergic rather than dopaminergic origin . Atomoxetine increases the levels of both catecholamines , noradrenaline and dopamine , but the cortical projection zones differ substantially between both systems: dopaminergic projections mainly target the prefrontal cortex [56] and only sparsely the occipital cortex [57 , 58] , whereas the noradrenergic projections are more widespread and strong to the occipital and parietal cortices [59] . Alternatively , this distribution may reflect the different receptor composition across cortical regions [59 , 60] . The relative frequency of the different noradrenaline receptors differs between prefrontal and posterior cortices [59] , which might translate a homogenous noradrenaline release into a heterogeneous effect on the activity in these different cortical regions . An important next step will be to investigate the differential role of different noradrenaline receptors and different regional receptor profiles in shaping the cortex-wide effect of noradrenaline on long-range temporal correlations . Consistent with our current results , previous studies also found a decrease in temporal autocorrelations of cortical activity due to external drive [26 , 42] . The observation is consistent with the insight from intracellular recordings of cortical neurons in animals that cortical responses to sensory stimulation in the awake state are dominated by inhibition [53 , 54 , 61] . One candidate source of this sensory-driven state change is thalamocortical inhibition [62] , but intracortical feedback inhibition might also contribute [63] . Modeling work shows that the driven state is associated with shortened temporal autocorrelations as well as a decrease in the entropy of activity states in large-scale cortical networks [64] . Correspondingly , the increase in long-range temporal autocorrelations under catecholaminergic modulation may be associated with an increase in entropy—that is , a tendency to explore a larger set of cortical activity states . It is tempting to link this to the prominent idea that high sustained noradrenaline levels promote an exploratory mode of cortical computation and behavior [5] . Cortical circuits maintain a tight balance between excitation and inhibition , which is largely preserved across contexts and levels of the cortical hierarchy [46 , 48] . However , even in the absence of changes in sensory input , neuromodulators such as noradrenaline and acetylcholine can change the cortical E/I ratio [65 , 66] . The E/I ratio , in turn , shapes the computational properties of cortical circuits [67 , 68] and thereby the behavior of the organism [36 , 44 , 69] . Substantial evidence already points to significant changes in E/I ratio in schizophrenia and autism [70–72] . Similar changes might be at play in other brain disorders as well [73] . Our simulations indicated that the temporal autocorrelation structure of neural population activity , as measured with the scaling exponent α , is sensitive to changes in E/I ratio produced through synaptic gain modulation ( see the white line in Fig 8H ) . In both versions of our model , the neuromodulatory effects were not perfectly symmetric ( see the deviations of peak scaling exponents from the main diagonal in Fig 8H ) . While the latter effect was small and may be specific to the details of the model , it remains possible that the subtle changes in scaling exponents we observed were produced through symmetric gain modulations that maintained the net E/I balance ( i . e . , along the main diagonal ) . However , two additional lines of evidence converge on our conclusion that catecholamines ( in particular , noradrenaline ) boosted E/I ratio . First , in the same participants , the catecholaminergic manipulation had a reliable effect on the perceptual switch rate , which is also indicative of cortical E/I ratio [32 , 33 , 35] . Second , results from invasive rodent work also point to an increase in cortical E/I ratio under noradrenaline: Noradrenaline was found to decrease spontaneous inhibition in the auditory cortex [66] and mediate a tonic depolarization of visual cortical neurons during locomotion [19] . The absence of an effect of donepezil on either perceptual fluctuations or long-range temporal correlations of cortical activity may be due to the small dosage or the single administration of the drug in our study . Even so , our donepezil manipulation was sufficient to robustly change heart rate variability and , more importantly , low-frequency power of cortical activity , an established marker of cholinergic action in the cortex [16 , 17 , 43 , 74] . The lack of the effects of donepezil on perceptual fluctuations and cortical scaling behavior might also be due to the specific properties of cholinergic action on the cortical net E/I ratio . Invasive evidence indicates that acetylcholine can rapidly disinhibit pyramidal cells by activating a chain of two inhibitory interneurons [20] , a mechanism that may alter E/I ratio mainly during stimulus-evoked responses [65] . By contrast , noradrenaline also alters the levels of the tonic inhibition of pyramidal cells occurring spontaneously [66] . This might explain the dissociation between the effects of atomoxetine and donepezil under the current steady-state conditions , which excluded ( or minimized ) stimulus-evoked transients . We observed a selective increase in the rate of spontaneous perceptual alternations under increased catecholamine levels , adding to evidence that these dynamics are under neuromodulatory control [75] . Such a change could be due to an increase in the intrinsic variability of cortical activity [32] . Future invasive studies should relate catecholaminergic changes in the variability of the spiking activity [76] of neurons contributing directly to the contents of multistable perception . We suspect that an increase in cortical E/I ratio will have particularly strong effects on behavior when affecting parietal and prefrontal cortical circuits characterized by slow intrinsic timescales [29 , 31 , 77] and involved in persistent activity during working memory and the slow accumulation of information over time [69] . It is possible that the catecholaminergic effects on the parietal cortex we observed here reflect an increase in recurrent excitation , which is essential for sustained processes , such as working memory [78] , as well as information integration during decision-making [31 , 79] . Future work should assess this through the use of tasks probing into network reverberation and information accumulation in the association cortex . In our model , long-range temporal correlations in the fluctuations of neural mass activity ( i . e . , activity summed across the entire local network ) [25] and avalanches within the neuronal network [50] jointly emerge at the same E/I ratio . Both phenomena are commonly interpreted as hallmarks of “criticality” [25 , 28 , 50 , 80]—the state of a complex dynamical system poised between order and chaos [81–83] . It has been proposed that the cortex operates in a narrow regime around criticality [83 , 84] , potentially optimizing its computational capacities [51 , 80 , 85–87] . A number of reports showed that cortical dynamics may continuously vary around the critical state [88–91] , but the source of these fluctuations has so far remained unknown . Here , we have identified catecholaminergic neuromodulation as an endogenous factor controlling these spontaneous variations in critical dynamics . In complex systems , critical dynamics can emerge in a self-organized fashion [81] or through an external control parameter that fine-tunes the system . The tuning of temperature in the Ising model of spin magnetization [83] is a common example of the latter case . It is tempting to speculate that catecholaminergic tone serves as such a control parameter in the cerebral cortex . We here used two readouts of catecholaminergic effects , constituting two distinct expressions of the resulting changes in cortical circuit state . The envelope of cortical alpha-band oscillations collapsed across large chunks of cortex is unlikely to encode the contents of perception in the phenomenon studied here . The perceived direction of 3D motion , which fluctuates spontaneously , is encoded in fine-grained patterns of neural population activity within motion-sensitive visual cortical areas [39 , 92] . The power of alpha-band oscillations is a more global feature of cortical population activity , which is likely insensitive to the fine-grained , within-area patterns of neural population activity . The widespread release of neuromodulators also changes the cortical circuit state , specifically E/I ratio , in a widespread manner . Such changes , in turn , alter the highly specific ( fine-grained ) interactions between percept-selective populations of visual cortical neurons that give rise to the perceptual dynamics [32 , 33 , 35] . Thus , although both readouts likely tap into similar changes in global cortical circuit state , there is no one-to-one mapping between them . While our model simulations provided important mechanistic insights , the model has limitations that should be addressed in future work . First , different from the MEG data , the power of alpha-band oscillations behaves similarly to the scaling exponents in the model ( S8E Fig ) . This is because in the model , oscillations emerge from the same recurrent neuronal interactions that also shape the long-range temporal correlations in the amplitude envelopes of these oscillations . By contrast , in the brain , alpha-band power of local cortical mass signals is likely affected by a variety of sources other than local circuits , for instance , alpha-frequency–modulated input from the thalamus [93] . This might lead to dissociations between changes in MEG power and long-range temporal correlations of the power fluctuations , which the model does not capture in its present form . Second , the model lacks long-range excitatory connections , which are prominent in the real cortex and the effects of which on the correlation structure of cortical fluctuations are largely unknown . Another limitation of our study concerns our mechanistic interpretation in terms of E/I ratio . This interpretation rests on the assumption that sensory drive has the same effect on E/I ratio in humans as previously shown in rodent visual cortex [53 , 54] . Our study and the previous ones differed in terms of behavioral task , stimulus , and species , all of which might have led to violations of the assumption . However , a number of observations from human electrophysiology point to the notion , consistent with our assumption and the above rodent work , that the awake human cortex operates in an inhibition-dominant regime [94] , which is further amplified during active stimulus processing [89 , 90] . The combined measurement of fluctuations in bistable perception as well as in cortical mass activity under steady-state conditions provides an easily assessable , multilevel readout of pharmacological effects on cortical computation . In our study , this readout supported the idea that catecholamines boost the intrinsic variability of perception and behavior , an effect that might be mediated by an increase in the net E/I ratio in the visual cortical system . This readout may be useful for inferring changes in the cortical E/I ratio in neuropsychiatric disorders or in their pharmacological treatment in future work .
All participants gave written informed consent before the start of experiment , in accordance with the Declaration of Helsinki . The study was approved by the ethical committee responsible for the University Medical Center Hamburg-Eppendorf ( Ethik-Komission der Ärztekammer Hamburg , ID PV4648 ) . MEG was recorded using a whole-head CTF 275 MEG system ( CTF Systems , Inc . , Canada ) at a sampling rate of 1 , 200 Hz . In addition , eye movements and pupil diameter were recorded with an MEG-compatible EyeLink 1000 Long Range Mount system ( SR Research , Osgoode , ON , Canada ) at a sampling rate of 1 , 000 Hz . In addition , electrocardiogram ( ECG ) as well as vertical , horizontal , and radial electrooculogram ( EOG ) were acquired using Ag/AgCl electrodes ( sampling rate 1 , 200 Hz ) . To simulate the effects of synaptic gain modulation on cortical activity fluctuations , we extended a previously described computational model of a local cortical patch [49] by means of multiplicative modulation of synaptic gain . All features of the model were identical to those of the model by [49] , unless stated otherwise . The model consisted of 2 , 500 integrate-and-fire neurons ( 75% excitatory , 25% inhibitory ) with local connectivity within a square ( width = 7 units ) and a connection probability that decayed exponentially with distance ( Fig 8A ) . The dynamics of the units were governed by: Ii=Ii+∑jNijWijSj ( 10 ) τidIidt=I0-Ii ( 11 ) where subscripts i , j indicated different units , Nij was a multiplicative gain factor , Wij were the connection weights between two units , Sj a binary spiking vector representing whether unit j did or did not spike on the previous time step , and I0 = 0 . For all simulations reported in this paper , we optimized the connection weights using Bonesa [118] , a parameter-tuning algorithm , such that the network exhibited alpha-band oscillations , long-range temporal correlations , and neuronal avalanches ( see below ) . The optimized values for the connection weights were WEE = 0 . 0085 , WIE = 0 . 0085 , WEI = −0 . 569 and WII = −2 , whereby subscript E indicated excitatory , subscript I indicated inhibitory , and the first and second subscript referred to the receiving and sending unit , respectively . On each time step ( dt = 1 ms ) , Ii was updated for each unit i , with the summed input from all other ( connected ) units j and scaled by a time constant τi = 9 ms , which was the same for excitatory and inhibitory units . The probability of a unit generating a spike output was given by: Psi=Psi+Ii ( 12 ) τPdPsidt=P0-Psi ( 13 ) with the time constant for excitatory units τP = 6 ms and for inhibitory τP = 12 ms . P0 was the background spiking probability , with P0 ( exc . ) = 0 . 000001 [1/ms] and P0 ( inh . ) = 0 [1/ms] . For each time step , it was determined whether a unit did or did not spike . If it did , the probability of that unit spiking was reset to Pr ( excitatory ) = −2 [1/ms] and Pr ( inhibitory ) = −20 [1/ms] . We used this model to analyze the dependency of two quantities on E/I ratio: ( i ) the power-law scaling of the distributions of the sizes of neuronal avalanches [50] estimated in terms of the kappa index , κ , which quantifies the difference between an empirically observed event size distribution and a theoretical reference power-law distribution , with a power-law exponent −1 . 5 [51] , and ( ii ) the scaling behavior ( scaling exponent α ) of the amplitude envelope fluctuations of the model’s local field potential . To this end , we summed the activity across all ( excitatory and inhibitory ) neurons to obtain a proxy of the local field potential . We band-pass filtered the local field potential in the alpha band ( 8–12 Hz ) and computed long-range temporal correlations in the alpha-band amplitude envelopes following the procedure described above ( see “Detrended fluctuation analysis” ) , using windows sizes ranging from 5–30 s . In order to assess the influence of structural excitatory and inhibitory connectivity on network dynamics ( Fig 4D–4F ) , we varied the percentage of units ( excitatory and inhibitory ) a given excitatory or inhibitory unit connects to within a local area ( 7 x 7 units; Fig 8A ) . These percentages were varied independently for excitatory and inhibitory units , with a step size of 2 . 5% . The gain factor Nij was the main difference to the model described by [49] . It was introduced to simulate the effects of neuromodulation on synaptic interactions in the cortical network [36] . For this , we kept all the structural parameters fixed ( 42 . 5% excitatory connectivity , 75% inhibitory connectivity; small square in Fig 4D–4F ) in a range in which the model exhibits both robust long-range temporal correlations as well as neuronal avalanches . Note that any other combination of parameters would yield similar results , as long as the model exhibits these two phenomena . From the chosen starting point , we systematically varied the synaptic gain factors in two different ways . In the first version , we only varied NEE and NIE to dynamically modulate the circuit’s net E/I ratio ( Fig 8B ) in a way consistent with recent modeling of the effects of E/I ratio on a cortical circuit for perceptual decision-making [44] . In the second version , we varied NEE , NIE , and NEI ( S8A Fig ) . Here , NEI was modulated independently from NEE and NIE , which in turn were co-modulated by the same factor . Per parameter combination , we ran 10 simulations using the Brian2 spiking neural networks simulator [119] . Each simulation was run for 1 , 000 s , with a random initialization of the network structure and the probabilistic spiking .
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The human brain is equipped with a number of modulatory neurotransmitter systems , which have widespread projections and regulate global brain states . Disturbances of these systems are implicated in several neuropsychiatric disorders , but how they modulate specific neural computations to shape perception , cognition , and behavior remains unknown . Here , we combined pharmacological interventions with electrophysiological and behavioral measurements to investigate the impact of two classes of such neuromodulatory systems—catecholamines and acetylcholine—on the variability of the activity of neuronal populations in the cerebral cortex and on cognition in humans . We addressed a prominent hypothesis , which holds that noradrenaline—a catecholamine—boosts the variability of inference and decision-making to promote exploration of alternative options—for example , exploring distinct perceptual interpretations of an ambiguous sensory input . Pharmacologically elevating catecholamine levels , but not acetylcholine levels , altered the temporal structure of intrinsic variability of population activity in two cortical regions and increased the rate of spontaneous perceptual alternations induced by ambiguous visual stimulation , in line with an increase in exploration . Computational modeling revealed that the observed changes can be explained by an increase in the ratio between excitation and inhibition in the circuits processing the stimulus .
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2018
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Catecholamines alter the intrinsic variability of cortical population activity and perception
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Various types of neural-based signals , such as EEG , local field potentials and intracellular synaptic potentials , integrate multiple sources of activity distributed across large assemblies . They have in common a power-law frequency-scaling structure at high frequencies , but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity . The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity . In intracellularly recorded neurons of cat primary visual cortex in vivo , the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent . We show that this exponent is not constant , but depends on the visual statistics used to drive the network . To investigate the determinants of this frequency-scaling , we considered a generic recurrent model of cortex receiving a retinotopically organized external input . Similarly to the in vivo case , our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input . This systematic dependence was also replicated at the single cell level , by controlling independently , in a parametric way , the strength and the temporal decay of the pairwise correlation between presynaptic inputs . This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques . These in vitro manipulations induced a modulation of the scaling exponent , similar to that observed in vivo and predicted in computo . We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity . Therefore , we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels , from Vm to LFP , EEG and fMRI .
Assigning a functional role to the correlations in network activity is still controversial . While many studies have proposed that the mean firing rate of the neuron contains much of the information about the sensorimotor interaction with the environment , or the behavioral task being performed [1] , [2] , other studies have suggested a specific role of higher-order interactions in cortical processing [3]–[5] . Here , we explore another way to extract correlations , through the scaling properties of the power spectrum ( hereby called “power spectral density” or PSD ) of the membrane potential of single neurons . A particularly common form of frequency scaling is the power-law , according to which the PSD scales as 1/fα at high frequencies , with some exponent which may be integer or fractional ( fractal ) . Power-law frequency-scaling is ubiquitous in electrophysiological measurements of neuronal population activity , from spiking activity [6] to fMRI signals [7] , but its function and origin are still controversial . Some studies consider it as the manifestation of neural “avalanches” , a special form of cell assembly dynamics which would appear when the cortical network is in a critical state [8] , [9] and which would be optimal for information processing . Power-law decay functions may also provide the basis for long-lasting interactions in adaptation [10] , [11] or memory storage [12] . Several explanations for the origin of power-law scaling have been proposed . At the intracellular level the membrane potential activity was shown to have power-law scaling at high frequencies , with exponent values around for synaptic background activity in vivo [13] , [14] and channel noise [15]–[17] . Cable equations predict values of between 3 and 4 for inputs distributed in soma and dendrites , and the non-ideality of the membrane capacitance was proposed to account quantitatively for these values [18] . However , it is unclear whether this exponent can also be modulated by extrinsic factors in vivo , and in particular by the synaptic bombardment evoked by sensory input . As we report in this paper , we decided to approach this issue by analyzing the activity of neurons recorded intracellularly in cat primary visual cortex in vivo , when the network is driven so as to be in an irregular activity regime . We found that the power-law scaling observed in the intracellular activity PSD at high frequencies is modulated by the stimulus . We examined whether the scaling exponent variations observed in vivo can be accounted for by theoretical models in computo , using paradigms where the correlation among inputs can be modulated . First , we designed a recurrent network model composed of a thalamic and a cortical layer and showed that when varying the correlation of the thalamic input to the cortical layer power-law exponent modulations were consistent with the in vivo results . The scaling exponent thus reflects in this model a specific correlational state of the network imposed by the input . We then dissected out those aspects in the activity impinging on the recorded neuron that can modulate the scaling exponent , and also explored the alternative hypothesis that intrinsic properties of the individual neuron are sufficient to explain the observed modulation . For this purpose , we applied different correlated synaptic inputs to neuron models . This confirmed that a change in the correlation of the synaptic input can modify the power-law exponent . Finally , we investigated this paradigm in cortical neurons in vitro using the dynamic-clamp technique and confirmed the results obtained with computational models . We discuss how these results are consistent with the theory that the power-law exponent modulation reflects changes in the correlation state of the network activity .
15 neurons were recorded intracellularly in the primary visual cortex of the anesthetized and paralyzed cat ( see Materials and Methods ) . Each neuron was recorded while presenting four full-field stimuli through the dominant eye ( Fig . 1 ) : a drifting grating at the cell's optimal orientation and spatial frequency ( DG ) , a high spatial definition dense noise ( DN ) , a natural image animated with a simulated eye movement sequence ( NI ) , and a grating animated with the same eye movement sequence ( GEM ) . After removing the spikes from the signals by interpolation , we computed their PSDs ( see Materials and Methods ) . These PSDs systematically exhibit a scaling behaviour in a broad , high-frequency band . To extract the scaling exponent , we fitted a linear function to the log-log representation of the PSD , for a range of frequencies from 75 to 200 Hz ( Fig . 2B ) , where the quality of the linear fit is high ( mean correlation coefficient ) . Note that this chosen band is also above the frequencies at which synaptic and membrane filtering cut-off appear [19] . Figure 2A shows the PSDs of the intracellular responses to the four stimuli for the same cell . In the log-log scale representation we observed a dependence of the slope , and hence the frequency-scaling exponent , on the stimulus . To confirm these effects at the population level , we compared for each cell the values of the exponent between pairs of stimuli . Figure 2C shows the comparison between stimuli DG and NI for each cell , and averaged over trials . Although the absolute value of the exponent was highly variable from cell to cell ( ranging from 2 . 0 to 3 . 5 ) , it was systematically lower , for the same cell , for NI than for DG ( paired Wilcoxon test , p0 . 003 ) . The magnitude of this difference was much larger than the standard error of the mean ( SEM ) among the different trials for the same protocol . We checked whether the value of the exponent could be correlated with the recorded cell's averaged or firing rate . The corresponding correlation coefficients were computed for each stimulus and then averaged together . We found that neither the firing rate ( ) nor the averaged ( ) were correlated enough to explain the variations of scaling exponent ( although these weak correlations were marginally significant ( p0 . 07 ) , except for the NI protocol where no correlation was found ) . We also estimated whether these systematic modulations were visible at the spiking level , or present only at the level . We computed the Fano factor exponent ( see Materials and Methods ) for the in vivo spiking responses . In contrast to the frequency-scaling of the , we did not observe any consistent variation of the spiking scaling-exponent with the visual stimulus . Moreover , there was no significant correlation between the and the spiking scaling exponents ( r = 0 . 2 , p0 . 1 ) . In some cells , the same protocol was repeated consecutively , interleaved with 2–3 s of spontaneous activity . We could not see any consistent difference between the power law exponents of the first trial and the others . This means that the dynamics reflected by the power law exponent appear in less than 10 seconds . These results indicate that the changes of frequency-scaling for the same cell as a function of the stimulus context are mainly determined by the differences in the visual stimulus statistics . Based on the comparison of the frequency-scaling exponents between all possible pairs of stimuli , we divided the stimuli into 2 groups . The exponents obtained from the intracellular responses to DG and GEM were not significantly different from each other but differed significantly from those obtained with NI and DN . We summarized these results by computing the relative changes from DN to the other protocols ( Fig . 2D ) . For a subset of cells , we also presented three additional stimuli designed as surrogates of the natural stimulus . The “Spatial Random” stimulus is composed of the natural image “scrambled” by randomizing the phases of its Fourier coefficients and animated with the same sequence of eye movements . The “Time Random” stimulus is composed of the natural image animated with a similarly “phase-scrambled” version of the eye movement trajectory . Finally , the “space and time random” stimulus is composed of the scrambled image animated with the scrambled eye movements ( plotted as Natural Image Surrogate or NIS in Fig . 2D ) . These three stimuli evoke power-law exponents similar to the DN protocol ( no significant difference , Wilcoxon paired test , p0 . 32 , p0 . 014 , p0 . 13 respectively , and see Fig . 2D for the third surrogate ) . Even though we did not see a significant difference between NI and DN or between DN and NIS , there is a significant difference between NI and NIS , the latter being the same stimulus with reduced phase coherence ( Wilcoxon paired test , p0 . 003 , p0 . 003 , p0 . 006 respectively for the three surrogate stimuli ) . From this study , we concluded that the value of the frequency-scaling exponent of the intracellular signal is strongly dependent on the visual input . It is interesting to note that the scaling exponent always seems to be smaller when the stimulus is less correlated ( DN being the extreme case where there is no correlation in the stimulus ) . We applied the frequency-scaling analysis to periods of spontaneous activity recorded in the same cells . Comparison between the frequency-scaling exponent of Spontaneous Activity ( SA ) and those in response to the five different stimuli was also performed at the population level . We observed a systematic increase from SA to the DG and GEM stimuli ( Fig . 2D and Fig . 2F; paired rank Wilcoxon test , p0 . 0003; the average difference between paired data SA-DG or SA-GEM is significantly different from zero , t-test , p0 . 0001 ) . In contrast , the SA frequency-scaling exponents are similar to those for DN , NIS and NI ( Fig . 2E; for NI r = 0 . 81 , paired rank Wilcoxon test , p0 . 5; slope = 0 . 82; the average difference between paired data SA-NI or SA-DN is not significantly different from zero , t-test , p0 . 1 ) . To estimate how much the frequency-scaling exponent tells us about the multiscale statistics of the intracellular signal , we performed a multifractal analysis ( see Materials and Methods ) . We therefore computed the two first moments of the singularity spectrum over the different cells and protocols . The first moment is linearly related to the frequency-scaling exponent measured on the PSD [20] . The respective values were indeed correlated over the population . The second moment is slightly above 0 for the four protocols ( DG: , GEM: , NI: , NIS: and DN: ) , while no significant differences were found between protocols . The intracellular signal is thus very close to a monofractal process , exhibiting self-similar behaviour . Furthermore , the first-order part of the singularity spectrum is the only one which varies with the visual stimulation . The functional sensitivity of our multiscale statistics can be reduced to the power-law behaviour of the trace . To study the effect of correlated input , we considered a simple model of a cortical network fed by an input with a controlled level of synchrony . This model was shown to be sufficient to reproduce the frequency-scaling exponent modulation measured above . In order to mimic the cortical network and the retinotopy of the input , we simulated topographically-connected networks of excitatory and inhibitory neurons using integrate-and-fire models and conductance-based synapses ( see Materials and Methods ) . We considered networks with topographically organized connectivity where each neuron is connected to its neighbours according to a Gaussian distribution ( Fig . 3A ) . The stimuli used during in vivo experiments have different levels of correlation ( Fig . 1A ) : the DG stimulus is highly correlated across space and time ( one Dirac impulse in the spatio-temporal spectral plane ) , while the DN is , by definition , fully uncorrelated ( flat spectrum in space and time ) . We chose to stimulate the recurrent network model with inputs having different levels of synchrony . The visually driven thalamic inputs project in a local region of space ( Fig . 3A ) , and the cortical response is thus the product of both the thalamic input and the recurrently mediated activity . The different levels of synchrony give rise to responses in the cortical area with different structures ( Fig . 3B ) , although the mean firing rate and the coefficient of variation of the cortical activity remain roughly constant over the different levels of input synchrony ( Fig . 3C ) . In particular , the cortical layer displays spontaneous waves of activity with an irregular and low frequency firing regime ( rate4 Hz and ISI CV1 ) when there is no synchrony within the thalamic discharge . The presence of correlation in the external input disrupts these waves and creates synchronous firing in the cortical layer ( Fig . 3B ) . The frequency-scaling exponent in the model was estimated from the traces of twenty cells ( see Materials and Methods ) . The values of the and frequency-scaling exponents both increased when the input synchrony increased ( Fig . 3D ) . This also held for the inhibitory conductance which behaved as its excitatory counterpart ( data not shown ) . This is consistent with the in vivo results where stimuli with more correlation ( DG , GEM ) evoke higher values of the scaling-exponent than the “decorrelated” stimuli ( NI , NIS and DN ) . We next examined which features of the network activity structure could be related to this modulation of the scaling exponent . Fig . 3E shows the spatial pairwise cross-correlation between pairs of neuron as a function of the interneuronal distance , for different levels of the input synchrony . The increase in input synchrony resulted in two simultaneous changes: a global increase of the cross-correlation values ( Fig . 3E , inset ) as well as a flatter spread profile over larger distances; when normalizing by the integral of the correlation over distance , it appears that the fall-off of the cross-correlation function ( CC ) is steeper for lower levels of synchrony ( Fig . 3E ) . In summary , the different levels of input synchrony modulate not only the global level of the correlation in the cortical network , but also its topographic extent and distance dependence . We next quantified the two features of the network activity that are modulated by the input synchrony and compared their modulation to that of the exponent . We first compared the exponent values to the integrated correlation , defined as the normalised cross-correlation integrated over distance . The frequency-scaling exponent increased linearly with the integrated correlation ( from 0 . 0 to 0 . 05 ) and saturated around 5 . 25 , for an integrated correlation of approximately 0 . 1 ( Fig . 3F ) . We also observed that the pairwise correlation between neurons scales with distance when expressed in logarithmic coordinates , which could be related to the frequency-scaling exponent . The corresponding cross-correlation scaling exponent ( CC SE ) , which reflects the fall-off gradient of the spatial correlation , decreases linearly when the exponent increases ( Fig . 3F , inset ) . To disentangle the influence of these two factors , we tested the effect of the spread of the thalamic projection to the cortical layer , which parameterizes the extent of the spatial correlation of the inputs . We ran the same simulations with an infinite spread ( i . e . , the thalamo-cortical connections were random ) . This condition might be related to the effect of a decorrelated background noise . While the relation between the cross-correlation scaling exponent and the exponent was shifted , the relation between the integrated correlation and the exponent remained unchanged . We found similar results by varying the spread between these two extreme values ( data not shown ) : the spread had no direct influence on the exponent value but shifted the baseline cross-correlation scaling exponent . Thus the variation of the spread , which determines the spatial structure of the input , did not alter the relation between the integrated cross-correlation and the exponent . This important relationship shows that , in this model , the integrated correlation is detected at the single-cell level through the membrane potential power spectrum scaling property for any stimulus . This measure thus provides a reliable hint about the actual functional state of the network . It also appears that , even if the spatial structure of the correlation is varied , the exponent value remains unchanged . This latter observation could explain why stimuli differing in their spatial structure can produce similar exponents in vivo . As in the previous in vivo study , we estimated the Fano Factor scaling exponent . Even when averaging over a population of randomly assigned neurons , the mean Fano Factor did not exhibit any systematic variation with the input synchrony , the integrated correlation or the cross-correlation scaling exponent . This is in accordance with the in vivo results . Finally , it is interesting to note that this network model can reproduce the changes in the frequency-scaling of the observed in vivo , despite its simplicity and the absence of any form of power-law in the spatial rules of connectivity: the thalamo-cortical and the cortico-cortical connectivities are drawn in our simulations from Gaussian distributions . Therefore it is not necessary to implement a scale-free connectivity to observe a frequency-scaling exponent emerging in the synaptic bombardment . We have shown that the scaling exponent is related to the integrated cross-correlation of the network activity . This integrated correlation depends on at least two factors: the global correlation level of the activity ( correlation strength ) and the spatial extent of the network correlation ( correlation extent ) . In our recurrent network model , both are modified simultaneously when varying the input , which makes the isolation of the precise feature modulating the scaling exponent difficult . We thus turned to the modeling of a single neuron receiving parameterized correlated synaptic noise in order to dissect out the influence of the different parameters of this correlated noise on the postsynaptic scaling exponent . Furthermore , although the network model provides a possible explanation for the frequency-scaling exponent modulation , this does not exclude a possible alternative hypothesis for our in vivo observations : due to the non-linearity in the neuronal transfer function , the frequency-scaling exponent variation in vivo could be due to the variation of the input firing rate or the different depolarisation levels from one protocol to the other . For these reasons we measured the frequency-scaling exponent in isolated neuronal models in response to several correlated synaptic inputs , where all these parameters can be varied independently . We also injected the same correlated synaptic patterns into biological neurons in vitro through dynamic clamp . This allowed us to test independently the effect of the correlation strength and extent , and to test the simpler hypothesis aforementioned . To further understand the relationship between the presynaptic activity and the frequency-scaling , we designed a model assuming that the irregular activity originates in the synaptic activity impinging on the recorded cell . Indeed , since the frequency-scaling exponent varies for the same cell and different visual stimuli , it must be linked to the activity of the network surrounding the observed neuron . Note that , being interested only by these relative changes , we did not search for the mechanisms shaping the absolute value of the PSD scaling , which may include intrinsic mechanisms [18] , [21] , [22] . For this reason we show the relative modulation of the values of the frequency-scaling exponent in different models and in vitro experiments , the baseline being the exponent in response to Poisson stimulation , unless otherwise noted . In the retinotopic model discussed in the previous section , synchronous input in the thalamic layer evoked synchronous firing in the cortical layer at random positions . These firing assemblies affect the recorded neuron through lateral connections with different propagation delays , which depend on the distance from the presynaptic neuron . The temporal correlations in the presynaptic spike train impinging on the recorded cell thus reflect both the direct thalamic input and the spatial correlations observed in the intracortical distance-dependent cross-correlation . Our aim was to determine how these temporal correlations present in the afferent pattern are conveyed from the presynaptic bombardment to the subthreshold activity through cell integration . Note that the propagation delays play a crucial role in the translation of spatial correlations into temporal correlations . Indeed , if the presynaptic population could interact instantaneously with the postsynaptic cell ( no propagation delay ) , synchronous firing would only increase the membrane potential variance . The model is composed of presynaptic neurons ( Poisson processes ) that all fire at the same mean rate , with a constant synchrony fraction . This means that each emission of a spike occurs simultaneously in neurons ( Fig . 4 ) . These presynaptic neurons then project with different conduction delays to the same postsynaptic neuron , which represents the recorded cell . This means that spikes emitted simultaneously by various presynaptic sources will arrive with different delays at the postsynaptic neuron , thus creating a high-order structured temporal correlation pattern . The delays are chosen randomly according to a distribution ( Fig . 4 ) . We emphasize that this model is not biologically realistic: it is a correlated spike train generator parameterized by the synchrony level and the delay distribution . To give more intuition about what these parameters represent , and to make a link with the recurrent model , we can interpret as the strength of the correlations in presynaptic activity , and as the way these correlations are temporally distributed . Note that both of these parameters would influence the integrated correlation measured previously in the recurrent model ( the spatial correlation in the recurrent model becomes a temporal correlation when considering the delays between distant neurons ) . In this model , it can be shown [23] , [24] that the analytical expression for the conductance PSD resulting from the synaptic integration of all these inputs is given by Eq . 5where is the Fourier transform of the synaptic time course ( when the synapse is exponential , this is a Lorentzian curve ) , and is the Fourier transform of the delay probability distribution . From this expression , we find that a controlled way to impose an activity-dependent frequency scaling behaviour in this model is to impose a temporal delay distribution having itself a power-law form . Furthermore , this form of correlation is what we found in the recurrent model , although it was not implemented in the connectivity . For this reason the delay distribution will have the form ( 1 ) The parameter parameterizes the extent of the delay distribution: the higher is , the narrower will be the delay distribution . An infinite value of would correspond to all delays equal to . We emphasize that this choice of delay distribution is not ad hoc , but rather is imposed in order to control the frequency-scaling exponent . Other forms of delay distribution might produce more realistic presynaptic patterns , but we focus here on the part of the correlations that will exert a direct control over the postsynaptic frequency scaling . The power spectral density of this delay distribution is [6]: ( 2 ) The synaptic conductance frequency-scaling exponent is thus equal to for frequencies beyond the synaptic filtering and the delay cut-offs . Note that , as already shown at the population level in Fig . 3F , the synchrony level detected in the presynaptic train has a “gating” role according to ( Equ . 5 ) : no synchrony at all would give a frequency-scaling exponent of 4 whatever the value of . Moreover , the relationship between the exponent and is here uncovered as soon as a minimal level of synchrony is present in the presynaptic population ( theoretically , any ) . We numerically simulated this model to check the previous analytical expression . We took a population of neurons and first fixed the presynaptic firing rate to = 10 Hz . For different values of the delay distribution parameter , and synchrony , we simulated the model to produce and traces . Figure 5A shows the resulting and PSDs , for a fixed synchrony level , and ranging from 0 to 1 . The PSD frequency scaling decreases when increases for frequencies above 20 Hz . We then measured the frequency-scaling exponents in these traces to quantify this result ( see Materials and Methods ) and plotted them as a function of the synchrony level and ( relative to the Poisson exponent ) . As predicted , the exponent decreases when the parameter increases ( Fig . 5B ) . This inverse relation between the frequency-scaling exponent and appears more and more clearly as the synchrony increases , and saturates for ( Fig . 5B ) . Nevertheless , even with an amount of synchrony as small as , the dependence of the power-law on is already monotonic . We obtained a linear relation between and the output frequency-scaling exponent , although the absolute values are not exactly those predicted by the analytical relation , most probably due to a finite-size bias of the estimation . To illustrate this “gating” effect of the synchrony , we plotted the frequency-scaling exponent against the synchrony level , for fixed ( Fig . 5C ) . When increasing , the exponent first increases and then saturates to a plateau which depends on . Identical results were obtained for but with a systematic shift of 2 corresponding to the membrane integration ( absolute exponent values were between 2 and 4 for the conductance , and between 4 and 6 for ) . This is what we would expect for a current-based model for which the effect of membrane integration results in a shift of 2 in the frequency-scaling exponent . This shows numerically that the non-linearity induced by the use of conductance-based synapses does not alter this relationship . Therefore , as long as few neuron assemblies are firing simultaneously in the presynaptic population , their correlations are made visible through the postsynaptic membrane potential PSD . Note that the results displayed in panels B and C of Fig . 5 are reminiscent of those obtained for the retinotopic cortical network in Fig . 3F . Indeed , increasing the synchrony or decreasing the parameter would both increase the integrated cross-correlation , which in turn increases the scaling exponent . The synaptic bombardment received by a cortical neuron is composed of both excitatory and inhibitory inputs . We extended our model by adding a population of presynaptic inhibitory neurons which has the same organization as the excitatory population described earlier , parameterized by the synchrony and the delay distribution parameter . While independently varying the inhibitory and excitatory exponents , we measured the corresponding frequency-scaling exponent . We first performed this analysis with the two presynaptic populations having a fixed amount of synchrony ( ) , to ensure the impact on the and frequency-scaling exponents , and being completely uncorrelated . Fig . 6A shows how the frequency-scaling exponent varies with and . The frequency-scaling exponent seemed to be dominated by the parameter , while the influence of the inhibitory inputs remained marginal . Since the firing rate is similar for excitatory and inhibitory neurons , this dominance was due to the excitatory-inhibitory ratio ( ) . We checked that it was not due to the closer inhibitory reversal potential in additional simulations where we changed the reversal potential ( data not shown ) . Note that when , the frequency-scaling exponent behaves as in the excitatory-only case ( Fig . 6D ) . We then examined the case where excitatory and inhibitory inputs are correlated , which is more realistic in view of most of the in vivo studies [25]–[27] . The functional relationship between conductance correlations and the frequency-scaling exponent is conserved for stronger excitatory-inhibitory correlation , although it is slightly affected , especially for small values ( Fig . 6B–C ) . To illustrate this effect , we plotted the variation of the frequency-scaling exponent for and different levels of correlation ( Fig . 6D ) . For a sufficient amount of synchrony , the final frequency-scaling exponent will thus be mainly influenced by the frequency-scaling exponent of the delay distribution , and , to a lesser extent , influenced by the correlation between excitatory and inhibitory conductances , and . We found that adding a constant delay between the excitation and inhibition as often observed experimentally does not change the PSD slope value . To conclude , our model shows how changes in the parameters which determine the correlation in the presynaptic bombardment affect the frequency-scaling exponent of the signal . These changes are of the same order of magnitude as that which was observed in vivo . Increasing synchrony increases the frequency-scaling exponent up to a limit which depends on the parameters . Increasing or , or the correlation between excitation and inhibition , decreases the exponent . However , it is much more affected by the correlations present in the excitatory neurons than in the inhibitory ones , since there are many more excitatory neurons . Previous work on power-law frequency-scaling has mainly been based on extracellular recordings , either to characterize single-cell spiking correlations [6] or self-organized avalanche dynamics in networks [8] . Intracellular recordings , as used in the present study , offer a larger sampling of the network dynamics . Indeed , we can ask whether correlations in the synaptic input visible at the level are still present in the spiking output . We estimated the Fano Factor ( FF ) for the numerical model to better understand the -spike frequency-scaling exponent relation . We measured the frequency-scaling exponent in the spiking activity in response to different correlated synaptic input patterns , built by varying the parameters and . Figure 7A illustrates the Fano factor scaling behaviour for ranging from 0 to 1 , and shows a linear increase of the spiking frequency-scaling exponent with for time bins between 10 and 100 milliseconds . However , we then tested whether the same relationship holds for different resting potentials of the postsynaptic neuron ( Fig . 7B ) . It appears that the relation between the spiking and the frequency-scaling exponents is strongly dependent on the depolarization level . This dependency is confirmed when varying and independently . Other parameters can drastically influence the spiking frequency-scaling exponent . As an illustrative example , figure 7C–D show the corresponding spiking frequency-scaling exponents for two different depolarization levels and excitation-inhibition correlation levels; in 7C the postsynaptic and there is no correlation , whereas in 7D and the correlation is set to 0 . 4% . In light of these results , the lack of correlation between and spiking frequency-scaling exponents , and the absence of systematic modulations for the spiking exponent in vivo and in the recurrent model can be explained . This is likely due to the sensitivity of the latter to other parameters that also vary with the stimulus , such as the depolarization level . The spiking frequency-scaling exponent for single-cell study is thus hardly sufficient to characterize the self-similar behaviour of the neural activity . In the in vivo data , the FF is measured across a high heterogeneity of depolarization levels , and is thus not reliably linked with the presynaptic correlation . In contrast , the subthreshold activity has shown its robustness to changes in depolarisation , and thus provides a much better insight into the network correlation state , being averaged over a large number of presynaptic spiking neural elements . So far our model has shown how the frequency-scaling exponent can be modulated by the correlations present in the presynaptic activity pattern . However , we had to control for a simpler alternative hypothesis . In in vivo data the evoked neuronal mean activity was modulated by the different stimuli ( on average 160% decrease from DG to NI ) , implying that the presynaptic firing rate of the recorded cell varies from one visual stimulus to the other . It is possible that this increase of firing rate induces a change in the frequency power-law scaling . In the following , we call this hypothesis the “first-order hypothesis” . The weak correlation between the cell firing rate and the frequency-scaling exponent observed in the in vivo section makes such an hypothesis rather unlikely . However , to directly test this hypothesis on our model , we changed the input mean firing rate from 2 . 5 Hz to 10 Hz for both excitatory and inhibitory synaptic inputs . For each condition , we computed the frequency-scaling exponent . Figure 8B ( left panel ) shows that it is almost unaffected by the input firing rate . Although we observed a small decrease in the frequency-scaling exponent when increasing firing rate , this could still not explain the in vivo results . Indeed , in the latter case , even though the correlation is weak , the frequency-scaling exponent increase is concurrent with an increase of the cell firing rate . We also checked whether the membrane potential level can influence the frequency-scaling exponent . To do so , we varied the recorded cell membrane potential level by adjusting the synaptic strengths ( see Materials and Methods ) . As for the firing rate , no significant influence in the frequency-scaling exponent can be attributed to the depolarization level ( Fig . 8C , left panel ) , confirming the weak correlation observed in vivo . Despite the lack of evidence for the “first-order hypothesis” , our model does not incorporate biologically realisitic integrative features . It has been shown in previous studies [15] , [21] , [22] that the cell's intrinsic properties , shaped by its ionic channels , could have an impact on the PSD form when the cell is submitted to noisy inputs . We performed the same analysis by replacing the integrate-and-fire model with a Hodgkin-Huxley model . The and ionic channels could have an influence on the variation of the frequency-scaling exponent . However , adding these mechanisms did not alter the frequency-scaling exponent's dependence on the input firing rate , nor on the mean postsynaptic membrane potential ( Fig . 8B–C , middle panel ) . The results are identical to those obtained with the integrate-and-fire model . Controls were also performed with normally distributed synaptic weights for various standard deviations and gave identical results ( Fig . S1A–B ) . On another set of controls , we changed the synaptic waveform by using synapses with a rise time on the order of 1 ms ( -synapse ) . The controls with this new type of synapse gave identical results to previous cases ( Fig . S1C–D ) . Apart from the intrinsic mechanisms present in the somatic membrane , a possible source of modulation of the absolute value of the frequency-scaling exponent is the integrative property of the dendritic tree . To test how the dendritic arborization might impact the somatic subthreshold activity , we simulated synaptic input distributed in the dendrites of reconstructed pyramidal neurons . As shown in Table S1 , the relative modulations of the exponent are well captured by correlation changes in the model , while global conductance changes had a negligible effect . However , it is important to note that these simulations were done using standard simulation tools ( NEURON in this case ) , and thus used the standard cable equations . It has previously been shown that the standard cable equations cannot reproduce the correct frequency-scaling of the PSD , and that taking into account the non-ideal character of the membrane capacitance could yield the correct frequency-scaling [18] . This could explain why the in vivo absolute values of the scaling exponent are not well reproduced here . However , the relative modulations of the exponent are well captured by correlation changes in the model , while global conductance changes had a negligible effect . Numerical simulations gave important insights about the role of intrinsic properties in the effects we see , but no computational model can guarantee an exhaustive exploration of such mechanisms . Indeed , even though the first-order hypothesis was invalidated for Hodgkin-Huxley models , we cannot exclude the influence of other ionic currents . Therefore , we performed the same test on real biological neurons through dynamic-clamp in vitro . The correlated conductance traces generated by our model were directly injected into rat visual cortex neurons recorded in vitro ( n = 9 ) using the dynamic-clamp technique ( see Materials and Methods and Fig . 4B ) . We performed the same control as above changing the mean input firing rate . The frequency-scaling exponent barely changed ( Fig . 8B , right panel; , p0 . 3 ) , confirming that the overall presynaptic activity level has a negligible effect compared to the conductance correlations ( characterized by the parameter ) . Even the weak correlation observed between the mean input firing rate and the frequency-scaling exponent has the opposite sign to what is observed in vivo . The relative variation for different has the same magnitude as the numerical models ( ) . The previous results were obtained for different resting membrane potentials and did not show any noticeable effect regarding the mean depolarization ( Fig . 8B , right panel , , p0 . 9 ) . In order to measure the influence of the depolarization level on the frequency-scaling exponent , we systematically varied the conductance strength to change the mean of the recorded cell . The frequency-scaling exponent did not exhibit significant variation ( Fig . 8C , right panel ) . In vitro experiments thus confirm our previously observed results from numerical models . In summary , the correlation in the activity impinging on the recorded cell plays a major role in determining the frequency-scaling exponent of the . Other parameters , such as the total conductance ( see also Fig . S3 ) and the balance between excitatory and inhibitory conductances , have negligible effects . These results support the idea that changes in the frequency-scaling exponent observed in vivo reflect changes in correlations in the external stimulus-driven activity .
Our central finding in vivo is that the frequency-scaling exponent in V1 is modulated by the visual stimulus statistics . Because such changes are detected in the same cells , they must necessarily reflect changes in the spatio-temporal structure of presynaptic activity . Guided by the fact that intracellular activity in sensory and prefontal cortex shows long lasting temporal correlations , we hypothesized that the main factor affecting frequency-scaling exponents is the correlation in presynaptic activity . This hypothesis was supported by numerical simulations . A similar modulation of the frequency-scaling exponent was also found in a recurrent network for which the input correlation was varied : the scaling exponent increased when the input correlation increased above a certain threshold ( required to be detectable ) . This threshold was not reached during decorrelated states , such as those imposed by surrogate natural scenes . In the recurrent model , the input correlation modulated both the the absolute strength and temporal structure of correlations . To investigate separate modulations of these two factors , we chose a model of presynaptic inputs with a temporal power-law structure . This choice was made for two reasons: first , because this temporal structure was observed in our network model , without implementing any scaling in the connectivity; second , because it provided an operational way to control the form of the correlations in the input , and isolate which factors influence the output frequency-scaling exponent . The input is thus characterized by its frequency-scaling exponent , and we found that the frequency-scaling exponent of the subthreshold output is linearly related to this input exponent . However , this relationship is present only if the correlation strength is large enough . According to these results , the frequency-scaling exponent increase observed in vivo could plausibly be due to a global correlation strengthening in the surrounding network and/or by a narrowing of the spatial spread of correlation . The hypothesis for a determinant role of correlations is also consistent with in vitro experiments , where we recreated artificial and controllable synaptic activity by dynamic-clamp . The fact that correlation changes are reflected by changes in the frequency-scaling exponent of the frequency-scaling means that intrinsic cellular properties do not have major dynamical influences on this scaling , and that it mostly reflects synaptic activity controlled by the visual stimulation context . In particular , we showed that neither the mean level of synaptic bombardement nor the postsynaptic depolarization level could significantly modulate the frequency-scaling exponent , even though the cell integrative properties shape its static absolute value [15]–[17] , [21] . The finding that activity presents power-law frequency-scaling is reminiscent of the power-law relationships of self-organized critical states , similar to those found from multi-site local-field potential recordings in vitro [8] , [28] . In the latter case , self-organized critical states are characterized by the production of “avalanches” of activity , whose size distribution follows a power-law . However , the power-law relations were found there in the frequency domain , which is very different from the distribution of event sizes detected in our study , so our results should not be taken as evidence for avalanche dynamics . We have performed an avalanche analysis on the recurrent network model , and as was reported in a previous study [29] , we did not find evidence for avalanche type dynamics in the network during AI states . Moreover , it has to be noted that the power-law relations found here depend on the stimulus , which means that the frequency-scaling exponent does not represent a unique signature of cortical network activity , but rather reflects a measure of the dynamic interplay between the sensory evoked activity and the ongoing recurrent network activity . Power-law frequency-scaling was reported previously in extracellularly-recorded spiking activity [6] , [30] , [31] . We observed that the and spiking frequency-scaling exponents are linearly related . However , the exact value of the frequency-scaling of spiking activity critically depends on the depolarisation level , and thus does not reliably reflect network correlation state . Our study shows that the frequency-scaling exponent , which reflects the integration of thousands of input sources , can uncover features of the population activity that were not visible at the single cell spiking level or when assigning a limited number of cells at random . Our results imply that tracking the relative changes of the frequency-scaling exponent could be a way to characterize dynamic changes in the correlations hidden in the global connectivity network , but read out at the subthreshold level by each member cell of these overlaid functional assemblies . Having interpreted the relative variations of the frequency-scaling exponent , we can now link these variations with the type of visual stimulus presented . In order to emphasize the role of dynamic cortical non-linearities in the stimulus-dependency of the power-scaling , we checked whether or not these exponent changes were already apparent in the linear prediction of the responses . To do so , we used the first-order kernel of the receptive field obtained by dense noise mapping to reconstruct linear predictions of the subthreshold dynamics for the different classes of stimuli and tested the contextual dependency of the spectral scaling properties of the linear predictor . The modulatory effects were not retrieved , which was expected since the estimation of the frequency-scaling exponent is performed on high frequencies ( between 75 Hz and 200 Hz ) that are not accounted for by the linear kernel ( data not shown ) . We conclude that the exponent variations are not a linear read-out of the scaling behaviour of the stimulus but rather the product of the non-linearities in the input-output relationship imposed by the cortical network . According to our recurrent network study , the frequency-scaling exponent decreases when switching from DG stimuli to NI or DN stimuli should correspond to a decrease in the correlation strength . Following this interpretation , it could appear surprising that stimuli with very different structures , such as NI and DN stimuli , evoke similar values of the V scaling exponent . However , our study showed that the V scaling exponent is invariant to changes in the spatial structure of the input . As a consequence , stimuli with different spatial structures can evoke similar scaling exponents provided their global correlation levels are all low . On the one hand , although it has not been demonstrated directly , natural movie stimuli probably induce decorrelation , for several reasons . First , our natural image is animated most of the time by fixational eye movements , which may already decorrelate activity at the LGN [32] . Second , the decorrelation theory [33] predicts that cortical responses to natural scenes should be decorrelated in order to maximize the transmitted information , and this prediction has been confirmed in V1 studies [34] . On the other hand , dense noise , as a fully uncorrelated stimulus , also evokes a very decorrelated response . These low correlation levels for both stimuli are probably what make them indistinguishable from the perspective of the scaling exponent . In short , even if the structures of these inputs are very different , thalamic and cortical processing may reduce the initial correlations down to a similar level . Furthermore the scaling exponent captures neither the difference in the spatial structure of these resulting activities nor the difference in the low frequency band dominated by the stimulus spectrum . Taken together these arguments can explain why we observed similar scaling exponents . The same remark holds for DG and GEM stimuli: despite their difference in temporal structure , they might evoke similar levels of correlation , and thus similar scaling exponents , despite the difference in input spatial structure and low frequency content . Finally , the same argument may explain why we found similar exponents for the spontaneous activity and the natural stimulus: for high frequencies , both exponents likely correspond to a very decorrelated activity , even if there might be a residual synchrony . Note however that this striking correlation between NI and AS is not necessarly present at lower frequencies . Several studies have compared the structure of the spontaneous activity to that of the evoked activity . The spatial structure of the spontaneous activity measured with voltage-sensitive dye ( VSD ) imaging has been found to be similar to the DG-evoked activity [35] , [36] , although this result could not be replicated in awake animals [37] . On the other hand , [38] found that the temporal correlations measured in multi-unit recordings seems to be similar for dense noise , natural scenes and spontaneous activity . Our results and a recent theoretical study [39] seem to be compatible with the latter observations . However , they are not necessarily in total contradiction with the VSD results since our measures concern different frequency bands: while we measured frequency-scaling exponents between 75 and 200 Hz , the VSD measures mostly concerned dye signal fluctuations at frequencies below 20 Hz . It thus appears most likely that V1 responses to natural scenes and spontaneous activity share similar correlation features in the high-frequency band . We have shown that the frequency-scaling exponents measured in the intracellular activity can vary under the influence of the visual context for the same cell . Our model relates this modulation to a dynamic change in the network correlation state and could be associated to the underlying dynamic dimensionality [40] . Further studies need to address at the population level ( LFP or VSD ) how the frequency-scaling exponents of the network activity may vary with the stimulus context [41] , and if such changes could be indicative of the detection of specific sensory statistics in the external drive or their spontaneous recall by the recurrent structure of the network .
All in vitro and in vivo research procedures concerning the experimental animals and their care adhered to the American Physiological Society's Guiding Principles in the Care and Use of Animals , to European Council Directive 86/609/EEC and to European Treaties Series 123 and were also approved by the regional ethics committee “Ile-de-France Sud” ( Certificate 05-003 ) . Cells in the primary visual cortex of anaesthetized ( Althesin ) and paralyzed adult cats were recorded in vivo using sharp electrode ( potassium methylsulfate 3 M , 70–100 M ) recordings ( average = −67 mV , 0 nA ) as described elsewhere [25] , [42] . Data processing and visual stimulation protocols used in-house software ( G . Sadoc , Elphy , CNRS-UNIC ) . The analyzed data come from in vivo experiments to be presented in full in a companion paper ( Baudot , Marre , Levy , Monier and Frégnac , submitted ) . Preliminary accounts have been given elsewhere [43] , [44] . Stimuli were displayed on a 21” CRT monitor with a pixel resolution and a 150 Hz refresh rate , with a background luminance of 12 cd/ . Receptive fields were mapped using sparse noise and classical tunings were determined by automated exploration . Intracellular responses were compared for four full-field visual stimuli of 10 s duration and increasing complexity ( see Fig . 1 ) : a ) a drifting grating of optimal orientation , direction , and spatial and temporal frequencies ( DG ) , b ) the same optimal grating animated by a modeled eye-movement sequence ( GEM ) , c ) a natural image animated by the same virtual scanpath ( NI ) , and d ) dense binary white noise ( DN ) . The mean luminance and contrast of each movie were equalized . Each movie was presented 10 times . For the NI condition , we used a high definition natural image ( pixels ) animated with a virtual eye movement sequence [43] , [44] ( note that the size of the image is larger than the size of the screen , so that no blank region appears when the image is moved along the oculomotor trajectory ) . White noise consisted of a dynamic sequence ( 13 . 3 ms refresh period ) of high spatial definition ( pixels of side length 0 . 39° ) binary dense noise . All the simulations ( including dynamic-clamp experiments ) were performed with the NEURON software [http://www . neuron . yale . edu] except for the recurrent model which was been run under NEST [45] using the PyNN interface [http://neuralensemble . org/PyNN] . A time step of 0 . 1 ms was used systematically . We ran some simulations with 0 . 01 ms to verify that our results were not dependent on the integration time step ( data not shown ) . The postsynaptic neuron follows an integrate-and-fire equation with conductance-based synapses whose time evolution is given by ( 3 ) with the resting membrane time constant , the leak membrane potential and the excitatory and inhibitory conductances given in units of leak conductance . When reaches the spiking threshold , a spike is generated and the membrane potential is reset to for a refractory period of duration . and are the reversal potentials for the excitatory and inhibitory exponential synapses whose dynamics follow ( 4 ) where is the synaptic time constant with and . and are the quantal synaptic strengths elicited by each presynaptic spike and is the point process modelling the incoming spike train . and are chosen in order to satisfy the ratio where the brackets signify an average according to , and so that the effective resting potential is on average . Identical results were been obtained for synapses with a finite rise time ( -synapses ) . Parameters for the Hodgkin-Huxley model were taken from [46] . The recurrent network is composed of 10000 excitatory and 2500 inhibitory neurons , sparsely connected , with a connection probability of 2% within each population and between the two populations . The synaptic weights are and . Each neuron has a topographic position on a cortical layer-like surface of , and connects to its neighbours according to a Gaussian distribution of standard deviation . Periodic boundary conditions are used . Conduction delays are distant-dependent with ( ms ) where is the distance between the two neurons expressed in millimetres . The slope value of ( giving a propagation speed of 0 . 2 mm/ms ) is taken from a previous in vivo study showing a lateral propagation speed ranging dominantly between 0 . 1 and 0 . 3 mm/ms [42] . The retinotopic drive was modelled as another thalamic layer-like network facing the previous one where each neuron acts as a Poisson process with a controlled amount of synchrony between the firing . To mimic a retinotopic mapping , each cell in the thalamic layer projects to the recurrent network in a topographically organized manner following a Gaussian distribution of standard deviation ( Fig . 3 ) . The connection probability from the thalamic layer to the cortical layer is also 2% . In some simulations , we used models based on morphologically-reconstructed neurons from cat cortex , obtained from two published reference studies ( layer II–III of cat primary visual cortex Douglas et al . [47]; layer VI of cat somatosensory cortex Contreras et al . [48] ) , where biological details were given . The three-dimensional morphology of the reconstructed neurons was incorporated into the NEURON simulation environment , which enables simulating cable equations in complex three-dimensional structures [49] . In vivo-like activity was simulated in passive models using a previously published model of synaptic bombardment at excitatory and inhibitory synapses [50] ( see this paper for details about the parameters and numerical simulations ) . The density of synapses was constant per unit membrane area according to published morphological studies , and was ( per 100 ) : 60 for dendritic AMPA synapses , 10 for dendritic GABA and 20 for somatic GABA synapses . This gives 9947 AMPA and 2461 GABAA synapses for the layer II–III cell , and 16563 and 3376 , respectively , for the layer VI cell . The release rates , chosen to yield synaptic bombardment consistent with in vivo measurements , were = 1 Hz and = 5 . 5 Hz for AMPA and GABAergic synapses , respectively ( see details in [50] ) . In order to produce spike trains with arbitrary temporal correlations , we used the theory of cluster point processes [23] , [51] . The presynaptic activity can be characterized by two main features: on the one hand , the specific temporal structure given by the spike train temporal auto-correlation form , and on the other hand , the correlation strength which measures the temporal coherence between individual presynaptic spike trains ( see [52] for a similar distinction ) . These two features can be controlled separately in the spike train generator composed of a population of presynaptic neurons following Poisson processes , and firing together with a certain amount of synchrony . They project to the postsynaptic neuron through different time delays , randomly chosen from a specific distribution ( Fig . 4 ) . The temporal structure is given by the delay distribution whereas the global synchrony in the presynaptic neuronal discharge gives the correlation strength . In our implementation , the presynaptic population is assumed to contain neurons ( for the excitatory population and for the inhibitory population , except stated otherwise ) ; at each time step it was decided randomly whether or not some neurons will fire . The probability was adjusted to give a mean firing rate of the inputs . If so , neurons were chosen randomly to fire among the constituting the population . This method allows to have always synchronous neurons , and still an apparent Poisson discharge at rate for each presynaptic neuron taken individually . Note that this gives back independent Poisson spike trains when . Correlation between excitatory and inhibitory neurons is implemented in the same manner . The delays are then attributed to each presynaptic spike train according to the chosen delay distribution . From point process theory , this can be seen as two nested point processes . The first point process follows a Poisson process which determines the cluster positions and the second one determines randomly the position of points within each cluster according to an arbitrary density probability function . The correspondance between both representations is straightforward and the power spectrum density can be computed analytically with the Neyman-Scott equation [23] , [24] , [51] ( 5 ) where is the Fourier transform of the delay distribution , is the number of synchronous neurons and is the Fourier transform of the synaptic filtering . In Eq . 5 , the factor can also be written where is the ratio of synchronous neurons which does not depend anymore on . In this paper , we are interested in the power-law frequency-scaling in the temporal power spectrum density ( PSD ) . Eq . 5 relates the delay distribution to the PSD so that a power-law behaviour at the conductance level needs a power-law scaling in the delay distribution . Therefore , the delay associated with each synapse was randomly chosen from a distribution proportional to . The exponential term is added to avoid oscillations in the PSD due to an abrupt cut-off [6] with 10 ms . The parameter is varied over the simulations and modulates the spread of temporal correlations . The presynaptic neurons are synchronously active according to the parameter . The output frequency-scaling exponent ( to be defined below ) measured in the PSD ( Eq . 5 ) is thus equal to . In vitro experiments were performed on 350 m-thick sagittal slices from the lateral portions of rat occipital cortex . Wistar Rats , 4–6 weeks old ( CNRS , Gif-sur-Yvette ) , were anesthetized with sodium pentobarbital ( 30 mg/kg ) before craniectomy and cortex removal . The slices were maintained in an interface style recording chamber at 34–35°C . Slices were prepared on a DSK microslicer ( Ted Pella , Redding , CA ) in a slice solution in which the NaCl was replaced with sucrose while maintaining an osmolarity of 314 mosM . During recording , the slices were incubated in slice solution containing ( in mM ) 126 NaCl , 2 . 5 KCl , 1 . 2 MgSO4 , 1 . 25 NaHPO4 , 2 CaCl2 , 26 NaHCO3 , and 25 dextrose and aerated with 95% O2-5% CO2 to a final pH of 7 . 4 . After 30 minutes to 2 hours of recovery , intracellular recordings were performed in deep layers ( layer IV–VI ) in electrophysiologically identified regular spiking and intrinsically bursting cells . Micropipettes were filled with 1 . 2–2 M potassium acetate and 4 mM KCl and had resistances of 80–100 M after bevelling . The dynamic-clamp technique [53] , [54] coupled with an Active Electrode Compensation ( AEC ) method that we developed and validated recently in vivo and in vitro [24] was used to inject computer-generated conductances in real neurons . The AEC method allows the removal in real time of electrode noise from intracellular voltage recordings . Dynamic-clamp experiments were run using the Real Time-NEURON environment [55] , which is a modified version of NEURON 6 . 0 [49] . The dynamic-clamp protocol was used to insert the fluctuating conductances underlying synaptic noise in cortical neurons using the previous model , the post-synaptic neuron being now the recorded neuron , similar to a previous study [56] . The injected current is determined from the fluctuating excitatory and inhibitory conductances as well as from the difference of the membrane voltage from the respective reversal potentials . Spikes were removed from the original traces and replaced by a low-pass filtered version of the trace . To control the validity of this procedure , we compared whenever possible the power spectrum obtained from the interpolated trace with an identical trace generated without threshold . In all cases we observed that injecting a given conductance trace into a neuronal model and then removing the spikes gave the same power spectrum as injecting the same conductance in a neuronal model without spike threshold ( Fig . S2 ) . The spectra were computed with the multi-taper method [57] , which allows a better estimation of the power-laws than the standard periodogram methods . Results were similar when using the Welch method and the Goertzel algorithm [58] . We then determined the frequency-scaling exponent by linear regression on a log-log representation of the PSD , for the range 75–200 Hz . Similar results were obtained for lower bounds above 50 Hz , and higher bounds below 200 Hz . Estimation of the scaling exponent from multifractal methods gave similar values . For the in vitro data , we also estimated the frequency-scaling exponent by fitting a generalized Lorentzian function [59] , which gave equivalent relative values . We chose to use the linear fit for its simplicity , and because it is easy to quantify the goodness of fit , and thus to assess the power-law scaling over the frequency band chosen . In comparison , the Lorentzian fit is very accurate when considering controlled models where the cut-off frequencies can be easily found or computed , but this model gave inaccurate results when applied to in vivo data because it can not account for the low frequency regime , which is strongly modulated by the stimulus . Finally , the multifractal analysis gave us no control over the goodness of fit . In the case of the recurrent network , the fit was performed between 75 and 200 Hz . Using narrower bands gives similar results . In the in vitro measurements , the absolute values of the frequency-scaling exponent displayed significant variations because of the available scaling region . Our study focused on the modulation of the frequency-scaling , rather than on absolute values , the relative values of the frequency-scaling exponent are shown for in vitro experiments and the corresponding models for each linear region of the PSD . For the model studies , unless otherwise mentioned , we systematically subtracted the value obtained for a classical Poisson input . For the in vitro study , the reference was the frequency-scaling exponent obtained with the input parameter , averaged over the different conditions tested . In this case , measuring the relative values also removed the cell-to-cell variability of the absolute values . The total input conductance is reported to be about three times the leak conductance in the anaesthetized cat [26] . This is also what we used in our model and in the conductance injection in vitro . As a consequence , the cut-off frequency of the synaptic and membrane filtering are below the frequency band used for our fitting ( they did not exceed 75 Hz ) , and could not affect our estimates ( this point is futher discussed in the Results section ) . The multifractal analysis characterizes the scaling behavior of a signal [60] . For each point , the Hölder exponent is defined as the maximal value such that there exists a polynomial , with , a positive constant , and an interval around where for any ( 6 ) This coefficient reflects the scaling behaviour around the point . The singularity spectrum is the Haussdorf dimension of . It thus describes how the singularities are distributed in the signal . A particular example is the self-similar process ( also called monofractal ) , where only at one point , where . The practical estimation of the singularity spectrum is made difficult by the finite size of the signal , and by its discrete nature . However , the wavelet formalism allows a robust estimation of , which is the Legendre transform of the singularity spectrum: ( 7 ) In the case of a monofractal/scale-invariant process , , H being its unique Hölder exponent . This corresponds to a fractional Brownian process . Note that H is related to the PSD slope which is equal to . The curvature of quantifies the deviation from monofractality . The slope and the curvature are respectively the first and second moments of the singularity spectrum . We used an algorithm based on wavelet leaders [20] , [61] which directly estimates these two values . Fano factors and power-laws on these Fano factors were measured as in [6] . To compute the Fano Factor for a given time bin , we counted the number of spikes in each time bin and took the ratio of the spike-count variance to the mean spike-count . The power-law was estimated by computing this Fano Factor over a large range of time bins . This function was then represented in a log-log scale , and the slope of the curve was estimated by linear regression . This gives the frequency-scaling exponent of the spiking activity through the Fano Factor where is the time bin and the scaling exponent .
|
Intracellular recording of neocortical neurons provides an opportunity of characterizing the statistical signature of the synaptic bombardment to which it is submitted . Indeed the membrane potential displays intense fluctuations which reflect the cumulative activity of thousands of input neurons . In sensory cortical areas , this measure could be used to estimate the correlational structure of the external drive . We show that changes in the statistical properties of network activity , namely the local correlation between neurons , can be detected by analyzing the power spectrum density ( PSD ) of the subthreshold membrane potential . These PSD can be fitted by a power-law function 1/fα in the upper temporal frequency range . In vivo recordings in primary visual cortex show that the α exponent varies with the statistics of the sensory input . Most remarkably , the exponent observed in the ongoing activity is indistinguishable from that evoked by natural visual statistics . These results are emulated by models which demonstrate that the exponent α is determined by the local level of correlation imposed in the recurrent network activity . Similar relationships are also reproduced in cortical neurons recorded in vitro with artificial synaptic inputs by controlling in computo the level of correlation in real time .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience/theoretical",
"neuroscience",
"neuroscience/sensory",
"systems"
] |
2009
|
Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons
|
We recently described a new , live-attenuated vaccine candidate for chikungunya ( CHIK ) fever , CHIKV/IRES . This vaccine was shown to be well attenuated , immunogenic and efficacious in protecting against CHIK virus ( CHIKV ) challenge of mice and nonhuman primates . To further evaluate its preclinical safety , we compared CHIKV/IRES distribution and viral loads in interferon-α/β receptor-incompetent A129 mice to another CHIK vaccine candidate , 181/clone25 , which proved highly immunogenic but mildly reactive in human Phase I/II clinical trials . Compared to wild-type CHIK virus , ( wt-CHIKV ) , both vaccines generated lower viral loads in a wide variety of tissues and organs , including the brain and leg muscle , but CHIKV/IRES exhibited marked restrictions in dissemination and viral loads compared to 181/clone25 , and was never found outside the blood , spleen and muscle . Unlike wt-CHIKV , which caused disrupted splenic architecture and hepatic lesions , histopathological lesions were not observed in animals infected with either vaccine strain . To examine the stability of attenuation , both vaccines were passaged 5 times intracranially in infant A129 mice , then assessed for changes in virulence by comparing parental and passaged viruses for footpad swelling , weight stability and survival after subcutaneous infection . Whereas strain 181/clone25 p5 underwent a significant increase in virulence as measured by weight loss ( from <10% to >30% ) and mortality ( from 0 to 100% ) , CHIKV/IRES underwent no detectible change in any measure of virulence ( no significant weight loss and no mortality ) . These data indicate greater nonclinical safety of the CHIKV/IRES vaccine candidate compared to 181/clone25 , further supporting its eligibility for human testing .
Chikungunya virus ( CHIKV ) is a reemerging arbovirus and the etiologic agent of chikungunya fever ( CHIK ) . The virus belongs to the Alphavirus genus in the Togaviridae family . As an alphavirus , CHIKV particles are approximately 70 nm in diameter , and contain a single-stranded , positive-sense RNA genome of 11 . 8 kb [1] . The virus was discovered in Tanzania in 1952 by Robinson after responding to an isolated outbreak of febrile illness . This agent was later classified as a novel mosquito-borne virus that causes signs and symptoms similar to those associated with dengue fever [2–4] . The word chikungunya translates roughly from the African Kimakonde language to “that which bends up , ” a reference to the hunched posture adopted by victims afflicted with severe arthralgia . CHIKF has a high attack rate , with only 2–25% of seropositive people remaining asymptomatic [5] . The symptoms and signs of CHIK include high fever , a maculopapular rash radiating outward from the trunk , intense arthralgia and myalgia , and in some rare cases neurological manifestations such as delirium and convulsions [5 , 6] . Although it has emerged repeatedly from enzootic , mosquito-nonhuman primate ( NHP ) cycles in Sub-Saharan Africa for decades if not longer , CHIKV re-emerged with explosive outbreaks originating in Kenya in 2004 [7] . From this initial outbreak , CHIKV spread at an accelerated rate to make landfall in several islands of the Indian Ocean . La Reunión experienced a large outbreak , with approximately 38% of its population ( 300 , 000 people ) contracting the illness [8] . In 2005 , the Indian subcontinent began reporting multiple outbreaks of CHIKF and , shortly thereafter , Southeast Asia was also afflicted [9 , 10] . Subsequently , also due to infected travelers , CHIKV was introduced into northern Italy and southern France , followed by autochthonous cases [11 , 12] . In October of 2013 , cases occurred for the first time in the Americas with autochthonous spread on the French portion of St . Martin Island [13 , 14] . Subsequently , the Caribbean outbreak spread with local transmission on most Caribbean islands , northern South America , Central America , and Florida ( http://www . cdc . gov/chikungunya/geo/americas . html ) [15] . There is currently no licensed vaccine or treatment for CHIK . Patients generally receive only supportive care and non-steroidal anti-inflammatory drugs can alleviate some of the arthralgic pain and joint swelling . Vaccine development began in the 1960s with inactivated formulations generated from wild-type ( wt ) CHIKV strains [16–18] , and later scientists at Walter Reed Army Institute of Research developed a live-attenuated vaccine , called 181/clone25 , by serial passaging a Thai isolate through MRC-5 cells [19] . Although this vaccine was shown to be strongly immunogenic in small animal models and NHPs as well as in humans , Phase II clinical trials generated transient arthralgia in 5 of 59 participants [20] , and virus isolated from the blood of vaccinees showed reversion of its attenuating mutations [21] . Later studies showed that strain 181/clone25 relies on only two point mutations for its attenuation phenotype , probably explaining its reactogenicity [21] . Subsequent vaccine development has included a wide variety of platforms [22] , with a virus-like particle ( VLP ) formulation [23 , 24] and a live-attenuated measles virus-vectored vaccine candidate [25 , 26] proceeding through Phase I clinical trials with minimal side effects and complete seroconversion after two doses . However , the multiple dose requirement of these vaccines represents a limitation for the control of an explosively emerging viral disease and one that occurs mainly in resource-poor nations . To develop a safe , rapidly immunogenic vaccine we capitalized on a strategy using the internal ribosome entry site ( IRES ) from encephalomyocarditis virus ( EMCV ) to attenuate alphaviruses [27] . The IRES is used to replace the subgenomic promoter for expression of the structural genes through internal initiation on the genomic RNA , greatly reducing structural protein production . Furthermore , the EMCV IRES functions inefficiently in insect cells , thereby preventing infection of mosquito vectors to enhance safety for use in non-endemic locations [28] . This attenuation strategy has been successfully implemented for CHIKV [29–32] , Venezuelan [33 , 34] , and eastern equine encephalitis viruses [35] as well as the arthritogenic relative of CHIKV , Mayaro virus [36] . All of these vaccines have been demonstrated to be safe , immunogenic and efficacious in rodent models , and the CHIK version has been shown to be similarly effective in NHPs [37] . With the recent introduction and autochthonous CHIKV spread in the Americas there has been a resurgence of interest in developing a vaccine for CHIK . To this end , we expanded our studies with CHIKV/IRES to further test its preclinical safety using the A129 murine model developed by Courderc et al . [38] . A129 mice lack functional type I interferon receptors , rendering them susceptible to fatal disease following infection by wt CHIKV . We capitalized on this highly stringent model to examine levels of replication and tropism of the CHIKV/IRES vaccine candidate , and compared it to the highly immunogenic but mildly reactogenic 181/clone25 vaccine candidate [19 , 20] , as well as wt CHIKV . We also conducted an extended histopathological analysis not only to further assess CHIKV/IRES preclinical safety , but also to improve our understanding of CHIKV pathogenesis in this model . Finally , we conducted serial murine brain passages in neonatal A129 mice to assess the stability of genetic attenuation of CHIKV/IRES versus 181/clone25 attenuation .
The study was done in adherence to the Guide for the Care and Use of Laboratory Animals from the National Institutes of Health . All procedures were performed under protocol 02-09-068 approved by the Institutional Animal Care and Use Committee of the University of Texas Medical Branch . African green monkey kidney cells ( Vero E6 ) , were obtained from the American Type Cell Culture Collection , ( Bethesda , MD . ) The cells were maintained at 37°C with 5% CO2 in Dulbecco’s Modified Eagle’s Medium ( DMEM ) from Gibco ( Grand Isle , NY ) . The medium was supplemented with penicillin/streptomycin from Gibco and 10% fetal bovine serum from Hyclone ( Logan , UT ) . Viruses were titrated by plaque assay on these cells as previously described [39] . Briefly , the Vero cells were grown in to 6- or 12-well plates until they reached 95% confluency . The medium was then removed and 10-fold serial dilutions of virus were added to individual wells . After a one hour incubation at 37°C with 5% CO2 , an overlay consisting of the medium listed above and 0 . 4% agarose was added . The plates were fixed 2–3 days later with formalin and were stained using 10% crystal violet . The CHIKV/IRES plasmid was produced previously as described by Plante et al . [29] . The wt-CHIKV/FfLuc plasmid was described previously [40] . Viruses were rescued by linearization of the plasmid and transcription of the viral genomic RNA using a Life Technology mMessage mMachine SP6 in vitro transcription kit ( Waltham , MA ) . The resultant RNA was electroporated into Vero cells . Briefly , 2 confluent T-150 flasks Vero cells per electroporation were trypsinized and washed multiple times . The cells were then resuspended in 700 μl of PBS , approximately 4 μg of RNA was added and transferred to a 4 mm cuvette from Molecular Bioproducts ( San Diego , CA ) . The RNA/Vero cell suspension was electroporated using 3 pulses at 250 V , 10ms , one-second intervals in a BTX830 electroporator from Harvard Apparatus ( Holliston , MA ) . Electroporated cells were then placed into a T-75 flask and virus was harvested 30–36 hours later . The vaccine virus derived from electroporated cells was used without further passage . The A129 type I interferon receptor-/- mice were kindly provided by Slobodan Paessler at the University of Texas Medical Branch . The mice were bred in triads ( 2 female 1 male ) and were weaned at 21 days post-birth . Adult 10-week-old animals were used for pathogenesis and in vivo imaging system ( IVIS ) experiments , whereas neonatal 2-day-old animals were used for serial brain passages . The lactating mothers were left in the cage during any procedure that required neonatal mice . Histopathologic studies used adult 10-week-old A129 mice that received a 104 PFU total dose delivered intradermally ( ID ) into the left footpad in a 10 μl volume . Footpads were measured daily for 14 days and animals were sacrificed on days 1–4 , 8 , 14 , 21 , or 28 post-infection after anesthetization with isoflurane . For a single experiment , infected cohorts included 3 animals per virus group for the day 1 , 2 , 3 , 7 , 14 , 21 , and 28 post-infection harvests . Day 4 harvests had 4 animals per infected cohort . A single mock-infected mouse was included at each timepoint . Blood was collected by cutting the aortic arch then transcardially perfusing mice with ~40 ml of PBS at a flow rate of 2 ml/min . The major organs were then harvested and a sample of the muscle from the left hamstring was extracted . Half each sample was placed in 10% formalin for histopathological analysis , while the remaining half ( same organ or contralateral ) was placed in DMEM containing 5% FBS for viral load assays . An IVIS experiment was completed using 10-week-old A129 animals to visualize patterns of CHIKV replication in vaccinated mice . Each cohort included four animals except for the mock vaccinated , wt-CHIKV cohort , which included two animals . At 26 days post-vaccination , the mice were prepared by shaving the dorsal and ventral surfaces; a small strip of hair was left on the dorsal surface adjacent to the front limbs , and extra bedding was supplied for comfort . At day 30 post-vaccination , the mice were injected ID in the footpad as described above with either wt-CHIKV or wt-CHIKV/FfLuc . Weights and footpad measurements were collected daily post-challenge for the duration of the study . On days 1–4 post-challenge , 170 μl of luciferin substrate ( GoldBio , St . Louis , MO ) at a concentration of 15mg/ml were injected into each animal IP . The animals were then returned to their cages for 7 minutes before anesthetization with inhalational isoflurane and imaging using the Xenogen IVIS system ( Lincolnshire , United Kingdom ) and LivingImage software from Perkin Elmer ( Waltham , MA ) . At day 3 post-challenge some animals were sacrificed and their tissues were harvested for analysis as described previously . Results represent a single experiment . To assess the stability of attenuation , the CHIKV/IRES and 181/clone25 vaccine strains were passaged intracranially ( IC ) in 2-3-day-old mice . Each vaccine strain was passaged in 2 independent experimental series . Mouse litters were randomized at 2 days of age , then cohorts of 3 mice were injected IC with 10 μl containing 104 PFU . The animals were then sacrificed 30–36 hr later and their brains were homogenized and titrated . The highest titer of the three possible samples was chosen for each experiment to continue the IC passage series after clarification through centrifugation dilution to a titer of 106 PFU/ml . After 5 brain passages , passaged vaccine strains was tested for changes in virulence by comparing mortality rates with the parental strain . Each passaged virus and its parent was tested in 3–4 animals , alongside 5 wt-CHIKV and 2 PBS controls . The procedure for fixation and staining was described previously [29] . Briefly , tissues were fixed in 10% buffered formalin ( RICCA , Arlington TX ) for a minimum of 72 hours . Samples that contained bone , such as whole leg , were then decalcified overnight ( VWR , Radnar , PA ) . The tissues were then embedded in paraffin wax and 5μm sections were prepared . The samples were hydrated using an ethanol gradient and stained with hematoxylin and eosin . The vaccine strains were sequenced following serial brain passages . Consensus sequences was determined by directly sequencing reverse transcription-polymerase chain reaction ( RT-PCR; conditions and primer sequences available from the authors upon request ) amplicons generated from RNA extracted from brain homogenates using TRIzol LS ( Life Technologies ) and the Titan One Tube RT-PCR System kit from Roche ( Penzberg , Germany ) . Sequencing reactions were performed using the BigDye kit from Applied Biosystems ( Foster City , CA ) and amplicon DNA was purified using EdgeBio Sequence Cleanup columns ( Gaithersburg , MD ) and sequenced on an Applied Biosystems 3500 Genetic Analyzer . Vero cell plaque purified virus was also collected for sequencing .
To assess the tropism of wt-CHIKV and the 181/clone25 and CHIKV/IRES vaccines , a serial sacrifice study was completed using the 10-week-old A129 mouse model inoculated in the footpad with 104 PFU . Ipsilateral footpad swelling was measured and tissues were harvested from 3 animals on days 1–4 , 8 , 14 , 21 , and 28 post-inoculation to determine viral loads by plaque assay . The first 4 days were used to compare the vaccines’ tropisms and histopathological findings versus wt-CHIKV . The later time points , at which the wt-CHIKV-infected animals had already succumbed to illness , were used to compare the two vaccine strains . By day 1 after infection , minor footpad swelling was observed in both vaccine cohorts ( Fig 1 ) . This phenotype has been observed previously [29] and was attributed to mechanical disruption during the footpad inoculation . The day 2 measurements began to show a difference between the wt-CHIKV-infected animals and the vaccine cohorts , with moderate to severe swelling maintained in the former through day 4 . During the later time points , the vaccinated animals maintained mild swelling through the 14-day timepoint . By the 21-day timepoint no residual swelling was observed in either vaccine cohort . Mice were sacrificed for terminal bleeds , and transcardial perfusions were performed , followed by necropsies to collect the major organs for titration and histopathological analyses . Virus was detected in some animals of all cohorts up to the day 4 timepoint ( Fig 2 ) . The two vaccine groups had tissues collected from later time points , but by day 8 there was no detectable virus present by plaque assay in any of the tested tissues . The wt-CHIKV-infected animals produced a systemic infection by day one . The highest titers were found in the hamstring muscle and spleen of these animals . The brain had the lowest viral load on day one , approximately 3 log10 PFU/g . The viral loads in the wt-CHIKV-infected animals increased through day 4 until the tissues uniformly contained large viral loads of approximately 6-7log10 PFU/g ( tissue ) or PFU/ml ( serum ) . The 181/clone25 vaccine strain also replicated well in the A129 model ( Fig 2 ) . By day 1 post infection animals had detectable viral loads in the muscle and spleen ( Fig 2 ) . The hamstring had the higher viral load , which was 6 log10 PFU/g compared the spleens 3 log10 PFU/g . By day 2 the animals had a high levels of viremia and all tissues except the brain contained virus . This trend continued by day 3 , when the animals were found to have a systemic infection with brain samples now containing virus . Overall virus production continued to increase through day 4 , with the highest titer tissues approaching 7 log10 PFU/g . However , 181/clone25 virus was cleared to undetectable levels in all tissues examined by day 8 . Day 14 , 21 and 28 samples were also negative by plaque titration ( <100 PFU/g ) . In contrast to wt-CHIKV and 181/clone25 , the CHIKV/IRES-infected animals never developed high virus loads ( Fig 2 ) . CHIKV/IRES could not be detected in any of the tissues tested on day 1 . By day 2 , 2/3 animals had low viral loads detected in the muscle , slightly above our limit of detection . By day 3 , virus could still only be detected in the muscle , with the titer increased to 3 log10 PFU/g . Day 4 samples revealed slow spread to the spleen ( 2/3 animals ) and one animal had a slight viremia . CHIKV/IRES was not detected at later timepoints . One-way ANOVA indicated a significant difference among the 3 virus/vaccine cohorts for the timepoints and tissues tested . Using Bonferroni post hoc analyses for pair-wise comparisons , virus titers in all tissues except for muscle were statistically indistinguishable between the vaccine strains on day 1 . In the muscle , the 181/25 clone was statistically indistinguishable from wt-CHIKV , and CHIKV/IRES was statistically different from both the 181/25 clone and wt-CHIKV . The CHIKV/IRES cohort titers remained significantly lower than wt-CHIKV at all timepoints in all tissues . The day 2 and 3 181/clone 25 animals had similar viral loads in the muscle and spleen compared to wt-CHIKV . The trend continued by day 4 the two viruses now present at similar titers in the brain , heart , spleen and muscle . Histopathological analyses were completed on all major organs at each time point . Only the spleen , liver , and leg muscle showed remarkable lesions . None of the animals that received either vaccine or PBS exhibited lesions in the spleen during the course of the study . However , the animals that received wt-CHIKV exhibited disrupted splenic architecture with depletion of the white pulp and deposition of proteinaceous debris by day 2 , which persisted through day 3 ( Fig 3 ) . By day 4 the spleen began to recover histologically ( Fig 3 ) . The wt-CHIKV-infected animals also showed small hepatic lesions , which appeared on day 3 and persisted at least until day 4 ( Fig 4 ) . Neither vaccine cohort showed any hepatic lesions during the study , similar to the PBS cohort . Likewise , the histological findings for the leg after 181/clone25 , CHIKV/IRES , or PBS infections were unremarkable through day 4 ( Fig 5 ) . Mild foci of myositis could be seen in the calf muscles of the wt-CHIKV-infected mice at day 4; the later timepoints revealed the development of moderate myositis and cellulitis in animals infected with each vaccine strain on day 8 and to a lesser degree on day 14 ( Fig 6 ) . These animals showed no outward signs of disease during this period ( none with hunched posture , ruffled fur , or lethargy ) . This inflammation was generally cleared by day 21 and no pathologic findings could be detected by day 28 . To assess the genetic stability of the CHIKV/IRES and 181/clone25 candidate vaccines , two independent passage series of each were performed in neonatal A129 mice inoculated intracranially . Peripheral inoculation did not generate enough replication to allow for serial passages , and strain CHIKV/IRES did not replicate to sufficient titers in the brains of wt mice to permit serial propagation . The viruses were inoculated intracranially into the brains of 2-day-old A129 mice in a volume of 10 μl containing 104 PFU , then the animals were euthanized and brain tissue was harvested 36 hours later . Each cohort consisted of 2 pups and their lactating mother . After titration , the animal with the highest viral brain titer was chosen to continue the passage series after the appropriate dilutions to inoculate 104 pfu into each of the next pair of mice . Overall , brain titers for the 181/clone25 vaccine strain were ca . 10-100-fold higher than those of CHIKV/IRES ( Fig 7A ) . There was significant variation among the groups in all passages by one-way ANOVA . However , with the exception of the day 2 samples for 181/clone25 , there was no significant variation between independent passages of each virus strain by Bonferroni post-hoc analysis . Plaque morphology was also determined throughout these passages , and some noticeably larger plaques appeared as early as the second passage for the 181/clone25 vaccine . In contrast , plaques of the CHIKV/IRES vaccine candidate remained relatively constant in size throughout the passages ( Fig 7B ) . Following 5 serial passages , viruses were analyzed for changes in virulence in a 6-7-week-old A129 mouse model inoculated intradermally in the left rear footpad ( intracranial inoculation of strain 181/clone25 resulted in rapid death with little opportunity to observe changes in survival time following passages ) using 104 PFU . Parent virus strains derived from electroporated stocks of each vaccine were compared to the 5th mouse passages , as well as to PBS controls . The animals were observed for 13 days post infection and weight change , footpad swelling , and mortality were noted . When weights of mice infected with the passaged viruses were compared , mice that received p5 of 181/clone25 began to lose weight at day 6 , and continuing through the end of the study ( Fig 8A ) . In contrast , the p5 CHIKV/IRES-infected mice did not differ significantly in weight change compared to the PBS-injected controls or the unpassaged vaccine strains . The two mouse-passaged lines ( p5A , p5B ) of the CHIKV/IRES vaccine candidate caused similar levels of footpad swelling compared to the parent virus and to the PBS control , with significantly less swelling compared PBS only on day 6 ( Fig 8B ) . On days 1 , 3 , and 4 , wt-CHIKV produced more swelling that either vaccine or PBS . Unlike CHIKV/IRES , both passaged181/clone25 strains exhibited an altered footpad swelling phenotype , with the latter causing increased swelling similar to that induced by wt-CHIKV . There were also significant differences in mortality among the 8 treatments as calculated by a Kaplan Meier test . Neither of the parent vaccines or either p5 replicate of the CHIKV/IRES vaccine candidate , or the PBS control caused any mortality ( Fig 8 ) . In contrast , both p5 181/clone25 replicates became 100% lethal . Interestingly , the p5B 181/clone25 strain appeared to be more virulent , killing all the animals tested by day 9 whereas some p5A-infected animals survived 13 days . However , the Kaplan Meier test showed a p-value of only 0 . 11 for this difference in average survival . Because stochastic events are likely involved in reversion or pseudoreversion to virulence , it would not be surprising to see different trajectories in the virulence increases when only 2 attenuating point mutations [21] must be overcome to regain virulence in strain 181/clone25 . To investigate the mechanisms of reversion to virulence or stability , we consensus sequenced the complete genomic RNA extracted from the 5th mouse passage of each series ( 2 parallel series for 181/clone 25 and CHIKV/IRES ) using RT-PCR followed by Sanger amplicon sequencing . The passaged CHIKV/IRES viruses had no consensus mutations located in the open reading frames . In contrast , the passaged 181/clone25 vaccine viruses acquired four mutations in the nonstructural protein genes , two encoding amino acid substitutions in the nsP1 , all found in both parallel passage series , and generally in mixed populations ( Table 1 ) . Because the attenuating mutations of vaccine strain 181/clone25 are found in the E2 envelope glycoprotein [21] , we harvested virus from 2 large and 2 small plaques for each p5 virus . For each of the 4 plaque-purified stocks , the PE2 gene region , spanning from the 3’ end of capsid gene to the 5’ end of 6K , was sequenced using the Sanger method [21] . The plaque purified CHIKV/IRES sequences derived from the unpassaged parent strain and each p5 derivative were unchanged compared to that of the parent clone . However , the 181/clone25 mouse p5 viruses acquired multiple E2 gene mutations ( Table 1 ) . In each case , the 2 sequences derived from the large plaques sampled from a given p5 brain harvest contained the same E2 mutation; the same was true for the small plaques . The p5A of 181/clone25 virus had an E2 substitution of R80T , involving a reduction in change near residue 82 where the vaccine strain had acquired a G82R substitution that is its major attenuation determinant [21] . In 181/clone25 p5B , there was direct reversion of amino acid 82 to Gly , and a second loss of charge mutation at E2-K57N . The substitutions resulting in the loss of positive charge suggest that increased mouse virulence was mediated by a reduction in heparan sulfate binding , as has been shown to differ between 181/clone25 and wt-CHIKV [41 , 42] . When these mutations were mapped using Pymol on the E2 glycoprotein , all three occurred on the apical side of the protein in domain B [43] ( S1 Fig ) , consistent in their putative role in binding to cellular receptors [43] . The highly efficacious nature of the CHIKV/IRES vaccine candidate has been previously demonstrated in mice and nonhuman primates [29 , 37] . However , none of these studies evaluated exhaustively the replication of challenge CHIKV in vaccinated animals . In an attempt to evaluate the fate of challenge virus without sacrificing large numbers of animals , we used a wt-CHIKV strain that expresses the firefly luciferase gene fused directly to the capsid protein [40] . This virus was initially compared to our standard , wt-CHIKV in a mortality study to determine the effect of the reporter gene on virulence using 10-week-old A129 mice infected with a 104 pfu dose in the footpad . Both viruses were 100% lethal; however wt-CHIKV/FfLuc exhibited a statistically significant ( Kaplan Meier ) delay in the mean time to death ( ca . 6 versus 3 days ) compared wt-CHIKV ( Fig 9 ) . Because both viruses caused 100% mortality , we used CHIKV/FfLuc virus for challenge of mice 30 days after CHIKV/IRES or PBS vaccination . One cohort was challenged with wt-CHIKV to determine if autofluorescence would confound imaging . The mice were given the fluorescein substrate daily and observed using the IVIS system . When the whole animal was imaged , a strong luminescence signal was present in the inoculated footpad of the PBS-vaccinated animals that were challenged CHIKV/FfLuc virus , but as expected no signal was detected in the animals that were challenged with wt-CHIKV ( S2 Fig ) . The animals that received the CHIKV/IRES vaccine candidate and were then challenged with CHIKV/FfLuc also produced no detectable signal . The footpad signal from the PBS-vaccinated , CHIIKV/FfLuc-challenged animals was so high that it masked weaker signals from elsewhere in the body ( S3 Fig ) . When the animals’ inoculated footpads were masked , a strong signal was detected in the musculature and splenic region on days 1–4 . In contrast , the CHIKV/IRES vaccine candidate completely protected against CHIKV/FfLuc replication detected by luciferase activity . The PBS-vaccinated animals did have lower levels of infectious virus replication following CHIKV/FfLuc challenge , compared to the wt-CHIKV ( S4 Fig ) . However , in the absence of vaccination , the infection was systemic by day 3 and caused minor splenic disruption ( S5 Fig ) .
The ideal vaccine for an explosively emerging viral disease like CHIK will cause no detectable disease after administration , will generate rapid and durable immunity after a single dose , and will prevent or greatly reduce the replication of challenge infections to reduce transmission ( since humans are the only amplification hosts in the urban cycle ) . The vaccine will also remain stably attenuated during use in large numbers of vaccinees , especially for viruses like CHIKV that place tens-of-millions of persons at natural risk for severe and chronic arthralgia . A live-attenuated Chikungunya vaccine may be capable of meeting these goals [22] . We previously described a new , live-attenuated vaccine candidate , CHIKV/IRES , developed by inactivating the subgenomic promoter and expressing the CHIKV structural proteins from the genomic RNA using IRES-mediated translation . This vaccine was shown to be well attenuated , immunogenic and efficacious in protecting against CHIK virus ( CHIKV ) challenge of mice [29] and nonhuman primates [37] . In this study , we performed more extensive studies to further evaluate CHIKV/IRES preclinical efficacy and safety using the A129 interferon-α/β receptor deficient murine model in which wt-CHIK produces a lethal infection . We also used the 181/clone25 live-attenuated vaccine strain as a benchmark , which exhibited excellent immunogenicity but some reactogenicity in Phase II human trials [20] , presumably due to the presence of only 2 attenuating point mutations [21] . To evaluate preclinical safety , we performed serial sacrifice experiments with adult A129 mice to measure viral loads in a wide variety of tissues and organs 1–8 days after vaccination . The CHIKV/IRES vaccine candidate replicated to far lower levels than wt-CHIKV , which generated a rapid and systemic infection by day one . The CHIKV/IRES viral loads were also generally much lower than those of 181/clone25 , which were intermediate between this vaccine and wt-CHIKV . Footpad swelling , another measure of virulence , was also lower following inoculation of both vaccines compared to wt-CHIKV . To assess the stability of CHIKV/IRES attenuation , we attempted to enrich for reversion to virulence by performing serial brain passages in 2-day-old A129 mice . Following 2 independent experiments involving 5 serial passages , no increase in virulence , as measured by mortality , weight loss or footpad swelling , was detected in the CHIKV/IRES vaccine candidate strain ( Fig 8 ) . In contrast , strain 181/clone25 consistently became 100% lethal and increased its ability to cause weight loss and footpad swelling . This presumed mechanism of strain 181/clone25 instability , reversions or pseudoreversions in the attenuating E2 envelope glycoprotein substitutions that accompanied its development during serial MRC5 cell passages , were confirmed by the identification of loss-of-charge substitutions of the same or nearby E2 residues during serial mouse passages . In contrast , no consensus mutations occurred in the open reading frames of either of the CHIKV/IRES mouse passage series . The only mutation detected was in the length of a polyA tract within the IRES itself , which is not translated . Finally , to assess the ability of the candidate CHIKV/IRES vaccine to inhibit challenge virus replication , a wt-CHIKV construct was modified to fuse the luciferase gene to the CHIKV capsid , producing a virus that expressed luciferase during replication in mice . Using this system and IVIS , we demonstrated that immunization by the candidate CHIKV/IRES vaccine protected completely against detectable luciferase expression , suggesting nearly sterilizing immunity to challenge with the CHIKV/Ffluc virus . This is an important finding because , in addition to preventing disease , a CHIK vaccine should diminish CHIKV viremia after exposure of vaccinated subjects to interrupt the urban transmission cycle . Taken together , our results demonstrate the CHIKV/IRES vaccine candidate is highly and stably attenuated , and produces nearly sterilizing immunity against CHIKV challenge in the highly susceptible A129 mouse model . Together with previously reported data on preclinical safety , immunogenicity and efficacy in mice [29] and cynomolgus macaques [37] , these results further support the further development of the CHIKV/IRES vaccine candidate for clinical trials .
|
Chikungunya fever is a reemerging , mosquito-borne viral disease that causes severe , debilitating , and often chronic arthralgia . The virus reemerged from Africa in 2004 and has since caused disease in millions of persons , including in over one million in the Americas since it arrived for the first time in modern scientific history in late 2013 . An effective vaccine is critically needed to protect against this medically and economically devastating disease as well as to interrupt the human-mosquito transmission cycle . To further test a new , live-attenuated vaccine candidate for chikungunya fever , we conducted extensive preclinical safety evaluations using another vaccine candidate tested in humans , 181/clone 25 , as a benchmark . The new vaccine candidate , CHIKV/IRES , replicated to lower levels in a mouse model and generated lesser signs of disease . Furthermore , it was more stably attenuated following mouse passages . These results support the further development of the new CHIKV/IRES vaccine candidate toward clinical testing in humans .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Extended Preclinical Safety, Efficacy and Stability Testing of a Live-attenuated Chikungunya Vaccine Candidate
|
The predominant mechanism of drug resistance in African trypanosomes is decreased drug uptake due to loss-of-function mutations in the genes for the transporters that mediate drug import . The role of transporters as determinants of drug susceptibility is well documented from laboratory-selected Trypanosoma brucei mutants . But clinical isolates , especially of T . b . gambiense , are less amenable to experimental investigation since they do not readily grow in culture without prior adaptation . Here we analyze a selected panel of 16 T . brucei ssp . field isolates that ( i ) have been adapted to axenic in vitro cultivation and ( ii ) mostly stem from treatment-refractory cases . For each isolate , we quantify the sensitivity to melarsoprol , pentamidine , and diminazene , and sequence the genomic loci of the transporter genes TbAT1 and TbAQP2 . The former encodes the well-characterized aminopurine permease P2 which transports several trypanocides including melarsoprol , pentamidine , and diminazene . We find that diminazene-resistant field isolates of T . b . brucei and T . b . rhodesiense carry the same set of point mutations in TbAT1 that was previously described from lab mutants . Aquaglyceroporin 2 has only recently been identified as a second transporter involved in melarsoprol/pentamidine cross-resistance . Here we describe two different kinds of TbAQP2 mutations found in T . b . gambiense field isolates: simple loss of TbAQP2 , or loss of wild-type TbAQP2 allele combined with the formation of a novel type of TbAQP2/3 chimera . The identified mutant T . b . gambiense are 40- to 50-fold less sensitive to pentamidine and 3- to 5-times less sensitive to melarsoprol than the reference isolates . We thus demonstrate for the first time that rearrangements of the TbAQP2/TbAQP3 locus accompanied by TbAQP2 gene loss also occur in the field , and that the T . b . gambiense carrying such mutations correlate with a significantly reduced susceptibility to pentamidine and melarsoprol .
The chemotherapy of human African trypanosomiasis ( HAT , also known as sleeping sickness ) currently relies on suramin or pentamidine for the first , haemolymphatic stage and on melarsoprol or eflornithine/nifurtimox combination therapy ( NECT ) for the second stage , when the trypanosomes have invaded the central nervous system ( CNS ) [1] . All five drugs have unfavorable pharmacokinetics and adverse effects . Melarsoprol is particularly toxic , causing severe encephalopathies in over 5% of the treated patients [2] . And yet , melarsoprol is the only treatment for late-stage T . b . rhodesiense infections . New and safer drugs are at various stages of ( pre ) clinical development , thanks largely to the Drugs for Neglected Diseases initiative ( www . dndi . org ) . Two molecules that have successfully passed clinical Phase I trials are now being tested in patients: the nitroimidazole fexinidazole [3] , [4] and the benzoxaborole SCYX-7158 [5] , [6] . Both are orally available and cure 2nd stage T . b . brucei infections in a mouse model [7] . However , until new drugs for HAT are on the market , the current ones – problematic as they are – need to be used in a sustainable way . This requires an understanding of the mechanisms of drug resistance . The mechanisms of drug resistance in African trypanosomes have been studied in the lab for over 100 years [8] . Two observations were made recurrently , namely ( i ) reduced drug uptake by drug resistant trypanosomes [9]–[14] and ( ii ) cross-resistance between melarsoprol and pentamidine [15] , [16] . Both phenomena were attributed to the fact that melarsoprol and pentamidine are taken up by trypanosomes via the same transporters , which appeared to be lacking in drug-resistant mutants . The first transporter identified was called P2 since it was one of two purine nucleoside transporters identified [17] , [18] . It is encoded by the gene TbAT1 for adenine/adenosine transporter 1 [19] . Homozygous genetic deletion of TbAT1 in bloodstream-form T . b . brucei resulted in pentamidine and melarsoprol cross-resistance , albeit only by a factor of about 2 . 5 [20] . This weak phenotype , together with the fact that the TbAT1−/− mutants still exhibited saturable drug import [21] , indicated that further transporters are involved in melarsoprol-pentamidine cross-resistance [16] , [21] , [22] . One such transporter was recently identified , the aquaglyceroporin TbAQP2 [23] , [24] . Aquaporins and aquaglyceroporins belong to the major intrinsic protein ( MIP ) family and form channels that facilitate transmembrane transport of water and small non-ionic solutes such as glycerol and urea [25] . The three aquaporins of T . brucei ( TbAQP1-3 ) are thought to physiologically function as osmoregulators and are involved in glycerol transport [26] . Aquaporins were described to mediate uptake of arsenite in mammalian cells [27] and in Leishmania , and loss of aquaporin function was implicated in heavy metal resistance [28] . Homozygous genetic deletion of TbAQP2 in bloodstream-form T . b . brucei increased the IC50 towards melarsoprol and pentamidine by about 2- and 15- fold , respectively [24] . Moreover , a T . b . brucei lab mutant selected for high-level pentamidine resistance [21] carried a chimeric TbAQP2 gene , where 272 nucleotides had been replaced by the corresponding sequence from a neighboring , very similar gene TbAQP3 [24] . Differences in the TbAQP2/TbAQP3 tandem locus on chromosome 10 were also observed between the reference genome sequences of T . b . gambiense DAL972 [29] and T . b . brucei TREU927 [23] , [30] . They possess identical versions of TbAQP2 but differ in TbAQP3 [31] . More recent field isolates of T . brucei ssp . have so far not been genotyped regarding their TbAQP2/TbAQP3 locus . The genotypic status of TbAT1 , located proximal to a telomere on chromosome 5 [32] , has been more intensely investigated . Point mutations in TbAT1 were described , both in selected lab strains and in clinical T . brucei ssp . isolates , which rendered the gene non-functional when expressed in yeast [19] . The occurrence of these mutations correlated to a certain degree with melarsoprol treatment failure in 2nd stage T . b . gambiense HAT patients [33]–[36] . However , the relationship between polymorphisms in TbAT1 , drug susceptibility , and treatment failure in patients is not fully resolved as the TbAT1 mutant T . b . gambiense were not analyzed phenotypically . Such investigations are notoriously difficult since clinical T . b . gambiense isolates are hard to obtain ( given the inaccessibility of HAT foci and the poor success rate of isolation and adaptation in rodents ) and cannot readily be propagated in axenic culture . Here we concentrate on clinical T . brucei ssp . isolates from drug refractory cases that have been adapted to axenic in vitro cultivation , aiming to investigate whether mutations at the known melarsoprol and pentamidine transporter loci also occur in the field – and if so , whether such mutations are accompanied by loss of drug susceptibility .
The 16 analyzed isolates are described in Table 1 ( origin ) and Table 2 ( clinical outcome ) . For more details on the recent isolates from the DRC please refer to Table S4 of Pyana et al ( 2011 ) [37] . All have previously been adapted to axenic cultivation . T . b . brucei and T . b . rhodesiense isolates were cultured in minimum essential medium ( MEM ) with Earle's salts with the addition of 0 . 2 mM 2-mercaptoethanol , 1 mM Na-pyruvate , 0 . 5 mM hypoxanthine , and 15% heat-inactivated horse serum as described by Baltz et al ( 1985 ) [38] . T . b . gambiense strains were cultured in IMDM medium supplemented according to Hirumi and Hirumi ( 1989 ) [39] , plus 0 . 2 mM 2-mercaptoethanol , 15% heat-inactivated fetal calf serum and 5% human serum . The cultures were maintained under a humidified 5% CO2 atmosphere at 37°C and were subpassaged 3 times a week to ensure growth in the exponential ( log ) phase . Drug sensitivity was determined with the Alamar blue assay as described by Räz et al ( 1997 ) [40] , using the redox-sensitive dye resazurin as an indicator of cell number and viability . The trypanosomes were cultivated in 96-well microtiter plates in serial dilutions of drugs for 70 h . 10 ul of resazurin ( 125 ug/ml ( Sigma ) dissolved in PBS pH 7 . 2 ) was added to each well . The plates were further incubated for 2–4 hours for T . b . rhodesiense and T . b . brucei , and 6–8 hours for T . b . gambiense , before being read with a SpectraMax Gemini XS microplate fluorescence scanner ( Molecular Devices ) at an excitation wavelength of 536 nm and an emission wavelength of 588 nm . IC50 values were calculated by non-linear regression to a sigmoidal inhibition curve using SoftMax Pro software ( V . 5 . 2 ) . The IC50 values given in Table 2 are averages ± standard deviation of at least 3 independent assays ( n = 3–12 ) , each determined in duplicate . Melarsoprol ( Sanofi-Aventis ) was obtained from WHO . Pentamidine isothionate and diminazene aceturate were purchased from Sigma . Genomic DNA was isolated from 10 ml dense trypanosome cultures . The cells were spun down and the pellets resuspended in 300 µl 10 mM TrisHCl pH 8 , 1 mM EDTA and 3 µl 10% SDS was added before incubating for 10–15 min at 55°C . After 5 min incubation 3 µl of pronase mix ( 20 mg/ml , Sigma ) was added to increase the stability of the extracted DNA . 90 µl of ice cold 5 M potassium acetate was added and the mixture was incubated for 5 min on ice . After spinning down for 5 minutes at max speed in a microfuge , the supernatant was transferred to a new tube and DNA was precipitated in 2–2 . 5 volumes of absolute ethanol , washed in 70% ethanol and dissolved in 20 µl ddH2O . PCR was performed with Taq polymerase ( Solis BioDyne , Estonia ) ; the primers and annealing temperatures are summarized in Table S1 . PCR products were run on a 0 . 8% agarose gel and purified on a silica membrane column ( Nucleospin gel and PCR clean up , Macherey Nagel , Germany ) . The purified PCR products were directly sequenced ( Microsynth , Switzerland or GATC , Germany ) with the same primers as used for PCR amplification . Only the TbAQP2/TbAQP3 locus of T . b . gambiense K03048 produced two PCR products , which were cloned in pCR2 . 1-TOPO ( Invitrogen ) . The assembled sequences were submitted to GenBank; accession numbers are listed in Table S2 .
To be able to compare – and possibly correlate – genotype and phenotype of T . brucei ssp . , we assembled a set of 16 isolates that had been adapted to axenic in vitro cultivation as blood-stream forms . These included 5 recent T . b . gambiense isolates from the Democratic Republic of the Congo ( DRC ) , 2 older isolates from the Republic of Côte d'Ivoire and one isolate from South Sudan , which were all isolated from patients who had relapsed after melarsoprol chemotherapy . Other T . b . gambiense isolates from the DRC , northwestern Uganda , and Liberia were from patients who were successfully treated with melarsoprol or the treatment outcome is unknown . T . b . gambiense STIB 930 is a fully drug-susceptible lab strain that was used as a reference strain . We further included the field isolates T . b . brucei STIB 940 , T . b . brucei STIB 950 and T . b . rhodesiense STIB 871 , which are multidrug-resistant to isometamidium , diminazene and tubercidin . The fully drug-susceptible reference strain T . b . rhodesiense STIB 900 was included as a reference . The different isolates and their origin are summarized in Table 1 . All isolates were genotyped regarding TbAQP2 and TbAT1 . When the TbAQP2/TbAQP3 genomic locus was amplified by PCR from the 16 T . brucei ssp . isolates , all the recent T . b . gambiense isolates from the DRC ( 40 AT , 45 BT , 130 BT , 349 BT and 349 AT ) exhibited a smaller band than expected for the wild-type locus . Direct sequencing of the PCR product in each of the five isolates revealed only one gene at the locus: a chimeric version of TbAQP2 and TbAQP3 . The first 813 bp of the open reading frame perfectly matched TbAQP2 while the remaining 126 bp derived from TbAQP3 ( Figure 1C ) . These 126 bp perfectly matched to TbAQP3 of T . b . rhodesiense STIB 900 but this exact sequence is not found in the published genome of T . b . gambiense DAL 972 . Note that the present TbAQP2-TbAQP3 chimeric gene ( Figure 1C ) differs from the one described by Baker et al . from a pentamidine-selected T . b . brucei lab mutant ( Figure 1B; [24] ) . T . b . gambiense K03048 from the South Sudan also gave rise to an abnormal pattern upon PCR amplification of the TbAQP2/TbAQP3 locus from genomic DNA: a distinctly smaller double band instead of the expected product , indicative of heterozygosity . The smaller band contained the upstream region of TbAQP2 followed by the open reading frame of TbAQP3 while the TbAQP2 open reading frame was missing ( Figure 1D ) . The larger band contained a TbAQP2/3 chimera similar to that encountered in the T . b . gambiense isolates of the DRC ( Figure 1C ) . Point mutations in TbAQP2 were encountered in the multidrug-resistant field isolates T . b . brucei STIB 940 , T . b . brucei STIB 950 and T . b . rhodesiense STIB 871 , all of which had the same 4 SNPs in TbAQP2 compared to the T . b . brucei 927 reference gene ( Tb927 . 10 . 14170 ) , leading to the amino acid change threonine159 to alanine ( Figure 1E ) . However , the same 4 SNPs also occurred in our drug-susceptible reference strain T . b . rhodesiense STIB 900 , so they are not likely to be involved in the mdr phenotype [41] , [42] of these isolates . All other isolates analyzed had a wild-type copy of TbAQP2 . The identified sequence polymorphisms are summarized in Table 2 , GenBank accession numbers are in Table S2 . All of the 12 analyzed T . b . gambiense isolates were identical in TbAT1 sequence to the reference STIB 930 as well as to the genome strain DAL972 . The previously described TbAT1R allele [19] , [33] was found in the 3 mdr lines T . b . brucei STIB 940 , T . b . brucei STIB 950 and T . b . rhodesiense STIB 871 . TbAT1R carries 5 coding and 4 silent mutations and a codon deletion as compared to the reference sequence ( STIB 900 ) , and the resultant protein appeared to be non-functional when expressed in Saccharomyces cerevisiae [19] or re-expressed in a tbat1 null T . b . brucei ( De Koning , unpublished results ) . The remainder of the isolates did not possess mutations in TbAT1 when compared to the respective reference isolate . The GenBank accession numbers of all the sequences are in Table S2 . Drug sensitivities of the bloodstream-forms of all isolates were determined in vitro regarding melarsoprol , pentamidine , and diminazene . The five T . b . gambiense that possessed the chimeric TbAQP2/3 gene ( 45 BT , 130 BT , 349 BT , 349 AT , 40 AT ) , as well as K03048 which carries a deletion of TbAQP2 in one allele , in addition to one chimeric TbAQP2/3 allele , all showed a similar drug sensitivity profile with markedly increased IC50 values towards pentamidine and , to a lesser extent , also melarsoprol ( Figure 2 ) . IC50 values were in the range of 70–92 nM for pentamidine and 22–42 nM for melarsoprol ( Table 2 ) ; compared to the median of the four drug sensitive T . b . gambiense lines STIB 930 , STIB 891 , STIB 756 and ITMAP 141267 , this corresponds to a 40- to 52-fold decrease in susceptibility to pentamidine and a 2 . 8- to 5 . 3-fold decrease for melarsoprol . The higher IC50 values of the isolates that carried a mutation in TbAQP2 ( n = 6 ) compared to the remainder ( n = 10 ) were statistically significant both with respect to pentamidine ( p = 0 . 0002 , two-tailed Mann-Whitney test ) and melarsoprol ( p = 0 . 0047 ) ; no association was observed regarding TbAQP2 status and sensitivity to diminazene . However , the isolates that carried the known resistance allele TbAT1R ( i . e . STIB 940 , STIB 950 and STIB 871 ) exhibited strongly increased IC50 values to diminazene ( p = 0 . 01 , two-tailed Mann-Whitney test ) but not to pentamidine ( Figure 2 , Table 2 ) . T . b . brucei STIB 950 also had an elevated IC50 against melarsoprol ( Figure 2 ) , but over all three TbAT1R isolates there was no significant effect on melarsoprol susceptibility . Across all 16 T . brucei isolates , pentamidine sensitivity positively correlated with that to melarsoprol ( Spearman's rank correlation coefficient of 0 . 67 , p = 0 . 005 ) while there was no correlation between the two structurally related diamidines , pentamidine and diminazene ( Figure 2 ) .
It is an intriguing phenomenon with African trypanosomes that drug resistance is predominantly linked to reduced drug import , typically arising from loss of function mutation of a non-essential transporter [12] , . Here we investigated the aminopurine transporter TbAT1 and the aquaglyceroporin TbAQP2 , two proteins known to be involved in uptake of – and susceptibility to – melarsoprol and diamidines in bloodstream-form T . brucei . While there is evidence for a link between TbAT1 mutations and melarsoprol treatment failure in the field [33]–[36] , the more recently identified gene TbAQP2 has so far not been analyzed in a clinical setting . TbAQP2 is dispensable for growth in culture [24] and partial gene replacement of TbAQP2 with TbAQP3 was observed in a pentamidine-selected T . b . brucei lab mutant [24] that displayed reduced infectivity to rodents [21] . However , it was unknown whether similar mutations also occur in the field , as they might bear a fitness cost in patients or during transmission by the tsetse fly . Concentrating on a panel of clinical T . brucei ssp . isolates that ( i ) derived from treatment-refractory cases and ( ii ) had been adapted to axenic in vitro culture , we have genotyped their TbAT1 and TbAQP2 loci , and phenotyped their in vitro sensitivity towards melarsoprol , pentamidine and diminazene . Our aim was to explore whether TbAQP2 mutations occur in the field and if so , whether mutant isolates exhibit reduced drug susceptibility . Five of the analyzed T . b . gambiense isolates , all from melarsoprol relapse patients of Dipumba Hospital in Mbuji-Mayi , DRC , carried only one gene at the TbAQP2/TbAQP3 tandem locus , an unprecedented TbAQP2/3 chimera . The high degree of sequence similarity between TbAPQ2 and TbAQP3 allows for homologous recombination between the two genes , leading to chimerization and gene loss . TbAQP2 has a unique selectivity filter with unusual NSA/NPS motifs instead of the characteristic NPA/NPA that occur in the vast majority of MIP family members [43] including TbAQP1 and TbAQP3 [24] . The published , pentamidine-resistant T . b . brucei lab mutant possessed a TbAQP2/3 chimera whose C-terminal filter triplet was from TbAQP3 , suggesting that the unusual NPS triplet may be involved in pentamidine transport . However , the presently described pentamidine-resistant T . b . gambiense isolates carry a TbAQP2/3 chimera encoding a predicted protein with both selectivity filter triplets from TbAQP2 . We hypothesize that the TbAQP2/3 chimera observed in the T . b . gambiense isolates fails to contribute to pentamidine and melarsoprol susceptibility despite having the proposed selectivity filter residues of TbAQP2 . Functional expression of the chimeric gene in tbaqp2 null cells will be necessary to test this hypothesis . The occurrence of rearrangements at the TbAQP2/TbAQP3 locus correlated with reduced susceptibility to pentamidine and , to a lesser extent , melarsoprol . Thus field isolates also exhibit the well known cross-resistance between melarsoprol and pentamidine 15 , 16 , 31 , while no cross-resistance was observed to diminazene aceturate . This is in agreement with TbAT1 being the primary uptake route for diminazene [44] , [45] and consistent with results obtained using TbAQP2−/− cells , which showed no resistance to the rigid diamidines diminazene or DB75 [24] , as opposed to pentamidine which has a highly flexible structure . It is also noteworthy that T . b . rhodesiense STIB 871 and T . b . brucei STIB 940 are susceptible to melarsoprol and pentamidine in vitro although both carry the TbAT1r allele . Loss of TbAT1 function has been described without mutations in the open reading frame of the gene [32] . However , since in the present study all isolates with a ‘wild-type’ TbAT1 ORF were fully susceptible to diminazene , we conclude that they possess a functional TbAT1 ( i . e . P2 ) transporter . Trypanosoma congolense and T . vivax appear to lack an AT1 orthologue [46] , therefore diminazene transport and resistance must have a different mechanism in these livestock parasites . The plasma levels of pentamidine in treated patients peak about 1 hour after injection and vary extensively from 0 . 42 µM to 13 µM , while the mean elimination half-life after multiple applications is approximately 12 days [47] . Thus , since pentamidine is very potent , even a 50-fold increase in IC50 of pentamidine as observed here for the T . b . gambiense isolates with mutations in TbAQP2 , is unlikely to jeopardize the success of treatment . With melarsoprol , however , the obtainable drug levels are more critical . Only 1–2% of the maximal plasma levels are seen in the CSF [48] , and a 5-fold reduced sensitivity to melarsoprol might allow trypanosomes to survive in the CSF during melarsoprol therapy . Thus mutations in TbAQP2 might indeed be responsible for melarsoprol treatment failures with T . b . gambiense . However , two of the T . b . gambiense isolates from relapse patients ( DAL 870R and DAL 898 R ) were sensitive to melarsoprol and pentamidine , and they possessed wild-type copies of TbAT1 and TbAQP2 , indicating that factors other than drug resistance can contribute to treatment failures . Larger sample sizes will be required to test the significance of TbAQP2 for successful treatment . We show here for the first time that a TbAQP2/3 chimera as well as loss of TbAQP2 occurs in T . b . gambiense clinical isolates , and that the presence of such rearrangements at the TbAQP2/TbAQP3 locus is accompanied by a 40- to 50-fold loss in pentamidine sensitivity and a 3- to 5-fold loss in melarsoprol sensitivity . We recommend genotyping of the TbAQP2/TbAQP3 locus to be integrated into larger field trials such as clinical studies with drug candidates .
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Human African Trypanosomiasis , or sleeping sickness , is a fatal disease restricted to sub-Saharan Africa , caused by Trypanosoma brucei gambiense and T . b . rhodesiense . The treatment relies on chemotherapy exclusively . Drug resistance in T . brucei was investigated mainly in laboratory-selected lines and found to be linked to mutations in transporters . The adenosine transporter TbAT1 and the aquaglyceroporin TbAQP2 have been implicated in sensitivity to melarsoprol and pentamidine . Mutations in these transporters rendered trypanosomes less susceptible to either drug . Here we analyze T . brucei isolates from the field , focusing on isolates from patients where melarsoprol treatment has failed . We genotype those isolates to test for mutations in TbAQP2 or TbAT1 , and phenotype for sensitivity to pentamidine and melarsoprol . Six T . b . gambiense isolates were found to carry mutations in TbAQP2 . These isolates stemmed from relapse patients and exhibited significantly reduced sensitivity to pentamidine and melarsoprol as determined in cell culture . These findings indicate that mutations in TbAQP2 are present in the field , correlate with loss of sensitivity to pentamidine and melarsoprol , and might be responsible for melarsoprol treatment failures .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
Aquaporin 2 Mutations in Trypanosoma brucei gambiense Field Isolates Correlate with Decreased Susceptibility to Pentamidine and Melarsoprol
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Tsetse flies transmit trypanosomes that cause nagana in cattle , and sleeping sickness in humans . Therefore , optimising visual baits to control tsetse is an important priority . Tsetse are intercepted at visual baits due to their initial attraction to the bait , and their subsequent contact with it due to landing or accidental collision . Attraction is proposed to be driven in part by a chromatic mechanism to which a UV-blue photoreceptor contributes positively , and a UV and a green photoreceptor contribute negatively . Landing responses are elicited by stimuli with low luminance , but many studies also find apparently strong landing responses when stimuli have high UV reflectivity , which would imply that UV wavelengths contribute negatively to attraction at a distance , but positively to landing responses at close range . The strength of landing responses is often judged using the number of tsetse sampled at a cloth panel expressed as a proportion of the combined catch of the cloth panel and a flanking net that samples circling flies . I modelled these data from two previously published field studies , using calculated fly photoreceptor excitations as predictors . I found that the proportion of tsetse caught on the cloth panel increased with an index representing the chromatic mechanism driving attraction , as would be expected if the same mechanism underlay both long- and close-range attraction . However , the proportion of tsetse caught on the cloth panel also increased with excitation of the UV-sensitive R7p photoreceptor , in an apparently separate but interacting behavioural mechanism . This R7p-driven effect resembles the fly open-space response which is believed to underlie their dispersal towards areas of open sky . As such , the proportion of tsetse that contact a cloth panel likely reflects a combination of deliberate landings by potentially host-seeking tsetse , and accidental collisions by those seeking to disperse , with a separate visual mechanism underlying each behaviour .
Tsetse flies ( Glossina spp . ) occur in sub-Saharan Africa and transmit the trypanosomes that cause nagana in cattle , and sleeping sickness ( human African trypanosomiasis , HAT ) in humans [1] . Riverine tsetse ( Palpalis species group ) are responsible for most cases of HAT [2] . In contrast to savannah tsetse ( Morsitans species group ) which respond strongly to odour cues , riverine flies characteristically respond weakly [3] . Effective odour cues for attracting riverine tsetse may yet be identified [4] , but at present odourless , insecticide-treated cloth panels are advocated for the cost-effective control of these flies [2 , 5 , 6] . Understanding the visually-guided behaviours that draw tsetse to such baits can contribute to current efforts to optimise the cost and efficiency of control operations , and one factor that has received much attention is the role of colour [7 , 8 , 9 , 10] . Studies to understand tsetse attraction to baits have often employed grids of electrocuting wires which can enclose simple panels of coloured cloth bait material ( e-cloths ) , or of fine net ( e-nets ) . E-cloths sample tsetse that land on the cloth bait , whilst e-nets are difficult for tsetse to detect and sample those flies that accidentally collide with them [11 , 12 , 13] . This allows tsetse to be sampled not only when they contact a particular bait but also when circling nearby , allowing sophisticated investigation of their behaviour ( e . g . [14] ) . As a result , it is recognised that tsetse are intercepted at baits as a function both of their initial attraction to approach the bait from a distance , and their propensity to land on the bait ( or enter a trap ) once close ( e . g . [15] ) . A variety of interacting olfactory and visual cues can contribute to these behavioural processes ( for reviews , [16 , 17] ) , but among them reflected light wavelength cues are both important , and relevant to the optimisation of the visual baits currently advocated for riverine tsetse control . The role of colour cues in enticing tsetse to approach a stationary visual bait is relatively well understood , and the phthalogen blue dye for cotton fabrics produces a particularly attractive colour ( e . g . [9] ) . Field studies monitoring combined tsetse catches at coloured e-cloths and flanking e-nets ( sampling tsetse landing on the coloured cloth , and those circling it ) , have found positive contributions of blue wavelengths , and negative contributions of green/yellow/red and UV wavelengths , to the tsetse catch [8 , 9] . The same trends were also found in studies of tsetse catches in three-dimensional traps of various designs , although these catches would have resulted both from attraction into the vicinity of the traps , and trap entry responses [7 , 8] . The above insights were gained by direct analysis of visual bait reflectance spectra , but it is the responses of photoreceptors to these spectra that guide a fly’s behaviour . Across the majority of ommatidia in the fly compound eye , excluding the male fovea and the polarisation-sensitive dorsal marginal area , there are five classes of photoreceptor with varying spectral sensitivities ( Fig 1 ) [18 , 19 , 20] . Recently , the datasets produced during the above tsetse field studies have been reanalysed using the calculated excitations of fly photoreceptors as predictors of attraction . The result of this reanalysis was that fly photoreceptors R7y ( UV-blue ) and R8p ( blue ) contribute positively , whilst R7p ( shorter wavelength UV ) and R8y ( green ) contribute negatively [10] . Perhaps because photoreceptors R7y and R8p provide somewhat redundant information , the attraction of tsetse to approach a visual bait , and the special attractiveness of phthalogen blue cotton , could be parsimoniously explained by a simple opponent mechanism involving the calculated excitations ( E ) of three of these photoreceptors as follows: +ER7y –ER8y –ER7p [10] . Videographic observations demonstrate that when tsetse alight on a black cloth target , they very rarely do so after having made a direct approach to it . Instead , the initial approach is followed by local circling or alighting on the ground before the fly eventually lands on the target [13] . This accords with data gained using combinations of e-cloth and flanking e-net , where the e-net sample of circling flies often exceeds the e-cloth sample of those that land directly [5 , 6 , 9] . Furthermore , intricate studies using e-nets reveal that only some of the flies attracted to a bait ultimately land at all , the others departing after having circled it [14 , 15] . Since the insecticide-treated cloth panels used for tsetse control can only be effective if tsetse make contact with them , insecticide-treated flanking nets are advocated to intercept and kill circling flies by inducing accidental collisions [5 , 6 , 21] . However , the cues that induce tsetse to alight remain an interesting and little understood area of investigation . Where field studies have employed combinations of e-cloth and flanking e-net , the catch of the e-cloth expressed as a proportion of the combined catch of the e-cloth and e-net ( henceforth , Pcloth ) is used to provide a measurement of tsetse preference for direct landing over circling ( see Fig 2 ) . As such , this measurement is commonly referred to as the ‘landing score’ . Pcloth is positively influenced by a bait’s reflectance of UV wavelengths , or low overall luminance , and the former observation has lead to the assertion that UV wavelengths are important cues for eliciting landing [8 , 15 , 22 , 23] ( but see also [9] ) . Hence , a number of studies have investigated dual-colour baits , incorporating panels of colour that strongly stimulate tsetse to approach , and others that provide the putative landing cues ( e . g . [22 , 24] ) . The idea that landing responses are positively influenced by UV wavelengths appears to be at odds with the negative contribution of these wavelengths to the chromatic mechanism of attraction to the vicinity of the bait [7 , 8 , 9 , 10] . This would imply that a visual cue that is unattractive at long-range is attractive at close-range , and that entirely different behavioural mechanisms underlie visual attraction , broadly defined , at these different ranges . In this study I aim to shed light on this apparently paradoxical aspect of tsetse behaviour by providing a mechanistic explanation for Pcloth measurements based upon calculated excitation values for fly photoreceptors ( c . f . [25 , 26 , 27 , 28] ) .
The distribution of tsetse catches between e-cloth and flanking e-net was analysed for two field datasets [8 , 9] , out of the four recently analysed to determine a photoreceptor-based model of tsetse attraction [10] . These datasets were selected because they were obtained using simple , two-dimensional e-cloths of various colours , with adjacent two-dimensional e-nets , both oriented vertically ( for a simplified schematic representation , see Fig 2 ) . Data for catches of G . fuscipes fuscipes at small e-cloths ( 0 . 25 m x 0 . 25 m ) with equal-sized flanking e-nets were obtained from [9] . In total 37 cotton or polyester e-cloths of different colours were tested in 15 separate experiments . Each experiment investigated tsetse catches at five differently coloured e-cloths , one of which was always a phthalogen blue-dyed cotton standard . Phthalogen blue is often reported to be extremely attractive to tsetse , but the dye can only be applied to cotton fabrics [9] . The original study reported the proportion of the combined catch taken from the e-cloth ( there termed the landing score ) , and absolute numbers of flies in the combined catch , for each e-cloth in each experiment . The number of landing flies was calculated from these data , rounding to the nearest whole number . Data for catches of G . palpalis palpalis at large e-cloths ( 1 . 0 m x 1 . 0 m ) with flanking e-nets ( 0 . 5 m x 1 . 0 m ) were obtained from [8] . In total , 27 e-cloths of different colours were tested in 10 separate experiments . Where the type of fabric comprising the e-cloths was stated , it was reported to be cotton [8] . Each experiment investigated tsetse catches at four differently coloured e-cloths , one of which was always a phthalogen blue standard . The original study reported the percentage of the combined catch taken from the cloth panel ( there termed the landing score ) for each e-cloth in each experiment , although the absolute numbers of tsetse in the combined catch was stated only for the phthalogen blue standards . Fly photoreceptor excitation values elicited by each coloured e-cloth in the above tsetse field studies were calculated during a previous study [10] . That study made freely available in its supplementary materials the calculated excitation values and the materials required to calculate them , and completely described the calculation procedure ( dx . doi . org/10 . 1371/journal . pntd . 0003360 ) [10] . A brief recap of those methods is provided here for convenience . Methods with which to calculate photoreceptor excitation from spectra of illumination , stimulus reflectance , background reflectance , and photoreceptor sensitivity are now well established and widely employed ( e . g . [25 , 29] ) . For each fly photoreceptor type the effective quantum catch ( P ) of reflected light from a given e-cloth was calculated according to: P=R∫310600IS ( λ ) S ( λ ) D ( λ ) dλ Where IS ( λ ) is the spectral reflectance function for the e-cloth; S ( λ ) is the spectral sensitivity function of the photoreceptor in question; and D ( λ ) is the illuminant function . R is the range sensitivity factor which adjusts photoreceptor sensitivity such that background stimulation would elicit a half maximal response in each receptor class , and was calculated by: R=1/∫310600IB ( λ ) S ( λ ) D ( λ ) dλ Where IB ( λ ) is the spectral reflectance function of the assumed background . Quantum catches were non-linearised to represent the transduction process in each photoreceptor , providing excitation ( E ) by: E=P/ ( P+1 ) Calculated photoreceptor excitations have values between 0 . 0 and 1 . 0 , and through the above procedures the adapting background elicits a half-maximal response of 0 . 5 units in each photoreceptor [10 , 29] . The reflectance spectrum of a typical green leaf was used as the background reflectance spectrum , and the illuminant used was the D65 standard expressed as relative quanta ( these are provided in S5 table ) . Both functions were obtained from [29] , and were linearly interpolated to achieve 2 nm wavelength resolution . E-cloth reflectance spectra were obtained from the supplementary materials of [9] , and linearly interpolated for 2 nm wavelength resolution , or extracted from figures in [8] using Datathief software [30] ( the latter are provided in S5 table , whilst the former are freely available online at dx . doi . org/10 . 1371/journal . pntd . 0001661 ) . Photoreceptor sensitivity functions were those typical of Musca and Calliphora extracted from [18] using Datathief ( see Fig 1 ) . Although sensitivity functions have been recorded for G . morsitans morsitans , the flies used lacked carotenoid screening pigments due to dietary deficiency [19] . Carotenoid pigments were , however , extracted from the retinae of G . p . palpalis raised on a different diet [19] . The extent of visual screening in wild tsetse is thus unknown and would presumably vary with diet , but the underlying organisation of photoreceptors in tsetse aligns with that for Musca and Calliphora [18 , 19 , 31] . The approach taken in this study was to seek statistical explanations for landing scores based upon individual photoreceptor excitation values , and/or indices representing the combined responses of two or more photoreceptor types ( c . f . [25 , 26 , 27 , 28] ) . One such combination was an opponent index representing the chromatic mechanism proposed to underlie attraction , calculated as follows: + ER7y –ER8y –ER7p [10] . This index was previously shown to predict combined e-cloth plus e-net catches in the G . f . fuscipes and G . p . palpalis datasets analysed here [10] . In order to aid in data interpretation , the opponent index was also calculated for leaves in the adapting background ( + 0 . 5–0 . 5–0 . 5 = -0 . 5 ) . The two tsetse catch datasets each included a number of separate experiments in which sub-sets of e-cloths were compared , and these were often clustered around similar values of the opponent index . Therefore , I used Generalized Estimating Equations ( GEEs ) to try to model the clustering of data within experiments [32 , 33] , without including ‘experiment’ as a factor in the analysis because this might have masked the overall relationship with opponent index or other predictors . The original experiments used latin squares designs to block out variation due to bait location and day , but the experiments themselves were separated in time . Thus , it was reasonable to expect that tsetse catches within each experiment would be related , but no particular structure was expected to the relatedness within experiment . As such , an exchangeable working correlation matrix was appropriate . Because Pcloth is calculated from a known total number of flies in each combined catch , it is appropriate to analyse these measurements using a binary logistic model which correctly models the variance of such proportions [34] . This was possible for the G . f . fuscipes dataset where the total numbers of flies in each combined catch were directly reported . For these data a binomial distribution—logit link GEE model was employed . However , in the G . p . palpalis dataset absolute combined catches were reported only for the phthalogen blue cloth , with percentage catches for each of the other cloths within an experiment . The stated percentage catches were often not achievable by dividing any absolute catch integer value by that stated for the standard , presumably because the percentage catches were calculated from detransformed means as in other previous studies [9] . Hence , the numbers of flies in each combined catch could not be determined with certainty , and I decided instead to analyse Pcloth values directly , after logit transformation [34] , using a normal distribution—identity link GEE model . Such approaches incorrectly assume equal variances across measured proportions , which can reduce their statistical power to detect differences [34] . Nevertheless , the distribution of the residuals from the normal—identity GEE analyses reported in the main text did not differ markedly from a normal distribution ( as determined by Kolmogorov-Smirnov tests and visualisation of Q-Q plots ) , or demonstrate a strongly marked pattern when plotted against values for the linear predictor . The goodness of fit of GEE models was assessed using the quasi-likelihood under independence model criterion ( QIC ) , and a version of this statistic that corrects for model complexity and small sample size ( QICC ) [35 , 36] . QIC is a modification to Akaike’s information criterion ( AIC ) for use with GEE models [35] , and lower values for such criteria indicate improved fit to the data . With respect to AIC , models within 2 units of the best model are sometimes considered to be competitive [37] . All analyses were conducted using SPSS version 22 . 0 ( IBM Corp . , Armonk NY , USA ) .
The chromatic mechanism proposed to underlie tsetse attraction can be approximated by a simple opponent index , and the combined catch of an e-cloth and flanking e-net was previously shown to have a positive relationship with this index ( see Fig 7 of [10] ) . Fig 3 shows the relationship between this same opponent index and Pcloth ( the proportion of the combined catch that was caught on the e-cloth ) , which represents the propensity of tsetse to directly contact the cloth panel in preference to first , or only , circling around it ( see Fig 2 ) . In contrast to combined catches , Pcloth did not have a simple , positive relationship with opponent index . GEE models containing a quadratic term had lower QIC and QICC versus simpler linear models for all datasets ( Table 1; Fig 3 ) . However , whilst the fit of the quadratic model was substantially better than that of the linear model for G . f . fuscipes , the two models were competitive for G . p . palpalis ( for which the linear model described a negative relationship between Pcloth and opponent index ) . The green vertical line in each panel of Fig 3 shows the opponent index value calculated for leaves in the adapting background , to which each photoreceptor responds with a half-maximal response of 0 . 5 units of excitation [10 , 29] . The fitted quadratic relationships suggest that Pcloth tended to increase with opponent index for visual baits that were more attractive than their background , although this trend was much more marked for G . f . fuscipes than for G . p . palpalis . Such a trend might be expected if the mechanism implicated in initial attraction also underlay landing responses ( Fig 3 , to the right of the green lines ) . However , inconsistent with this explanation , the fitted quadratic relationships also tended to increase as visual baits became increasingly less attractive than their background , although in this respect the trend was more marked for G . p . palpalis than G . f . fuscipes ( Fig 3 , to the left of the green lines ) . These quadratic relationships between Pcloth and opponent index were not considered to be biologically meaningful in themselves , but were hypothesised to be evidence that a second behavioural mechanism interacts with opponent index in determining tsetse catch distribution . I next conducted GEE analyses to model Pcloth based upon the opponent index describing visual attraction , excitation values of photoreceptors that may drive a second behavioural mechanism , and the interaction between these two mechanisms ( Tables 2 and 3 ) . With the exception of the model containing photoreceptor R8p excitation for the female G . f . fuscipes dataset , all of these models resulted in reductions in QIC and QICC over the linear relationships with opponent index alone presented in table 1 . Of these models , that which used the shorter wavelength UV photoreceptor R7p’s response consistently fitted each dataset better than models using excitation values for any other photoreceptor type , and in the R7p models the effects of all predictors were significant ( Tables 2 and 3; Fig 4 ) . Judged by differences in QIC >2 , no other model was deemed competitive with the R7p model , although for the G . p . palpalis dataset QICC differences <2 provided some support for the alternative models other than that using R8p excitation . Removing the interaction term from any R7p model reduced its fit to the data . Elaborating any R7p model with an additional photoreceptor excitation value and its interaction term also reduced its fit to the data ( S1 and S2 tables ) . To further support the adequacy of the opponent index/R7p model , I also computed sums of , and differences between , the excitation values of every possible combination of photoreceptor pairs and used these in GEE models that also contained opponent index and an interaction term ( S3 table ) . Alongside opponent index , summed excitation values of photoreceptor pairs ( representing an additional achromatic mechanism ) generally fitted the data better than computed differences between the excitation values of photoreceptor pairs ( representing an additional chromatic mechanism ) . In the G . f . fuscipes datasets , the model including summed R7p and R7y excitations alongside opponent index was the only one for which there was a substantial improvement in QIC or QICC over the opponent index/R7p model , but this was evident only for males and not for females ( S3 table ) . In the G . p . palpalis datasets , many summed photoreceptor models provided largely equivalent QIC or QICC values ( i . e . within 2 units ) to the opponent index/R7p model , but among these reductions were only evident in QICC , and only for models in which R7p excitation was part of the photoreceptor sum ( S3 table ) . In order to rule out the potentially simpler possibility that Pcloth might result entirely from a single achromatic or chromatic mechanism , I examined GEE models containing every possible combination of between one and five photoreceptor types to predict Pcloth ( S4 table ) . These models fitted the data substantially less well than the above models with two interacting mechanisms , indicating that they did not provide a better explanation for tsetse behaviour . Thus , overall , these analyses support the assertion that Pcloth can be predicted by the colour opponent model that was proposed to underlie initial attraction , and an additional , interacting achromatic mechanism reliant on excitation from photoreceptor R7p ( Fig 4 ) . However , the additional contribution of other photoreceptors to that achromatic mechanism should not be ruled out .
In this study I reanalysed tsetse catch distribution across coloured e-cloths and flanking e-nets based upon a chromatic mechanism recently proposed to explain tsetse attraction to approach visual baits . I found that Pcloth increased as cloth panels became more attractive by an index describing this mechanism , as expected if the same chromatic mechanism of attraction underlay both the approach to a bait , and subsequent landing upon it . However , I also found that Pcloth increased as excitation of the UV-sensitive photoreceptor R7p increased , indicating that tsetse are also driven to contact cloth panels as a result of a separate but interacting achromatic mechanism . It seems intuitive that tsetse should more readily alight upon cloth panels that are more attractive by the chromatic mechanism implicated in their initial attraction to approach them . However , the involvement of a second , achromatic mechanism in causing tsetse to directly contact such cloth panels is less easy to explain . Flies are well-known to display an innate attraction to UV light and in Drosophila the R7 photoreceptors are important in driving this response [38 , 39] . This behaviour is often called the ‘open space response’ , and is presumed to guide flies towards areas of open sky . This is because the sky is strongly radiant in UV wavelengths , whilst many features of the terrestrial environment are characterised by strong UV absorption ( e . g . see [40] ) . Earlier tsetse work has already suggested that UV wavelengths may functionally represent skylight , causing highly UV-reflective cloth panels to elicit high Pcloth values not by eliciting landing responses , but as a result of accidental collisions by tsetse attempting to disperse [15 , 23] . The R7p-driven achromatic mechanism suggested by my analysis appears well aligned with these explanations , which would suggest that tsetse catch distributions are affected by two distinct behavioural motivations . In further support of this idea , G . tachinoides caught on e-cloths tended to have lower fat content than those caught on flanking e-nets , which was interpreted as an indication that the relatively more starved flies were more prone to land directly in preference to circling , due to their requirement to be less discriminating in host seeking [24] . In the same study , female flies caught over the UV-reflective white portion of a half-blue , half-white e-cloth had higher fat content than those caught over the blue portion , and their fat content was equivalent to that of flies caught at flanking e-nets of other target designs in the same experiment [24] . This trend was , however , not evident for males . Nevertheless , since highly UV-reflective baits are unattractive to host-seeking tsetse [7 , 8 , 9 , 10] , the fact that better-nourished and potentially more discriminating female flies tended to make contact with them [24] , would be consistent with the explanation that these flies were attempting to disperse rather than land on a perceived host . However , detailed observations of tsetse behaviour prior to interception on UV- and non-UV-reflective cloth panels , as have been made of tsetse behaviour prior to alighting on black panels [13] , will be required to directly test this hypothesis and provide persuasive evidence for the above explanation . A UV effect on Pcloth was not evident in the authors’ original analysis of the G . f . fuscipes dataset [9] , and in this reanalysis the analogous R7p effect was notably weaker than that seen for G . p . palpalis . A plausible explanation for this difference between datasets is the different size of the e-cloths in the two studies: those in the G . f . fuscipes study were 1/16th the size of those in the G . p . palpalis study . Alighting responses of savannah tsetse increase with the size of blue or black targets [41 , 42] , whilst the alighting responses of riverine species are relatively little affected by changes in the size of such a target [5] . A potential explanation for this is the effect of habitat geometry on tsetse movement and expression of host-seeking behaviour [43] . However , if some of the tsetse intercepted by UV-reflecting baits are in fact attempting to orient towards perceived open spaces rather than alighting on perceived hosts , it is plausible that the larger area of those open spaces enhanced this separate behavioural response , resulting in the difference between the datasets . However , a number of other explanatory factors cannot be ruled out . The UV effect was clearly evident in a study of the riverine tsetse G . p . palpalis [8] , but only for a sub-set of UV-reflective baits which also allowed some light to pass through them in a study of the savannah tsetse G . pallidipes [23] . It is certainly possible that species differences in behaviour explain such discrepancies , but it must also be borne in mind that the highly UV-reflective baits that elicit high Pcloth values also tend to attract the lowest combined catches , resulting in greater error around Pcloth measurements for such baits . This factor has special relevance to the current analysis , since the binomial—logit GEE model applied to G . f . fuscipes data correctly modelled the variance of Pcloth measurements , whilst this was not true of the normal—linear GEE model that was applied to logit-transformed G . p . palpalis Pcloth values for reasons of data availability . For this reason , some caution should be exercised in evaluating the trends for G . p . palpalis , although trends in that dataset were strongly evident , and substituting binary logistic models for linear ones might be expected to reduce statistical power [34] . An additional factor that might cause variation in the UV effect on Pcloth is the specific positioning of a visual bait . This might lead to variability in the effect of colour cues on attraction and landing as a result of variation in the background they are viewed against , or their spectrum of illumination . In possible support of this general notion , a study of G . tachinoides in Cote d’Ivoire found significant differences in attraction to blue , violet , red , and black e-cloths between replicates conducted in gallery forest , and those conducted on more open riverbank habitat [24] . Furthermore , Pcloth was significantly higher for males in the riverbank replicate . However , whilst this supports the general notion that bait positioning may be an important factor affecting visual cues and behavioural responses to them , the same study provided no evidence that such factors might affect the UV effect on Pcloth specifically . This was because there were no apparent differences between replicates of an experiment incorporating high- and low-UV reflectance white baits in the same two habitats , and the UV effect on Pcloth was only evident for females in the combined data from both replicates [24] . An additional way in which bait positioning may affect Pcloth is via active avoidance of e-nets , which has been shown to be greater in shade than full sun [13] . Active avoidance of the e-net would cause an increase in Pcloth , as a result of a reduction in combined catch . Finally , other cues which were not quantified in the original field studies may also influence landing responses . For example , polarotaxis has been implicated in attraction and landing of tabanid flies on potential hosts and artificial baits [44 , 45 , 46] , but the visual baits analysed in this study were not quantified with respect to reflected polarised light . Alongside the R7p-driven achromatic mechanism , this study also provides evidence that the chromatic mechanism guiding tsetse attraction towards a visual bait might also encourage them to land upon it . This suggests that the same mechanism underlies attraction at both long- and close-range . By comparison with findings for plant-seeking insects ( e . g . [28] ) it was argued that blue-green ( R7y-R8y ) opponency provides a means to distinguish vegetation from other objects , such as potential vertebrate hosts [10] , which aligns with previous explanations for the blue preference of tsetse [16] . The additional , negative input of photoreceptor R7p improved the fit to the data , and was thus implicated in the opponent mechanism underlying attraction [10] . Given the above interpretation of the functional role of R7p and UV wavelengths , this input may function to distinguish patches of open sky from vegetation and potential hosts . However , in light of the analyses presented in this study , it might be debated whether R7p’s effect on attraction comes about as a result of its input to the proposed chromatic mechanism , or solely via the interaction of the achromatic mechanism suggested here . Low luminance black fabrics are also well known to elicit strong tsetse landing responses ( e . g . [15 , 42] ) . Such fabrics are characterised by low reflectance at all wavelengths , including the UV , so these landing responses cannot be explained by the R7p-driven achromatic mechanism suggested here . The opponent index used to describe attraction in this analysis simply subtracts the excitation of photoreceptors R7p and R8y from that of R7y , and as a result the value is negative for all stimuli in this analysis with those closest to zero the most attractive . Because black fabrics have uniformly low luminance , they elicit low excitation values in all photoreceptors , and as a result of that also have opponent indices that are relatively close to zero and , therefore , are predicted to be attractive [10] . With the important caveat that neural computations in a fly’s brain will differ to a greater or lesser extent from their simplified representation here , the ability of black fabrics to elicit tsetse landing responses is compatible with the scheme described in this analysis . However , other explanations must not be ruled out , such as a separate role for low luminance in attraction , or the involvement of polarotaxis for which dark surfaces are particularly effective in providing polarised light cues [20 , 46] . Studies of a range of Glossina species have reported decreased catches using blue/black combination e-cloths , when the cloth panels inside the electrocuting grids were covered by an adhesive sheet that absorbed UV wavelengths [47 , 48 , 49] . This resulted from decreased tsetse catch over the black portion of the cloth panel only . In these studies the UV reflectance of the black cloth was low , meaning that this result is unlikely to be explained by an effect of the UV manipulation on the R7p mechanism described in the current analysis . Since the adhesive film absorbed wavelengths below 400 nm [49] , it would have affected not only the repellent R7p response ( shorter wavelength UV ) , but also the attractive R7y response ( UV-blue ) , and may thus have had complex effects on the mechanism of attraction . It is also possible that the adhesive sheet affected other visual cues , such as the polarisation of reflected light [44 , 45 , 46] . Intercepting circling tsetse has great potential to augment catches since the majority of the tsetse attracted into the vicinity of a bait circle around it rather than landing [6 , 9 , 14 , 15] . This has motivated the use of insecticide-treated flanking nets to intercept circling flies , and these are important additions to the small cloth panels currently advocated for riverine tsetse control , where their small size and the use of modern netting materials make them robust [5 , 6] . By contrast , larger visual baits are employed for savannah tsetse , and large flanking nets to accompany these have sometimes been suggested to be damage prone [5 , 6] . However , although flanking net damage did reduce a bait’s efficacy in field trials , replacement rates were higher for net than cloth portions but low in both cases ( 0 . 2 versus 0 . 1% monthly replacement rate , respectively ) [50] . Furthermore , savannah tsetse landing responses increase with bait size [41 , 42 , 51] , such that large cloths can function just as efficiently as cloth and flanking net combinations of the same size [42] . Therefore , although some riverine tsetse may mistake highly UV-reflective cloths for patches of open sky , even if this finding were transferable to savannah tsetse it is unlikely to mean that UV-reflective cloths can provide a useful substitute for the flanking net . Nevertheless , the suggestion that UV-reflecting cloths likely catch tsetse attempting to disperse rather than host-seek does have implications for visual bait optimisation . Short wavelength excitation of photoreceptor R7p was previously shown to contribute negatively to the chromatic mechanism of attraction [10] , and in the current analysis strong excitation of R7p was implicated as interacting with that mechanism . As such , the attractiveness of visual baits is likely best enhanced by reducing UV reflectance . The currently preferred phthalogen blue dye has these properties , but can only be applied to cotton material ( e . g . [9] ) . Modern polyester fabrics offer a number of advantages in terms of cost and robustness , but the blues currently produced for tsetse control have broader reflectance peaks than phthalogen blue that extend into the UV ( e . g . see reflectance spectra for blues 7 and 8 in [9] ) . Curtailing reflectance at low wavelengths and enhancing it in the attractive region using fluorescent dyes , as has been suggested previously [8] , may be the key to optimising these fabrics . In addition , the use of stand-alone insecticide-treated , UV-reflective cloth panels without flanking nets might potentially provide a useful complement to the standard baits , if they do indeed attract a different sub-set of the tsetse population .
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Tsetse flies transmit trypanosomes that cause sleeping sickness . Visual baits to attract and kill tsetse are an important method of vector control , and the rational improvement of these baits depends on a mechanistic understanding of tsetse behaviour . Visual baits are often panels of insecticide-treated cloth which tsetse must contact to become dosed with insecticide . However , most of the tsetse that are attracted to approach visual baits circle them rather than landing . Colour is one factor that might be important in eliciting landing responses , and thus bait optimisation . Visually-driven tsetse behaviour can be understood by investigating how a fly’s five types of photoreceptor respond to differently coloured baits , and determining how each of these photoreceptors contributes to behaviour . I applied this approach to data recorded in two previous field studies . I found that tsetse contacted visual baits due to two behavioural mechanisms: a comparison between the responses of several photoreceptors that underlies attraction and landing , and a UV photoreceptor-driven mechanism that likely drives dispersal towards open sky and causes tsetse to collide with visual baits accidentally . If the mechanistic basis of tsetse behaviour is understood , it may be possible to design baits that exploit these mechanisms and optimise tsetse control .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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A Receptor-Based Explanation for Tsetse Fly Catch Distribution between Coloured Cloth Panels and Flanking Nets
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Many genetic and physiological treatments that extend lifespan also confer resistance to a variety of stressors , suggesting that cytoprotective mechanisms underpin the regulation of longevity . It has not been established , however , whether the induction of cytoprotective pathways is essential for lifespan extension or merely correlated . Using a panel of GFP-fused stress response genes , we identified the suites of cytoprotective pathways upregulated by 160 gene inactivations known to increase Caenorhabditis elegans longevity , including the mitochondrial UPR ( hsp-6 , hsp-60 ) , the ER UPR ( hsp-4 ) , ROS response ( sod-3 , gst-4 ) , and xenobiotic detoxification ( gst-4 ) . We then screened for other gene inactivations that disrupt the induction of these responses by xenobiotic or genetic triggers , identifying 29 gene inactivations required for cytoprotective gene expression . If cytoprotective responses contribute directly to lifespan extension , inactivation of these genes would be expected to compromise the extension of lifespan conferred by decreased insulin/IGF-1 signaling , caloric restriction , or the inhibition of mitochondrial function . We find that inactivation of 25 of 29 cytoprotection-regulatory genes shortens the extension of longevity normally induced by decreased insulin/IGF-1 signaling , disruption of mitochondrial function , or caloric restriction , without disrupting normal longevity nearly as dramatically . These data demonstrate that induction of cytoprotective pathways is central to longevity extension and identify a large set of new genetic components of the pathways that detect cellular damage and couple that detection to downstream cytoprotective effectors .
Lifespan can be extended in C . elegans and other organisms by a variety of ostensibly deleterious interventions: disruption of mitochondrial function , disruption of translation , disruption of insulin/IGF-1 signaling , caloric restriction , exposure to xenobiotics and others . The counterintuitive benefits of these stressful stimuli suggest a hormetic mechanism rooted in the beneficial induction of cytoprotective pathways that respond to environmental challenges , such as starvation , heat , or exposure to xenobiotics . These cytoprotective pathways may represent the mechanisms that drive lifespan extension . While the correlation of stress tolerance and longevity is well established , the underlying cytoprotective pathways have not been fully explored . Many of the gene inactivations that extend lifespan encode core , conserved components of cells , such as translation factors or mitochondrial proteins , many of which are the molecular targets of known xenobiotics [1] . Lifespan-extending inactivation of cytochrome C reductase , ATP synthase , F59C6 . 5 in electron transport chain ( ETC ) complex I , or cytochrome C oxidase may induce the same cytoprotective responses as the xenobiotics that target them , which include antimycin , oligomycin , rotenone and sodium azide , respectively . Similarly , a wide variety of xenobiotics disrupt translation , including hygromycin , genetecin and emetine . Disruption of endoplasmic reticulum ( ER ) function also extends longevity and may induce cytoprotective mechanisms effective against ER-targeted xenobiotics such as tunicamycin or thapsigargin . The parity of essential cell components targeted by xenobiotics and those that extend longevity upon inactivation suggests that long-lived animals engage cytoprotective mechanisms that evolved as cellular homeostatic and detoxification responses to xenobiotics and virulence factors produced by other organisms . Cytoprotective responses , including chaperones , antioxidants and pathogen response genes , as well as xenobiotic detoxification mechanisms , can protect extant components of the cell and may contribute to lifespan extension . Genetic studies have identified over 50 mutations that extend the lifespan of C . elegans , and each is resistant to one or more stressors , such as oxidative damage , heat stress or irradiation [2] , [3] . The oxidative stress theory of aging has driven extensive analysis of oxidative damage in particular , and while long-lived animals are resistant to compounds that generate ROS , such as paraquat , identification of underlying mechanisms has proven challenging [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] . Longevity is also correlated with thermotolerance , and expression of the heat shock response gene hsp-16 . 2 predicts longevity in C . elegans [13] , [14] , [15] , [16] , [17] , [18] . In a genetic screen for enhanced thermotolerance , the majority of isolated mutants were long-lived by at least 15% [19] . Other protein folding mechanisms , such as the ER and mitochondrial unfolded protein responses , contribute to longevity as well [20] , [21] , [22] , [23] . Cellular damage may result from the production of toxic metabolic byproducts or exposure to xenobiotics , consistent with the extension of lifespan by overexpression of the detoxification transcription factor skn-1 , a gene that is also required for lifespan extension in daf-2 mutants [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] . The potential influence of diverse cytoprotective functions on longevity is underscored by the heat , ROS and toxin resistance of long-lived animals . Consistent with the stress-tolerant phenotypes of long-lived animals , cytoprotective mechanisms are activated in long-lived mutants . Disruption of insulin/IGF-1 signaling induces heat shock ( hsp-16 . 49 , hsp-16 . 11 , hsp-16 . 1 , hsp-16 . 2 and hsp-12 . 6 ) , antioxidant ( ctl-1 , ctl-2 and sod-3 ) , and pathogen response ( lys-7 , lys-8 and spp-1 ) genes [32] . The long-lived mitochondrial mutants isp-1 , clk-1 and cyc-1 induce the mitochondrial unfolded protein response [33] . Inactivation of the translation initiation factor ifg-1 induces the transcription of 51 stress responsive genes , including daf-16 and skn-1 [34] . In each of these long-lived mutants , evidence suggests concurrent induction of detoxification mechanisms . The detoxification of xenobiotics in many systems , including C . elegans , involves the upregulation of cytochrome P450's ( CYPs ) , UDP-glucuronosyltransferases ( UGTs ) , and glutathione S-transferases ( GSTs ) . Transcriptional profiling of the long-lived daf-2 insulin/IGF-1 signaling mutant reveals daf-16-dependent upregulation of these functions [32] , [35] . A xenobiotic response is similarly induced in long-lived mitochondrial mutants and lifespan extension by disruption of translation requires skn-1 , which participates in xenobiotic stress tolerance [36] , [37] . While the response to xenobiotics is , in part , the upregulation of detoxification , other cytoprotective mechanisms , such as chaperones , mitigate cellular damage; detoxification and cytoprotection may both be components of a xenobiotic response apparatus mobilized by various aging interventions . Mechanistic evidence supports the causality of cytoprotective gene activation in lifespan extension . Loss of chaperone expression through inactivation of hsf-1 , the transcriptional regulator of the heat shock response genes , abrogates lifespan extension in a daf-2 mutant , while overexpression extends the lifespan of wild-type animals [38] . The ER unfolded protein response ( UPR ) underlies lifespan extension in daf-2 mutants and in response to caloric restriction [22] , [39] . The mitochondrial UPR is required for lifespan extension in the mitochondrial mutants isp-1 and clk-1 [21] . Lifespan regulatory factors , including daf-2 , hif-1 , skn-1 and hsf-1 are required for pathogen defense , further suggesting that these pathways coordinate critical elements of cytoprotection [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] . These findings highlight the potential contributions of a range of cytoprotective pathways to lifespan extension , but a systematic genetic analysis of the regulation of cytoprotective mechanisms in diverse models of lifespan extension has not been conducted . We tested the hypothesis that the regulation of cytoprotective gene expression in long-lived animals underlies lifespan extension across mechanistically diverse models of this phenotype . We utilized xenobiotic and genetic stimuli in an RNAi screen to identify regulatory genes required for the appropriate induction of hsp-6 , hsp-4 , gst-4 and sod-3 in long-lived animals . We directly addressed the activity of these cytoprotection regulatory genes in lifespan extension by inactivation and subsequent lifespan analysis in three functionally diverse long-lived mutants , isp-1 , eat-2 and daf-2 ( overview in Figure S1 ) . We find that these cytoprotective regulatory genes are critical to lifespan extension in all three longevity backgrounds . These results provide mechanistic support for the hypothesis that lifespan extension occurs through the activation of cytoprotective pathways triggered by xenobiotic or genetic means .
Long-lived mutants express cytoprotective genes at elevated levels . To identify the cytoprotective pathways induced by the diverse conditions that confer lifespan extension , we analyzed the induction of 13 stress-responsive GFP fusion genes functioning in the response to heat , ER stress , mitochondrial stress , oxidative damage , pathogenesis , osmotic stress , xenobiotics , and decreased insulin/IGF-1 signaling ( Table S1 ) by each of 160 gene inactivations found to increase longevity in high-throughput RNAi screens ( Table S2 ) [1] , [20] , [23] , [32] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] . Clustering the expression of phsp-6::gfp ( Mt UPR ) , phsp-60::gfp ( Mt UPR ) , phsp-4::gfp ( ER UPR ) , pgst-4::gfp ( detoxification ) , psod-3::gfp ( ROS ) , pF55G11 . 7::gfp ( pathogenesis ) and pgpdh-1::gfp ( osmotic stress ) generates distinct groups of longevity gene inactivations ( Figure S2 ) . Overall , cytoprotective gene activation is a hallmark of the most potent lifespan extension mechanisms . The average mean lifespan extension amongst the 88 gene inactivations that induce at least one fusion gene ( Figure S2 ) is 27 . 3% , while the 72 gene inactivations that do not activate a single fusion gene exhibit an average extension of 12 . 5% ( t-test p = 7 . 7E−12 ) ( Figure S2 , Table S2 ) [1] , [50] , [51] . To discern how interventions that extend longevity couple to the activation of cytoprotective pathways , we sought to identify the genes required for the activation of hsp-6 ( Mt UPR ) , hsp-4 ( ER UPR ) , sod-3 ( ROS response ) and gst-4 ( detoxification ) by drugs or genetic triggers . Activation of cytoprotective genes requires the capacity to detect the disruption of an essential cell function , such as translation or mitochondrial function , and to generate signals that activate downstream responses . The mechanisms by which these events occur remain largely unknown . We reasoned that components of longevity signaling might emerge from an RNAi screen to identify gene inactivations that disrupt the induction of cytoprotective pathways . Because many gene inactivations that confer longevity extension encode targets of naturally occurring xenobiotics , analysis of toxin response may explore the same cytoprotective pathways activated in long-lived mutant animals . We raised C . elegans to young adulthood before treating the animals with toxins to induce the expression of longevity-correlated cytoprotective genes . Tunicamycin , an antibiotic that disrupts N-linked glycosylation in the ER , was employed for the activation of the ER UPR reporter phsp-4::gfp [20] . Antimycin disrupts complex III of the electron transport chain , activating the mitochondrial UPR reporter phsp-6::gfp . Sodium azide treatment activates the skn-1 target pgst-4::gfp , and the activity of the daf-16 target psod-3::gfp was modulated via a temperature sensitive allele of daf-2 [53] , [58] . We used RNAi to screen for gene inactivations that blocked the expected cytoprotective response to each drug or genetic stimulus . The screen encompassed ∼1500 gene-inactivating RNAi constructs , assayed for inhibition of each of the four cytoprotective responses . This library was composed of 395 kinases and 610 transcription factors , as well as two cherry-picked small RNA and longevity sublibraries . Because small RNA pathways have been implicated in stress responses , we screened 317 gene inactivations that have emerged from screens for defects in microRNA or RNAi functions [59] , [60] . Gene inactivations that abrogate the increase in longevity conferred by low insulin/IGF-1 signaling have also been identified and potentially regulate cytoprotective functions . We therefore included this set of 179 gene inactivations as well [61] . The primary screen identified 73 gene inactivations required for appropriate activation of cytoprotective responses ( Table 1 , Table S3 ) . Known stress response regulatory factors ( ire-1 , skn-1 , daf-16 ) score strongly and each is highly specific to its known function ( Table 1 ) . Quantification of fluorescence intensity for 32 gene inactivations that scored most strongly in the primary optical screen confirmed the role of 29 genes in cytoprotective gene induction ( Table 1 ) . Induction of phsp-16 . 2::gfp following heat shock and the expression of a non-stress-induced fusion gene , psur-5::gfp , were quantified to control for generic transgene silencing phenotypes; none of these gene inactivations were potent transgene silencers ( Table S4 ) . Expression of the chromosomal loci corresponding to the screened fusion genes was analyzed by quantitative PCR to distinguish transgene dysregulation from regulation of the endogenous loci ( Table S5 ) . Results confirmed that the majority of these gene inactivations decouple the chromosomal cytoprotective loci from activation by toxins . While results were largely consistent , measured decreases in fluorescence from the GFP fusion genes were more dramatic than those detected by quantitative PCR for the corresponding genetic loci . The use of multi-copy transgenic constructs may contribute to this observation . Tissue specificity may do so as well , since quantitative PCR averages gene induction over all C . elegans cells while cytoprotective gene induction may be isolated to particular tissues . In addition , the efficacy of RNAi is reduced in neurons and other excitable cells . The 29 regulators of cytoprotection identified are annotated to function in RNA processing ( cpsf-2 , cpsf-4 , cpf-2 ) , protein degradation ( pas-3 , let-70 , ufd-1 , skr-1 , cul-1 ) , deacetylation ( sdc-2 , hda-1 , dcp-66 , lin-40 ) , phosphorylation ( wnk-1 , F18F11 . 5 , let-92 , kin-1 , nekl-2 ) , transcription ( mdt-26 , dpy-22 , elt-2 ) and other activities ( Table 1 ) . Fourteen have been annotated as candidate cofactors for miRNA function , four as cofactors of RNAi and eight as positive regulators of lifespan extension in the long-lived daf-2 mutant [59] , [60] , [61] . Of the eight putative insulin/IGF-1 signaling factors , five were found to potently regulate the transcription of sod-3 downstream of daf-2 [61] . Sixteen gene inactivations that disrupt the coupling of cellular dysfunction to cytoprotective gene activation demonstrate specificity to one of our four tested cytoprotective gene fusions , including the canonical stress response regulatory factors daf-16 , skn-1 , and ire-1 ( Table 1 , Figure 1 , Figure S3 ) . The canonical factors score most strongly amongst these , with the exception of nekl-2 , which regulates the expression of gst-4::gfp 50% more potently than skn-1 . F53F4 . 11 , cpf-2 and dcp-66 are also noteworthy as the most potent previously unidentified pathway-specific gene inactivations , regulating the expression of hsp-4 , hsp-6 and sod-3 fusion genes , respectively . We observe the greatest degree of regulation , however , amongst the 16 regulators of cytoprotective gene expression that regulate 2 or more of the tested cytoprotective pathways . lin-40 gene inactivation disrupts psod-3::gfp induction 17-fold , and pgst-4::gfp 2-fold . let-92 gene inactivation results in the most potent disruption of pgst-4::gfp induction , decreasing expression 25-fold , and inhibiting induction of psod-3::gfp and phsp-6::gfp by 3- and 4-fold , respectively . ima-3 and elt-2 gene inactivations both dramatically decrease induction of phsp-6::gfp by antimycin . ima-3 gene inactivation additionally inhibits the induction of pgst-4::gfp ( 4-fold ) . elt-2 , like let-70 and cpsf-2 , is required for the appropriate regulation of all four tested cytoprotective pathways ( Table 1 , Figure 1 ) . We conclude that the cytoprotective pathways upregulated by conditions that increase longevity are regulated by both distinct and shared genetic components ( Table 1 , Figure S3 ) . We speculate that shared regulatory genes may be upstream of pathway-specific factors , though the complexity of this regulatory network remains unexplored . If the cytoprotective pathways normally induced by conditions that confer increased longevity are essential for that increase , decoupling their induction might shorten the lifespan of long-lived mutants more than that of wild-type animals . To test this hypothesis , we asked whether the 29 gene inactivations that disrupt cytoprotective gene induction also abrogated the increase in lifespan conferred by mitochondrial dysfunction ( isp-1;ctb-1 ) , reduced feeding ( eat-2 ) or disruption of insulin/IGF-1 signaling ( daf-2 ) . Inactivation of an idealized lifespan regulatory gene would reduce the lifespan of a long-lived strain to that of the control strain ( N2 ) without perturbing wild-type lifespan . In these experiments , 12 of 29 tested gene inactivations abrogate 2/3 or more of the lifespan extension observed in eat-2 , isp-1 and/or daf-2 mutants ( Table 2 ) . These gene inactivations shorten wild type lifespan much less dramatically , differentiating these lifespan-regulatory gene inactivations from generalized sickness . While dcp-66 , pas-3 and arf-3 exert their largest suppression of lifespan in isp-1 , inactivation of cpf-2 , wnk-1 and nekl-2 are most potent in the eat-2 mutant ( Table 2 , Figure 2 ) . Of these , however , only dcp-66 , which reduces lifespan extension in a mitochondrial mutant ( isp-1 ) by 87% , does not significantly influence lifespan extension in at least one additional mutant . The remaining 6 gene inactivations , including phi-50 , ima-3 , gob-1 , ufd-1 , let-70 , and elt-2 , are critical to lifespan extension in both the isp-1 and eat-2 mutants ( Table 2 , Figure 2 ) . Two of these , phi-50 and ima-3 , also reduce the lifespan of daf-2 mutants by more than 2/3 ( Table 2 , Figure 2 ) . These phenotypes represent the most potent inhibitions of lifespan extension . Applying a less conservative standard of 15% reduction in lifespan extension , 25 of 29 regulators of cytoprotective gene induction suppress the extension of lifespan in at least one long-lived strain ( Table 2 ) . Cumulatively , we find that genes required for the appropriate transcriptional response to xenobiotic stress and disruption of insulin/IGF-1 signaling contribute to diverse axes of lifespan extension , and in some cases , in particular phi-50 and ima-3 , to all three studied lifespan extension axes ( Table 2 , Figure 2 ) . Many gene inactivations that inhibit lifespan extension also decrease stress tolerance . To reveal the role of cytoprotective response regulatory genes in the tolerance of xenobiotic stress , we inactivated the 29 genes identified in the screen and challenged animals with sublethal ( LD30 ) doses of antimycin , sodium azide , cadmium chloride and paraquat . These toxins parallel the conditions utilized in the screen . Antimycin targets the function of mitochondrial ETC complex III and activates phsp-6::gfp . Sodium azide disrupts the final step of electron transport , blocking energy production and releasing reactive oxygen species , leading to the induction of pgst-4::gfp [58] . Cadmium , like tunicamycin , induces the ER stress response and cadmium tolerance is dependent upon a functional ER UPR [62] . Paraquat survival has been utilized as a measure of ROS tolerance , known to result from the activation of cytoprotective genes downstream of insulin/IGF-1 signaling , such as the superoxide dismutase sod-3 [53] . Inactivation of 16 of 29 genes that disrupt induction of the cytoprotective GFP fusion genes , also disrupted the ability of animals to survive exposure to xenobiotics ( Figure 3 ) . Eleven of the sixteen xenobiotic-sensitive gene inactivations ( phi-50 , wnk-1 , nekl-2 , mdt-26 , let-70 , arf-3 , elt-2 , dpy-22 , let-92 , F18F11 . 5 , and C06A8 . 2 ) enhance sensitivity to the xenobiotic that pairs with the compromised cytoprotective response . The strongest examples of this correlation include phi-50 and nekl-2 ( pgst-4::gfp/sodium azide ) , elt-2 , wnk-1 and mdt-26 ( phsp-4::gfp/cadmium chloride ) , let-92 , elt-2 and mdt-26 ( phsp-6::gfp/antimycin ) and dpy-22 ( psod-3::gfp/sodium azide ) . Of the eleven total gene inactivations that pair in this way , seven are also susceptible to additional xenobiotics , suggesting that the pathways examined serve cytoprotection more extensively than previously predicted . In addition , five gene inactivations ( dcp-66 , pas-3 , kin-1 , cpf-2 and cul-1 ) are sensitive only to xenobiotics that do not directly pair with the observed deficit in cytoprotective gene induction , further demonstrating the complexity of stress responsive gene networks and their protective functions . None of the 29 gene inactivations significantly decreased survival following treatment with drug solvent controls alone ( Figure S4 ) . Cumulatively , phi-50 , ima-3 , elt-2 , nekl-2 , wnk-1 , let-92 , mdt-26 , and let-70 stand out amongst the 29 genes that disrupt cytoprotective response ( Table S6 ) . wnk-1 , phi-50 and elt-2 are severely sensitive to multiple xenobiotic stress conditions , but not control conditions , and modulate lifespan extension in all three tested axes ( insulin/IGF-1 signaling , mitochondrial function and caloric restriction ) . While ima-3 and let-92 do not demonstrate xenobiotic sensitivity under the tested conditions , and let-70 only a subtle sensitivity to sodium azide , they are amongst the most robust suppressors of cytoprotective transcription and suppress lifespan extension in all three long-lived mutants . mdt-26 and nekl-2 are sensitive to all four xenobiotic treatments and suppress lifespan extension in two of the three long-lived mutants tested ( daf-2 and eat-2 or isp-1;ctb-1 and eat-2 , respectively ) . In total , we identify 15 regulators of cytoprotection that are required for tolerance of xenobiotic stress and lifespan extension ( Table S6 ) .
Stress tolerance and lifespan extension are remarkably correlated . The contradictory extension of lifespan by ostensibly deleterious conditions , and the concomitant induction of stress tolerance , suggests that lifespan extension may occur through the hormetic induction of damage-buffering cytoprotective mechanisms . We have identified the cytoprotective pathways that are upregulated by conditions that extend lifespan . In a screen of 160 gene inactivations that increase lifespan , the most potent lifespan extension phenotypes were defined by the induction of suites of cytoprotective genes ( Figure S2 ) [32] , [35] , [63] . To identify upstream regulatory genes in xenobiotic responses , we designed an RNAi screen to detect gene inactivations that disrupt the normal induction of phsp-6::gfp , pgst-4::gfp and phsp-4::gfp by toxins , and the activation of psod-3::gfp by low insulin/IGF-1 signaling . The induction of cytoprotective longevity-modulatory pathways by toxins may be the normal biological context in which these pathways function , having evolved as countermeasures to the xenobiotic and environmental challenges that animals encounter . Because xenobiotics and lifespan extending gene inactivations engage the same cytoprotective , physiological and behavioral responses , xenobiotic responses may be triggered by direct surveillance of cell functions , which would provide broad , adaptive utility in toxin detection [64] , [65] , [66] . We identified 29 gene inactivations that decouple normal transcriptional responses to toxins and environmental stress . While some gene inactivations were specific to one toxic modality , such as mitochondrial dysfunction , others affected multiple , distinct toxin response pathways . The identified genes may act in damage surveillance , signaling , or the transcriptional coordination of cytoprotective responses by acting either within cells and tissues or across tissues by an as-yet-undefined endocrine mechanism . Gene inactivations specific to one toxin may act in dedicated surveillance pathways , while those that affect multiple , distinct responses may identify points of signal convergence . Many of the gene inactivations that disrupt the coupling of cellular dysfunction and the transcription of cytoprotective responses in the screen are annotated phosphorylation or transcription factors . The kinase nekl-2 , which we identify in the regulation of gst-4 expression , is necessary for the nuclear localization of skn-1 following oxidative stress [67] . The kinase wnk-1 , which we identify as a regulator of the ER stress response , has previously been placed upstream of effector genes in the osmotic stress response [68] . let-92 , the catalytic subunit of protein phosphatase 2A , stands out as a potent regulator of gst-4 expression , suggesting it may play a critical role in the regulation of skn-1 activity . The transcription factor elt-2 is expressed exclusively in the intestine , a critical tissue in xenobiotic detection and detoxification in C . elegans . Targets of elt-2 include osmoprotective and innate immune responses , detoxification and oxidative defenses and metal detoxification , as well as the transcription factor pha-4 and , potentially , skn-1 . This multitude of key cytoprotective functions is consistent with our finding that elt-2 is required for appropriate expression of hsp-6 , hsp-4 , sod-3 and gst-4 . We identified five components of the ubiquitin proteasome system ( pas-3 , let-70 , ufd-1 , skr-1 , cul-1 ) . Proteasome regulatory components like aip-1 , and potentially skn-1 , are believed to prolong lifespan by stimulating the degradation of damaged proteins [69] . Others , such as the E3 ubiquitin ligase vhl-1 , serve regulatory functions by degrading key signaling components [70] . The E2 ubiquitin conjugation factor let-70 regulates all four tested cytoprotective responses ( phsp-6::gfp , phsp-4::gfp , psod-3::gfp , pgst-4::gfp ) , but not the heat shock response ( phsp-16 . 2::gfp ) or a constitutively expressed control ( psur-5::gfp ) . It has been previously shown that let-70 interacts with numerous chaperones , contributes to the DNA damage response , and that inactivation of let-70 increases the size of aggregates in a polyglutamine model of protein aggregation [71] . The diverse functions of let-70 , one of 22 E2 ubiquitin conjugation factors in C . elegans , are consistent with its interaction with a much larger pool of target-specifying ubiquitin-protein ligases ( E3s ) . We identify three deacetylases ( hda-1 , dcp-66 , lin-40 ) , all of which regulate the expression of psod-3::gfp downstream of insulin/IGF-1 signaling . Studies of chromatin modification have demonstrated that both silencing and desilencing epigenetic marks regulate lifespan [72] , [73] . Other genes of interest include ima-3 and phi-50 ( Table 1 , Table 2 ) . ima-3 , one of three importin alphas that channel NLS-tagged molecules into the nucleus , stands out as the most potent regulator of the mitochondrial UPR we identify . Our results suggest that ima-3 may participate in the transport of stress regulatory factors into the nucleus . phi-50 , which regulates psod-3::gfp and pgst-4::gfp , is orthologous to the human hydroxymethylglutaryl-CoA synthases 1 and 2 ( HMGCS1 , 2 ) [74] . HMGCS1 is required for the prenylation of proteins such as GTPases , which are essential to organelle homeostasis and many signaling cascades , while HMGCS2 mediates the response to fasting [75] , [76] . Eighteen genes found to disrupt the induction of cytoprotective responses serve putative microRNA or RNAi functions , suggesting that small RNAs regulate stress response . As most microRNAs in C . elegans are not individually essential for viability under standard laboratory conditions , the possibility that they fulfill conditional functions such as stress response is particularly appealing [77] . Small RNAs are attractive candidates for stress response regulation , as they are rapidly inducible and the suppression of protein levels they mediate is rapidly reversible [78] , [79] . Small RNAs may be generated without translation and spread easily amongst cellular compartments or from cell to cell [79] , [80] . Roles for small RNAs in stress responsive gene regulation are emerging in bacterial , plant and mammalian systems [81] , [82] , [83] , [84] , [85] , [86] , [87] . The capacity of small RNA pathways to mediate the expression of duplicated and genetically linked genes may contribute to their potential to act as key regulators of xenobiotic response , as detoxification genes are known to form long tandem arrays by duplication [88] . Like canonical stress response regulatory genes , some small RNAs have been found to regulate longevity [89] . Regulation of cytoprotective genes by siRNA- or miRNA-mediated silencing is likely indirect under the tested conditions because inactivation of genes required for silencing would be expected to increase , not decrease , the expression of a direct target . Additionally , the transgenes utilized were promoter fusions . The complexity of stress response is increasingly evident , challenging the presumption that cytoprotective pathways are genetically independent . Translation initiation factor 2 ( eIF2α ) integrates signals from four stress-activated kinases , each responding to diverse stress stimuli including oxidative damage , amino acid starvation , infection and ER stress . Another example is found in the transcription factor slr-2 , which co-regulates a suite of diverse stress response genes , including hsp-16 . 2 ( heat shock ) , hif-1 ( oxidative stress ) and gpdh-1 [90] . The insulin/IGF-1 signaling pathway contributes to the tolerance of heat , radiation , osmotic stress , oxidative damage and heavy metals , as well as pathogens . It is not surprising that some of the genes identified in our study engage more than one cytoprotective mechanism . The regulators of cytoprotection we identified contribute to lifespan extension in three distinct long-lived mutants: isp-1 ( mitochondrial function ) , eat-2 ( caloric intake ) and daf-2 ( insulin/IGF-1 signaling ) . Nearly all of the identified regulators of cytoprotection contributed to lifespan extension in at least one of these mutants and many modulate lifespan extension in at least two conditions . Because previously identified positive regulators of lifespan , such as daf-16 and hsf-1 , manifest the cumulative benefit of large suites of co-regulated genes , we suggest that the stress response and lifespan regulatory genes identified here similarly abrogate the induction of many downstream cytoprotective effectors . Our finding that nearly all gene inactivations that disrupt the induction of cytoprotective pathways by toxins also disrupt longevity extension suggests a tight coupling of these pathways . Increased longevity may be the cumulative result of cytoprotective pathway induction or , alternatively , a coregulated output of xenobiotic response analogous to the hormonal pathways of lifespan regulation engaged by insulin/IGF-1 signaling mutants . Our results suggest that xenobiotic and environmental response mechanisms underpin diverse models of longevity extension , with the potential to unify the study of long-lived animals . Lifespan poses an evolutionary conundrum , as the genetic determination of lifespan ostensibly suggests post-reproductive selection . Our data suggests that lifespan-determining genes do not specify lifespan per se , but rather the activity of damage-buffering cytoprotective pathways normally engaged only in response to stress stimuli , such as toxins . Cytoprotective programs must be subject to Darwinian evolution , selected pre-reproductively to maintain the viability of larval and young adult animals in the presence of xenobiotic and environmental challenges . Post-reproductive adults could engage the same programs . The functions of these pathways are expected to be highly regulated , since they marshal essential resources , such as iron for cytochrome p450s or ATP for chaperones and transporters , away from anabolic pathways and reproduction; organisms that upregulate these pathways continuously would be outcompeted by those who regulate them conditionally [91] . We have identified upstream regulatory components of longevity and xenobiotic response pathways , the overlap of which supports our hypothesis that longevity pathways evolved as xenobiotic and environmental stress response programs . Our results reveal the complex networking of cytoprotective gene regulation . The genes we have identified may act in the detection of stress stimuli , the transduction of a resulting signal or the direct regulation of the transcription of stress response effectors . We find that these upstream regulators play central roles in both xenobiotic stress tolerance and the extension of lifespan in several canonical long-lived strains , including eat-2 , isp-1 and daf-2 . Xenobiotic and environmental stress response pathways may underpin many current models of longevity extension . The xenobiotic hypothesis of aging invokes hormesis , a phenomenon observed from microorganisms to humans , highlighting the possibility of a xenobiotic approach to longevity extension in humans .
Fluorescent strains phsp-6::gfp ( sj4100 ) , phsp-60::gfp ( sj4058 ) , phsp-4::gfp ( sj4005 ) , psod-3::gfp ( cf1553 ) , pgst-4::gfp ( cl2166 ) . , phsp-16 . 2::gfp ( tj375 ) and pfat-7::gfp ( bc15777 ) were obtained from the C . elegans genome center ( CGC ) . pgpdh-1::gfp ( vp198 ) was obtained courtesy of Kevin Strange . Bristol ( N2 ) , rrf-3 ( pk1426 ) and daf-16::gfp ( gr1352 ) were obtained from the Ruvkun laboratory . pF55G11 . 7::gfp ( hd92 ) , plys-1::gfp ( hd102 ) , plys-7::gfp ( hd100 ) and pnlp-29::gfp ( hd101 ) were obtained courtesy of Scott Alper . Long lived strains were daf-2 ( e1370 ) , eat-2 ( ad465 ) and isp-1;ctb-1 ( mq989 ) . RNAi clones were grown overnight in LB with 100 µg/ml ampicillin and seeded 100 µl/well to 24-well 5 mM IPTG worm plates . Clones were induced overnight at room temperature . Synchronized L1 worms were raised on RNAi at 20°C , 50 animals/well . Fluorescence was assayed at 48 , 72 and 96 hours . Scores were recorded from 0 ( no expression ) to 4 ( strong expression ) . For lethal clones , worms were grown to young adulthood at 20°C on empty-vector RNAi ( L4440 ) before treatment , with subsequent scoring after 24 , 48 and 72 hours . Each clone was scored in three trials . Resulting data was clustered using the open source software Cluster 3 . 0 with hierarchical uncentered correlation of average linkage and visualized using Java TreeView . RNAi clones were cultured overnight in LB with 100 µg/ml ampicillin and seeded 100 µl/well to 24-well 5 mM IPTG worm plates , each well containing 1 . 5 mL agar . Synchronized L1 transgenic strains ( SJ4100 , sj4005 , cf1553;e1370 , cl2166 ) were distributed to RNAi , 50 animals/well , and raised to young adulthood ( 56 hours ) at 20°C . At this time , each well was treated with 0 . 5 µl 20 mg/ml antimycin in EtOH ( sj4100 ) , 1 . 8 µl10 mg/ml tunicamycin in DMSO ( sj4005 ) , 17 µl1 mg/ml sodium azide in water ( cl2166 ) or 1 h 37°C heat shock ( tj375 ) . Toxins were diluted in water to 20 µl/well and applied directly to the agar wells . Expression was assayed after 8 hours ( tunicamycin , heat ) , or 24 hours ( antimycin , sodium azide ) . For sod-3 ( cf1553 ) , psod-3::gfp;daf-2 ( e1370 ) ts was raised to young adulthood at 15°C and shifted to 25°C with GFP expression assayed after 12 hours . All clones in the primary screen were scored in three replicates . Candidate stress response regulatory genes were subsequently verified in five or more additional replicates . Treated worms ( see activation of fusion genes by stress in materials and methods ) were washed into 200 µl M9 containing 0 . 3 mg/ml lavamisole and 0 . 005% Triton X-100 , and concentrated in a 96-well pate by centrifugation for 1 minute at 500 RPM , then transferred to 96-well glass slides with a final liquid volume of 5 µl/well . Imaging of slides was automated using Molecular Devices ImageXpress Micro imaging platform with MetaXpress software . Device captures four images per well , which are tiled to construct full wells . Images are captured in both in both GFP and bright field channels . Custom MATLAB ( The Mathworks , Natick , MA ) scripts distinguish well boundaries by blurring the image and applying a threshold of L*F where L is determined by Otsu's method and F = 0 . 9 . To identify worms , bright field images are bottom hat filtered to decrease variability in background intensity . Otsu's method and a size filter are applied to distinguish objects from background and debris . An outlier method is applied in place of Otsu's when effectiveness is low ( <0 . 7 ) . Fluorescence of worm objects is averaged from the median intensity of each well after background subtraction . Results were averaged from four to eight replicates for each experimental condition and the psur-5::gfp control , with two replicates for the phsp-16 . 2::gfp control . Significance was determined by a one-tailed t-test , p = 0 . 05 , and fold decreased expression >1 . 5× without multiple tests correction . Venn diagrams were generated using the online BioInfoRx Venn diagram tool available at <bioinforx . com/free/bxarrays/venndiagram . php> . Wild-type N2 animals were raised on HT115 bacteria on 10 cm agarose plates at 20°C and treated with cytoprotective response-inducing toxins ( see activation of fusion genes by stress in materials and methods ) . Animals were harvested after 8 hours ( antimycin , tunicamycin ) or 24 hours ( sodium azide ) . In the case of sod-3 , e1370 animals were raised to young adulthood at 15°C and shifted to 25°C and harvested after 12 hours . To isolate total RNA , animals were washed , resuspended in trizol , frozen and homogenized by grinding . RNA was isolated by chloroform extraction followed by ethanol precipitation . Reverse transcription was carried out with the Ambion AM1710 Retroscript RT-PCR kit . Quantitative PCR reactions utilized 12 . 5 µl Bio-Rad iQ SYBR Green PCR Supermix ( 170-8880 ) with 2 µl template , 5 . 5 µl water and 5 µl each of two of the following paired oligos: hsp-4 GAGAACACAATTTTCGACGCC/GACTTGTCGACGATCTTGAACGG; hsp-6 GATAAGATCATCGCTGTCTACG/GTGATCGAAGTCTTCTCCTCCG; sod-3 CACTATTAAGCGCGACTTCGG/CAATATCCCAACCATCCCCAG; gst-4 GCCAATCCGTATCATGTTTGC/CAAATGGAGTCGTTGGCTTCAG . Fold change was measured in comparison to Y45F10D . 4 using oligos GTCGCTTCAAATCAGTTCAGC/GTTCTTGTCAAGTGATCCGACA as described by Hoogewijs et al . 2008 [92] . Quantitative RT-PCR was carried out using a Bio-Rad C1000-CFX96RT thermocycler ( 3 m 95°C; 44 cycles of 95°C 10 s , 60°C 30 s , 72°C 30 s; 5 m 72°C ) . Experiments were carried out with four replicates of approximately 4 , 000 animals per replicate . Long-lived strains daf-2 ( e1370 ) , eat-2 ( ad465 ) and isp-1;ctb-1 ( mq989 ) were raised to young adulthood at 20°C on 10 cm worm plates with HT115 E . coli . RNAi clones were cultured overnight in LB with 100 µg/ml ampicillin , seeded 400 µl/well to 6-well 5 mM IPTG worm plates and induced overnight . The young adult animals were transferred to the prepared IPTG worm plates , 40 animals/well , and FuDR was immediately applied to the plate to a final concentration of 80 µg/ml agar . On day 4 adulthood , wells were supplemented with 400 µl of additional bacteria concentrated to 10× in 5 mM IPTG M9 with 100 mg/ml ampicillin and induced for 2 hours at room temperature before application to worm plates . Lifespan was scored by touch response on alternate days with censoring . Survival statistics were calculated using SPSS Kaplan-Meier . All analyses are based upon mean lifespan . Experiments were performed with three replicates per condition and an average of 103 worms scored per condition . Significance was held to p = 0 . 05 within each tested strain; significance of differences in lifespan extension are based upon a threshold of 15% decrease . The DNA and RNA synthesis inhibitor 5-fluoro-2′-deoxyuridine ( FUdR ) was used to inhibit progeny production . Although the use of FUdR is well established and does not affect the lifespan of wild-type animals , FUdR can affect lifespan of particular mutants [93] , [94] , [95] , [96] . For example , mutation of tub-1 or gas-1 extends lifespan in the presence of FUdR , but not in its absence [93] , [97] . Our experiments included FUdR in both control and experimental trials , so that any FUdR effects on lifespan were controlled . L1 rrf-3 ( pk1426 ) ts worms were synchronized overnight in M9 and raised to young adulthood at 25°C on 10 cm worm plates with HT115 E . coli . RNAi clones were cultured overnight in LB with 100 µg/ml ampicillin , seeded to 6-well 5 mM IPTG worm plates and induced overnight . Young adult rrf-3 worms were distributed to RNAi , 50 animals/well , and raised for 3 days at 25°C . Worms were transferred to wells of M9 containing 24 mg/ml paraquat , 5 . 2 mg/ml cadmium chloride , 22 µg/ml sodium azide , or 696 µg/ml antimycin , with a total volume of 230 µl per well in a 24-well format . Animals were incubated in solution for 16 hours . Survival was then analyzed by scoring for spontaneous movement . Experiments were conducted with three replicates and an average of 94 animals scored per condition . Significance of proportion survival was determined by a one-tailed t-test , p = 0 . 05 without multiple tests correction . Error bars display S . D .
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Many mutations that increase animal lifespan also confer stress tolerance , suggesting that cytoprotective mechanisms underpin the regulation of longevity . It has not been established , however , whether the induction of individual cytoprotective pathways is essential for lifespan extension , or merely correlated . To establish whether the regulatory pathways for the induction of cytoprotective responses are key in the extension of lifespan , we performed an RNAi screen for gene inactivations that decouple the activation of cytoprotective pathways from xenobiotic stimuli that normally induce them . The screen identified 29 genes that constitute the regulatory cascades of the unfolded protein response , oxidative stress response , and detoxification . These upstream regulatory genes are critical to stress tolerance and the extension of lifespan conferred by decreased insulin/IGF-1 signaling , disruption of mitochondrial function , or caloric restriction , but have little effect on normal longevity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"cellular",
"stress",
"responses",
"animal",
"genetics",
"gene",
"regulation",
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"animal",
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"caenorhabditis",
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2012
|
Induction of Cytoprotective Pathways Is Central to the Extension of Lifespan Conferred by Multiple Longevity Pathways
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Attribution of biological robustness to the specific structural properties of a regulatory network is an important yet unsolved problem in systems biology . It is widely believed that the topological characteristics of a biological control network largely determine its dynamic behavior , yet the actual mechanism is still poorly understood . Here , we define a novel structural feature of biological networks , termed ‘regulation entropy’ , to quantitatively assess the influence of network topology on the robustness of the systems . Using the cell-cycle control networks of the budding yeast ( Saccharomyces cerevisiae ) and the fission yeast ( Schizosaccharomyces pombe ) as examples , we first demonstrate the correlation of this quantity with the dynamic stability of biological control networks , and then we establish a significant association between this quantity and the structural stability of the networks . And we further substantiate the generality of this approach with a broad spectrum of biological and random networks . We conclude that the regulation entropy is an effective order parameter in evaluating the robustness of biological control networks . Our work suggests a novel connection between the topological feature and the dynamic property of biological regulatory networks .
Biological regulatory networks play an essential role in all living organisms . The investigation of their general behaviors is an important subject in the current research of systems biology . Recently , the reliable functionality of these networks has attracted much attention [1]; it has been widely recognized that some important biological networks are globally stable against external perturbations and can perform their functions without much fine-tuning of their internal parameters [2]–[5] . These properties of biological control systems are well demonstrated by the recent successful works on the Boolean approximation of regulatory networks [6]–[8] . It was shown that a bare Boolean dynamics is often good enough to describe the essence of biology . Moreover , biological networks simplified by the Boolean approximation often still show a significant dynamic stability , characterized by their global attractors of the biological stationary states and the stability of the biological pathways [7]–[9] . It is widely believed that the topological properties of a biological control network largely determine its dynamic behavior . Therefore , the robustness of biological systems should have its root in the special arrangement of the links in the control networks . Several authors have made various attempts to quantitatively identify this structural origin of network robustness , yet their studies were mainly focused on the distribution of connections , such as the scale-free distribution of degrees [10] , or the modularity [5] , while the function of links was totally overlooked . Although these studies provide important insights into the emergence of biological robustness , their descriptions miss the important ingredient of the network systems and thus are incomplete . In this paper , we try to make a step forward in this line of researches by including the sign ( positive/negative ) of links . To this end we define a new order parameter , named regulation entropy , to measure the signed topology of a biological network . Using the cell-cycle control networks of the budding yeast ( Saccharomyces cerevisiae ) and the fission yeast ( Schizosaccharomyces pombe ) as examples , we first show that this parameter puts a constraint on the robustness of the two control networks , then we provide additional evidences showing that it can serve as a good indicator of the robustness of biological control systems in general .
It is well known that the components of a biological system are often connected by complicated interactions , such as binding , ( de ) phosphorylation , transcription , synthesis and degradation . To model such intricate systems , one needs to employ various approximations . Guided by the balance between model accuracy and computational efficiency , one may neglect the details of biochemical kinetics while preserve the crucial regulatory relations among key players of the original interaction network . Specifically , lots of biochemical interactions are realized by a cascade of reactions , with the fate of the final product almost completely determined by the upstream signal . In practice , such an indirect interaction between the upstream and the downstream of a cascade is often simplified into a direct link in network modeling , especially for the Boolean case . Thus it does not make much sense to take too seriously the difference between direct and indirect interactions on an interaction map . From this point of view , we should place direct links and indirect ones in a biological network on a more or less equal footing . On the other hand , the regulation coherency of the control network is often an essential property of the system . Here coherency denotes the situation that commands from different controllers do not contradict , but to strengthen each other . Based on these considerations , we introduce an order parameter to describe the structural or topological property of a biological control network . In a simple form , a biological regulatory network can be expressed in a signed directed graph . The nodes of this graph represent biomolecules in the system , and the directed edges denote interactions between these biomolecules , with positive and negative signs indicating up and down regulations respectively . For example , a link may represent the fact that the transcription factor promotes the synthesis of the protein , or the protein activates the protein , etc . Following the direction of arrows , one may take a ‘walk’ on the graph . And a path can be conventionally defined as a self-avoiding walk on the graph , i . e . , distinct nodes sequentially connected by arrows present in the graph . To handle self-interactions , this definition can be extended to allow the starting and the ending nodes to be the same , while still require intermediate nodes to be distinct from each other and from the starting node . Each path from node to node represents a regulation pathway from node to node . The overall regulation effect of a path may be either positive ( up ) or negative ( down ) , depending on the sign of each link and the total number of links in the path . For example , a chain composed of an even number of negative regulations behaves like a positive regulation . We thus can associate each path with a sign , which is determined by the product of the signs of all the links in the path . By this definition , a path becomes a concrete representation of regulation in general , both direct and indirect . Next step we define as the set of all paths from node to node . Obviously , elements in the set may carry different signs , which means that the regulations of node by node may be self-contradictory , with some components activating and others inhibiting node . In this case , the overall effect of regulation would delicately depend on the coupling of all these components , and thus more sensitive to the details of interactions and the status of intermediate nodes , especially when they are on the overlap with other sub-circuits . However , if most of the paths in have the same sign , there would be less potential conflicts among the instructions sent from node through different routes to node . As a result , we can expect a reliable regulation that is insensitive to biochemical details and leads to a relatively ordered dynamic behavior of the network . To quantify this ambiguity of interactions , we define the regulation entropy for each pair of node that is connected by at least one path from node to node :in which is the ratio of positive paths in . If tends to zero or one , will tend to zero , which is the minimum of this function; whereas reaches its maximum value of 1 at , which corresponds to the cases of most probable conflicts among the instructions sent from node through different routes to node . It is natural to introduce the entropy of node by averaging over regulators of node . Then , we can use the averaged entropy of the nodes in a network as a measure of the entropy of the whole network; we name it the regulation entropy of the network . We should point out that the nodes that have only one incoming link and one outgoing link should be excluded during these averaging processes . In this way , the value of the regulation entropy will be invariant under trivial transformations like inserting a node into the middle of an existing link , i . e . changing into , and vice versa . Figure 1 provides a concrete example . Evidently , from the definition we have: Thus the regulation entropy of the network in Fig . 1A is: Admittedly , different regulations may have diverse timescales , which makes the coupling of different chains of interactions non-trivial , as shown in the analysis of the function of various feed-forward loops [11] . But to construct an order parameter describing the overall coupling coherency of networks , it is justifiable to take a coarse-graining approach , assuming that the difference of interaction details can be neglected in a first approximation . In the following , we will demonstrate the relevance of this crudely constructed quantity to the functional and dynamic properties of some real biological control systems . We use two specific examples , the cell-cycle regulatory networks of the budding yeast and the fission yeast , to investigate the relation between the regulation entropy and the functional and dynamic properties of biological networks . As pointed out by Davidich et al [8] , these two simplified cell-cycle control networks ( see Fig . 2 ) are diverse in nature , providing an ideal test-bench for the investigation of general properties of network dynamics . Extensive literature has been devoted to the construction of Ordinary Differential Equation ( ODE ) models [12] , [13] as well as Boolean network models [7] , [8] to reveal the dynamic properties of the underlying control systems . To avoid getting lost in the details of the parameter-setting of the ODE models , we use the Boolean approach to gain a first impression of the effect of the regulation entropy on network dynamics . For a brief introduction to Boolean network dynamics and a recapitulation of the two model networks , see Materials and Methods . The behavior of these two biological networks is compared with randomly generated networks . Following Lau et al [9] , we refer to the combined structural and functional ensemble for this comparison , i . e . , the ensemble of networks that have the same number of connections as the corresponding cell-cycle network and can produce the same Boolean sequence of the corresponding cell cycle . For succinct reference , we shall denote this kind of ensemble by in subscript . Specifically , we employ the basic procedure described in Ref . [9] to generate 106 samples from each of the and ensembles of candidate networks , where and denote the budding yeast and the fission yeast , respectively . First , we check the regulation entropy distribution of the random networks and the position of the corresponding cell-cycle network in the distribution . Figure 3 summarizes the results . One observes that most random networks have high regulation entropy values , while those of the two biological control networks are ranked among the lowest 1% or so . Considering that these two biological networks are fundamentally different in the control mechanism ( strongly damped vs . auto-excited , transcriptional vs . translational ) [8] and are diverse in their average connectivity and their ratio of negative links , the departure of the biological networks from the majority of random networks may be quite general . And the regulation entropy may reveal an important topological characteristic of biological control networks in general . All of the networks in the and ensembles can produce the right cell-cycle trajectory . However , they are diverse in their ways to fulfill the function . The high regulation entropy values indicate that most of these random networks are sending self-contradictory commands , and it is probably the crude Boolean approximation that covers up these inconsistencies by totally suppressing interactions from the nodes that are not ‘active’ enough , and therefore produces the trajectory as it happens . In contrast , biological networks have delicate wiring , with most of their components well tuned , as suggested by its distinctively low regulation entropy . This makes it more likely that a subset of interactions can represent the overall effect of regulation . This redundancy enables the cell-cycle networks to reliably produce the target trajectory . In this and next sections , we discuss in detail the correlation of the regulation entropy with the robustness of the yeast networks . From the point of view of nonlinear dynamics , the robustness of a system means that it is stable against external perturbations on the state of the system ( state or dynamic stability ) , and it is stable against perturbations on its control parameters ( structural stability ) . Here , we measure the state stability by the basin size of the biggest attractor of the dynamic system [7] and the network sensitivity [14] in the Boolean model , and we measure the structural stability by the Q value of parameter insensitivity in the ODE model [5] . Previous studies on the Boolean models of the yeast cell-cycle networks showed that these systems are globally stable in dynamics [7]–[9] . Observing that such networks are characterized by their low regulation entropy , we investigate the relation between the regulation entropy and the dynamic stability of these networks . To this end , we calculate the regulation entropy , as well as the basin size of the biggest attractor ( we shall call it ‘basin size’ for abbreviation ) and the network sensitivity [14] for the networks generated from the and ensembles . ( For a brief review of the definition and implication of these dynamic properties , see Materials and Methods . ) To compensate for the highly unbalanced distribution of the regulation entropy and to get a well-rounded estimation of the dependence of the dynamic properties on , we divided the [0 , 1] interval of the regulation entropy into equal segments of length 0 . 02 , and randomly sampled 105 networks from each of the segments . The correlation of the dynamic stability with the regulation entropy is shown in Fig . 4 . The green and cyan lines indicate the bottom 5% and the top 5% levels of robustness , respectively . These skew outlines show that networks with relatively low regulation entropy tend to have relatively stable dynamics , i . e . the lower regulation entropy is , the larger basin size and the lower network sensitivity they are most likely to have . This positive correlation is evidently expectable , since redundancy enhances robustness . The parameter insensitivity or structural stability of a network is an important facet of the robustness of the system . The discussions in the previous section are based on the synchronous Boolean approximation of chemical kinetics , which already implies the parameter insensitivity of the systems . To discuss the structural stability of the two cell-cycle networks , we need to use continuous models based on Ordinary Differential Equations ( ODE ) . Previous studies have shown that some biological networks are extremely insensitive to the variation of parameters . For example , it has been demonstrated that a large proportion of parameter space can support the proper functioning of the Drosophila segment polarity network [2]–[5] . Here , we should point out that dynamic stability and structural stability characterize different properties of a network system , although they may in some cases be correlated , as pointed out by Ciliberti et al [15] . In general , dynamic stability addresses the resistance of a biological state or a biological pathway to external perturbations , while structural stability measures the functional stability of a system under internal fluctuations of parameters . In this section , we discuss the relation between the regulation entropy and the structural stability of biological networks . For this purpose , we carried out extensive simulation of the ODE models of the two cell-cycle systems , and compared the results with the behavior of random networks . For each network , we randomly selected a set of control parameters , and checked if the system can perform its biological function ( following the biological pathway ) . By repeating this process we got an estimation of the value of the network , which is defined as the fraction of the parameter space that can perform the biological function [2] , [4] , [5] . For details regarding the simulation of the ODE models and the functionality judgment , see Materials and Methods . Since only an extremely tiny fraction in the huge network configuration space can fulfill the cell-cycle function [9] , we limited our simulations to the networks that can produce the cell-cycle sequence in the Boolean scheme . Moreover , in order to rule out networks with an unrealistically large number of connections , we fixed our scope to the networks with the same number of connections as the corresponding cell-cycle network , i . e . , we focused on networks in the and ensembles . Figure 5 gives the value distribution of these random ensembles , and the position of the corresponding biological network in the graph . One observes that the two cell-cycle networks have very high values ( about top 1% ) , even among the networks that can support the cell-cycle function under the Boolean approximation . This provides further examples of parameter-insensitive biological networks . More importantly , our calculation shows a strong negative correlation between the regulation entropy and the value ( and thus structural stability ) , which is more evident if we check the ratio of ‘functional networks’ , i . e . the fraction of networks with at least one parameter set that can pass the functionality judgment in the simulation [5] . In this calculation , we divided the [0 , 1] interval of the regulation entropy into equal segments of length 0 . 02 , and randomly sampled 500 networks from each of the segments to estimate the value and the ratio of functional networks , with 104 parameters tested for each network . Figure 6 shows the calculation results . We believe that this correlation originates from the essence of the regulation entropy as a measure of conflict among individual interactions: networks with lower entropy , i . e . more consistent coupling of interactions , would have less dependence on the details of the relative strengths of interactions , and thus enjoy a larger degree of freedom in their parameters . It is generally accepted that the structure of a network defines its dynamics; the regulation entropy we propose captures one of possibly many conditions on network structure under which the dynamic stability and the structural stability arise . Up till now , we have exemplified our theory with the two cell-cycle control networks . The fundamental difference in their control architectures makes it reasonable to expect that the above results may generally hold for other circuits of biological control systems that demand high functional reliability . For this purpose , we used additional four well-studied biological networks to test our theory . The networks include the guard cell abscisic acid signaling network in plants ( ABA ) [16] , [17] , the T cell receptor signaling network ( TCR ) [18] , the survival signaling network in T cell large granular lymphocyte leukemia ( T-LGL ) [17] , [19] , and the network of physical interactions between nuclear proteins in the budding yeast ( PI ) [20] , [21] . For each of the networks , we calculated the value of the regulation entropy of the system and checked its relative rank in the corresponding background distributions . ( For more details regarding these additional networks , see Materials and Methods and the supplementary online material Tables S1 , S2 , S3 , S4 , S5 , S6 . ) As these networks have highly non-trivial functions , we did not introduce any functional constraint in the random ensembles . Instead , for each biological network , we generated more than 105 random networks with the same number of activation and inhibition link as in the real network , and we kept constant the in- and out-degree of each node as well . Table 1 presents the calculation results of the relative rank of the regulation entropy values in the corresponding background ensembles for each biological network . For comparison , we also list the results of the two cell-cycle networks ( abbreviated as and ) . One can see that these diverse biological systems , ranging from signal transduction pathways to the physical interaction network of proteins , also exhibit relatively low values of the regulation entropy . This provides further evidences that biological control networks in general possess relatively low regulation entropy . ( For more details about the randomization algorithm , see Materials and Methods . ) The next step is to check whether the observed correlation between the regulation entropy and robustness also holds generally . We could not study this correlation in any of the above large-scale networks , since all of them are too huge for the calculation of the global dynamics . Instead , we randomly generated 100 trajectories in the phase space of a 11-node network , each of them having 11 steps ending with a fixed point . We then built the combined structural and functional ensembles derived from each of them as we did with the budding yeast cell cycle , and calculated their distributions of the basin size , the network sensitivity , the Q value and the ratio of functional network . Figure 7 summarizes the calculation results . It shows that networks with relatively low regulation entropy tend to have relatively stable dynamics and low parameter sensitivity , as in the cell-cycle control networks .
In this work , we defined a novel order parameter , the regulation entropy , to characterize the signed topology of regulatory networks , and showed that in general biological networks have very low values of . We also established a link between network topology and robustness via this order parameter . First , we identified the correlation between the regulation entropy and the dynamic stability of networks; i . e . , a coherent regulation structure of a network will lead to a relatively stable dynamic behavior . Second , we showed an association between the regulation entropy and the parameter insensitivity , which is another aspect of biological robustness concerning the resistance against structural perturbations , i . e . the structural stability . In the perspective of system biology , these results can shed new light on two important but pending questions . First , why can the yeast cell-cycle control networks be successfully modeled by Boolean networks [7] , [8] ? Our study suggests it is the extremely low regulation entropy that guarantees large arbitrariness in the choice of parameter , and thus makes the Boolean approximation successful . Second , how do the yeast cell-cycle control networks achieve convergent dynamics and guarantee a globally attracting stationary state ? Our work indicates that these networks achieve dynamic stability partly by arranging the coupling of components to guarantee low regulation entropy and thus relatively convergent dynamics . Actually , Lau et al [9] already pointed out that the functional constraint of the budding yeast cell-cycle spurs networks to have larger attractor basin , which partly shows the origin of the large basin size of the cell-cycle regulatory network . Our results further illuminate this scenario by identifying the regulation entropy as another source of the attractor enhancement . Several remarks are in order . First , our results emphasize the significance of the coherent coupling of interactions , while Mangan et al pointed out that special functions realized by incoherent feed-forward loop , such as non-monotonic input [22] and the acceleration of response time [23] , are common in biological control . These apparently conflicting observations , however , are actually complementary , because they address different facets of the intricate relationship between structure and function . For circuits carrying out specific subsidiary functions , delicate designs such as incoherent feed-forward loops prove to be convenient and powerful , realizing special functions with relatively simple construction . But these subtleties may depend more on the fine-tuning of interaction details , and more likely to fail if intermediate nodes are subject to external control when embedded into a larger system . Such strategy of achieving function at a cost of robustness may be well suited for certain purposes , but might be improper for core networks that have to operate with great reliability and stability , against strong internal as well as external noises . In the latter scenario , networks with low regulation entropy would probably rule . Second , previous studies on the relationship between structure and function mainly focused on the dynamic effects of feedback loops , from early work of Thomas et al [24] to more recent articles of Sontag et al [25] and Kwon et al [26] , providing mathematical explanation and detailed estimation of the phase space structure of the Boolean dynamics . Our work , however , is aimed at elucidating the emergence of general robustness observed in biological networks . The correlations identified within and beyond Boolean models justify our approach of comprehensively checking the consistency of indirect regulations , rather than limiting our scope to feedback loops . Finally , we address a technical issue concerning computational feasibility . One may note that the calculation of regulation entropy might be handicapped by computational complexity . It requires the exhaustive enumeration of paths on a directed graph , which seems to limit its application to large-scale networks . However , if we are only interested in the relative rank of a network in an ensemble , we can introduce a cutoff on the length of paths that we take into consideration , ignoring contributions from longer paths to the regulation entropy . Our study shows that the relative rank of the regulation entropy of a network is not very sensitive to this cutoff on path length ( see Fig . 8 ) .
We adopt the most simplified model similar to those in Refs . [7] , [9] . The activity of a node is discretized into a binary bit: 0 denotes inactivity; 1 denotes activity . In this way , the state of the whole system can be cast in a Boolean vector , which is evolved forward by the network in discrete time steps , according to specific updating rules . A straightforward setting for such rule is the synchronous ‘majority vote’ updating: assigning +1/−1 weight to each incoming activation/inhibition link , and updating the state of all nodes at once by turning them on/off according to the sign of the simple sum of the inputs from the node active at the previous time step [7] . This is actually a special case of the threshold network model [27] . A slightly modified version of this rule assumes the dominance of incoming negative regulations over positive ones , since it is widely observed in biological networks that inhibition is often much stronger than activation . But self-degradation should still be overruled by incoming activations , if any . We call this latter model ‘strong inhibition’ for reference . All the transitions governed by a network of nodes form a flow pattern in the phase space constituted by states , with each trajectory ended in an attractor ( either a limit cycle or a fixed point ) [7] , [25] . The basin size of an attractor is the number of states flowing into it . A large basin size of the biological steady state is an indication of the system's stability against state perturbations [7] , [8] . Besides , If the Hamming distance over the phase space is introduced as the number of different digits of two Boolean vectors , the network sensitivity can be defined as the average Hamming distance of the state pairs evolved one step from all of the Hamming neighbors , which quantifies the dynamic order of the system: higher indicates more chaotic dynamic behavior of the system , and is a critical point separating ordered and chaotic phases [14] . In this work , we performed experiments on the following biological networks . ( See the supplementary online material for the detailed documentation of the signed topology of these networks . ) First , we used the budding yeast cell-cycle network model of Li et al [7] with 11 nodes , as shown in Fig . 2A and Table S1 . We adopted the ‘majority vote’ updating rule , in accordance with the original work , which produces a global attracting trajectory resembling the actual sequence of the budding yeast cell cycle . ( For more details , see Ref . [7] . ) We should point out that all the investigations under the ‘strong inhibition’ model give virtually the same results ( but not listed here ) , which shows the independence of our results on the details of the Boolean model . Second , we used the Boolean model of the fission yeast cell-cycle network with 10 nodes [8] , as shown in Fig . 2B and . The original work used a similar updating rule as Ref . [7] , but introduced non-zero thresholds for the nodes Cdc2/Cdc13* and Cdc2/Cdc13 , to guarantee the fulfillment of a trajectory similar to the cell cycle . Here , we adopted an alternative solution , introducing an additional self-degradation for the former , and a self-activation link for the latter . Then , the ‘strong inhibition’ rule produces exactly the same trajectory as Ref . [8] . We made this choice of updating rule merely for convenience in generating random networks , avoiding random shift of thresholds . In addition , we used another four networks without discussing their dynamics . The first is the guard cell abscisic acid signaling network in plants ( ABA ) . It was first synthesized in the Figure 2 of Ref . [16] from experimental literature . Following Ref . [17] , we amputated the nodes without a regulator , but we kept the node ‘ABA’ denoting the upstream signal of abscisic acid in our simplified 39-node network , as shown in Table S3 . The second is the T cell receptor signaling network ( TCR ) shown in Table S4 . It was built from the logic model described and validated in Ref . [18] ( see its Fig . 2 and Table S2 ) . The third is the survival signaling network in T cell large granular lymphocyte leukemia ( T-LGL ) . It was constructed in Ref . [19] ( see its Fig . 1 ) , and simplified in Ref . [17] . We note that in the original network in Ref . [17] , the ubiquitous outgoing inhibitions from the conceptual node ‘Apoptosis’ constitutes more than half of the total inhibition links . In order to limit the artifacts that may arise in randomization , we deleted the links starting from ‘Apoptosis’ in our version of this network , as shown in Table S5 . We note that TCR and T-LGL only share three nodes , and thus are not redundant but addressing distinct aspects of the T cell biology . The fourth is the 80-node network of physical interactions between nuclear proteins in the budding yeast ( PI ) , shown in Table S6 . It was taken from the Fig . 1a of Ref . [21] , which is a simplified version of the 329-node network in the Fig . 1 of Ref . [20] . We adopted a systematic reshuffling algorithm for the randomization of signed directed networks . First , two connected pairs of nodes are randomly selected , and then they are randomly rewired by switching the two ending nodes or the two starting nodes with equal probability , as long as no multiple edges form between the same pair of nodes; for example , and are rewired into either and , or and . This procedure preserves the total number of inhibitions/activations , and keeps constant the in- and out-degree of each node . The repeated application of this reshuffling , starting from the biological network , enabled us to probe the regulation entropy characteristics of the background ensembles of biological networks , and we set the number of reshuffling steps between two adjacent samplings comparable to the square of the number of nodes , so as to ensure the whole configuration space of the relevant networks is well sampled . Yet we did not use this routine for the cell-cycle networks due to its low efficiency to carry out the functional constraint; instead , we adopted the efficient algorithm developed in Ref . [9] for the cell-cycle networks . And we should point out that for the cell-cycle networks , the ensemble formed by the above random-walk algorithm and the combined structural and functional ensemble constructed in Ref . [9] have almost the same distribution of the regulation entropy . The random M-step trajectories in the phase space of an N-node network were constructed as follows . For each node in the network , two different moments are randomly selected from the points of time 1 , 2 , … , and the time series of the states of this node are set in the following manner: either the states between the two moments are set ‘on’ and the rest moments ‘off’ , or the states in between are set ‘off’ and the rest moments ‘on’ , with equal probability . Repeating this procedure for each node in the network results in a cascade of activation [9] , and we set the ending state of the system to be a fixed point . We note that this construction captures the main characteristics of the Boolean trajectories produced by real biological networks , that the state of each node does not flip frequently ( noise-dominated ) , but to vary orderly and slowly ( regulation-dominated ) . We modeled each of the nodes of a regulatory network by an ODE with a self-degrading term characterized by a variable timescale , and each regulation between nodes by an independent Hill function term , with variable strength , threshold and stiffness , and we modeled multiple regulations as the sum of individual regulations . Such translation into ODEs looks crude compared with the delicate cell-cycle models [12] , [13] , but it provides a systematic way to model the dynamic behavior of random networks . After non-dimensionalization similar to that in Ref . [5] , we arrived at an equation for each node , ( 1 ) with the Hill function defined as ( 2 ) and the normalization constant given by ( 3 ) in which we summed over negative and positive regulators for each node . Additionally , we modeled the absence of self-degradation as a positive self-regulation term . In this set of ODEs , we had independent parameters: , , , and . In the random setting of these parameters , we used Latin Hypercube sampling [28] to ensure the minimal correlation between different dimensions of the parameter space . The ranges of the parameters to sample were set as follows: , , ( dimensionless time unit ) , , with uniformly sampled on the log scale and others on the linear scale , in accordance with previous studies [5] . Then , we employed the function rkf45 in the GNU Scientific Library [29] to solve these ODEs by numerical integration for a simulation time of , from initial states set according to the Boolean sequence of the cell cycle: specifically , the concentration of initially active nodes is set to 1 and the rest 0 . For a dynamic function ( trajectory ) in the form of activation cascades like the simplified cell-cycle , we can judge by the following criteria whether a set of parameter has enabled the ODE system to fulfill it . For each node , a score was given to quantify the simulation's resemblance of the target Boolean trajectory , with ( 4 ) and similarly ( 5 ) where denotes the Hill function defined by equation ( 2 ) , while the activity of a node refers to that in the Boolean sequence , and , and are the maximal , minimal and final value of in the ( continuous ) time course , respectively . We note that the credibility of this score function , which places no weight on the order of extrema but only their amplitudes , is limited to the trajectories produced by networks that can fulfill the target sequence in the Boolean approximation . Then we used the average of individual scores to represent the degree of function fulfillment . Calculations showed that 0 . 4 happened to be a rough cutoff for the top 2% on the tail of the distribution for the two yeast cell-cycle networks , so we defined the fulfillment of function as having a score higher than 0 . 4 , and finally counted out the value as the ratio of the parameter sets fulfilling the given function . It should be noted that general results hardly depend on the exact choice of such thresholds or cutoffs , and the above procedure can be readily applied to our construction of random trajectories .
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Living organisms exert very complicated control on the functionality of their components . Such control systems can often operate in a surprisingly robust manner , in spite of constant perturbations from fluctuating internal conditions and a volatile external environment . What feature makes such control mechanisms robust ? Is there a general way to achieve robustness ? Here , we address these questions by investigating the wiring of interaction networks , which contains the most condensed information about the control mechanisms of biological systems . We suggest that one of the most important factors in the realization of biological robustness rests in the global coherency of the control strategy , i . e . , the consistency of commands flowing through different routes in the network to the same destination . To implement this idea , we propose an order parameter termed ‘regulation entropy’ to quantitatively describe this control consistency of networks . We find that this order parameter correlates with the resistance of the system to external perturbations as well as internal fluctuations . Our results suggest that the self-consistency of the control strategy is important for the vitality and robustness of living organisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"computational",
"biology/signaling",
"networks",
"computer",
"science/systems",
"and",
"control",
"theory",
"computational",
"biology/systems",
"biology",
"computational",
"biology"
] |
2009
|
Identification of a Topological Characteristic Responsible for the Biological Robustness of Regulatory Networks
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The threadworm , Strongyloides stercoralis , endemic in tropical and temperate climates , is a neglected tropical disease . Its diagnosis requires specific methods , and accurate information on its geographic distribution and global burden are lacking . We predicted prevalence , using Bayesian geostatistical modeling , and determined risk factors in northern Cambodia . From February to June 2010 , we performed a cross-sectional study among 2 , 396 participants from 60 villages in Preah Vihear Province , northern Cambodia . Two stool specimens per participant were examined using Koga agar plate culture and the Baermann method for detecting S . stercoralis infection . Environmental data was linked to parasitological and questionnaire data by location . Bayesian mixed logistic models were used to explore the spatial correlation of S . stercoralis infection risk . Bayesian Kriging was employed to predict risk at non-surveyed locations . Of the 2 , 396 participants , 44 . 7% were infected with S . stercoralis . Of 1 , 071 strongyloidiasis cases , 339 ( 31 . 6% ) were among schoolchildren and 425 ( 39 . 7% ) were found in individuals under 16 years . The incidence of S . stercoralis infection statistically increased with age . Infection among male participants was significantly higher than among females ( OR: 1 . 7; 95% CI: 1 . 4–2 . 0; P<0 . 001 ) . Participants who defecated in latrines were infected significantly less than those who did not ( OR: 0 . 6; 95% CI: 0 . 4–0 . 8; P = 0 . 001 ) . Strongyloidiasis cases would be reduced by 39% if all participants defecated in latrines . Incidence of S . stercoralis infections did not show a strong tendency toward spatial clustering in this province . The risk of infection significantly decreased with increasing rainfall and soil organic carbon content , and increased in areas with rice fields . Prevalence of S . stercoralis in rural Cambodia is very high and school-aged children and adults over 45 years were the most at risk for infection . Lack of access to adequate treatment for chronic uncomplicated strongyloidiasis is an urgent issue in Cambodia . We would expect to see similar prevalence rates elsewhere in Southeast Asia and other tropical resource poor countries .
Strongyloides stercoralis , a soil-transmitted nematode , is a neglected tropical helminthiasis [1] , [2] and endemic in tropical , subtropical and temperate settings where sanitary and hygiene conditions are poor [3] , [4] . However , the worldwide prevalence of S . stercoralis is heterogeneously distributed [2] and the current estimation of infection remains underestimated due to the use of inadequate diagnostic method [5] . The available information about S . stercoralis infection in developing countries mostly comes from studies in Brazil and Thailand [2] . The gastrointestinal symptoms of the disease include diarrhea and abdominal pain , while dermatological symptoms include itching , rash ( urticaria ) and migrating larvae in the skin ( larva currens ) [6]–[9] . However , more than 50% of all infections remain asymptomatic [4] . Due to its particular ability for autoinfection , S . stercoralis is the only soil-transmitted helminth ( STH ) that can lead to systemic infection with high parasite densities and severe to potentially fatal complications , especially in immunosuppressed hosts [3] , [7] , [10] . Ivermectin is recommended as the most effective treatment [11] . The presence of S . stercoralis larvae in stool specimens is proof of infection [12] . Koga-agar plate ( KAP ) culture [13] and the Baermann method [14] are specific diagnostic methods for strongyloidiasis . However , their sensitivity is not satisfactory when testing a single stool sample in cases of chronic , uncomplicated strongyloidiasis [15]–[18] . In Cambodia , data from several cross-sectional studies in community and hospital settings revealed S . stercoralis prevalences between 2 . 6% and 31 . 5% . However , in all but three studies , a diagnostic approach with low sensitivity was used on a single stool sample [19]–[22] . Three recent studies used a combined diagnostic approach ( KAP culture and Baermann technique ) on two [9] , [23] and three stool samples [16] . We aimed to determine the prevalence , risk factors and spatial distribution of S . stercoralis infection in Preah Vihear province . We conducted a cross-sectional study of S . stercoralis infection , using KAP culture and the Baermann method on two stool samples from each participant in 60 villages of Preah Vihear province , northern Cambodia .
The research was approved by the Ethics Committee of the Cantons of Basel-Stadt and Baselland ( EKBB , #16/10 , dated 1 February 2010 ) , Switzerland , and by the National Ethics Committee for Health Research , Ministry of Health , Cambodia ( NECHR , #004 , dated 5 February 2010 ) . Written informed consent was obtained from each participant prior to the start of the study . For participants between the ages of 1 and 18 years , written informed consent was obtained from the parents , legal guardian or appropriate literate substitute . All participants were informed of the study's purpose and procedures prior to enrolment . All participants infected with S . stercoralis were treated with a single oral dose of ivermectin ( 200 µg/kg BW ) [24] . All other parasitic infections were treated according to the guidelines of the National Helminth Control Program of Cambodia [25] . The study was conducted in 60 rural villages of Preah Vihear province , Northern Cambodia ( Figure 1 ) . The villages were randomly selected from a list of all villages in six of the seven districts in Preah Vihear province ( total number of villages: 184 ) . The district of Chhaeb was not included as most villages in this district are difficult to access by car , which was necessary to ensure the rapid transfer ( three hours by car ) of stool samples to one of the two temporary laboratories established in the health centers of Kulen and Rovieng districts . A cross-sectional study was carried out from February to June 2010 among all the population living in 60 villages . Fifteen households were randomly selected from the list of all households in the selected villages . All household members one year of age and older were eligible for inclusion in the study and all household members present on the day of the survey were enrolled . After obtaining written informed consent from participants , an individual questionnaire was administered to obtain demographic information ( age , gender , educational level and profession ) , personal risk-perception ( knowledge about worm infections ) , and behavioral data ( personal hygiene practices , wearing shoes , and latrine use ) . The head of household was interviewed , based on a household questionnaire , about socioeconomic indicators such as house type , household assets , latrine and livestock . All questionnaires were pre-tested . After the interview , each participant was given a pre-labeled plastic container ( ID code , name , sex , age and date ) for stool sample collection . The next morning , the filled stool container was collected and a second empty , pre-labeled one was handed out for a second stool sample of the following day . Stool samples were transported at ambient temperature and arrived at the laboratory within three hours of collection . Laboratory technicians from the National Center for Parasitology , Entomology and Malaria Control ( CNM ) , Phnom Penh , processed the stool specimens in one of two laboratories established in Kulen and Rovieng health centers , respectively . First , a single Kato-Katz thick smear [26] was prepared using the WHO standard template and examined under a light microscope to detect helminth eggs . Eggs were counted and recorded for each helminth species separately . Second , KAP culture [13] was used to detect S . stercoralis larvae . A hazelnut-sized stool sample was placed in the middle of the agar plate and the closed Petri dish was incubated in a humid chamber for 48 hours at 28°C . Afterwards , the plates were visually examined for the presence of larval tracks . The plates were then rinsed with sodium acetate-acetic acid-formalin ( SAF ) solution . The eluent was centrifuged and the sediment was examined under a microscope for the presence of larvae . Based on morphology , larvae were identified ( i . e . , size of buccal cavity , presence of genital primordium ( L1 ) , presence of forked tail-end ( L3 ) ) as either S . stercoralis or hookworm larvae . Finally , the Baermann technique [14] was performed to detect S . stercoralis larvae . A walnut-sized stool sample was placed on gauze inserted into a glass funnel and covered with water . The apparatus was exposed for two hours to artificial light directed from below . After centrifuging the collected liquid , the sediment was examined under a microscope for the presence of S . stercoralis larvae . For quality control , the technicians were specifically trained on the morphological criteria for distinguishing hookworm and S . stercoralis larvae . Throughout the study period , technicians were rigorously supervised by a qualified microscopist from the Swiss Tropical and Public Health Institute ( Swiss TPH ) , Basel , Switzerland . Any unclear diagnosis was immediately discussed with both the qualified microscopist and the study supervisor . Day and night land surface temperature ( LST ) , enhanced vegetation index ( EVI ) and land use/land cover ( LULC ) were extracted at 1×1 km resolution from Moderate Resolution Imaging Spectroradiometer ( MODIS ) Land Processes Distributed Active Archive Center ( LP DAAC ) , U . S . Geological Survey ( USGS ) Earth Resources Observation and Science ( EROS ) Center ( http://lpdaac . usgs . gov ) . Rainfall estimates ( RFE ) at 0 . 1 degree ( about 10×11 km ) resolution were obtained from the National Oceanic and Atmospheric Administration's ( NOAA ) Climate Prediction Center ( CPC ) Famine Early Warning System ( FEWS ) Rainfall Estimates South Asia , version 2 . 0 ( http://www . cpc . ncep . noaa . gov/products/fews/SASIA/rfe . shtml ) . Digital elevation data at a resolution of 90×90 m were retrieved from the NASA Shuttle Radar Topographic Mission's ( SRTM ) Consortium for Spatial Information of the Consultative Group for International Agricultural Research ( CGIAR-CSI ) database . Soil type data at a spatial resolution of 9×9 km , including bulk density , soil organic carbon content and pH , was extracted from the International Soil Reference and Information Center's ( ISRIC ) World Inventory Soil Emission Potentials ( WISE ) , version 1 . 0 ( http://www . isric . org ) . The 18 land cover type 1 classes ( IGBP ) were merged into five categories according to similarity and respective frequencies . Yearly means , as well as minima and maxima of EVI , monthly LST and RFE were calculated for May 2009 to April 2010 .
Overall , 3 , 560 individuals from 616 households ( average household size: 5; range: 1–12 ) were enrolled , of which 2 , 748 ( 77 . 2% ) participants submitted two stool samples . The final analysis included 2 , 396 ( 67 . 3% ) participants with complete data records , i . e . , two stool specimens examined with all diagnostic tests and all questionnaires completed . The median age of the participants was 20 years , with a range from 1 to 85 years . One thousand three hundred and fifty-five ( 56 . 5% ) participants were females . Half of the participants ( 48 . 5% ) were farmers and 33 . 0% were pupils . The majority of participants ( 58 . 3% ) had attended primary school; one third ( 32 . 2% ) had not received primary education . Seven intestinal parasite species were found in the stool samples . Hookworm and S . stercoralis were most common , with a prevalence of 46 . 7% and 44 . 7% , respectively . Taenia sp . was found in 0 . 4% of participants , while Hymenolepis nana and Enterobius vermicularis were observed in 0 . 2% and 0 . 1% of participants , respectively . Both Ascaris lumbricoides and Trichuris trichiura were observed in 0 . 3% of participants . Of the 1 , 071 S . stercoralis cases , 642 ( 59 . 9% ) were co-infected with hookworm . Table 1 summarizes the results of KAP culture and Baermann tests for the 1 , 071 S . stercoralis cases ( 44 . 7% ) detected . KAP culture and the Baermann technique detected 877 and 823 cases , respectively . The total of all positive cases diagnosed by any of the two methods was considered the “diagnostic gold standard” . The sensitivity of the KAP culture was 81 . 9% , and that of the Baermann technique , 76 . 8% . The negative predictive values were 87 . 2% and 84 . 2% , while the positive predictive values were 81 . 8% and 76 . 8% for KAP culture and Baermann technique , respectively . Of 1 , 071 S . stercoralis cases , half were females ( 50 . 1% ) , half were farmers ( 51 . 1% ) , and 425 ( 39 . 7% ) cases were diagnosed in individuals under 16 years . The majority ( 57 . 0% ) attended primary school , while one third ( 33 . 6% ) reported no schooling . Figure 2 shows the smoothed age prevalence stratified by gender . The prevalence of S . stercoralis increased rapidly with age , particularly in the first eight years of life , where after it leveled off in females but continued to rise slowly in males . Prevalence rose from 31 . 4% in children , aged five , to 51 . 2% in participants older than 50 . In all age groups , prevalence was higher in males than in females . The multivariate GEE found that gender was significantly associated with S . stercoralis infection ( mOR: 1 . 7; 95% CI: 1 . 4–2 . 0; P<0 . 001 ) . Compared to children under six years old , all age groups had a higher risk for infection . Participants who reported having been treated for worms were less frequently infected with S . stercoralis than those who did not report taking anthelminthic drugs ( mOR: 0 . 7; 95% CI: 0 . 6–0 . 8; P<0 . 001 ) . In addition , participants who usually defecated in latrines were significantly less infected with S . stercoralis than those who did not use latrines ( mOR: 0 . 6; 95% CI: 0 . 4–0 . 8; P = 0 . 001 ) . No additional predictor of S . stercoralis infection relating to personal disease perception and hygiene was found in the multiple regression analysis . Looking at environmental factors , risk significantly decreased with increasing rainfall ( mOR: 0 . 8; 95% CI: 0 . 7–0 . 9; P = 0 . 004 ) and soil organic carbon content ( mOR: 0 . 6; 95% CI: 0 . 5–0 . 9; P = 0 . 003 ) . The land cover class corresponding to croplands was associated with an increased risk for infection ( mOR: 1 . 7 , 95%CI: 1 . 2–2 . 4; P = 0 . 004 ) ( Table 2 ) . During the two weeks preceding examinations for S . stercoralis , 50 . 5% of participants reported an episode of diarrhea , 12 . 7% had experienced nausea and 59 . 1% complained about abdominal pain . However , none of these clinical symptoms was significantly associated with S . stercoralis infection . Population attributable risk analysis found that the number of strongyloidiasis cases would be reduced by 39% if all participants used a latrine for defecation . The spatial model run without covariates indicated very little spatial correlation of infection risk , as indicated by the 1 km range . The small residual ( unexplained ) within village variance ( σ ) also indicated a weak clustering tendency of S . stercoralis infection risk . Parameters of these models are presented in Table 3 . After introducing LST night , rainfall , soil carbon content and land cover , the model with an exchangeable random effect fitted the data slightly better , as indicated by the lower DIC . Environmental covariates explained 45% of the village-level variability and the range dropped under a kilometer after covariates were introduced in the model . Mixed bivariate logistic regressions revealed no association at 15% significance level between S . stercoralis infection risk and any yearly summary measure of altitude , LST day , EVI , soil pH or bulk density . LST night ( P = 0 . 072 ) , yearly means of rainfall estimates ( P<0 . 0001 ) , soil organic carbon content ( P = 0 . 002 ) and land cover ( P = 0 . 107 ) were associated with infection risk and were used to predict S . stercoralis infection risk throughout Preah Vihear province . Apart from LST night , all covariates remained significant in the multivariate model and ORs were similar to those obtained in the multivariate GEE for the risk factor analysis ( data not shown ) . Maps of the covariates used predict infection in Preah Vihear province are presented in Figure S1 . Model validation revealed that both models were able to correctly predict prevalence for 100% of the test locations , within a 95% credible interval . However the non-spatial model , i . e . with an exchangeable random effect , had slightly better predictive ability ( MSE: 0 . 0226 and 0 . 0229 , χ2: 13 . 22 and 13 . 59 for the non-spatial and spatial models , respectively ) . Therefore , the non-spatial model was used to predict S . stercoralis infection risk in Preah Vihear province , Cambodia . Figure 3 displays the S . stercoralis predicted median prevalence in Preah Vihear province ( Figure 3A ) , together with the uncertainty of the estimates ( Figure 3B ) as expressed by the error coefficient ( the ratio between the predicted median and its standard deviation ) . The lower ( 2 . 5% ) and upper ( 97 . 5% ) credible intervals of the predicted S . stercoralis prevalence are presented in Figure ( 3C ) and ( 3D ) , respectively . Results were consistent with observed prevalence at surveyed locations .
Many epidemiological aspects of S . stercoralis infection are poorly understood [34] . The available information on the prevalence of S . stercoralis comes from studies on other STHs , where diagnostic methods with low-sensitivity for S . stercoralis and only a single stool sample were mostly used [1] , [2] , [16] . To reach an acceptable estimate of the “true” prevalence of S . stercoralis , Siddiqui and Beck [12] , and Khieu et al . [16] proposed analyzing multiple stool samples with multiple diagnostic techniques simultaneously . In our study of S . stercoralis among a rural population living in 60 villages in northern Cambodia , we examined two stool samples using two diagnostic techniques ( KAP culture and Baermann method ) specifically targeting S . stercoralis and found that 44 . 7% of the participants were infected . Children under the age of six accounted for 5 . 5% of the infections , while prevalence increased with age . Almost every second individual in our study population was infected with S . stercoralis . To our knowledge , this is one of the highest prevalence ever reported , compared to other studies in highly endemic areas like Cambodia [2] , [16] , [35] , Laos [36] , Thailand [37] , Brazil [34] and China [38] , or in other countries . The main reason for such high prevalence is likely to be due to the more rigorous diagnostic approach employed in our study ( number of stool specimen , multiple diagnostic methods ) , compared to the other studies , where a single method to examine a single fecal sample was used . Yet , the prevalence we observed is also substantially higher than that of other studies using the similar diagnostic approaches . Two recent studies in Kandal and Takeo provinces in Cambodia reported that about a quarter ( 24 . 4% ) of schoolchildren and 21 . 0% of the general population were infected , respectively [16] , [35]; while Steinmann et al . , and Knopp et al . found a prevalence rate of 11 . 7% in a village in Yunnan , China and of 10 . 8% among schoolchildren in Zanzibar , respectively [39] , [40] . Hence , other factors such socioeconomic and sanitary conditions are likely to contribute to the differences observed . In the absence of a gold standard for diagnosing S . stercoralis , KAP culture [13] and the Baermann method [14] are widely used for detecting the parasite microscopically . Our study found that KAP culture was more sensitive than the Baermann method , which is consistent with reports from Cambodia [16] , [35] , rural Côte d′Ivoire [41] , Brazil [42] and Honduras [43] . However , the opposite was observed in studies in south-central Côte d′Ivoire [44] , Zanzibar [40] , China [39] and Uganda [45] . This seems to indicate that neither method is superior . As either technique will fail to identify a certain number of infections , the combined use of both methods is recommended for optimal sensitivity . We found that about one third of children under six ( 59 of 188 children ) were already infected with S . stercoralis . This hints at a high contamination of the environment , such that children easily become infected when playing on the ground around the house or barefoot in the village . The fact that prevalence steadily increases with age can be explained by the fact that once infected at a young age , an infection can persist in an untreated individual for their entire life [46] , [47] . Personal hygiene ( not using a toilet for defecating ) as a significant predictor of S . stercoralis infection was also observed in a study in south-central Cambodia [16] . This connection is obvious: with proper disposal of the feces , contamination of the surrounding area with infective larvae decreases . We calculated that 39 . 0% of S . stercoralis cases in the study area could be prevented if everyone were to defecate in a toilet . The cycle of S . stercoralis transmission could thus be interrupted by improving personal hygiene and sanitation . Strongyloidiasis is almost non-existent in countries where sanitation and human waste disposal have improved [48] . S . stercoralis infections were ubiquitous the study setting and exhibited a weak tendency to spatial clustering in the Preah Vihear province , as indicated by the low location-specific variance parameter . A low clustering tendency was also observed for hookworm , in the Region of Man , Côte d′Ivoire and Ghana [49] , [50] . However , the lack of spatial correlation in this analysis is likely due to the study's small scale . This does not preclude S . stercoralis infection risk from spatially clustering at country or regional level , since environmental factors delimit suitable ecological zones for parasites at larger scales . [51] . Still , even at this provincial scale , we found significant associations with rainfall , soil organic carbon content and croplands both in the predictive model and after adjusting for demographic and behavioral factors . Our risk predictions yielded two broad risk zones: a lower risk zone in the East of the province and a higher risk zone in the West , characterized by lower rainfall and soil organic carbon content and a higher proportion of zones occupied by cropland . Since there was no indication of spatial correlation , risk prediction was carried out using an exchangeable random effect and relied on the predictors only . While a negative association between rainfall and infection risk was also identified in Thailand , a laboratory study found that S . stercoralis development was impaired by submersion of stools in water [52] . Hypothetically , the decreased risk of S . stercoralis infection in the East of the Province where rainfall was higher , might relate to more extensive or long lasting flooding that could negatively affect S . stercoralis transmission . Another possibility might be that higher rainfall in the East reduces parasite survival rates , as parasites are washed away by run-off water down steeper slopes . We found that lower soil carbon content was associated with increased risk of infection ( in the West ) . A full profile of soil type information was unavailable for this setting and soil organic carbon content depends on a complex interplay of environmental and soil features , so interpretation is limited . But , in general , soil organic content tends to decrease with increased forest destruction , burning of savannas and land use for agriculture [53] . Hence , the association of increased risk of infection with lower soil carbon contents in our setting might relate to human activities such as slash-and-burn practices that destroy forests to create agricultural lands . Moreover , risk of infection was found to increase in croplands , a MODIS land cover category that specifically corresponds to soils that are alternately bare and cultivated . In our setting , these are rice fields [54] . Half ( 51 . 7% ) of the study villages are surrounded by rice fields and 54 . 9% of participants infected with S . stercoralis live in such environments . Risk might be increased further by regular soil contamination by defecation around the fields and exposure during agricultural activities . Indeed , open defecation was the usual habit for 88 . 5% of participants . Finally , the small cluster size ( 1 km ) of infection risk suggests that S . stercoralis transmission occurs within villages rather than between them and may relate to the location of defecation sites within and close to the villages . We conclude that S . stercoralis infection is highly prevalent in rural communities of Cambodia . School-aged children and adults over 45 years were the most at risk for infection . Almost 40% of infections could be avoided by proper personal hygiene . Access to adequate treatment for chronic uncomplicated strongyloidiasis is low . Given its potential to produce potentially fatal disseminated infections , further epidemiological data on this parasite in other endemic areas are urgently needed
|
Data on the prevalence and distribution of Strongyloides stercoralis ( threadworm ) is scarce in many resource-poor countries . We carried out a cross-sectional study during the dry season among 2 , 396 rural Cambodians of all ages . We used a rigorous diagnostic approach , involving two stool samples per person and two examination techniques , namely , Koga agar plate culture and the Baermann method . We predicted the spatial distribution of S . stercoralis using Bayesian Kriging analysis . Almost half of the participants ( 44 . 7% ) were infected with S . stercoralis . Of the S . stercoralis cases , 39 . 7% involved participants under 16 years old . S . stercoralis infection prevalence was significantly higher in males than in females . Participants younger than 10 years old had a lower risk of infection than did older participants . Furthermore , our study showed that toilet use could prevent threadworm infections by 39% . Infection prevalence in the province was negatively associated with rainfall and soil organic content and positively associated with land covered by rice fields . We conclude that access to adequate treatment for S . stercoralis must be addressed in Cambodia . Infection prevalence is likely to be similar in other countries of the region and the developing world .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"helminth",
"infections",
"medicine",
"and",
"health",
"sciences",
"primary",
"care",
"environmental",
"epidemiology",
"epidemiology",
"global",
"health",
"strongyloidiasis",
"neglected",
"tropical",
"diseases",
"spatial",
"epidemiology",
"infectious",
"disease",
"control",
"tropical",
"diseases",
"soil-transmitted",
"helminthiases",
"parasitic",
"diseases",
"health",
"care"
] |
2014
|
High Prevalence and Spatial Distribution of Strongyloides stercoralis in Rural Cambodia
|
Formation of spatial gene expression patterns in development depends on transcriptional responses mediated by gene control regions , enhancers . Here , we explore possible responses of enhancers to overlapping gradients of antagonistic transcriptional regulators in the Drosophila embryo . Using quantitative models based on enhancer structure , we demonstrate how a pair of antagonistic transcription factor gradients with similar or even identical spatial distributions can lead to the formation of distinct gene expression domains along the embryo axes . The described mechanisms are sufficient to explain the formation of the anterior and the posterior knirps expression , the posterior hunchback expression domain , and the lateral stripes of rhomboid expression and of other ventral neurogenic ectodermal genes . The considered principles of interaction between antagonistic gradients at the enhancer level can also be applied to diverse developmental processes , such as domain specification in imaginal discs , or even eyespot pattern formation in the butterfly wing .
With the availability of complete genome sequences and quantitative gene expression data , it becomes possible to explore the relationships between sequence features of regulatory DNAs and the transcriptional responses of their associated genes [1–7] . Developmental genes regulated by multiple enhancer regions and their spatio–temporal dynamics of expression are of particular interest [8–11] . The enhancers of developmental genes , such as gap and pair-rule genes , interpret maternally deposited information and participate in the formation of progressively more complex expression patterns , thus increasing the overall spatial complexity of the embryo . In part , the information required to generate these downstream patterns ( e . g . , gap and pair-rule ) is present in the enhancer sequences . Much attention has been paid to the investigation of transcription factor binding motifs and motif combinations , and to interpreting their role in the formation of spatial gene expression patterns . [5 , 12 , 13] . However , some early enhancers of Drosophila contain virtually identical sets of binding motifs , yet they produce distinct expression patterns [6 , 14] . It has been argued extensively that binding site quality ( affinity ) and site arrangement within enhancers ( grammar ) contributes to the levels and precision of enhancer responses [6 , 15–21] . In fact , some experimental studies of differentially arranged binding sites confirm the dependence of enhancer response on distances between binding sites and on binding site orientation [6 , 16 , 22–24] , and some structural enhancer features such as motif spacing preferences and characteristic binding site linkages . “Composite elements” and other syntactical features were identified in many model organisms using computational analyses of binding site distributions throughout entire genomes [5 , 25 , 26] . Recent studies involving in vivo selection of optimal binding-site combinations in yeast also revealed a number of preferred motif combinations and structural features [27] . Nevertheless , some phylogenetic studies indicate significant flexibility in the regulatory code [28–31] . The analysis of unrelated , structurally divergent , but functionally similar enhancers aids in defining the balance between the stringency of the functional cis-regulatory “code” and its flexibility as demonstrated by changes in primary enhancer sequence over the course of evolution . [6 , 18 , 32] . Requirements for multiple cofactors that influence transcription via protein–protein interaction complicate computational predictions and studies of enhancers . While known binding motifs are easy to find , most protein–protein interactions leave no clear footprints in the DNA sequence of enhancers—some developmental coregulators such as CtBP ( C-terminal binding protein ) and Groucho influence the transcriptional response through interactions with sequence-specific transcription factors ( e . g . , [33] ) . Finally , regulatory signals from enhancers must be transmitted to the basal transcriptional machinery; this involves enhancer–promoter communication of some sort , as well as the recruitment of mediator complexes [2 , 21 , 34–36] . Both aspects further complicate the in silico prediction and analysis of enhancer activity . Until recently , most models explaining enhancer responses in development were largely qualitative [37 , 38] . Davidson's group [2 , 39] and Hwa's group [21] undertook quantitative modeling of enhancer–promoter interactions and investigated the responses of architecturally complex regulatory units . The elaborate nature of developmental enhancers in Drosophila was described in quantitative models introduced by Reinitz's group [1 , 7] . Here , we summarize some basic structural considerations and investigate mechanisms of enhancer regulation to demonstrate how such features may affect the transcriptional responses . Our quantitative analyses involve models based on the fractional occupancy of transcription factor binding sites present within enhancers [2 , 21 , 40 , 41] . On the one hand , the described models are similar to those developed by Hwa's group [21] as they consider structural enhancer details . On the other hand , the models include biological assumptions for developmental enhancers ( i . e . , quenching ) , similar to those introduced by Reinitz's group [7] . Technically , our models use a homotypic array ( a unit containing a number of identical sites ) of binding sites as an elementary unit for modeling . Based on quantitative analysis of transcriptional responses , we analyze some models for developmental pattern formation . In particular , we explore the outcome of the interplay between two antagonistic transcription factors , an activator and a repressor . We demonstrate that a pair of antagonistic gradients with similar or even identical spatial distributions is sufficient to initiate stripes of expression of a downstream gene . Given that the antagonistic gradients may be deposited by the same localized or terminal signal ( e . g . , in the fly embryo ) [42] or by a focal signal ( e . g . , in the case of a butterfly eyespot ) [43] , the models explain how initiation from a single point in space can lead to efficient gains in spatial complexity .
The transcriptional state of enhancers of developmental genes is among major factors in developmental pattern formation [6–8 , 10] . If a transcription factor is present in a concentration gradient , the probability of that factor occupying a binding site in a target enhancer at a given position along the gradient depends on the factor's concentration at that position ( coordinate ) . This logic suggests that in the case of activator and repressor gradients , calculating the probability of activator , but not repressor , binding ( i . e . , the successful transcriptional state resulting in transcription ) may serve well to model the spatial expression patterns of the early developmental genes . Let us consider an elementary enhancer , which contains two binding sites: one for an activator and one for a repressor . Let us assume that binding of the activator A in the absence of the repressor R brings the elementary two-site regulatory unit i ( the enhancer; see Figure 1A ) into a successful transcriptional state . The equilibrium probability of the successful state pi depends on the binding probabilities of A ( pA ) and R ( pR ) , which depend on the concentrations of the regulators ( [A] and [R] ) and on the binding constants ( KA and KR ) of the binding sites for the corresponding transcription factors ( see Equations S1–S5 in Protocol S1 ) : Extending this formula to multiple different activators or repressors may be easily obtained with the same logic ( see Equation S6 in Protocol S1 ) . Bintu and coworkers recently introduced a number of similar models , describing DNA–protein and protein–protein interactions on proximal promoters [21] , where the authors used an “effective dissociation constant , ” which is the inverse of the binding constant ( K ) used in this study . Developmental enhancers usually contain homotypic or heterotypic binding site arrays for multiple activators and repressors [44] . The probability of achieving a successful transcriptional state for the binding site array ( enhancer ) i , containing M identical , noninteracting activator sites and N identical , noninteracting repressor sites , is equal to ( see Equation S7 in Protocol S1 ) : Here , Ψ is the sum of the statistical weights of molecular microstates for a homotypic site array and the denominator ΨAMΨRN is the sum of the statistical weights for all microstates of the system ( i . e . , the partition function; see Protocol S1 , “Binding site arrays” ) . In such site arrays , bound transcription factors may cooperate or compete for binding . Let us consider a cooperative array as an element of enhancer architecture ( Figure 1B ) . Assuming presence of lateral diffusion [41 , 45] , equal binding affinities for all sites in the array and expressing cooperativity C as the ratio between the second and the first binding constants , one can approximate the sum of statistical weights Ψ of all possible molecular microstates for a cooperative array as follows ( see Equations S8 and S9 in Protocol S1 ) : Binding sites for an activator and a repressor may overlap , and the corresponding proteins compete for binding . Well-known examples in Drosophila development include Bicoid and Krüppel [46] , Caudal and Hunchback [44] , and Twist and Snail [6] . The classic example outside Drosophila is the competition between CI and Cro in the phage lambda switch [47] . The sum of microstates for a competitive site array , containing M overlapping A/R binding sites ( Figure 1C; also see Figure S1 and Equations S8–S12 in Protocol S1 ) , can be approximated by: In addition to competitive interactions , this model also includes homotypic cooperative interactions between the regulators ( see Equations S10–S12 in Protocol S1 ) . Structural elements within an enhancer ( single sites or entire site arrays ) may be distributed over extended genomic regions ( thousands of bases , e . g . , the Drosophila sna enhancer ) [48 , 49] . In these cases , the distant regulatory elements within the enhancer may represent relatively independent units—modules [15 , 26] ( see Figure 1D ) . Each independent module may include a single binding site or a binding site array . Redundancy of the enhancer elements ( binding sites and modules ) is a well-known biological phenomenon [44] . If the modules within an enhancer are independent from one another , bringing any one module into a successful transcriptional state may be sufficient for bringing the entire enhancer into a successful state , even if another module ( s ) is repressed . Given the probabilities pi of successful states of all i independent modules or enhancers ( Equations 1–4 ) , the probability PEnc of the multimodule enhancer being in a successful state is equal to: This is the reverse probability of the enhancer being in an inactive state , which is the product of the probabilities of each independent module being in an inactive state ( 1 − pi ) ; Reinitz's group [1 , 7] implemented similar expressions for the quenching mechanism . While distinct modules may provide simultaneous responses to different inputs , multiple equivalent modules may allow for the boosting of an enhancer's overall response to a single input [50] ( see Figure S1E and S1F ) . In practice , however , the modules may not be completely independent from each other . Short-range repression and other factors ( discussed below ) may be involved in distance-dependent module responses [22–24 , 48] . Let us consider an enhancer containing two modules , a and b . Module a contains an activator site and a repressor site; module b contains an activator site only ( see Figure 1E ) . Potentially successful enhancer states include all combinations in which at least one activator molecule is bound . However , the mixed state KaA[A]KaR[R] is always inactive as the repressor , and the activator sites in the module a are “close” . If module b is not “too far” from module a , short-range repression from a may reach the activator site in b . We can account for this possibility ( and for its extent ) by introducing a multiplier δ , depending on distance between the modules a and b ( see also Equations S14–S16 in Protocol S1 ) : In this formula , Ψab is the sum of weights for all microstates , and Ψaboff is the sum of weights for the microstates that are always inactive ( see Protocol S1 , Equation S14 ) . If modules a and b are “far , ” δ = 1; if they are “close , ” δ = 0 . If the distance between a and b is somewhere in between , so that a repressor bound in a partially affects the activator bound in b , we could introduce a distance function δ = f ( x ) ( 0 ≤ δ ≤ 1 ) , where δ depends on the distance x between a and b ( and perhaps other variables , such as the repressor type ) . However , all we currently know about the distance function is that short-range repression is effective at distances less than 150–200 bases , and long-range repression may spread through entire gene loci ( i . e . , 10–15 kb [23 , 24 , 48] ) . Without exact knowledge about the distance function , the module concept ( Equation 5 ) allows modeling of distance-dependent responses , but only in a binary close/far ( yes/no ) fashion . Most of the enhancer response models ( Equations 1–6 ) consider inputs from two antagonistic gradients , but enhancers may be under the control of a larger number of regulators ( see Figure 1D ) . However , gradients of some of these regulators may either have similar spatial distributions ( e . g . , Dorsal and Twist ) [51] , or non-overlapping spatial expression domains ( e . g . , Krüppel and Giant ) [37] . Therefore , in many cases the combination of all inputs may be parsed down to one or more pairs of antagonistic interactions . Based on the described quantitative models approximating enhancer responses ( see above ) , we analyzed possible spatial solutions produced by gradients of two antagonistic regulators . The examples in Figure 2A–2C demonstrate that the spectrum of possible enhancer responses is quite rich . One surprising result of these simulations is that even identically distributed antagonistic gradients can yield distinct spatial expression patterns such as stripes ( Figure 2B ) . We identified conditions for the “stripe” solutions using differential analysis of the site occupancy function shown in Equation 1 . For example , if both regulators are distributed as identical gradients and if their concentrations and binding constants are equal ( KA = KR; [A] = [R] ) , then it is sufficient to identify conditions for the maximum of a site-occupancy function y ( x ) depending on the spatial coordinate x: In this variant of Equation 1 , k is the product of absolute concentration of the regulators [Abs] and the binding constant KA ( k = KA[Abs] ) . The function f ( x ) is the distribution of the relative concentration ( 0 ≤ f ( x ) ≤ 1 ) of the transcription factors along the spatial coordinate x ( i . e . , the embryo axis ) . The function's maximum y′ ( x ) = 0; x > 0 is f ( x ) = 1/k . In the Gaussian , logistic , and exponential decay forms of the function f ( x ) ( see details in [52] ) , the maximum 1/k exists only if KA[Abs] > 1 ( i . e . , if binding constants and/or the absolute concentrations are high ) ( see also Figure S2 ) . In the simple case ( Equation 7 ) , the absolute value of the fractional occupancy at the maximum is not very high ( 0 . 25 ) ; adding more sites or modules ( see Figure S1 ) allows for the function's values to approach 1 ( see Figure 2B ) . However , if the antagonistic gradients are not identical ( e . g . , if the activator gradient is “wider” than the repressor gradient ) , the solutions for the stripe expression are more robust ( Figure 2A ) . Shifting the peak of the activator gradient relative to the repressor gradient produces even more robust stripe patterns , as in the case of classical qualitative models [37] , where a repressor “splits” or “carves out” the expression of a target gene ( Figure 2C ) . The formation of distinct gene expression domains ( e . g . , stripes ) in response to similarly or even identically distributed gradients is of interest because this mechanism can lead to the very efficient gain of spatial complexity in just a single step: based on primary sequence , enhancers of target genes can translate two similarly distributed gradients into distinct gene expression domains or stripes . Such similarly distributed antagonistic gradients may come about by induction due to a single maternal gradient or due to a terminal ( focal ) signal emanating from a discrete point or embryo pole . The general pattern formation mechanism in the case described can be represented as follows: ( 1 ) maternal/terminal signal initiates two antagonistic gradients; and ( 2 ) interactions between the two gradients produce multiple stripe patterns . In an extreme case ( e . g . , Figure 2B ) , the described “antagonistic” mechanism could use only a single gradient/polar signal to produce multiple stripes of target gene expression . The interaction between two antagonistic gradients is an example of a feed-forward loop . Due to a cascade organization of the developmental transcriptional networks , feed-forward loops are among the most common network elements ( network motifs ) ; a detailed analysis of the feed-forward networks and potential solutions can be found in a recent work by Ishihara et al [53] . To explore the interplay of antagonistic gradients in detail , we considered particular examples , such as the regulation of rhomboid ( rho ) by gradients of Twist and Snail and the regulation of knirps by the maternal gradients of Hunchback and Bicoid [54] . The enhancer associated with rho directs localized expression in ventral regions of the neurogenic ectoderm ( vNEs ) [51] . The rho vNE enhancer , as well as enhancers of other vNE genes such as ventral nervous system defective ( vnd ) , is activated by the combination of Dorsal and Twist , but is repressed by Snail in the ventral mesoderm [13 , 51] . Both Twist and Snail are targets of the nuclear Dorsal gradient , which is established by the graded activation of the Toll receptor in response to maternal determinants [55] . The Twist and Snail expression patterns occupy presumptive mesodermal domains in the embryo , yielding slightly distinct protein distributions . Our recent quantitative analysis indicates that the boundaries of rho and vnd expression are defined largely by the interplay of the two antagonistic Twist and Snail gradients ( see Figure 2D and 2F ) [6] , and the expression patterns of rho and vnd resemble the predicted solutions shown in Figure 2A . The patterning mechanism in this case can be represented as follows: ( 1 ) a terminal signal ( Toll/Dorsal gradient ) initiates two similar antagonistic gradients , Twi and Sna; and ( 2 ) Twi and Sna gradients produce multiple ( distinct ) stripe patterns ( rho , other vNE genes ) . Another example of the interplay between an activator and a repressor gradient is the early expression of the gap gene knirps in response to maternal gradients of Bicoid and Hunchback . Bicoid and Hunchback are deposited maternally and have similar , but distinct distributions—high in the anterior and low in more posterior regions of the embryo ( see Figure 2E ) . The graded drop-off of the knirps repressor Hunchback at 50%–60% egg length is steeper than that of the knirps activator Bicoid . This is similar to the theoretical case shown in Figure 2C , where a narrow repressor “splits” a wider activator expression domain , thus producing two peaks of expression of the downstream gene . Known enhancer elements of knirps drive kni expression in the anterior and the posterior embryo domains and contain binding sites for Bicoid , Hunchback , Caudal , Tailless , and Giant [44 , 56–58] . However , tailless , caudal , and giant are downstream of Bicoid; it is likely that these and some other genes participate in the later maintenance of kni expression . It has been extensively argued that gap genes ( and hunchback ) stabilize their patterns along the anterior–posterior axis by mechanism of mutual repression [49] . At later stages ( after cycle 14 ) , the inputs from Bicoid and Hunchback into knirps regulation may stabilize fluctuations in knirps expression and fluctuations in the entire gap gene network due to mutual repression . Dynamic models from Reinitz's group based on slightly different logistic response functions support the sufficiency of Bicoid and Hunchback in the establishment of the early knirps expression [59] . To explore the role of Bicoid and Hunchback interplay in the early expression of knirps , quantitative expression data for Bicoid , Hunchback , and Knirps were downloaded from the FlyEx database [60] , and models simulating the knirps enhancer response were generated based on Equations 1–4 . One model assumed that Bicoid and Hunchback bind independently from each other; another model assumed that there is an interference ( possibly competition ) between the Bicoid and the Hunchback sites ( Equation 7: competitive binding ) . Fitting the available quantitative data with the models ( see parameter values in Table 1 ) shows that both models are sufficient to explain the posterior expression of knirps . However , the competitive model ( Figure 2G ) also predicts the anterior expression of knirps . This result was especially striking , as the anterior knirps expression data were not included in some of the fitting tests . Bicoid and Hunchback motifs are quite different , so it is unlikely that this is a case of direct competition for overlapping binding sites . Other mechanisms may account for the negative interaction between the two regulators; for instance , binding of Bicoid may prevent Hunchback dimerization [61] and/or efficient binding . Shifting the knirps expression data by more than 5% along the anterior–posterior axis ( see Materials and Methods ) results in reduction of the data-to-model fit quality for the posterior kni expression domain ( see Table 1 for exact parameter values ) . The robustness of knirps regulation was emphasized earlier [59 , 62] , and the present analysis using site occupancy confirms that the interplay of the two antagonistic gradients , Bicoid and Hunchback , is sufficient to explain the initial formation of both the anterior and the posterior strips of knirps expression . To test the models describing gene response to antagonistic gradients , we introduced mutations in the rho enhancer and compared the expression patterns produced by the reporter gene in vivo with the simulated expression patterns simulated in silico ( Equations 1–6 ) . Specifically , the models for rho and vnd expression predicted the following [6]: ( 1 ) The position of the dorsal expression border of rho is highly sensitive to Twist and/or its cooperativity with Dorsal . Reducing Dl–Twi cooperativity or Twist–Twist cooperativity shifts the dorsal border ventrally . ( 2 ) The number of independent elements ( groups of closely spaced Dorsal-Twist-Snail sites , or “DTS” elements ) contributes to the expression pattern of rho and vnd according to Equation 5 ( boost ) : a higher number of DTS elements in vnd is responsible for the shift of the ventral vnd expression border relatively to rho [6] . These two specific predictions , based on the model analysis and simulations , were tested by modifying the structure of the minimal rho enhancer . First , the distance between the Dorsal and the Twist sites in the DTS element was increased ( see Figure 3 ) . The increased distance between the two sites reduced the cooperative potential between the Dorsal and Twist sites . Indeed , the observed effect in vivo is consistent with the effect of the same mutation simulated in silico , causing a ventral shift of the dorsal border of the reporter gene expression ( compare Figure 3E with 3A ) . An additional mutation eliminating the weaker Twist site from the DTS element affects Twist–Twist cooperativity in the enhancer and shifts the dorsal rho–lacZ expression border . In fact , the combined effect produced by these two mutations in vivo ( Figure 3G; compare with 3C ) and the deletion of the weak Twist site alone ( Figure 3F; compare with 3B ) demonstrate shifts of the dorsal expression border of the rho-lacZ transgene in concordance with the models . Last , a second DTS module was introduced into the rho enhancer in the context of the previous two mutations . The predicted in silico effect is a “boost” in expression , resulting in the shift of both ventral and dorsal expression borders . Again , the predicted changes in the expression pattern were observed in vivo—not only were the positions of the ventral and the dorsal border shifted ( Figure 3H; compare with 3D ) , but the overall level of expression of this transgenic construct appears higher ( unpublished data ) . The described in vivo tests of the in silico predictions using site-directed mutagenesis of the rho enhancer have demonstrated that though the quantitative models based on fractional site occupancy are approximations , they can produce reasonable predictions for the response of complex regulatory units ( such as fly enhancers ) to gradients of transcriptional regulators . Using transcriptional response models and quantitative expression data , we demonstrated how two similar terminal gradients can determine stripes of expression of downstream genes . Related examples are quite frequent in development . For instance , the posterior stripe of hunchback is the result of activation by Tailless and repression by Huckebein [63 , 64] . As in the case with Twist and Snail , the posterior gradient of Tailless is slightly broader than the gradient of Huckebein . Therefore , the mechanisms of posterior hunchback expression may be similar to the mechanisms shown in Figure 2A , 2B , 2D , and 2F . However , while the examples above involve direct transcriptional regulation in the embryonic syncytial blastoderm , extracellular morphogen gradients may produce similar outcomes if the cellular response is transcriptional in nature . Formation of eyespot patterns in butterfly wings is an elegant example of axial ( here focal ) patterning in a cellular environment ( see Figure 4A ) . The interplay between Notch and Distalless specifies the position of focal spots and intervein midline patterns in the butterfly wing [65] . Subsequent Hedgehog signaling from the focal spots is believed to induce the formation of concentric rings of gene expression and the pigmentation of the eyespots in the adult butterfly wing [66] . Known targets of the Hedgehog gradient are the butterfly homologs of engrailed and spalt [67] . Initially , both genes are expressed around the focal spot , but at later stages an external ring of engrailed expression appears around the spalt expression pattern ( see Figure 4B and 4C ) . In the case of engrailed pattern formation , a simplified mechanism [67] may include elements of the following feed-forward network: ( 1 ) focal signal ( focal spot/Hedgehog signaling ) initiates two antagonistic gradients , the activator Engrailed and the repressor Spalt; and ( 2 ) subsequent interactions between Engrailed and Spalt produce multiple ring patterns . An extension of the model in Equation 1 , ( k is the rate of synthesis and c is the rate of decay; d[R]/dt = 0 ) reproduces the dynamic changes in the engrailed pattern ( Figure 4A , 4D–4E ) : Examples of axial or focal patterning using a single source of signaling or a combination of similar antagonistic gradients are common . The interplay between maternal hunchback and maternal nanos during development of the short germ-band insect Schistocerca is an example of axial patterning similar to the interplay between Bicoid and Hunchback [68] . Specification of segments during insect limb development is comparable to the mechanisms of Twist/Snail interplay and the butterfly eyespot formation [69] . Nature uses many combinations of signals and gradients in pattern formation , but the most effective mechanism/combination may be one that allows maximal informational gain in a minimal number of steps . From this perspective , the interplay between similar or identical gradients is of significant interest .
Quantitative distribution data for Dorsal , Twist , and Snail were published previously [6] . Quantitative expression data for mRNA levels of mutated rho enhancers were generated by in situ hybridization ( the data are available at the DVEx database: http://www . dvex . org ) . Multiplex in situ hybridization probes were used for colocalization studies , including co-stainings for the endogenous mRNAs and lacZ reporter gene expression as described previously , and confocal microscopy and image acquisition were performed as described [6] . In short , signal intensity profiles of sum projections along the dorso–ventral axis of mid-nuclear cleavage cycle of 14 embryos were acquired using the ImageJ analysis tool ( National Institutes of Health , http://rsb . info . nih . gov/ij ) . Background signals were approximated by parabolic functions and subtracted according to existing methods [70] . Online programs for the automated background subtraction and data alignment are available from the University of California Berkeley Web resource ( http://webfiles . berkeley . edu/∼dap5 ) . After background subtraction , the data were resampled and aligned according to the position of Snail gradient and the distribution of endogenous rho message . Expression datasets for anterior–posterior genes were downloaded from the FlyEx database ( with options: integrated , without background ) [60] . In all cases , signal amplitude was normalized to the 0–1 range , and the data was resampled to 1 , 000 datapoints along the coordinate of the corresponding axis . In all models , we used the relative concentration multiplied by a maximal absolute concentration . This absolute concentration is an independent unknown parameter ( range , 10−8–10−9 M ) equal for all reaction components . The minimal rho enhancer [6] was mutated via site-directed mutagenesis in pGem T-Easy ( Promega; http://www . promega . com ) using the following primers: Dl-Twi distance , RZ65mut: 5′-GTTGAGCACATGTTTACCCCGATTGGGGAAATTCCCGG-3′; deletion of Twist site , RZ66mut: 5′-GGCACTCGCATAGATTGAGCACATG-3′; creation of a second DTS , RZ67mut: 5′-GCAACTTGCGGAAGGGAAATCCCGCTGCAACAAAAAG-3′; and RZ68mut: 5′-CACACATCGCGACACATGTGGCGCAACTTGC-3′ . Mutated enhancers were cloned into the insulated P-element injection vector E2G as described previously [13]: constructs were introduced into the D . melanogaster germline by microinjection as described previously [71] . Between three and six independent transgenic lines were obtained and tested for each construct; results were consistent across lines . To fit our models with actual quantitative data , we maximized the agreement r ( Pearson association coefficient ) between the model output predictions and the observed ( measured ) expression patterns: The best set of parameters X* from the parameter space I is defined by the binding constants , cooperativity values , and the number of binding sites . We used a standard hill-climbing algorithm ( full neighborhood search ) for the main parameter space ( e . g . , [72] ) . For each identified maximum , we measured the value of the site occupancy function and discarded maxima that produce site saturation values below selected thresholds , as well as such that are located beyond selected realistic parameter ranges for binding constants and cooperativity values . All maxima producing the highest data-to-model agreement were found multiple times , suggesting that exhaustive mapping of the parameter space was achieved . Fitting “shifted data” ( wrong data ) for Knirps was performed by exploring exactly the same parameter space and exactly the same number of seed points for each shift value . Quantitative gene expression data for dorso–ventral genes are available at http://www . dvex . org; the analysis tool “E-response , ” fitting utilities , and online data-treatment programs are available at the University of California Berkeley Web resource http://webfiles . berkeley . edu/∼dap5 .
|
The early development of the fruit fly embryo depends on an intricate but well-studied gene regulatory network . In fly eggs , maternally deposited gene products—morphogenes—form spatial concentration gradients . The graded distribution of the maternal morphogenes initiates a cascade of gene interactions leading to embryo development . Gradients of activators and repressors regulating common target genes may produce different outcomes depending on molecular mechanisms , mediating their function . Here , we describe quantitative mathematical models for the interplay between gradients of positive and negative transcriptional regulators—proteins , activating or repressing their target genes through binding the gene's regulatory DNA sequences . We predict possible spatial outcomes of the transcriptional antagonistic interactions in fly development and consider examples where the predicted cases may take place .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"drosophila",
"developmental",
"biology",
"computational",
"biology"
] |
2007
|
Enhancer Responses to Similarly Distributed Antagonistic Gradients in Development
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MicroRNAs ( miRNAs ) are small regulatory RNAs processed from primary miRNA transcripts , and plant miRNAs play important roles in plant growth , development , and response to infection by microbes . Microbial infections broadly alter miRNA biogenesis , but the underlying mechanisms remain poorly understood . In this study , we report that the Rice stripe virus ( RSV ) -encoded nonstructural protein 3 ( NS3 ) interacts with OsDRB1 , an indispensable component of the rice ( Oryza sativa ) miRNA-processing complex . Moreover , the NS3-OsDRB1 interaction occurs at the sites required for OsDRB1 self-interaction , which is essential for miRNA biogenesis . Further analysis revealed that NS3 acts as a scaffold between OsDRB1 and pri-miRNAs to regulate their association and aids in vivo processing of pri-miRNAs . Genetic evidence in Arabidopsis showed that NS3 can partially substitute for the function of double-stranded RNA binding domain ( dsRBD ) of AtDRB1/AtHYL1 during miRNA biogenesis . As a result , NS3 induces the accumulation of several miRNAs , most of which target pivotal genes associated with development or pathogen resistance . In contrast , a mutant version of NS3 ( mNS3 ) , which still associated with OsDRB1 but has defects in pri-miRNA binding , reduces accumulation of these miRNAs . Transgenic rice lines expressing NS3 exhibited significantly higher susceptibility to RSV infection compared with non-transgenic wild-type plants , whereas the transgenic lines expressing mNS3 showed a less-sensitive response . Our findings revealed a previously unknown mechanism in which a viral protein hijacks OsDRB1 , a key component of the processing complex , for miRNA biogenesis and enhances viral infection and pathogenesis in rice .
MicroRNAs ( miRNAs ) , a class of endogenous small RNAs processed from their primary transcripts ( pri-miRNAs ) , are crucial for plant development and responses to abiotic and biotic stresses [1–3] . Invading pathogens can manipulate the biogenesis and stability of many miRNAs to promote infection , or affect plant defense . For example , the accumulation of miR168 is elevated by infections with Cymbidum ringspot virus ( CymRSV ) , crucifer-infecting Tobacco mosaic virus ( crTMV ) , Potato virus X ( PVX ) , and Tobacco etch virus ( TEV ) in Nicotiana benthamiana and by Rice stripe virus ( RSV ) and Rice dwarf virus ( RDV ) in rice ( Oryza sativa ) . Indeed , this induced accumulation was found to promote the infection process of these viruses [4–9] . Rice ragged stunt virus ( RRSV ) and Rice black streaked dwarf virus ( RBSDV ) infections in rice increase the level of miR319 , which suppresses jasmonic acid mediated antiviral defense in rice [10] . Overexpression of miR528 in transgenic rice plants reduces the accumulation level of reactive oxygen species ( ROS ) compared with that of wild type ( WT ) , and these plants are more sensitive to RSV infection [11] . Arabidopsis miR393 suppresses auxin signaling in response to bacterial infection [12] , while miR398b is involved in defense against fungal pathogens [13] . In addition , some miRNAs that function in basal metabolism are regulated by pathogen invasion . For instance , miR395 and miR399 , which are involved in the regulation of sulfur assimilation [14 , 15] and response to phosphorus starvation [16–18] , respectively , are up-regulated by RSV infection in rice [19] . In plants , the miRNA biogenesis pathway is regulated by the cooperation of several host factors [20] . Work in Arabidopsis showed that miRNAs are processed from primary transcripts that contain partially complementary fold-back regions of variable lengths ( pri-miRNAs ) by a processing complex consisting of the RNAse III enzyme DICER-LIKE 1 ( AtDCL ) , the double-stranded RNA ( dsRNA ) binding protein HYPONASTIC LEAVES1 ( AtDRB1/AtHYL1 ) , and the zinc finger protein SERRATE ( AtSE ) [21–23] . AtHYL1 and AtSE are essential for the accurate and efficient cleavage of pri-miRNAs by AtDCL1 [21] . Homodimerization/self-interaction of AtHYL1 ( orthologous to the OsDRB1 ) ensures the correct selection of cleavage sites on pri-miRNAs [24] . After processing , the miRNA/miRNA* duplex is methylated by HUA ENHANCER 1 ( AtHEN1 ) at the 3’-terminus to ensure the stability of mature miRNA [25 , 26] . The miRNA strand is recruited by diverse ARGONAUTE ( AtAGO ) proteins to form miRNA-induced silencing complexes to mediate post-transcription gene silencing or translation repression [27–29] . Although the miRNA biogenesis pathway in plants has been well documented and many studies have indicated that miRNAs are involved in host–virus interactions , little is known about how pathogens regulate miRNA processing and accumulation . Rice stripe virus ( RSV ) , the type member of the genus Tenuivirus , causes severe disease and yield losses in many Asian rice cultivars . The RSV genome comprises four negative-sense , single-stranded RNA segments , RNA1 , 2 , 3 , and 4 . RNA1 uses a negative sense coding strategy while RNA2 , 3 , and 4 use ambisense coding strategy [30–33] . RNA1 encodes RNA-dependent RNA polymerase ( RdRp , 337 kDa ) [34] . RNA2 encodes NS2 ( 22 . 8 kDa ) , a weak suppressor of RNA silencing [35] and NSvc2 ( 94 . 2 kDa ) , a glycoprotein that targets the Golgi body and the endoplasmic reticulum ( ER ) [36] . RNA3 encodes nonstructural protein 3 ( NS3 , 23 . 9 kDa ) and the coat protein ( CP , 35 . 1 kDa ) [37 , 38] . NS3 was identified as a viral-encoded RNA silencing suppressor ( VSR ) which suppresses post-transcriptional gene silencing ( PTGS ) in N . benthamiana and binds single- or double-stranded RNA without sequence preference [37 , 38] . RNA4 encodes disease specific protein ( SP , 20 . 5 kDa ) which interferes with photosynthesis by interaction with an oxygen-evolving complex protein [30] and NSvc4 ( 32 . 4 kDa ) , a cell-to-cell movement protein [39] . Several studies have demonstrated that RSV infection perturbs miRNA accumulation , for example , 38 miRNAs including miR167 , miR168 , miR395 , miR399 etc . , were induced upon RSV infection between 7 to 15 dpi [9 , 19] , but the underlying mechanism for this is unclear . In this study , we found that RSV-encoded NS3 is responsible for the over-accumulation of several miRNAs , many of them known to regulate biotic or abiotic stress response genes , in a dsRNA-binding activity-dependent manner both in rice and Arbidopsis . In vivo experiments demonstrated that NS3 enhanced pri-miRNA processing through its dsRNA binding domain . Additionally , NS3 interacts specifically with the second dsRNA-binding domain ( dsRDB2 ) of OsDRB1 . Importantly , the NS3-interacting sites in OsDRB1 are also required for the homodimerization of the OsDRB1 and NS3 acts as a scaffold to regulate the association of OsDRB1 and pri-miRNA . Genetic analysis showed that NS3-ΔdsRBD-AtHYL1 fusion protein could partially rescue the phenotype of hyl1-2 , but NS3 or ΔD1-AtHYL1 could not do so . NS3-overexpressing transgenic rice lines enhanced RSV pathogenicity compared with the control transgenic lines expressing a mutant form of NS3 ( mNS3 ) and the WT plants . Our data revealed that the RSV NS3 protein regulates the association between OsDRB1 and pri-miRNAs , induces accumulation of a number of miRNAs , and enhances viral pathogenicity in rice .
RSV-infected rice plants show stunting , rolled-leaf and chlorotic mottling in leaves symptom compared to mock-infected rice plants at early stage ( Fig 1A to 1C ) . In a previous study , we found that RSV infection resulted in an increased accumulation of several rice miRNAs , including miR168 , miR395 , miR398 , miR399 and miR528 ( 20-nt and 21-nt forms ) [5 , 9 , 11 , 19] . To confirm the RSV-induced increase in the accumulation of these miRNAs in a different set of plants , we analyzed the differential expression of these rice miRNAs in mock-inoculated and RSV-infected rice plants by northern blotting . All tested miRNAs , with the exception of miR528 ( 21-nt ) , were up-regulated by RSV infection ( Fig 1D ) , which was consistent with previous reports [5 , 11 , 40 , 41] . To determine which RSV-encoded protein triggers the up-regulation of miRNAs in the RSV-infected rice plants , we measured the expression of these miRNAs in rice plants overexpressing various RSV-encoded proteins , driven by the ACTIN 1 promoter , with a 4×Myc epitope tag at the N-terminus . Western blot assays confirmed the expression of myc-NS2 , myc-NS3 , myc-SP , and myc-NSvc4 in the corresponding transgenic rice lines ( Fig 1E ) ( bottom two panels ) . As shown in Fig 1E , ( top three panels ) , the levels of miR168 and miR395 , measured by northern blot hybridizations , were strongly elevated by RSV infection and transgenic expression of NS3 ( Fig 1E , lane 2 and 4 ) , but not other RSV proteins . Also , RSV infection and NS3 transgene expression is produced similar elevations of miRNA levels ( Fig 1E , lane 2 and 4 ) . Therefore , expression of NS3 alone fully recapitulates the perturbation of miRNA levels caused by RSV infections . The dsRNA binding domain of NS3 is important for its activity [37] . To determine whether NS3-promoted miRNA accumulation depends on its dsRNA binding activity , we constructed a transgenic rice line overexpressing Myc-tagged mNS3 , in which the dsRNA binding domain was disrupted by the replacement of a 173K174K175R motif with173E174D175E ( Fig 2A ) . We then conducted de novo sequencing of small RNAs in the WT , and the NS3 overexpression ( OX ) #1 and mNS3 OX#1 rice lines . The results showed that miR168 , miR395 , miR398 , miR399 , and miR528 were all up-regulated in the NS3-overexpressing rice line , but down-regulated or unchanged in the mNS3-overexpressing rice line compared with the WT ( See S1 Table for more details ) ( Fig 2B ) . To verify the de novo sequencing results , we carried out northern blot and western blot assays in two independent overexpression lines each for NS3 and mNS3 ( NS3 OX#1 and #7 and mNS3 OX#1 and #4 ) . As shown in Fig 2C , most miRNAs accumulated at higher levels in the NS3 overexpression lines compared with the mock-inoculated WT , whereas lower levels were detected in the mNS3 overexpression lines . These results indicated that NS3 plays a critical role in the positive regulation of miRNA accumulations in rice and this regulation is dependent on its dsRNA binding activity . To verify that the increased accumulation of these miRNAs further reduced the expression levels of their target genes , we examined the expression levels of their target mRNAs by quantitative RT-PCR ( RT-qPCR ) . As shown in Fig 2D , mRNA levels of OsAGO1a ( miR168 ) , OsSULTR2;1 ( miR395 ) , OsCDS1 ( miR398 ) , Os08g45000 ( miR399 ) and OsRFPH2-10 ( miR528 ) were all reduced in the NS3 overexpression lines compared with the mock-inoculated WT plants , whereas expression levels in the mNS3 overexpression lines were relatively unchanged compared to the WT . To further test whether NS3 triggers accumulation of miRNAs through its dsRNA binding domain in dicots , we constructed NS3 and mNS3 overexpression transgenic Arabidopsis plants . Northern blot showed that the levels of miR168 and miR395 were up-regulated by RSV infection and NS3 overexpression in these Arabidopsis plants ( S1A and S1B Fig ) . Although overexpression of NS3 in rice did not result in disease-like symptoms , NS3-overexpressing Arabidopsis plants exhibited a severely stunted phenotype that phenocopied the disease symptoms of the RSV infected Arabidopsis ( S1C and S1D Fig ) . In contrast , overexpression of mNS3 had no influence on the growth or the levels of miR168 and miR395 in the Arabidopsis plants ( S1B and S1D Fig ) . These results indicate that induction of miRNA accumulations by NS3 overexpression is conserved in dicots and monocots . To determine whether RSV infection and NS3 overexpression increases the accumulation of these miRNAs through promoting primary miRNA ( pri-miRNA ) processing , we measured the primary transcript levels of miR168 , miR395 , miR398 , miR399 , and miR528 by RT-qPCR . The results demonstrated that NS3 , but not mNS3 , down-regulated all of the pri-miRNAs , except for pri-miR399d , which suggests that NS3 is involved in pri-miRNA processing in a dsRNA-binding activity-dependent manner ( Fig 2E ) . Given that NS3 reduces the accumulation of a set of pri-miRNAs but induces the accumulation of corresponding mature miRNAs , we suggested that NS3 may promote the recruitment of pri-miRNAs by affecting the miRNA-processing complex . Since the thermo stability of the end of the miRNA/miRNA* duplex is important for mature miRNA accumulation [42] , we designed an experiment in which a pri-miRNA can be recognized by NS3 , but not by the processing complex , which would indicate that NS3 directly assists in the association of the processing complex with pri-miRNAs when NS3 is co-expressed with the pri-miRNA . We constructed a 35S promoter-driven artificial primary miR528 ( apri-miR528 ) and mutant artificial primary miR528 ( mapri-miR528 ) with three additional C/G pairs at the end of the miRNA/miRNA* duplex using an Arabidopsis primary miR159a backbone ( Fig 3A ) . miR528 is only expressed in monocot plants; therefore , we carried out an in vivo transient expression assay to co-express apri-miR528 and vector/NS3/mNS3 and also mapri-miR528 and vector/NS3/mNS3 in the leaves of the dicots N . benthamiana and measured mature artificial miR528 ( amiR528 ) levels in each group by northern blotting at 3 days post-infiltration ( dpi ) . We found that expression of NS3 had little influence on the accumulation of amiR528 in the apri-miR528 group , but expression ofmNS3 reduced the levels of miR528 relative to the vector control . As expected , no miR528 accumulation was observed in the negative control . Additionally , no mature miR528 accumulation was detected in the mapri-miR528 group in the absence of NS3 expression . The accumulation of mature miRNA was only detected in the leaves co-expressing mapri-miR528 with NS3 ( Fig 3B ) . To test if NS3 interacts with apri-miR528 and mapri-miR528 , an in vitro microscale thermophoresis assay with GST-mNS3 serving as the negative control was used to reveal that both apri-miR528 and mapri-miR528 were recognized by GST-NS3 ( Fig 3C ) . These results provide further evidence for the role of NS3 in mature miRNA biogenesis . NS3 does not contain an RNase III domain; therefore , it cannot promote miRNA accumulation by itself . We hypothesized that NS3 may function in miRNA processing through its association with components of the Dicing body ( D-body ) to promote the recruitment of the pri-miRNA by the processing complex . To test this hypothesis , we performed bimolecular fluorescence complementation ( BiFC ) assays to co-express Arabidopsis DCL1 ( AtDCL1 ) , SE ( AtSE ) , HYL1 ( AtHYL1 ) , or CBP20 ( AtCBP20 ) with NS3 in N . benthamiana leaves . NS3 associated with AtHYL1 and AtSE but not with AtDCL1 or AtCPB20 . In addition , we found that AtHYL1 , but not AtSE , specifically interacted with NS3 in the D-body , and the interaction between NS3 and AtHYL1 may involve in miRNA maturation ( S2A Fig ) . Using the basic local alignment search tool ( BLAST ) and the UniProt protein database ( uniprot . org ) , we found six AtHYL1 homologs in rice , OsDRB1a , OsDRB1b , OsDRB1c , OsDRB2 , OsDRB3 and OsDRB4 ( S2B Fig ) . To confirm that OsDRB1 has the same function as AtHYL1 , we measured the levels of miR164 , miR166 , and miR168 by small-RNA RT-qPCR in an OsDRB1-knockdown rice line and found that these miRNAs were down-regulated ( S2C Fig ) . We also analyzed OsDRB1 protein levels , and found that the level of OsDRB1 was reduced in the OsDRB1-knockdown line ( S2D Fig ) . OsDRB1a contains all the domains of the other two OsDRB1s , as well as a unique C-terminus , so we chose OsDRB1a to test the interaction between NS3 and OsDRB1 , BiFC and co-immunoprecipitation ( CoIP ) assays demonstrated that OsDRB1a does interact with NS3 ( Fig 4A and 4B ) . We also tested interactions between NS3 and the other OsDRBs by BiFC assay , and found that only OsDRB2 has a weak interaction with NS3 in cytoplasm ( S2E Fig ) . Previous studies have shown that homodimerization of AtHYL1 is required for AtDCLs to locate the correct cleavage sites in pri-miRNAs , while the G147 and L165 residues of AtHYL1 are critical for homodimer formation [24] . The G162 and L180 residues of OsDRB1a correspond to the G147 and L165 residues of AtHYL1 and may be essential for homodimer formation according to amino acid alignment ( S2F Fig ) . To test our hypothesis , we constructed an OsDRB1a mutant ( mOsDRB1a ) by replacing the G162 and L180 residues with E162 and E180 , respectively ( Fig 4C ) . In a subsequent BiFC assay , we found that mOsDRB1a could not interact with itself ( Fig 4D ) , as expected . We further confirmed that wild-type OsDRB1a , like AtHYL1 , formed a homodimer ( Fig 4E ) [24 , 43] . Also , mOsDRB1a could not interact with NS3 or mNS3 ( Fig 4F ) , but NS3 interacts with itself , mNS3 has weak interaction with NS3 , and OsDRB1a has weak interaction with mOsDRB1a too ( S2G Fig ) . We summarize the interaction of each pair between NS3 , mNS3 , OsDRB1a and mOsDRB1a in Table 1 . These results demonstrated that the association between NS3 and OsDRB1a depends on the dsRBD2 domain of OsDRB1a and is essential for miRNA processing . Given that the dsRNA-binding activity of NS3 is important for the processing of pri-miRNAs and the accumulation of miRNA , and NS3 interacts with DRB1 , we deduced that NS3 , rather than DRB1 , recognizes pri-miRNAs during NS3–DRB1 interactions . To test this hypothesis , we transiently co-expressed OsDRB1a/mOsDRB1a , apri-miR528/mapri-miR528 , or empty vector/NS3/mNS3 in N . benthamiana and detected the CoIP products of OsDRB1a/mOsDRB1a by RT-PCR . We found that both OsDRB1a and mOsDRB1a associated with apri-miR528 , but neither of them recognized mapri-miR528 , and NS3 and mNS3 associated with OsDRB1a instead of mOsDRB1a . With the expression of NS3 , both OsDRB1a and mOsDRB1a associated with apri-miR528 . However , with the expression of mNS3 , only mOsDRB1a associated with apri-miR528 . Additionally , only co-expression with NS3 resulted in an OsDRB1a interaction with mapri-miR528 ( Fig 5A ) , see Table 2 for more details . Using a microscale thermophoresis assay , we also found that OsDRB1a associated with apri-miR528 but not mapri-miR528 ( Fig 5B ) . We also found that NS3 , but not mNS3 , could bind with the endogenous miRNA precursors ( pre-miR168a , pre-miR395d , pre-miR398a , pre-miR399d , and pre-miR528 ) in an electrophoretic mobility shift assay ( EMSA ) ( S3A Fig ) . These results indicated that by interacting with DRB1 , NS3 replaced the dsRNA-binding activity of DRB1 . To test this hypothesis , we overexpressed AtHYL1 , ΔD1-HYL1 , a double-stranded RNA binding domain 1 deletion form of AtHYL , NS3-ΔD1D2-HYL1 , a fusion protein of NS3 and ΔD1D2-HYL1 and NS3 ( Fig 5C ) in Arabidopsis hyl1-2 mutant background , the transgenes were all driven by a 35S promoter with the proteins tagged with a myc-epitope tag at the N-terminus , we found that the fusion protein NS3-ΔD1D2-AtHYL1 could ameliorated the phenotype ( Fig 5D ) and miRNA ( miR156 , miR164 , miR168 and miR395 ) levels ( Fig 5E ) of hyl1-2 mutant as HYL1 did but NS3 or ΔD1-HYL1 could not ( Fig 5D and 5E ) , we test these transgenic Arabidopsis plants by western blotting ( Fig 5F ) , and this results indicated that NS3 could substitute for the dsRBD domain of AtHYL1 in miRNA processing . Because NS3 induced miRNA accumulation along with a decreased antiviral defense response in rice , we speculated that NS3 may play a role in regulating viral pathogenicity . We used virus-free ( mock ) and viruliferous ( RSV ) planthoppers ( Laodelphax striatellus ) to inoculate the WT , and NS3- and mNS3-overexpression rice lines ( NS3 OX#1 and OX#7 and mNS3 OX#1 and OX#4 ) and found that the NS3 OX#1 and OX#7 lines , but not the mNS3 OX#1 and OX#4 lines , were hypersensitive to RSV infection compared with WT plants , with most serious stunted and chlorisis phenotypes ( Fig 6A ) . To examine whether the increased susceptibility of the NS3 OX lines was due to the increased accumulation of RSV , we used RT-qPCR to measure the transcript levels of the RSV CP gene . We found that the expression of CP mRNA in the NS3 OX#1 and #7 lines was much higher than that in the WT plants , with no obvious changes detected in the mNS3 OX#1 and #4 lines when compared with the WT ( Fig 6B ) . We also monitored differences in RSV infection rates among the WT and the NS3 OX#1 , NS3 OX#7 , mNS3 OX#1 , and mNS3 OX#4 lines every 3 days until 21 dpi . These observations indicated that NS3 increased RSV pathogenicity in rice ( Fig 6C and S3 Table ) . To test if NS3 OX plants are also more sensitive to infection with other viruses , we used Rice ragged stunt virus ( RRSV ) , a member of the genus Oryzavirus , to infect the WT , and the NS3 OX#1 , NS3 OX#7 , mNS3 OX#1 , and mNS3 OX#4 lines . The results showed that NS3 OX plant lines displayed a stunted phenotype ( S4A Fig ) and accumulated more RRSV CP genes ( S4B Fig ) compared with other rice plant lines .
RNA interference ( RNAi ) is a conserved and effective antiviral mechanism in plants and insects [44] . To counter the host’s antiviral defense mechanisms , viruses encode VSR ( s ) to interfere with the host’s RNAi . Most reported VSRs act to suppress the host’s RNA silencing system , such as by inhibiting viral RNA recognition , blocking dicing , suppressing assembly of the RNA-induced silencing complex ( RISC ) , and preventing siRNA amplification [44–50] . RSV NS3 , a reported RNA silencing suppressor , suppresses post-transcriptional gene silencing ( PTGS ) in N . benthamiana through its dsRNA binding ability [38] . Our previously obtained small RNA sequencing data of changes associated with RSV infection revealed that many miRNAs are induced by RSV infection [9 , 19 , 41] . Up to now , only a few studies have focused on how a virus hijacks the RNA silencing pathway to regulate miRNA accumulation and advance its own pathogenicity , with very limited reports focusing on how virus regulates miRNA processing . In the present study , we revealed that NS3 exploits OsDRB1 , a key component of the D-body , to promote the processing of pri-miRNA along with the regulation of miRNA target gene expression ( Fig 7 ) . NS3 showed a weak interaction with OsDRB2 in the cytoplasm ( S2G Fig ) . Since miRNA processing is occurred in the nucleus , we deduced that the association of NS3 and OsDRB2 would not affect NS3-mediated miRNA processing . Previous studies showed that NS3 suppression of the PTGS pathway may depend on its siRNA and long dsRNA binding activity [37 , 38] , which is different from the results of this study showing that NS3 enhanced the miRNA pathway through its function of bridging pri-miRNA and OsDRB1 , a key player in miRNA processing complex . These two different functions may act in parallel . These results widen our knowledge of the molecular mechanisms underlying viral-host interactions . Maturation of miRNAs is a complex process . miRNA-coding genes ( MIRs ) are transcribed by DNA-dependent RNA polymerase II ( Pol II ) and subjected to splicing and addition of a 5’ 7-methyguanosine cap and a 3’ polyadenylated tail [51] . Besides Pol II , many other Pol II-associated factors such as RNA-binding proteins PLEIOTROPIC REGULATORY LOCUS 1 and DAWDLE , CAP BINDING PROTEIN 20 and 80 , SE , CAM33/XAP CIRCADIAN TIMEKEEPER and TOUGH have been reported to regulate miRNA transcription [52–59] . The structures of pri-miRNAs also affect pri-miRNA processing [60 , 61] . RSV infection results in the differential expression of pri-miRNAs in rice . For example , pri-miR168a and pri-miR399d were up-regulated , while other pri-miRNAs were down-regulated ( Fig 2E ) . We revealed that NS3 mainly decreases the accumulation of pri-miRNAs , but the mechanism by which NS3 influences pri-miRNA expression levels remains unclear . De novo smallRNA sequencing showed that NS3 and mNS3 have a strong influence on the accumulation of miRNAs , but not on other types of smallRNAs . The total number of miRNA reads in the NS3 OX lines was higher than that in the WT , and the total number of miRNA reads in the mNS3 OX lines was lower than that in the WT ( S3B Fig ) , suggesting that NS3 may directly promote pri-miRNA recruitment by the processing complex . This hypothesis was supported by the finding that NS3 aids in in vivo pri-miRNA processing ( Fig 3B ) . Further investigations showed that NS3 associated with OsDRB1a at sites required for OsDRB1a self-interaction ( Fig 4F ) and promoted the association of OsDRB1a with pri-miRNAs ( Fig 5A ) . SmallRNA sequencing showed that the lengths and sequences of miRNAs were not changed by NS3 or mNS3 overexpression in rice ( S1 Table ) . These results suggested that NS3 promotes recruitment of pri-miRNAs by the processor complex through an interaction with DRB1 . The observation that NS3 overexpressing rice plants showed no notable developmental abnormities at vegetative stages under normal growth conditions may relate to the presence of target genes of miRNAs , regulated by NS3 , in a complex genome , and a similar phenomenon was previously described [3 , 56 , 62] . A study in Arabidopsis showed that a set of miRNAs is regulated by DRB1 [63] . NS3 up-regulates several miRNAs in a DRB1 dependent manner ( Fig 5D ) . These results may explain why NS3 could not enhance some miRNA which are DRB1-independent . But it’s still unknown how the preferential regulation of DRB1-dependent miRNA by NS3 , we did not find similarities of sequences or secondary structure between precursors of DRB1-dependent miRNA . Understanding the effects of NS3 of miRNA biogenesis will widen our understanding of the functions of virus-encoded proteins in regulating miRNA metabolism . Our findings of the association of NS3 with miRNA processing and accumulation provides insights into RSV pathogenicity and helps identify important targets for RSV infection , which can offer new strategies for the breeding or genetic engineering of RSV-resistant rice .
Rice ( Oryza sativa spp . japonica ) seedlings were grown in a greenhouse at 28–30°C and 60 ± 5% relative humidity under natural sunlight for 4 weeks . Arabidopsis thaliana plants were grown at 22°C under long-day conditions ( 16-h light/8-h dark ) . When the seedlings were about 10 days old , they were inoculated using viruliferous ( RSV ) or virus-free ( mock ) planthoppers ( L . striatellus ) at a ratio of four insects per plant for 72 h . After removal of the insects , the inoculated plants were returned to the greenhouse and monitored daily for the appearance of viral symptoms . The number of symptomatic rice plants of each line was recorded ( Fig 6C ) . The Gateway system ( Invitrogen ) and the enzyme digestion connection method were used to make binary constructs . Binary gateway vectors pSAT4A-DEST-N ( 1–174 ) EYFP-N1 , pSAT5A-DEST-C ( 175-END ) EYFP-N1 , pEarleyGate202 , and pEarleyGate203 [64] were used for transient expression in N . benthamiana and stable transformation of Arabidopsis , while binary vectors pGEX-4T-1 and pCam2300:Actin1::OCS were used for Escherichia coli and stable rice transformations . Most cDNA and miRNA genes were cloned into pENTR/D and pEASY Blunt Zero vectors , and pENTR/D-mNS3 and mOsDRB1a clones were prepared using a Quik Change site-directed mutagenesis kit ( Stratagene , La Jolla , CA , USA ) . After confirmation by sequencing , all clones were transferred to the appropriate destination vector by recombination using the Gateway LR Clonase II Enzyme mix ( Invitrogen ) or T4 DNA ligase ( TransGen Biotech , Beijing , China ) . Agrobacterium tumefaciens-mediated rice transformation was carried out at Weiming Kaituo Co . , Ltd . ( Beijing , China ) . Transgenic Arabidopsis plants were selected by their resistance to Basta on soil . Total RNAs were extracted from plants using Trizol ( Invitrogen ) . The extracted RNAs were treated with RNase-free DNase I ( Promega , Madison , WI , USA ) to remove DNA contamination and then reverse-transcribed with SuperScript III reverse transcriptase ( Invitrogen ) using oligo ( dT ) primers and a Mir-X miRNA first-strand synthesis kit ( Clontech Laboratories ) . The resulting cDNAs were then used as templates for RT-qPCR and RT-PCR . RT-qPCR was performed using SYBR Green Real-time PCR Master Mix ( Toyobo , Osaka , Japan ) . The rice OsEF-1a gene was detected in parallel and used as an internal control . Northern blot analyses were performed as previously described [65] . Briefly , RNA samples were separated by 15% denaturing gel electrophoresis and transferred onto Hybond-N+ membranes ( Amersham , Fairfield , CT , USA ) . Membranes were UV cross-linked and hybridized to 32P end-labeled oligonucleotide probes . Sequences of primers used in RT–qPCR and northern blot assays are listed in S2 Table . The Microscale Thermophoresis assays were performed as previously described [66 , 67] . GST-NS3 , GST-OsDRB1a , and GST-mNS3 proteins were individually labeled with NHS red fluorescent dye according to the instructions of the RED-NHS Monolith NT Protein Labeling kit ( NanoTemper Technologies GmbH , München , Germany ) . In protein and RNA interaction assays , the concentration of NHS-labeled protein was maintained at 100 nM , whereas RNA concentrations were gradient-diluted ( 20 , 000 nM , 10 , 000 nM , 5 , 000 nM , and then 2-fold dilutions until 10 nM ) . The RNA was denatured and annealed to form dsRNA , followed by addition of RNase inhibitor ( 1 U per group ) . After a short incubation , the samples were loaded into MST standard-treated glass capillaries . Measurements were performed at 25°C and LED and MST powers of 20% in buffer containing 20 mM Tris ( pH 8 . 0 ) and 150 mM NaCl . The assays were repeated two times for each affinity measurement . Data analyses were performed using Nanotemper Analysis and OriginPro 8 . 0 software provided by the manufacturer . The pGEX-4T-1-NS3 , pGEX-4T-1-mNS3 , and pGEX-4T-1-OsDRB1a constructs , as well as empty pGEX-4T-1 vectors , were individually transformed into E . coli Transetta ( DE3 ) ( Transgene , Beijing , China ) with protein expression induced by IPTG . The soluble GST fusion proteins were extracted and immobilized onto glutathione sepharose beads ( Amersham ) . RNAs were labeled with 32P–UTP using a T7 RNA Production system ( Promega ) and purified with a MEGAclear kit ( Ambion , Waltham , MA , USA ) . Approximately 2 , 000 cpm of radioactive RNA was denatured and then annealed to form dsRNA . The dsRNA was incubated with individual GST fusion proteins in the presence of RNase inhibitor ( 1 U per group ) for 30 min on ice . The mixtures were then run on an RNase-free native PAGE gel for 1 h at 4°C . The procedures used for protein extraction and western blotting have been described previously [5] . The following antibodies were purchased from commercial sources: anti-flag-peroxidase ( Sigma , St . Louis , MO , USA ) , anti-Myc-peroxidase ( Sigma ) , anti-Myc ( Sigma ) , anti-FLAG ( Sigma ) , anti-Actin ( Easybio , Beijing , China ) , and anti-HYL1 ( Agrisera AB Box 57 , SE-911 21 Vännäs , Sweden ) . Small RNA cloning for Illumina sequencing was carried out at Bainuodacheng Co . , Ltd ( Beijing , China ) . Rice miRNA annotations were obtained from miRBase ( http://microrna . sanger . ac . uk/sequences , Release 14 ) . Statistical analysis of the small RNA data sets was performed using in-house Perl scripts [5] . BiFC assays were performed as previously described [22] . Briefly , we transformed the recombinant constructs into A . tumefaciens strain EHA105 and injected the Agrobacterium cultures into N . benthamiana leaves . After 3 days , we observed the tobacco epidermal cells with a confocal laser scanning microscope ( LSM 710 NLO & DuoScan System , Zeiss ) . RNA immunoprecipitation was carried out essentially as previously described [68] using 1% formaldehyde-treated leaves , transiently expressed protein , and RNA for 72 h . RIP was carried out with the following modifications: nuclei were isolated , resuspended in high salt nuclear lysis buffer ( 20 mM Tris-HCl , pH 7 . 5 , 500 mM NaCl , 4 mM MgCl2 , 0 . 2% NP-40 ) . The chromatin supernatant was diluted five times with dilution buffer ( 20 mM Tris-HCl , pH 7 . 5 , 4 mM MgCl2 , 0 . 2% NP-40 ) and immunoprecipitated using antibody . Pri-miRNAs were detected in the immunoprecipitates by RT-PCR using the artificial primers miR528-5p and -3p . Sequence data generated in this study can be found in GenBank ( https://www . ncbi . nlm . nih . gov/genbank/ ) , the Rice Genome Database ( http://rice . plantbiology . msu . edu/ ) and the Arabidopsis Information Resource ( http://www . arabidopsis . org/ ) under the following accession numbers: NS3 ( AY284945 . 1 ) , AGO1a ( LOC_Os02g45070 ) , SULTR2;1 ( LOC_Os03g09930 ) , CDS1 ( LOC_Os07g46990 ) , LOC_Os08g45000 , OsRFPH2-10 ( LOC_Os06g06050 ) , OsDRB1a ( LOC_Os11g01869 ) , OsDRB1b ( LOC_Os12g01916 ) , OsDRB1c ( LOC_Os05g24160 ) , OsDRB2 ( LOC_Os10g33970 ) , OsDRB3 ( LOC_Os09g33460 ) , OsDRB4 ( LOC_Os01g56520 ) , AtHYL1 ( At1g09700 ) , AtDCL1 ( At1g01040 ) , AtSE ( At2g27100 ) , and AtCBP20 ( At5g44200 ) , AtDRB2 ( At2g28380 ) , AtDRB3 ( At3g26932 ) , AtDRB4 ( At3g62800 ) , AtDRB5 ( At5g41070 ) .
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MicroRNAs ( miRNAs ) regulate gene expression at the transcriptional or post-transcriptional level and have emerged as key players in regulating plant growth , development and response to biotic and abiotic stresses . Accumulating evidences suggest that miRNAs are pivotal modulators of host–virus interactions , but how virus regulates miRNA accumulation remains poorly understood . Here , we report that NS3 protein encoded by Rice stripe virus ( RSV ) regulates the processing of several primary miRNA transcripts ( pri-miRNAs ) by acting as an intermediary to modulate the association of pri-miRNAs and OsDRB1 , a key factor of the pri-miRNA processing complex . NS3 increases recruitment of pri-miRNA to the processing complex by its association with OsDRB1 at the sites required for OsDRB1 dimer formation and induces several miRNAs accumulations as well as target genes repression , promoting the sensitivity of rice to RSV infection . Together these findings reveal a novel mechanism by which RSV regulates pri-miRNA processing , leading to enhanced viral infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[] |
2017
|
Rice stripe virus NS3 protein regulates primary miRNA processing through association with the miRNA biogenesis factor OsDRB1 and facilitates virus infection in rice
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Snake bite causes greater mortality than most of the other neglected tropical diseases . Snake antivenom , although effective in minimizing mortality in developed countries , is not equally so in developing countries due to its poor availability in remote snake infested areas as , and when , required . An alternative approach in this direction could be taken by making orally deliverable polyvalent antivenom formulation , preferably under a globally integrated strategy , for using it as a first aid during transit time from remote trauma sites to hospitals . To address this problem , multiple components of polyvalent antivenom were entrapped in alginate . Structural analysis , scanning electron microscopy , entrapment efficiency , loading capacity , swelling study , in vitro pH sensitive release , acid digestion , mucoadhesive property and venom neutralization were studied in in vitro and in vivo models . Results showed that alginate retained its mucoadhesive , acid protective and pH sensitive swelling property after entrapping antivenom . After pH dependent release from alginate beads , antivenom ( ASVS ) significantly neutralized phospholipaseA2 activity , hemolysis , lactate dehydrogenase activity and lethality of venom . In ex vivo mice intestinal preparation , ASVS was absorbed significantly through the intestine and it inhibited venom lethality which indicated that all the components of antivenom required for neutralization of venom lethality were retained despite absorption across the intestinal layer . Results from in vivo studies indicated that orally delivered ASVS can significantly neutralize venom effects , depicted by protection against lethality , decreased hemotoxicity and renal toxicity caused by russell viper venom . Alginate was effective in entrapping all the structural components of ASVS , which on release and intestinal absorption effectively reconstituted the function of antivenom in neutralizing viper and cobra venom . Further research in this direction can strategize to counter such dilemma in snake bite management by promoting control release and oral antivenom rendered as a first aid .
The World Health Organization ( WHO ) [1] has enlisted snake bite as one of the neglected tropical diseases . About 5 . 5 million snake bites resulting in about 40 thousand amputations and 20 to 125 thousand deaths have greater mortality than that from other neglected tropical diseases viz . dengue , hemorrhagic fever , cholera , leishmaniasis , schistosomiasis , Japanese encephalitis , and Chagas' disease [2] . In India the magnitude of mortality is grave , at about 0 . 47% of total deaths [3] . Although antisnake venom serum ( ASVS ) is effective in keeping the mortality low in developed countries , in developing countries the same solution is rendered ineffective by several factors typical to neglected tropical diseases . Brown [4] has encountered lack of effective , safe and affordable therapy in developing countries while , Warrel [5] , suggested improving production and clinical use of antivenom . Critical analysis of high mortality from snake bite not only indicates shortcomings of ASVS alone , but also insufficiency of infrastructure in snake infested developing countries . Prognosis depends on early ASVS administration which needs hospitalization for intravenous delivery and for treating hypersensitive reaction from ASVS . Transit time to hospital thus is an important determinant factor in outcome as bites mostly occur in remote places . In most of the developing countries remoteness , cost and heat-instability of ASVS are major contributing factors of the inaccessibility of ASVS [6] . Remoteness increases the cost further than the production cost by adding to the cost of distribution , storage , administration and of providing infrastructure for reaching remote areas . So , making ASVS effectively available is a critical factor which requires globally integrated knowledge based strategy [7] . An approach to address the problem of remoteness suggested use of Geographical Information Systems for cost effective utilization of ASVS [8] . In this work we have elaborated another approach to develop readily available and orally deliverable polyvalent ASVS formulation for use it as first aid by local health practitioners during transit to hospital . This approach can change the prognosis of snake bite by preventing the irreversible damage from venom resulting during transit time . Oral and controlled ASVS delivery as first aid prior to hospitalization can change the prognosis by multiple factors – 1 . Use of easy-to-administer ASVS as first aid , 2 . Less irreversible damage from venom during transit time 3 . Less reliance on faith healers if treatment could be started immediately , 4 . Less chance of adverse effect during transit due to controlled release properties of oral formulation . These factors together can make local and timely availability of ASVS feasible . Moreover , as a part of a global strategy , it can help in designing a global oral formulation for first aid . Many approaches were taken for delivering protein drugs through oral route like the use of polymers [9]–[11] , liposome based drug delivery [12] , [13]or by using nanotechnology [14]–[16] . Present study was aim to encapsulate a drug which unlike insulin , BSA , or immunoglobulin , is a combination of multiple heterogenous proteins of different molecular weight and isoelectric pHs . Drug delivery research has not yet dealt with such problems where the components are not only multiple but also not studied individually . Therefore , we took alginate a biocompatible , biodegradable , non toxic polymer , known to encapsulate a wide variety of molecules [17] , for bead preparation and studied , whether upon entrapment , the beads retain the advantages of alginate while the antivenom , retains its structural components which , on pH dependent release , preserve the capacity to neutralize venom activity .
All animal experiments were approved by the Animal Ethics Committee of the University of Calcutta and were in accordance with the guidelines of the Committee for the Purpose of Control and Supervision of Experiments on Animal ( CPCSEA ) , Government of India ( IAEC Ref no:820/04/ac/CPCSEA . 2010 ) . Wistar strain male albino rats of about 9–12 weeks old ( 240±20 g ) were used in the experiment . Animals were collected from Chakraborty and company , Calcutta , and housed under controlled environment ( RT: 22±2°C , relative humidity: 60±5% , 12 h day/night cycle ) with balanced diet and water ad libitum . All animal experiments were approved by the animal ethical committee , Department of Physiology , Calcutta University and were in accordance with the guideline of the committee for the purpose of control and supervision of experiments on animal ( CPCSEA Ref no: 820/04/ac/CPCSEA . 2010 ) , Government of India . Indian spectacle cobra ( Naja naja ) venom and Russell's viper ( Daboia russelii ) venom was gifted by Dr . Debanik Mukherjee , Field Biologist ( Herpetology ) , Centre for Environmental Management of Degraded Ecosystems ( CEMDE ) , University of Delhi , India . Venoms were lyophilized and stored at 4°C in amber colored bottle until further use . For the experiments , relevant venoms were weighed , dissolved in 0 . 9% saline and used at appropriate dilutions . ASVS solution was prepared using physiological saline and protein concentration was measured [18] . 1 gm of ASVS: alginate:: 1∶1 beads were taken in a beaker with 4 ml of phosphate buffer solution ( 10 mM , pH 7 . 0 ) and kept for 4 h to complete the release of encapsulated ASVS . Released protein was collected and concentration was measured [18] . Native polyacrelamide gel electrophoresis ( 12 . 5% ) ( PAGE ) was performed to observe the banding pattern of normal ASVS and released ASVS after coomassie brilliant blue staining . High performance liquid chromatography of ASVS and released ASVS were performed using C18 column ( 4 mm×250 mm , flow rate: 0 . 5 ml/minutes , solvent: 60∶40∶0 . 2:: methanol: water: acetic acid ) . Acid digestion study was performed according to Li et al . [19] . 20 mg of alginate entrapped ASVS beads were taken in a 10 ml beaker . 0 . 5 ml of hydrochloric acid ( 0 . 01M ) was added into it and was placed in 37°C for 2 h . After 2 h reaction was stopped and neutralized by addition of 0 . 01M sodium hydroxide solution . After neutralization volume of the solution was made up to 4 ml with phosphate buffer ( pH 7 . 0 ) and placed in 37°C for 24 h for complete release of ASVS . It was then centrifuged and ASVS concentration of the supernatant was measured as protein concentration according to ultraviolet absorption . The activity of the released ASVS after digestion was assessed by phospholipase A2 ( PLA2 ) inhibition assay . To study ASVS absorption ASVS was tagged with FITC . Overnight dialysis was performed to washout unbound FITC from the solution . This FITC tagged ASVS was then used in isolated intestinal preparation for absorption kinetic study . At first jejunum section of intestine ( about 10 cm ) was isolated from male Swiss albino mice under phenobarbital anesthesia and washed with Krebs-Ringer bicarbonate solution , pH 7 . 4 . One side of the intestine was tied and FITC tagged ASVS in Krebs-Ringer bicarbonate solution was poured into the intestine through the hypodermic needle and then other side was also tied . The intestine was placed in a medium standard with 95% O2 , 5% CO2 in phosphate buffer solution pH 7 . 4 at 37°C . O2 and CO2 mixture was bubbled into the solution to obtain intestinal peristaltic movement . After every one hour interval , 50 µl of buffer samples from the medium outside the intestine were collected and FITC intensity of that solution was measured using spectroflurometer ( Jasco FP-6200 , Japan ) . Excitation at 490 nm was used for FITC tagged ASVS and 505 nm to 530 nm emission spectra was recorded . A bandwidth of 5 nm was used for the assay . At the end of the assay the intestinal tissue was washed to remove unabsorbed FITC into the intestinal layer and it was homogenized . Intensity of FITC in the supernatant from the homogenate was again analyzed in same measurement by spectroflurometer using the same excitation and emission wavelengths respectively , to indentify incorporation of ASVS within the intestinal layers . ASVS absorbed from the ex vivo intestinal preparation was collected from the medium outside the intestine compartment of the preparation , concentrated and pre-incubated with venom for neutralization studies . Phospholipase A2 activity of Naja naja and Daboia russelii venom was measured according to Dole , 1956 [24] . In this method free fatty acids released from egg yolk phospholipids suspended in 1% triton ×100 , 0 . 1 M Tris HCL , 0 . 01M CaCl2 , pH-8 . 5 buffer was titrated against snake venom ( 5 µg ) . It was incubated at 37°C for 15 minutes . Results are depicted as inhibition percentage , where 100% is the activity induced by snake venom alone . Naja naja venom was taken for study of hemolytic activity . Blood was collected aseptically from left radial vein of male healthy volunteers in heparinised vial and centrifuged at 3000 rpm for 15 minutes . The supernatant was discarded and the pellet containing human red blood cells ( HRBC ) were washed thrice with normal physiological saline by repeated centrifugation at 3000 rpm for 15 minutes . An erythrocyte suspension ( hematocrit of 20% ) was prepared and 250 µl of it was taken in each tube to which 50 µl Naja naja venom of different doses ( 2 . 5 µg–40 µg ) were added . All the tubes were incubated for 1 h at 37°C . After incubation RBC solution was centrifuged at 3000 rpm for 15 minutes . The supernatant was separated and hemoglobin concentration was measured spectrophotometrically at 540 nm . Median hemolytic dose was calculated and ∼60% of the median hemolytic dose ( 10 µg ) was used for neutralization study with normal ASVS , ASVS released from alginate beads . In brief , different concentration of normal ASVS ( 2 . 5 µg–50 µg ) /released and absorbed ASVS ( 2 . 5 µg–50 µg ) was incubated with 10 µg of venom for 1 h at 37°C . After incubation it was centrifuged at 2000 rpm for 15 minutes and the supernatant was incubated with 20% RBC suspension ( 250 µl ) for 1 h at 37°C . After incubation RBC solution was centrifuged at 3000 rpm for 15 minutes . The supernatant was separated and hemoglobin concentration was measured spectrophotometrically at 540 nm [25] . Different concentration of ASVS ( 25 µg ) and released ASVS ( 25 µg ) was incubated with 10 µg of Naja naja venom for 1 h at 37°C . After incubation it was centrifuged and the supernatant was incubated with 20% RBC suspension ( 250 µl ) for 1 h at 37°C . After incubation RBC solution was centrifuged at 3000 rpm for 15 minutes supernatant was separated and lactate dehydrogenase level of the supernatant was measured spectrophotometrically at 340 nm as indicated in the assay kit . Hemorrhagic activity was quantitatively determined by the method of Kondo et al . [26] using mice as described by Sânchez et al . [27] . Hemorrhagic activity was assessed by injecting 0 . 1 ml ( 10 µg ) of Daboia russelii venom in saline intradermaly into the back of the mice ( 20±2 gm ) . The hemorrhagic activity was estimated by measuring the diameter ( cm ) on the visceral side after 24 hour of intradermal injection . Neutralization was performed by both normal ASVS and released ASVS at the same concentration ( 25 µg ) . The lethality of Daboia russelii venom and Naja naja venom was assessed by injecting different concentration of venom in 0 . 2 ml of 0 . 9% physiological saline ( pH 7 . 4 ) into tail vein of male albino mice ( 20±2 gm ) [28] and survivality was recorded up to 24 h . After determining the minimum lethal dose ( MLD ) , neutralization of MLD was studied with ASVS and absorbed ASVS . In brief , normal ASVS ( 25 µg ) /released and absorbed ASVS ( 25 µg ) were incubated with venom ( MLD dose ) for 1 h at 37°C and centrifuged at 2000 rpm for 10 minutes . The supernatant was injected into the tail vein and neutralization was assessed for 24 h . Thermo stability of alginate coated ASVS was evaluated by keeping those protein loaded beads at room temperature for 30 days and then neutralization study was performed with the released protein from those beads as described above against russel viper venom . All the results were expressed as mean±SEM , n = 6 . Level of significance was determined by one way ANOVA followed by Tukey's post hoc test . p<0 . 05 was considered as significant .
Entrapment efficiency and loading capacity were studied by varying alginate and ASVS concentration in three different ratios ( alginate: ASVS:: 2∶1 , alginate: ASVS:: 1∶1 , and alginate: ASVS:: 1∶2 in v/v ) to determine optimum condition for maximum entrapment of ASVS into alginate . The entrapment efficiency was found to be 20 . 2±0 . 67% in alginate: ASVS:: 2∶1 , 37 . 2±0 . 73% in alginate: ASVS:: 1∶1 and 33 . 7±0 . 99% in alginate: ASVS:: 1∶2 when chelated in 2% calcium chloride solution and 21 . 6±0 . 86% in alginate: ASVS:: 2∶1 , 37 . 3±0 . 94% in alginate: ASVS:: 1∶1 and 35 . 0±0 . 71% in alginate: ASVS:: 1∶2 when chelated in 3% calcium chloride . Entrapment efficiency was found to be in the order of alginate: ASVS:: 1∶1 beads>alginate: ASVS:: 1∶2 beads>alginate: ASVS:: 2∶1 in 2% as well as 3% in calcium chloride solution ( Fig . 1D ) . Loading capacity was found to be in the order of alginate: ASVS:: 1∶2 beads>alginate: ASVS:: 1∶1 beads>alginate: ASVS:: 2∶1 beads in 2% as well as in 3% calcium chloride solution ( Fig . 1B ) . But as the entrapment efficiency is maximum in alginate: ASVS:: 1∶1 ratio , it was used for further study ( Fig . 1E ) . The scanning electron microscopy study showed that beads were spherical and has more homogenous surface in alginate: ASVS:: 1∶1 beads as compared with alginate: ASVS:: 1∶2 beads ( Fig . 1F , 1G , 1H ) . Respective surface analysis result confirmed that the surface heterogeneity was increased in alginate: ASVS:: 1∶2 beads as compared with alginate: ASVS:: 1∶1 beads ( Fig . 1F′ , 1G′ , 1H′ ) . The swelling ability of alginate entrapped ASVS beads were studied using different pH solution of 1 . 2 , 6 . 8 , 7 . 0 and 7 . 4 . Alginate entrapped ASVS beads did not show any significant swelling at the pH 1 . 2 after 40 minutes of incubation whereas it showed significant gradual increase in swelling after 10 , 20 , 30 and 40 minutes of incubation respectively at pH 6 . 8 , 7 . 0 and 7 . 4 solution which was expressed as weight change of beads , shown in Fig . 2A . Weight of alginate entrapped ASVS beads were increased by 1 . 4 , 3 . 3 , 3 . 3 times at pH 6 . 8 , 7 . 0 , 7 . 4 after 10 minutes incubation , Weight of alginate entrapped ASVS beads were increased by 5 . 4 , 9 . 2 , 7 . 04 times at pH 6 . 8 , 7 . 0 , 7 . 4 after 20 min incubation , by 8 . 2 , 10 . 62 , 7 . 24 times at pH 6 . 8 , 7 . 0 , 7 . 4 after 30 minutes incubation , and by 8 . 4 , 12 . 48 , 7 . 4 times at pH 6 . 8 , 7 . 0 , 7 . 4 after 40 minutes incubation respectively . In vitro release of ASVS from alginate entrapped beads was studied in different pH solution of 1 . 2 , 6 . 8 , 7 . 0 , and 7 . 4 , simulating gastrointestinal conditions for 4 h ( Fig . 2B ) . From the figure it was found that burst release of ASVS took place after 1 h of incubation at 6 . 8 and 7 . 0 pH solution respectively which then gradually reached its maximum capacity . At pH 7 . 4 solution alginate entrapped beads showed a steady and firm release of ASVS which continuously increased from 1 h and reached to the maximum at 4 h . The release of ASVS from alginate entrapped beads at pH 1 . 2 was found to be significantly lower from pH 6 . 8 , 7 . 0 and 7 . 4 solution . Mucus glycoprotein assay was performed and the result was depicted in Fig . 2Ci . Result was calculated by measuring the unbound mucin concentration spectrophotometrically at 555 nm . Concentration of the free mucin in the solution were 34 . 22% for alginate beads , 36 . 04% alginate: ASVS:: 2∶1 beads , 45 . 49% for alginate: ASVS:: 1∶1 beads and 49 . 33% for alginate: ASVS:: 1∶2 beads . Thus , from the above result it was found that mucoadhesive property of alginate is altered with the amount of ASVS entrapped . Maximum free mucin was found in the condition where alginate: ASVS:: 1∶2 beads were incubated with mucin solution and lowest free mucin was found in case of pure alginate beads . Ex vivo mucoadhesion study was performed using rat intestine . Results showed that particle binding capacity of alginate: ASVS:: 2∶1 beads were 91 . 07% , alginate: ASVS:: 1∶1 beads were 85 . 6% and alginate: ASVS:: 1∶2 beads were 49 . 56% , when binding capacity of pure alginate beads were consider as 100% . Thus , maximum particle binding was observed in alginate: ASVS:: 2∶1 beads which was not significantly different from pure alginate beads and alginate: ASVS:: 1∶1 beads and with increase in ASVS concentration particle binding capacity significantly decreased in alginate: ASVS:: 1∶2 beads ( Fig . 2Cii ) . Normal ASVS and released ASVS showed presence of similar banding patterns after coomassie brilliant blue staining in native PAGE ( Fig . 2Di , 2Dii ) . Result of HPLC showed the presence of multiple peaks in normal ASVS solution ( Fig . 2E ) . Similar HPLC peak pattern of released ASVS ( 2F ) indicated the presence of all protein fractions in released ASVS solution . Acid digestion of particle was made by placing alginate: ASVS:: 1∶1 beads in 0 . 1M HCl for 1 h and then the encapsulated ASVS was recovered by placing the beads in phosphate buffer ( pH 7 . 0 ) for assessing its activity . In the activity study it was found that the released ASVS from alginate: ASVS:: 1∶1 acid digested beads showed significant protection by 41 . 09% against PLA2 enzyme activity of Naja naja venom and by 43 . 16% against PLA2 enzyme activity of Daboia russelii venom which was not significantly different in released ASVS from alginate: ASVS:: 1∶1 beads which was not acid digested . This data indicates that alginate entrapment might protect ASVS to overcome the damage caused by the acidic environment of the gastrointestinal tract ( Fig . 3A , 3B ) . Presence of FITC emission at 520 nm after excitation at 490 nm of the fluid from outer intestinal medium indicated that FITC tagged ASVS was permeated across the intestinal barrier . A time dependent gradual increase in FITC tagged ASVS concentration was observed up to 4 hour which was started from 15th minutes ( Fig . 3C , 3D , 3E ) . The permeated concentrated ASVS also showed neutralization of Daboia russelii venom and Naja naja venom induced lethality in male albino mice . Emission of FITC from the intestinal tissue homogenate in the same experimental condition also indicated the transport of FITC tagged ASVS through the intestine ( Fig . 3F ) . The PLA2 enzyme activity study was performed to find out the activity of released ASVS from alginate: ASVS:: 1∶1 beads as well as normal ASVS . In the present experiment it was found that ASVS showed a significant 47 . 88% , 48%and79% protection against PLA2 enzyme activity of Naja naja venom and Daboia russelii venom whereas the released ASVS from alginate: ASVS:: 1∶1 beads showed a significant 45 . 79% and 47 . 35% protection against PLA2 enzyme activity of Naja naja venom and Daboia russelii venom respectively as shown in Fig . 3B . In vitro hemolysis of RBC was increased dose dependently after Naja naja venom incubation ( 2 . 5 µg–40 µg ) as was observed from hemoglobin concentration of sample supernatant . The median hemolytic dose of Naja naja venom was found to be 17 . 45 µg in 20% RBC solution ( Fig . 4A ) . Released ASVS dose dependently ( 2 . 5 µg–50 µg ) neutralized the hemolytic activity of Naja naja venom ( 10 µg ) significantly . The median neutralization dose of normal ASVS and released ASVS hemolytic activity of Naja naja venom was found to be 12 . 5 µg and 12 . 05 µg respectively ( Fig . 4B , 4C ) . The neutralization capacity of hemolytic activity for released ASVS did not show any significant alteration as compared with normal ASVS in a similar 25 µg dose ( Fig . 4D ) . Lactate dehydrogenase concentration of RBC hemolysate significantly increased after incubation with 10 µg of Naja naja venom . Normal ASVS , and released ASVS treatment significantly decreased lactate dehydrogenase concentration of RBC hemolysate after incubation with 10 µg of Naja naja venom . No significant difference was observed in lactate dehydrogenase level of HRBC hemolysate in normal ASVS , and released ASVS treatment ( Fig . 4E ) . Normal and released ASVS ( 25 µg ) showed to neutralize hemorrhagic activity of Daboia russelii venom ( 10 µg ) significantly ( Fig . 4F ) . Minimum lethal dose ( MLD ) of Naja naja venom was found to be 6 µg/20 gm mice while that of Daboia russelii venom was found to be 4 µg/20 gm mice . These respective MLDs of Naja naja and Daboia russelii venoms were significantly neutralized by 24 . 108 µg of released ASVS as well as similar amount of normal ASVS . The minimum lethal dose of Daboia russelii venom was found to be 4 µg/20 gm mice . Protein released from the alginate coated ASVS beads after 30 days incubation at 37°C showed a complete neutralization of lethality at a dose of 26 µg protein which did not show any significant difference from normal ASVS .
The aim of present study was to entrap multiple proteins of ASVS in alginate beads which will be functionally active against snake venom upon release . So far oral delivery systems have dealt with simple and small proteins like insulin or BSA [9] , [30] or vaccine , which use different strategy for delivery system [31] . From drug delivery point ASVS has some peculiarities which make it worth individual attention . The ASVS comprises of immunoglobulins raised biologically in animals against venom . The ASVS which was used in this study provided antibody against four major Indian snake venoms ( cobra , common krait , russell viper , saw scaled viper ) . ASVS contains multiple proteins which vary and thus , the chemical properties , isoelectric point or neutralization of venom components by individual proteins has not been studied in detail . Moreover , as ASVS is not a lucrative business [32] , making sophisticated oral delivery system is a commercially unviable project . To overcome these problems the different protein components were not given individual consideration; rather those were encapsulated as a whole in alginate , a cheap polymer , cross linked by divalent cation calcium . This delivery system has the capacity to entrap proteins with different isoelectric pHs unlike ionic gelation method [33] and therefore may proof efficient for entrapping every component of the multiple components of ASVS . Results showed ( Fig . 2D , 2E , 2F ) that this system indeed was proved efficient in entrapping every component of the multiple component ASVS . The influence of preparation condition like the concentration of the polymer alginate as well as the concentration of calcium chloride solution will be different for encapsulation of different proteins [34] , [35] . In the present study alginate: ASVS:: 1∶1 beads were used as it showed maximum entrapment efficiency though the loading capacity was higher at alginate: ASVS:: 1∶2 . The SEM study too confers less heterogeneity of alginate: ASVS:: 1∶1 beads . The calcium chloride concentration ( 2% or 3% ) variation did not show significant alteration in entrapment efficiency or loading capacity . Therefore 2% calcium chloride solution was used for the preparation of beads for the biological studies . pH dependent swelling property is one of the advantageous phenomenon of alginate beads , widely used in oral drug delivery . The alginate meshwork swells or shrinks respectively in alkaline and acidic pH , which helps to release or entrap proteins within alginate beads [36] . Shrinking the beads in low pH ensure protection of entrapped protein from digestion within stomach . Result showed that at pH 1 . 2 the alginate beads did not swell but with increase in pH the swelling property increases . At pH 7 . 0 the beads swell maximally , ensuring release of entrapped protein in the intestine . In vitro release of protein from polymer was studied in different pH , simulating gastrointestinal condition . The release pattern of ASVS from alginate beads showed an initial burst release in alkaline pH . Though it is considered an undesirable and uncontrollable phenomenon as it prevent sustain release and creates problem in many controlled delivery systems but burst release is beneficial in situations like wound healing [37] . An initial burst is necessary to provide immediate relief , followed by sustained release to maintain the action , as is the situation in snake envenomation , similar to wound healing , the burst release may be beneficial . One of the main purposes of the study was to encapsulate all the protein components of ASVS within polymer and released them in the intestinal environment so that the polyvalent nature of ASVS and its bioactivity could be maintained . Similar banding pattern in native PAGE and similar chromatogram pattern in HPLC of released ASVS in comparison with normal ASVS indicated that all the components of normal ASVS were present in released ASVS . Thus , these results confirm the entrapment of every protein component of ASVS into alginate beads . The released proteins are susceptible to intestinal protease activity . Mucoadhesive drug delivery systems increase the bioavailability of drug by increasing the contact and residence time of the drug on gastrointestinal tract , the absorption surface . Mucoadhesive polymers can bind to mucus by physical or chemical interactions . Alginate , consisting of mannuronic acid and guluronic acid forms , has more hydroxyl groups than the other polymers and thereby binds more strongly with the oligosaccharide chains of mucin [38] . Mucoadhesive property of alginate and alginate encapsulated ASVS of different concentrations were studied in two different models . Despite the difference between alginate beads and ASVS loaded alginate beads in mucin binding property , ASVS encapsulated alginate beads adhere to rat gastric mucosa significantly . So it could be assumed that alginate encapsulated ASVS beads could be able to bind with gastric mucosa in significant amount . After characterization of the bead it was necessary to study whether released ASVS could permeate the intestinal barrier . An isolated intestinal preparation was used to study this phenomenon . The flurochrome FITC was tagged with ASVS and after excess unbound FITC was washed out , the permeability was assessed by measuring FITC intensity kinetically from the outer intestinal fluid . This is an alternative model of in vivo absorption study where it was found that FITC intensity was started to increase from 15 minutes of the experiment gradually up to 4 hours . FITC tagged ASVS concentration was also increased gradually from 15 minutes to 4 hours . FITC tagged ASVS from outer intestinal fluid significantly neutralized the venom induced lethality which signify that all the major components required for venom induced lethality neutralization was absorbed through the intestine . Further experiments with the released proteins from beads were performed to study whether any alteration in biological functions of the released ASVS had occurred due to entrapment or during release from the beads as compared with normal ASVS . The released protein was thus used to evaluate the functional aspects of ASVS . WHO [39] has recommended assay of ASVS based on neutralization of lethality in experimental animals . In the present study this assay was performed using two different kinds of venom viz Naja naja and Daboia russelii . It was found that the released ASVS neutralizes the lethality of both venoms . But neutralization of lethality does not necessarily correlate with neutralization of specific venom actions [40] and death can occur due to these actions beyond the time frame taken for lethality studies . For this reason in vitro neutralization of the PLA2 activity; [41] , [42] , passive agglutination [43] , hemolytic activity [44] , hemorrhagic activity and ELISA [28] , [45] have been substituting the in vivo method . In the present study phospholipase A2 enzyme activity was measured to study the functional ability of the released ASVS protein in neutralizing the snake venom . Snake venom possesses PLA2 enzyme activity , which manifests many of the toxic effects of the venom [46] . In this study released ASVS showed significant inhibition of venom PLA2 activity . Naja venom possesses direct hemolytic factor [27] which dose dependently showed hemolysis in washed RBC solution . Released ASVS also showed significant protection against hemolytic activity dose dependently . Lactate dehydrogenase is a marker for cellular toxicity and hemolysis . Released ASVS also significantly restored LDH released from RBC lysate . These activities of released ASVS were comparable with freshly constructed ASVS which clearly indicate that the biological activity of ASVS remains unaltered after entrapment and also after release from the alginate beads . A similar result was obtained from hemorrhagic activity study with viper venom . Intradermal injection of viper venom causes local manifestations such as hemorrhage which was also neutralized by released ASVS . The gold standard to test ASVS activity is neutralization of venom lethality in animals done by injecting preincubated venom and antivenom in experimental animals . Since oral formulation cannot be pre-incubated we have shown lethality neutralization of ASVS released from alginate bead after crossing the intestinal barriers . This mimics the real situation as empirically as preincubated models and needs new methodology to study efficacy of ASVS as well as oral ASVS ( Fig . 7 ) . But the above studies cannot confirm that how much this oral delivery is beneficial as there are lots of other factors involved in it like peristaltic movement , effects of protein digestion enzymes as well as the uptake kinetics . Thus the feasibility of alginate coated ASVS bead to provide sufficient amount of antivenom required for neutralization of venom was studied in animal model . Alginate coated ASVS was given orally by the method of Matsuno et al 2008 [47] with minute modification ( Fig . 7 ) . Viper venom induced lethality , hemotoxicity and renal toxicity was chosen as venom toxicity models as these effects are irreversible and are affected by delay in ASVS administration . Alginate coated ASVS was per orally administered in every 30 minutes interval up to 6 hours for hemotoxic and nephrotoxic models and till 24 h for lethal model . Peroral administration of ASVS significantly prevented venom induced toxicity which is depicted by delay in increase of clotting time , inhibition of morphology alteration of RBC , decrease release of free hemoglobin , and low plasma urea and creatinine level as compared with venom induced group of animals . Above all neutralization of MLD has been found after peroral delivery of alginate coated ASVS . From these studies it has been found that oral delivery of alginate coated ASVS prevented the damage caused due to venom administration . The above study confers that oral delivery of ASVS could be possible and it could help to decrease number of deaths occurring due to snake bite in remote places . Intestinal absorption does not delay ASVS availability in blood in a dose sufficient to neutralize venom infiltrated from muscle site before toxic effects of venom could be recognized . Being immunoglobulin in nature might have helped intestinal absorption of ASVS by direct binding with IgG-Fc receptor ( FcRn ) mediated transport , which can account for timely action of oral ASVS in venom neutralization in animal models . In this study we have selected alginate for its low cost . However , ASVS is expensive and the low loading capacity of alginate system has to be improved for better economy of ASVS . Alginate- antivenom bead will not reduce the cost of antivenom production . But , the increment in price of this novel antivenom when compared to the traditional one would not be much . Another limitation of this study which has to be detailed is the thermostability of alginate-ASVS preparation at room temperature . We have not studied whether the thermal stability of alginate-antivenom would be greater than that of the antivenom alone and beyond one month . This would imply that the alginate-antivenom mixture could not be stored at room temperature and would require cold chain conditions unless the polymer formulation is developed to address this problem . Approaches like addition of the polyols [48] to increase thermostability of liquid ASVS can address both the problems of cost and thermostability of freeze dried ASVS , and hence , such approaches have to be taken for improving our formulation . Moreover , oral ASVS can be given only as first aid and the minimum dose to prevent the irreversible damages taking place during transit . This warrants detail study with ASVS dosing separately for venom toxicities for choosing optimum dose and release kinetics as well as for pharmacokinetic and pharmacodynamic analysis . Antisnake venom serum is the only drug available against snake envenomation but is limited by infrastructure required to administer this intravenously as soon as the bite occurs . In this paper we have addressed this limitation by testing the feasibility of controlled oral delivery of the drug ( Fig . 8 ) for future use as first aid . Advantages of alginate system were retained after ASVS loading and venom neutralizing properties of ASVS was unaltered after release from the beads . Hence , alginate could be used to develop controlled oral delivery system of ASVS but requires polymer modification for animal experiments and clinical trials . Moreover , this is the first report of encapsulating multiple proteins of a drug to reconstitute a functional drug , for which new strategies should be proposed .
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Antivenom , the only effective therapy against snake bite in practice , is successful in controlling mortality in developed countries , but not in developing countries . Unavailability of antivenom at the proper time and place of snake bite in developing countries is a major factor in this account , which results not only from production deficit but also from dependence on hospitals located too faraway for intravenous administration . It lengthens the period between bite and treatment , and thereby worsens the outcome . To make antivenom available immediately after bite , we need to develop an oral formulation which , by its property of controlled release , can supply antivenom as first aid until further hospitalization . In this work , multiple components of antivenom were entrapped in alginate , an economic , biodegradable polymer , which retained the functional property of the antivenom even after intestinal absorption and showed in vivo and in vitro venom neutralization effects . This study promises the development of an effective first aid against snake envenomation , thereby increasing chances of survival of the victim .
|
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"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
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"medicine",
"and",
"health",
"sciences",
"clinical",
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2014
|
Viper and Cobra Venom Neutralization by Alginate Coated Multicomponent Polyvalent Antivenom Administered by the Oral Route
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Invasion of erythrocytes by Plasmodium falciparum involves a complex cascade of protein-protein interactions between parasite ligands and host receptors . The reticulocyte binding-like homologue ( PfRh ) protein family is involved in binding to and initiating entry of the invasive merozoite into erythrocytes . An important member of this family is PfRh5 . Using ion-exchange chromatography , immunoprecipitation and mass spectroscopy , we have identified a novel cysteine-rich protein we have called P . falciparum Rh5 interacting protein ( PfRipr ) ( PFC1045c ) , which forms a complex with PfRh5 in merozoites . Mature PfRipr has a molecular weight of 123 kDa with 10 epidermal growth factor-like domains and 87 cysteine residues distributed along the protein . In mature schizont stages this protein is processed into two polypeptides that associate and form a complex with PfRh5 . The PfRipr protein localises to the apical end of the merozoites in micronemes whilst PfRh5 is contained within rhoptries and both are released during invasion when they form a complex that is shed into the culture supernatant . Antibodies to PfRipr1 potently inhibit merozoite attachment and invasion into human red blood cells consistent with this complex playing an essential role in this process .
Malaria is caused by parasites from the genus Plasmodium , of which Plasmodium falciparum is associated with the most severe form of the disease in humans . Sporozoite forms of these parasites are injected into humans during mosquito feeding and they migrate to the liver where they invade hepatocytes and develop into merozoites , which are released to invade erythrocytes in the blood stream . The blood stage cycle of P . falciparum is responsible for all of the clinical symptoms associated with malaria [1] . Once a merozoite has invaded an erythrocyte it develops , within this protected intracellular niche , to form around 16 new merozoites that are released and then bind and invade other red blood cells . Invasion of merozoites into the host erythrocyte is a rapid process involving multiple steps in a cascade of protein-protein interactions ( see for review [2] ) . The reticulocyte binding-like homologues ( PfRh or PfRBP ) and erythrocyte binding-like ( EBL ) proteins play important roles in merozoite invasion [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] . The PfRh family consists of PfRh1 ( PFD0110w ) , PfRh2a ( PF13_0198 ) , PfRh2b ( MAL13P1 . 176 ) , PfRh3 ( PFL2520w ) , PfRh4 ( PFD1150c ) and PfRh5 ( PFD1145c ) [4] , [5] , [7] , [9] , [16] , [17] , [18] , [19] , [20] . PfRh3 is a transcribed psuedogene in all the P . falciparum strains that have been analysed [21] . PfRh1 , PfRh2b , PfRh2a , PfRh4 and PfRh5 bind to erythrocytes and antibodies to them can inhibit merozoite invasion thus showing they play a role in this process [11] , [13] , [18] , [19] , [20] , [22] , [23] , [24] . Polymorphisms in the PfRh5 protein have been linked to differential virulence in infection of Aotus monkeys suggesting that amino acid changes in its binding domain can switch receptor recognition [19] . PfRh5 has been shown to bind red blood cells but its putative receptor has not been identified [18] , [19] , [20] . In contrast to other members of the PfRh protein family , PfRh5 is considerably smaller and lacks a transmembrane region , which combined with its role as an invasion ligand , suggests it may be part of a functional complex . It has not been possible to genetically disrupt the gene encoding PfRh5 and antibodies to it can partially inhibit merozoite invasion , pointing to an essential role of this protein in the invasion process [20] . The EBL family of proteins includes EBA-175 ( MAL7P1 . 176 ) [3] , [26] , EBA-181 ( also known as JESEBL ) ( PFA0125c ) [27] , [28] , EBA-140 ( also known as BAEBL ) ( MAL13P1 . 60 ) [6] , [29] , [30] , [31] and EBL-1 [32] . Whilst these parasite ligands function in merozoite invasion by binding to specific receptors on the erythrocyte , they appear to have a central role in activation of the invasion process . For example , it has been shown that binding of EBA-175 to its receptor , glycophorin A restores the basal cytosolic calcium levels after interaction of the merozoite with the erythrocyte and triggers release of rhoptry proteins and it is likely that the PfRh protein family plays a similar role [37] . The PfRh and EBL protein families are responsible for a mechanism of phenotypic variation that allows different strains of P . falciparum to invade erythrocytes using different patterns of host receptors [7] , [28] , [38] . This is important for evasion of host immune responses and also provides a means to circumvent the polymorphic nature of the erythrocyte surface in the human population [39] . Indeed , analysis of immune responses from individuals in malaria endemic areas has suggested that the PfRh proteins are targets of human invasion inhibitory antibodies and therefore a critical component of acquired protective immunity [39] , [40] , [41] . In order to understand the function of PfRh5 in merozoite invasion , we purified it from parasite culture supernatant and show this protein exists as a complex with a cysteine-rich protein containing 10 epidermal growth factor-like domains ( PFC1045c ) . This novel protein functions with PfRh5 to play an essential role in invasion of merozoites into human erythrocytes .
PfRh5 binds to erythrocytes and is functionally important in merozoite invasion of erythrocytes [18] , [19] , [20] . Whilst other members of the PfRh family also bind erythrocytes , they have a transmembrane region , a feature that PfRh5 lacks suggesting that it may exist as a complex . In order to determine if PfRh5 was shed as a complex into culture supernatant during merozoite invasion we set out to purify it using ion-exchange chromatography . PfRh5 , that had been partially purified from culture supernatant by ion-exchange chromatography , migrated on SDS-PAGE gels as an approximately 45 kDa fragment as detected using 2F1 , a anti-PfRh5 specific monoclonal antibody ( data not shown ) , consistent with our previous results showing that the 63 kDa mature PfRh5 was processed in late schizonts and shed into culture supernatant during merozoite invasion [20] . However , analysis of PfRh5 by gel-filtration chromatography revealed that the 45 kDa polypeptide was eluted in the peak ( # 19–24 ) ( Fig . 1A ) which is immediately before the peak of 158 kDa bovine Υ-globulin ( #21–25 ) ( Fig . S1 ) , suggesting PfRh5 is indeed in a complex with a molecular weight of approximately 150–200 kDa . To confirm PfRh5 exists as a higher molecular weight species , blue native gel electrophoresis was performed and showed the PfRh5 migrated at an approximate molecular weight of 150–200 kDa ( Fig . 1 B ) . This was in contrast with the processed PfRh1 fragment that migrated at the expected size of 110 kDa ( Fig . 1B ) [12] . This suggested that PfRh5 either forms a homo-oligomer or was in complex with other molecule/s . To distinguish between these possibilities , gel-filtration chromatography was used to identify the size increment of PfRh5-containing species after it was incubated with a monoclonal anti-PfRh5 antibody [20] . It was found that pre-incubation with anti-PfRh5 antibody caused the PfRh5 protein peak to appear two fractions earlier , which correspond to an increase in size of the PfRh5-containing species by 150 kDa , indicating that one antibody molecule was associated with the complex ( Fig . 1C and D ) . The free PfRh5 co-eluted with the excess antibody in the protein peak comprising fraction 22 to 25 ( Fig . 1D ) , further indicating PfRh5 is in a complex of approximate 150 kDa . Therefore we proposed that the processed 45 kDa PfRh5 species formed a complex with another molecule/s rather than existing as a homo-oligomer . To identify the proteins present within the PfRh5 complex , we performed a large-scale purification using culture supernatant from the parasite line 3D7-Rh5HA in which the PfRh5 gene had been tagged with haemagglutinin epitopes [20] . The PfRh5 protein was enriched and partially purified using ion-exchange chromatography followed by further purification on an anti-HA affinity matrix . The bound proteins were eluted and subjected to trypsin digestion followed by analysis of the resulting peptides by mass spectrometry ( LC-MS/MS ) [42] . Since powerful anion ion-exchange chromatography was employed to enrich the complex and remove the majority of contaminants before it was subjected to anti-HA antibody affinity pull-down ) , the mass spectrometry analysis yielded a very clean result ( Table S1 ) . Database analysis identified parasite protein peptides that were predominantly derived from two proteins , which included PfRh5 and a conserved hypothetical protein ( PFC1045c ) , designated here as P . falciparum PfRh5 interacting protein ( PfRipr ) ( Table 1 and Table S1 ) . The most N-terminal peptide found for PfRh5 was amino acid 187–197 , consistent with it being shed into culture supernatant as an approximately 45 kDa fragment produced by cleavage between amino acid 125 and 135 of the mature protein . In the case of PfRipr , we identified peptides from both N-terminal and C-terminal regions of the protein suggesting that the full-length protein was present within the complex and this was confirmed by co-immunoprecipitation experiments with specific antibodies ( see below section ) . The mature form of PfRipr was 123 kDa and this together with the 45 kDa PfRh5 fragment would produce a complex with an overall molecular weight of approximately 170 kDa in agreement with the size observed by gel-filtration chromatography and blue native gel electrophoresis ( Fig . 1A and B ) . The PfRh5/PfRipr complex was stably purified by ion-exchange chromatography and 350 mM NaCl in the elution buffer did not disrupt their association , suggesting the tight interaction of two proteins . Such specific high affinity protein-protein interaction implies the biological significance of the complex . In order to confirm that PfRh5 and PfRipr form a complex , we inserted a plasmid by single cross-over homologous recombination at the 3′ end of the Pfripr gene to fuse three HA epitopes and derived the parasite line 3D7RiprHA ( Fig . 2A ) [20] . Successful tagging was confirmed by immunoblot experiments using anti-HA antibodies on both saponin lysed schizont pellet preparations and proteins from culture supernatants ( Fig . 2B ) . In parasite schizont pellets a minor band of approximately 125 kDa and a major band of approximately 65 kDa were detected whereas in culture supernatants only the smaller protein band was predominantly observed . The 125 kDa protein presumably represents the full-length mature protein in schizonts that was processed and shed into the culture supernatant . Since only one processed fragment of 65 kDa was detected in culture supernatants using anti-HA antibody , we propose that PfRipr was processed into at least two fragments of approximately the same size . Mobility of the 65 kDa fragment remains similar under the reducing and non-reducing condition ( Fig . 2C ) demonstrating that the processed fragments are not linked by a disulphide bond . Both 65 and 125 kDa fragments migrated slightly faster on SDS-PAGE gels under non-reducing conditions consistent with the presence of multiple intramolecular disulfide linkages creating tightly folded domains ( Fig . 2C ) . To further confirm that PfRh5 and PfRipr form a complex , we performed co-immunoprecipitation experiments with anti-PfRh5 and anti-HA antibodies using proteins from the parasite line 3D7RiprHA ( Fig . 2D to G ) . Immunoprecipitation with anti-HA antibodies , to pull-down tagged PfRipr from schizont-infected erythrocytes , also brought down PfRh5 as shown by immunoblots with a specific monoclonal anti-PfRh5 antibody ( Fig . 2D ) . The full-length mature protein ( approximately 63 kDa ) as well as two processed fragments of similar molecular weight ( approximately 45 kDa ) was observed ( Fig . 2D ) . The reciprocal experiment in which we immunoprecipitated PfRh5 , using the monoclonal anti-PfRh5 antibody , also pulled down PfRipr ( Fig . 2E , left panel ) but not PfRh2a or PfRh2b , two other members of PfRh protein family ( Fig . 2E , right panel ) . Using culture supernatants , a similar immunoprecipitation experiment with the anti-HA antibodies to pull down PfRiprHA also co-precipitated the 45 kDa doublet of PfRh5 but not PfRh2a or PfRh2b ( Fig . 2F ) . The reciprocal experiment using culture supernatants in which anti-PfRh5 antibody was used for immunoprecipitation detected HA-tagged PfRipr but not PfRh2a or PfRh2b in the pull-down sample ( Fig . 2G ) . As an additional control , immunoprecipitaion of the culture supernatant with rabbit anti-PfRh2a/b antibody recognizing the 85 kDa fragment of both PfRh2a and PfRh2b was performed [24] . The anti-PfRh2a/b antibodies immunoprecipitated the 85 kDa PfRh2a/b but did not pull-down PfRh5 or PfRipr , further confirming that the PfRh5/PfRipr complex was specific . It was not possible to determine with these experiments whether the PfRipr/PfRh5 proteins associate in schizont stage parasites or at a later stage . However , since PfRh5 and PfRipr appear to be located in different compartments within the schizont ( see below section ) , it is likely that they form a complex during detergent extraction as has been shown for the AMA1 and RON complex [43] , [44] , [45] . PfRipr is highly conserved in Plasmodium spp . and the gene is syntenic with other genes , all of which are annotated as hypothetical ( http://plasmodb . org ) . The full-length PfRipr protein consists of 1 , 086 amino acids with a molecular weight of 126 kDa . It has a putative hydrophobic signal sequence at the N-terminus consistent with it being secreted and the rest of the protein contains 87 cysteine-residues , many of which are clustered in epidermal growth factor ( EGF ) -like domains ( Fig . 3A and B ) . There are ten EGF-like domains in the protein with two in the N-terminal region and eight clustered towards the C-terminus ( Fig . 3A ) . An EGF domain has 6 cysteine residues and the position of each is relatively conserved in the ten EGF-like domains of PfRipr ( Fig . 3B ) [46] , [47] . Processing of PfRipr occurs in the area between the second and third EGF-like domain ( Fig . 3A ) ; however , the processed N- and C-terminal regions remain associated as peptides from both regions were obtained in the mass spectrometry analysis of the immuno-precipitated PfRh5/PfRipr complex ( Table 1 ) and the two regions were co-immunoprecipitated ( see below ) . To provide additional tools to probe the function of PfRh5/PfRipr complex , we expressed amino acids 238–368 of PfRipr that encompass the two EGF-like domains in the N-terminus as well as amino acids 791–900 of PfRir that encompass two EGF-like domains in the C-terminus ( Fig . 3A ) , in E . coli as a recombinant protein tagged at the N-terminus with six histidines . The recombinant proteins of approximately 17 kDa were purified ( Fig . 4A ) and used to immunise rabbits . The anti-PfRipr/1 and -PfRipr/2 IgG antibodies , raised in two rabbits , were tested by western blot against solubilised pellets from schizont stages and a protein band of approximately 65 kDa was observed ( Fig . 4B ) . Specificity of the antibodies was confirmed with HA-tagged PfRipr immunoprecipitated from culture supernatant with anti-HA antibodies and in both cases a 65 kDa protein was detected ( Fig . 4B ) . This was the molecular weight expected for the processed C-terminal region of PfRipr; however , the mature full-length protein of 123 kDa was not easily observed as it was mostly processed at late schizont stage and in the supernatant . The anti-PfRipr/3 antibody has low reactivity to the denature protein but recognizes native PfRipr well . The anti-PfRipr/3 was therefore used in co-immunoprecipitation experiments to confirm that the cleaved N-terminus and C-terminus of PfRipr remain associated . Immunoprecipitation of the HA-tagged C-terminus of PfRipr from 3D7RiprHA parasites using anti-HA antibodies co-precipitated a fragment of approximately 60 kDa recognized by the anti-PfRipr/3 ( Fig . 4C ) . The size of the fragment is expected for the N-terminal domain of the processed PfRipr . The full-length mature protein of approximately 125 kDa was also observed as expected since it should react with both the anti-PfRipr/3 and anti-HA antibodies . The reciprocal experiment to immunoprecipitate the N-terminal domain with schizont material using anti-PfRipr/3 co-precipitated the 65 kDa C-terminal fragment and the 125 kDa full length protein ( Fig . 4D , left panel ) . An identical experiment using culture supernatant co-precipitated the 65 kDa C-terminal domain ( Fig . 4D , right panel ) ; the full-length protein was not detected in this case , presumably because it was processed . Immunoprecipitation using anti-PfRipr/3 and immunoblots with anti-HA antibodies detected the expected bands of the 65 kDa C-terminal domain and the 125 kDa full-length protein in 3D7RiprHA but not from the parental parasite line 3D7 ( Fig . 4E ) , further confirming the specificity of the antibodies . Together these experiments show that PfRipr is processed into two fragments of approximately 65 kDa each and they remain associated with each other to form a complex with PfRh5 , consistent with mass spectrometry results ( Table 1 ) . Their association does not involve intermolecular disulfide linkages ( Fig . 2C ) . The anti-PfRipr/1 was used in western blot to probe the PfRipr separated on a blue native gel ( Fig . 5A ) . Indeed anti-PfRipr/1 recognizes the same protein complex detected by anti-PfRh5 , with the size of the complex ( approximately 200 kDa ) consistent with that seen in Fig . 1B . Expression of PfRipr in the asexual blood stage life cycle was determined using synchronised ring stages parasites from 3D7RiprHA . Saponin pellets were prepared from the samples taken during the parasite life cycle as indicated ( Fig . 5B ) . Anti-HA antibody detected both the 125 kDa full-length and 65 kDa fragment of PfRipr and expression was evident in late trophozoites to schizonts , the expected pattern for proteins that play a role in merozoite invasion [20] . The 65 kDa processed form appeared late in parasite blood stage development indicating that this processing event occurs in the schizont stage before merozoite egress . The same samples were probed for PfRh5 , with which PfRipr forms a complex , and it showed a similar timing of expression but processing of this protein appears to occur late in schizogony , as observed earlier [20] . The similar pattern of PfRipr and PfRh5 expression late in the asexual blood stage life cycle of P . falciparum was consistent with formation of a complex between them either in the schizont stage or during merozoite egress before interaction of the parasite with the host cell . Whilst both PfRipr and PfRh5 have a signal sequence at the N-terminus for entry into the ER , neither has a hydrophobic domain that would provide the means for anchoring in the membrane . As PfRh5 binds to the host erythrocyte [18] , [19] , [20] it would be expected that the PfRh5/PfRipr complex would be bound to the membrane of the merozoite to provide a junction and perhaps transmit an appropriate signal [37] . To determine if this complex was associated with a membrane , we used differential solubilisation of the parasite to determine the properties of the PfRipr/PfRh5 complex ( Fig . 6 ) . P . falciparum parasites were hypotonically lysed in water and centrifuged to obtain a pellet and supernatant fraction . Treatment of the pellet with 10 mM Tris showed that both PfRipr and PfRh5 remained predominantly in the pellet fraction suggesting they were associated with a membrane ( Fig . 6A ) . However , treatment of the pellet fraction with Na2CO3 showed that both proteins moved to the soluble fraction suggesting they were extrinsically associated as peripheral membrane proteins of the parasite . PfRipr was mainly insoluble in Triton X-100 but the smaller processed form of the protein was more soluble in CHAPs detergent . In contrast , PfRh5 was more soluble in Triton X-100 and CHAPS implying either PfRipr/PfRh5 complex involves hydrophobic interactions that can be disrupted by non-ionic detergents [48] , or PfRipr and PfRh5 have different membrane association properties . Similar solubility characteristics were obtained from saponin treated parasites isolated with saponin treatment ( Fig . 6B ) . In summary , these results showed that the PfRh5/PfRipr complex was peripherally associated with the parasite membrane and it is possible that another protein ( s ) may be involved in the membrane attachment of the PfRh5/PfRipr complex . With anti-PfRipr antibodies , we performed immuno-fluorescence assays to determine the subcellular localization of PfRipr with respect to known markers of compartments such as micronemes and rhoptries , organelles that are important for erythrocyte invasion . We firstly confirmed that the HA-tagged PfRipr protein showed the same localisation when detected using both anti-HA and anti-PfRipr/1 antibodies , with both showing a punctate pattern in schizonts ( Fig . 7 , panel a ) . Co-labelling of schizonts and merozoites with PfRipr and the rhoptry neck marker RON4 ( Fig . 7 , panel b and c ) or the rhoptry body marker , RAP1 ( Fig . 7 , panel d and e ) , both showed close alignment but no significant overlap of signal . These results suggest that PfRipr localises to the apical tip of the merozoite in an area distinct from the very apical tip and the bulb of the rhoptries . Comparison of the subcellular localisation of PfRh5 and PfRipr in schizont stages showed that whilst there appeared to be some overlap , there were substantial areas of the punctate pattern that were clearly in a separate part of the cell ( Fig . 7 , panel f ) . In contrast , PfRh5 and PfRipr showed substantial co-localisation in merozoites suggesting that the two proteins may form a complex in late schitzonts or merozoits ( Fig . 7 , panel g ) . Furthermore , the substantial overlap of PfRipr and the micronemal protein EBA-175 in the schizont stage suggest that PfRipr may be predominately present within this organelle at this stage ( Fig . 7 , panel h ) . However , in merozoites they clearly did not co-localise although there was some overlap ( Fig . 7 , panel i ) . Taken together , these data are consistent with PfRipr localising to micronemes and being released in merozoites . To more finely determine the subcellular localisation of PfRipr , immuno-electron microscopy was performed on 3D7RiprHA merozoites ( Fig 8A to D and Fig . S2 ) . HA-tagged PfRiprHA was observed towards the apical end of merozoites in more electron dense structures , suggesting localisation in micronemes ( Fig . 8A ) . In merozoites fixed during erythrocyte invasion we observed a concentration of PfRipr reactivity at the leading edge of the tight junction , consistent with the protein being shed and released into the culture supernatant during invasion ( Fig . 8B ) . This suggests that PfRipr was shed from the merozoite surface as the tight junction moves across its surface so that it concentrates at the posterior end before sealing of the parasitophorous vacuole membrane . Whilst there were clearly concentrations of PfRipr in structures at the apical end ( Fig . 8C ) we also observed considerable surface localisation on free merozoites suggesting it is released prior to invasion ( Fig . 8D ) . In comparison PfRh5 seems to localise to rhoptries ( Fig . 8E , F ) as we have observed previously [20]; however , there was also some labelling at the apical surface , suggesting this protein was also being released prior to invasion ( Fig . 8 E , F ) . These results are consistent with that observed by immuno-fluorescent microscopy ( Fig . 7 ) . From these localization data , we suggest that PfRipr and PfRh5 localise to separate subcellular compartments and thus do not form a complex in schizont stage parasites . Presumably following fusion , or secretion , of these distinct compartments , they then come together in merozoite or late schizont stages . Once complexed and at the apical end of the merozoite , they interact with a receptor on the erythrocyte surface . Such an interaction between proteins originating in separate organelles is not unprecedented , with one prominent example being the interaction between AMA1 and RON4 to form the invasion tight junction across apicomplexan species [43] , [44] , [45] . To determine if PfRipr was required for development of P . falciparum , we tested the ability of anti-PfRipr/1 and/2 antibodies ( Fig . 3 ) to block parasite growth ( growth inhibition assays , GIA [7] ) using the P . falciparum strains FCR3 , W2mef , T994 , CSL2 , E8B , MCAMP , 7G8 , D10 , HB3 , and 3D7 ( Fig . 9A ) . The antibodies inhibited parasite growth of all strains tested and significantly , FCR3 was inhibited to 80% whilst in comparison 3D7 was inhibited to 35% with αPfRipr/1 at 2 mg/ml ( Fig . 9A ) . The inhibition observed for 3D7 was comparable to that observed for other antibodies raised to regions of the PfRh or EBL protein families [7] , [12] . Similar results were observed for 3D7 using the αPfRipr/2 ( data not shown ) . As for the anti-PfRipr/1 and/2 antibodies , rabbit polyclonal to the N-terminal EGF-like domains ( anti-PfRipr/3 ) also inhibited parasite growth for all strains tested ( Fig . S3 ) . The αPfRipr/1 antibody was titrated in GIAs in comparison with IgG from normal serum for both FCR3 and 3D7 parasite strains ( Fig . 9B and C ) . Growth of FCR3 , a parasite that invades preferentially by sialic acid-dependent pathways , was almost completely abolished at 3 mg/ml and significant inhibition still remained at 1 mg/ml ( 40% ) ( Fig . 9B ) . In comparison , the 3D7 parasite strain , which can efficiently use sialic acid-independent invasion pathways primarily by using the ligand PfRh4 and complement receptor 1 [49] , was inhibited at significantly lower levels of 50% at 3 mg/ml and this decreased to 20% at 1 mg/ml of antibody ( Fig . 9C ) . This suggests that the PfRipr/PfRh5 complex may be more functionally important in P . falciparum strains that efficiently use sialic acid-dependent invasion pathways , which is supported by our finding ( Fig . S4 ) that anti-PfRipr/1 inhibits W2mef , a sialic acid-dependent strain , more effectively than W2mefΔ175 , a sialic acid-independent strain [8] . Among other P . falciparum strains tested , the αPfRipr/1 antibody also exhibited significantly higher inhibitory activity for those that invade erythrocytes preferentially using sialic acid-dependent receptors ( ie . glycophorins ) , which includes T994 , CSL2 and E8B ( Fig . 9A ) [9] . To investigate the mechanism of inhibition by anti-PfRipr antibodies , we performed merozoite attachment assays ( Fig . 9D ) . Viable merozoites from 3D7 and FCR3 parasites were purified [45] , [50] and mixed with erythrocytes in the presence of αPfRipr/1 or αPfRipr/2 antibodies . Both antibodies inhibited merozoite attachment for 3D7 to a significant level ( 45% and 60% respectively ) ( Fig . 9D , first panel ) . In contrast , the same antibodies inhibited attachment of FCR3 merozoites to a considerably greater extent ( 80% for both antibodies ) ( Fig . 9D , second panel ) . The level of inhibitory activity observed with the αPfRipr/1 and αPfRipr/2 antibodies for 3D7 and FCR3 parasites was similar to that observed in the growth inhibition assays ( Fig . 9A , B and C ) demonstrating that inhibition was occurring at merozoite invasion rather than during growth of the parasite . This inhibition was likely due to interference of these antibodies with the function of the PfRipr/PfRh5 complex since we could not detect PfRipr binding to erythrocytes while consistently observing PfRh5 binding ( data not shown ) [20] . It was possible that the PfRipr/PfRh5 complex disassociated on binding of PfRh5 to red blood cells . To test this possibility we purified the PfRh5/PfRipr complex using ion-exchange chromatography ( Fig . S5A ) and tested its binding to red blood cells ( Fig . S5B ) . This showed that PfRh5 bound to red blood cells but PfRipr was not detected suggesting that the complex was disrupted on binding . The region of PfRipr to which the αPfRipr/1 , 2 antibodies were raised was from the 3D7 strain of P . falciparum; however , this domain does not show any polymorphisms compared to other strains so far sequenced ( http://plasmodb . org/and http://www . broadinstitute . org/ ) ( Fig . S6 ) . Also we did not observe any cross-reactivity of the antibodies with other proteins that contain EGF-like domains such as MSP1 ( data not shown ) . This was not surprising as the only conserved amino acids are the six cysteine residues that define each EGF-like domain ( Fig . 3 ) . Therefore the differences in inhibition observed in GIA with the various strains was unlikely to be due to cross reactivity with other proteins containing EGF-like domains or polymorphisms within this region of PfRipr . It more likely reflects the reliance of these strains on the PfRh5/PfRipr complex to mediate a specific invasion pathway in comparison to the function of other members of the PfRh and EBA protein families [7] , [51] . To test this we used a combination of antibodies raised to PfRipr , EBA-175 [51] , PfRh4 [13] , [49] , PfRh2a and PfRh2b [7] to determine if they increased the level of inhibition in GIAs for 3D7 parasites ( Fig . 9E ) . Both αPfRipr/1 and αPfRipr/2 antibodies at 1 mg/ml inhibited 3D7 parasite growth to 24 and 21% respectively ( Fig . 9E ) , similar to our previous experiments ( Fig . 9C ) . Anti-EBA-175 antibodies on its own also inhibited 3D7 parasite growth to 25% . Anti-PfRh2a/b and PfRh4 antibodies inhibited the 3D7 parasite growth at 39% and 8% respectively . The combination of αPfRipr/1 with αEBA-175 antibodies showed an additive inhibition of 45% . This was a similar result to that observed for the combination of αPfRipr/1 with αPfRh2a/b or αPfRh4 antibodies ( 50% and 40% respectively ) . Significantly , a combination of αPfRipr/1 , αPfRh2a/b and αPfRh4 as well as αPfRipr/1 , αEBA-175 , αPfRh2a/b and αPfRh4 showed a much higher level of inhibition ( 67% and 74% respectively ) . This additive effect was consistent with parasites using multiple invasion pathways to gain entry to the erythrocyte . A similar additive inhibitory effect was obtained with FCR3 parasites ( Fig . S7 ) . Our inability to disrupt the PfRipr gene ( Fig . S8 and S9 ) suggests the function of PfRipr and thus the PfRipr/PfRh5 complex is essential for parasite invasion and survival . Therefore PfRipr represents a novel protein that plays a critical role in merozoite invasion and thus it is a new candidate for a combination vaccine with other PfRh and EBL proteins to produce antibody responses to block a broad array of invasion pathways .
Invasion of P . falciparum into host erythrocytes requires specific ligand-receptor interactions that identify the appropriate host cell followed by activation of the parasite actomyosin motor for entry ( see for review [2] ) . The PfRh family of proteins are important ligands that bind directly to specific receptors on the red blood cell during invasion [4] , [5] , [7] , [9] , [11] , [13] , [16] , [18] , [19] , [20] , [22] , [49] . Differential expression and activation of these ligands provide a mechanism of phenotypic variation to evade host immune responses and to circumvent the polymorphic nature of the red blood cell surface in the human population [7] , [8] , [39] . PfRh5 is an important member of the PfRh family that appears to be essential in P . falciparum [19] , [20] . This ligand binds to an unknown receptor and plays a key role during merozoite invasion; however , in contrast to all other PfRh family members , it lacks an identifiable transmembrane region , suggesting that it may function in a complex with another protein ( s ) [20] . We have shown here that PfRh5 exists in a membrane-associated complex with a novel cysteine-rich protein that we have called PfRipr . The ability of PfRipr antibodies to inhibit merozoite attachment and invasion and our inability to genetically disrupt the corresponding gene implies that this protein plays an important role in these processes . Before invasion , the merozoite undergoes a step of irreversible attachment to the host red blood cell and current evidence suggests that the PfRh and EBL protein families play a key role in this process [37] , [45] . Attachment triggers downstream events presumably through signalling from the cytoplasmic domain of each molecule [37] , [45] . Merozoite attachment can be inhibited with anti-PfRipr antibodies consistent with the PfRh5/PfRipr complex playing a role in the irreversible binding to erythrocytes and commitment to invasion . The ability to inhibit merozoite invasion was not due to cross reactivity of the antibodies with other proteins such as MSP-1 , that also have EGF-like domains . Whilst neither PfRh5 nor PfRipr has a transmembrane region they are tightly associated with the membrane , suggesting they most likely complex with another protein ( s ) that would hold the complex on the parasite surface after their release from the apical organelles . This would link the parasite to the erythrocyte by binding of PfRh5 to its specific receptor during merozoite invasion . The subcellular localisation of PfRh5 and PfRipr appears to be different in schizont stages of the parasite suggesting that they may not form a complex until egress of the merozoite and activation of erythrocyte invasion . PfRh5 was present within the rhoptries whilst PfRipr appears to be in micronemes at the apical end and these proteins would be released during apical interaction where they could associate and form a complex . This is similar to the formation of the AMA-1 and RON complex in both T . gondii and P . falciparum where the former protein is located in micronemes and the latter in the neck of the rhoptries [43] , [44] , [52] , [53] . Components of the RON complex translocate onto and under the erythrocyte membrane forming a bridge across the host cell to the invading merozoite through AMA-1 and this is associated with the tight junction . It is possible that PfRh5/PfRipr interacts with a membrane associated protein and our immuno-electron microscopy analysis has suggested that the complex is at the leading edge of the tight junction ( i . e . basal to the merozoite ) and shed into the culture supernatant , or onto the exterior of the red blood cell , before membrane fusion to form the enclosing parasitophorous vacuole membrane at the posterior end of the invading merozoite . The PfRh and EBL proteins may have overlapping functions as loss of EBA-175 can be compensated by increased function of PfRh4 [8] , [51] , [54] . Furthermore , different parasite lines can differentially express these proteins and in some cases polymorphisms affect their function or potentially alter receptor selectivity [7] , [8] , [19] , [28] , [38] . For example , both PfRh2a and PfRh2b are not expressed in FCR3 line [7] and additionally , both EBA140 and EBA-181 encode polymorphisms that greatly reduce their binding to the red blood cell surface and render them non-functional when tested in growth inhibition assays with specific antibodies [55] . Similarly , in the W2mef parasite line PfRh4 is not expressed and polymorphisms of EBA-140 and EBA-181 also weaken their binding abilities and function in invasion [55] . Since both FCR3 and W2mef have a reduced complement of functional PfRh and EBL proteins , one would predict that these parasites would have greater reliance on the PfRh5/PfRipr complex for their invasion . That is what we have observed in GIAs with the anti-PfRipr antibodies that efficiently inhibit merozoite invasion of these strains . In addition , the additive effect observed with anti-PfRipr antibody in combination with antibodies raised against other PfRh and/or EBA in GIAs would be consistent with the PfRh5/PfRip complex having a similar function to other members of the PfRh and EBL protein families [54] . However , in contrast to the other PfRh and EBL proteins , both PfRh5 and PfRipr could not be genetically disrupted , pointing to the possibility that the complex may also play a broader role that is essential to the parasites . The PfRh1 , PfRh2a and PfRh2b proteins undergo a complex series of proteolytic cleavage events in the schizont when trafficked to the neck of the rhoptries as well as during merozoite invasion and these events are important in their function [9] , [12] , [24] . In the case of PfRh2a and PfRh2b , the proteins are processed to produce the N-terminal binding domain that remains associated with the C-terminal domain [24] . It makes sense for N-terminal binding domain and C-terminal domain with a transmembrane region to associate together after processing for the parasites to attach to the erythrocytes . PfRh5 is also located in the neck of the merozoite rhoptries and is processed to a 45 kDa binding region [20] . Here we showed that the 45 kDa binding region forms a complex with PfRipr . Since neither of them contain a transmembrane region , it is likely that the complex binds another protein ( s ) to link it to the merozoite surface for PfRh5 to bind erythrocyte during invasion . Our results that the PfRipr/PfRh5 complex is peripherally attached to the parasite membrane are consistent with this possibility . Epidermal growth factor-like domains are found widely throughout eukaryotes and are defined by six cysteine residues that link to form a tightly folded structure [46] , [47] . In Plasmodium spp . this domain is contained in a large number of proteins in different stages of the parasite lifecycle . The merozoite surface antigen 1 ( MSP-1 ) has two EGF-like domains and it is localised to the merozoite surface where antibodies can access it and inhibit invasion , which has suggested it is involved in interacting with the erythrocyte surface [56] , [57] . Other proteins with EGF-like domains include the P28 family that protect the parasite from the harsh proteolytic environment in the mosquito gut [58] . PfRipr would not play such a protective role as it is released only during invasion . Instead , it may act as a scaffold on which PfRh5 can be mounted so that it is displayed for binding to its erythrocyte receptor . As the N-terminal and C-terminal of PfRipr remains associated after processing , it is likely that the EGF-like domains are responsible for their association as well as interaction with PfRh5 . PfRipr has previously been predicted to act as a parasite adhesin by a bioinformatic method based on protein physiochemical properties [59] . However , we could not detect direct binding of PfRipr to erythrocytes and our results suggest that the PfRipr/PfRh5 complex dissociates upon PfRh5 binding to red blood cells . This may be similar to the processed N-terminus of PfRh2a and PfRh2b that form a complex with the C-terminus of the same proteins . However , only the free processed N-terminus can be detected in erythrocyte binding assays suggesting that this complex also disassociates [24] . The PfRh and EBL protein families function in binding the erythrocyte and activating the invasion process [54] , [55] . It has been proposed that a combination of these ligands may be a useful blood stage vaccine against P . falciparum malaria . Indeed it has been shown in immunisation studies that vaccination with combined portions of EBA-175 , PfRh2a/b and PfRh4 proteins can raise antibodies that efficiently block merozoite invasion [54] . The functional importance of PfRh5 has also led to suggestion that it has potential to be included in such a combination vaccine . However , this has not been tested due to the difficulty of producing functional protein . Identification of the PfRh5/PfRipr complex and the ability of anti-PfRipr antibodies to block merozoite invasion open the possibility for its inclusion in a combination vaccine . Indeed , the efficient inhibition of invasion was observed when a combination of antibodies to PfRipr , PfRh2a/b and PfRh4 was used . This further supports PfRipr as a new vaccine target , especially in a combination approach to efficiently block merozoite invasion .
Antibodies were raised in mice and rabbits under the guidelines of the National Health and Medical Research Committee and the PHS Policy on Humane Care and Use of Laboratory Animals . The specific details of our protocol were approved by the Royal Melbourne Hospital Animal Welfare Committee . P . falciparum asexual parasites were maintained in human erythrocytes ( blood group O+ ) at a hematocrit of 4% with 10% ( w/v ) AlbumaxTM ( Invitrogen ) [60] . 3D7 is a cloned line derived from NF54 from David Walliker at Edinburgh University . FCR3 is a cloned line . Cultures were synchronised as previously described [61] . Culture supernatants enriched in parasite proteins were prepared by growing synchronized parasite cultures to high parasitemia , typically to 5% and allowing schizonts to rupture . Harvested culture supernatant was spun at 1200 rpm to remove residual erythrocytes and then 10 , 000 rpm to remove insoluble materials before use for experiments . Total proteins from schizont stage parasites were obtained by saponin lysis of infected erythrocytes . To prepare culture supernatants for affinity purification of the PfRh5 complex , the PfRh5HA parasite line was synchronized , grown to late schizont stage in normal culture conditions and then culture medium was replaced by RPMI without Albumax to allow the schizonts to rupture [5] . This culture supernatant has a minimal amount of BSA to facilitate purification of the PfRh5 complex . Culture supernatant ( 900 ml ) , prepared as described above , was dialysed against 12 . 5 mM Tris . Cl pH 7 . 2 overnight at 4°C and loaded onto a 15 ml Q-Sepharose column ( GE Heathcare ) equilibrated with 12 . 5 mM Tris . Cl , pH 7 . 2 . The bound proteins were eluted with NaCl in 12 . 5 mM Tris . Cl pH 7 . 2 . PfRh5HA was eluted with 350 mM NaCl . The eluted PfRh5HA-containing fractions were further purified using anti-HA affinity matrix ( Roche Apply Science ) and the bound proteins eluted with 0 . 1 M glycine , pH 2 . 6 . The eluted proteins were then subjected to trypsin digestion and analyzed by mass spectrometry ( LC-MS/MS ) and proteins identified by database searches [42] . Gel-filtration chromatography was performed on an analytical Superdex 200 column ( 24 ml , GE Healthcare ) and proteins were eluted with PBS or Tris buffer . Blue Native Gel Electrophoresis was conducted using the company's protocols ( Invitrogen ) . NativePAGETM Novex 4–16% or 3–12% Bis-Tris gels were used to resolve the proteins and the NativeMarkTM Unstained Protein Standard used as molecular weight markers . To attach a triple HA tag ( 3xHA ) to the 3′ end of the Pfripr gene , an 844 bp fragment of Pfripr was amplified from 3D7 genomic DNA using the primers 5′-ATCCCGCGGTGAATGTATATTAAATGATTATTG-3′ and 5′-TTATCTCGAGATTCTGATTACTATAATAAAATACATTTTC-3′ ( Sac II and Xho I restriction sites underlined ) . The DNA fragment was digested with Sac II and Xho I , and cloned into pHAST , a derivative of pGEM-3Z containing a 3xHA tag and single Strep II tag in tandem . Parasites were transfected as described previously [62] , [63] . Successful integration of the 3xHA tag was determined by Southern and Western blot analysis using a mouse monoclonal anti-HA antibody . Immunoprecipitation of PfRh5 from culture supernatant was performed using anti-PfRh5 monoclonal antibody ( clone 2F1 ) coupled to Minileak resin ( KEM-En-Tec ) . Briefly , 1 . 5 ml culture supernatants from both 3D7 and 3D7-PfRiprHA parasite lines were incubated with 20 µl anti-PfRh5-Minileak resins at 4°C for 4 hr . Also 1 . 5 ml culture supernatant of 3D7-PfRiprHA parasites was incubated with just 20 µl Mini-bead as an additional control . After incubation , the samples were spun to remove the supernatant and the resin washed three times with PBS containing 0 . 1% Tween-20 . Bound proteins were eluted with SDS sample buffer and separated by SDS-PAGE , transferred to nitrocellulose membrane and probed with a monoclonal anti-HA antibody ( 12CA5 ) . The membrane was then stripped and re-probed with rabbit anti-Rh2a/b polyclonal antibodies recognizing 85 kDa domain . Immunoprecipitation of HA-tagged PfRipr from culture supernatant of 3D7-PfRiprHA was performed using rat anti-HA affinity matrix ( Roche Applied Science ) . The culture supernatants from 3D7 parasites were used as a control . The bound material was analysed by western blot to probe for PfRh5 using monoclonal anti-PfRh5 antibody ( 2F1 ) or to probe for PfRipr using polyclonal antibodies raised against N-terminal fragment ( α-PfRip/3 ) . The material was also probed with anti-Rh2a/b polyclonal antibodies as a control . Immunoprecipitation of PfRipr from culture supernatant of 3D7-PfRipHA with α-PfRip/3 was performed using protein G . The IgG from a pre-bleed of the same rabbit was used as a control . Immunoprecipitation of PfRipr from solubilised saponin pellets of the 3D7RiprHA parasite line was performed similarly . The proteins were extracted from the saponin pellet using 2% n-Dodecyl-N , N-Dimethylamine-N-Oxide in PBS . Immunoprecipitation of culture supernatant from 3D7-PfRipHA with rabbit anti-Rh2a/b polyclonal antibodies was also performed with protein G . The bound material was probed for PfRh2a , PfRh2b , PfRh5 and PfRipr using corresponding antibodies raised in mice . The proteins were detected by enhanced chemiluminescence ( ECL , Amersham Biosciences ) . Synchronized 3D7RiprHA early ring stage parasites were harvested and the red blood cells lysed using saponin . A second aliquot was harvested 16 hr later , the third aliquot another 8 hr later and subsequent samples every 6 hr later until the end of schizogony . Proteins were extracted from the saponin pellets using SDS-PAGE sample buffer , separated by 4-12% SDS-PAGE gels ( Novagen ) and transferred to a nitrocellulose membrane . The membrane was firstly probed with monoclonal anti-HA antibody for PfRiprHA and then stripped to probe for PfRh5 and PfHsp70 [64] . To produce recombinant C-terminal fragment of PfRipr comprise of amino acid 791-900 , a 345 bp DNA fragment was amplified from genomic DNA prepared from 3D7 parasites using oligonucleotides ( 5′ CGCTAGCCATATGAATGAAGAAACAGATATTGTAAAATG 3′ and 5′ CGAGGATCCCTAATCTTCTAAAACACATTTTCC 3′ ) . The resulting PCR fragment was cloned into pET14b vector ( Novagen ) with Nde I and Bam HI , transformed into BL21 RIL E . coli strain to express the recombinant PfRipr-791-900 as a hexa-His-tagged protein ( Fig . 3 A ) . The His-tagged protein was purified from soluble lysate of bacteria cells by affinity purification on Ni-NTA agarose resin ( Qiagen ) followed by gel-filtration chromatography on SuperdexTM 75 column . To produce recombinant N-terminal fragment of PfRipr consisting of amino acid 238–368 , a codon-optimised gene was synthesized and cloned into pET28a vector ( Novagen ) with Nhe I and BamHI sites , transformed into BL21 RIL E . coli strain for expressing recombinant PfRip-238-368 as a hexa-His-tagged protein . The His-tagged protein was expressed in E . coli as inclusion body . Protein isolated from inclusion bodies was refolded in vitro and purified on Ni-NTA agarose resin ( Qiagen ) under native conditions . Both purified PfRip-791-900 and PfRip-238-368 proteins were used to immunise a rabbit . Rabbit immunoglobulins were purified on Protein A or G-Sepharose and buffer exchanged to PBS for subsequent experiments . The antibody production was done by the WEHI antibody production facility . Immunofluorescence assays ( IFA ) were performed as described [46] , using primary antibodies as follows: rabbit anti-Ripr [1∶1000]; rat anti-HA ( monoclonal CF10 , Roche ) [1∶50]; rabbit anti-RON4 [44] [1∶250]; mouse anti-RAP1 [65] [1∶500]; mouse anti-Rh5 2F1 [1∶200]; rabbit anti-EBA175 [66] [1∶300] . Images were captured on a Zeiss Axiovert 200 m microscope ( Zeiss ) with a 100x/1 . 40NA PlanApochromat phase contrast oil immersion objective lens ( Zeiss ) , an Axiocam MRm camera ( Zeiss ) and running Axiovision version 4 . 8 software ( Zeiss ) . PfRiprHA parasite ( late schitzont stage ) -infected red blood cells were hypotonically lysed with water , centrifuged and the pellet fraction washed with PBS . The pellet was then divided into four eppendorf tubes and incubated on ice for 2 hr with 10 mM Tris/pH 8 . 0; 100 mM sodium carbonate/pH 11 . 5; 2% Triton X100 and 2% CHAPS in 50 mM Tris/pH8 . 0 containing 1 mM EDTA and 100 mM sodium chloride respectively . The samples were then centrifuged to separate soluble and insoluble fractions . The insoluble fraction was washed twice with PBS and analyzed by Western blot together with the soluble fraction . Saponin pellet , prepared from the late schizont stage PfRiprHA parasites , were subjected to the same analyses as described above . GIA were performed as described [67] . Briefly , late trophozoite stage parasites were added to erythrocytes to give a parasitemia of 0 . 2% and haematocrit of 2% in 45 µl of 0 . 5% Albumax II ( Gibco , Auckland , New Zealand ) in 96 well round bottom microtiter plates ( Becton Dickinson , Fanklin Lakes , NJ , U . S . A . ) . 5 µl of purified rabbit IgG was added to a final concentration of 2 mg/ml . For the titration experiments , the antibodies were added to a final concentration of 0 . 125 , 0 . 25 , 0 . 5 , 1 , 2 . 0 and 3 . 0 mg/ml . Cultures were incubated for 72 hr ( 2 cycles ) of growth and parasitaemia of each well was counted by flow cytometry , a FACSCalibur with a plate reader ( Becton Dickinson , Fanklin Lakes , NJ , U . S . A . ) after ethidium bromide ( 10 µg/ml , Biorad , Hercules , CA , U . S . A ) staining trophozoite stage parasites . For each well , more than 50 , 000 cells were counted , and all samples were tested in triplicate . Growth was expressed as a percentage of the parasitaemia obtained from the pre-immunization IgG control . Three independent assays were performed . Merozoite attachment was performed using viable cells purified as described [45] , [50] . Briefly , late shizonts of 3D7 and FCR3 ( 40–46 hr post invasion ) were purified from culture by magnet separation ( Macs Miltenyi Biotec ) to >95% purity . The purified schizonts were incubated with 10 µM E64 ( Sigma ) for 7–8 hr , then pelleted at 1900g for 5 min . Resulting parasitophorous vacuole membrane enclosed merozoites ( PEMS ) [68] , [69] were resuspended in a small volume of incomplete culture medium ( containing no protein ) and filtered through a 1 . 2 µm , 32 mm syringe filter ( Sartorius Stedim biotech , France ) . Antibodies were added to filtered merozoites at a final concentration of 2 mg/ml and incubated for 2 min after which uninfected erythrocytes were added and incubated for 10 min . Following fixation at room temperature for 30 min using 0 . 0075% glutaraldehyde/4% paraformaldehyde ( ProSciTech , Australia ) in PBS [70] the merozoite-bound erythrocytes were washed and stained with 0 . 1 ng/µL 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Invitrogen ) . Imaging was performed using a either Plan-Apochromat 100x/1 . 40NA or 40x/1 . 3NA oil immersion Phase contrast lens ( Zeiss ) on an AxioVert 200 M microscope ( Zeiss ) equipped with an AxioCam Mrm camera ( Zeiss ) . The MosaiX application from the Axiovision release 4 . 8 software ( Zeiss ) was used for counting . For each condition , at least 2000 red blood cells with bound merozoites were scored . Merozoites from 3D7RiprHA parsites were purified according to a previously described method [50] and mixed with uninfected human erythrocytes . After a 2 min incubation , invading merozoites were fixed in 1% glutaraldehyde ( Pro SciTech , Australia ) in RPMI Hepes for 30 min on ice and prepared for Transmission electron microscopy as described [71] . Briefly , samples were dehydrated with increasing ethanol concentrations and embedded in LR Gold resin ( Pro SciTech , Australia ) . The resin was polymerised using benzoyl peroxide ( 0 . 5% ) ( SPI-Chem , USA ) and the preparation was sectioned on a Leica Ultracut R ultramicrotome ( Wetzlar ) . The sections were blocked with 5% BSA and 0 . 1% Tween-20 in PBS for 30 min . Immunolabeling was performed with mouse anti-Rh5 clone 6H2 ( dilution 1∶100 ) and mouse anti-HA clone 12AC5 ( dilution 1∶100 ) . Samples were incubated with 18 nm colloidal gold-conjugated goat anti-mouse secondary antibody ( Jackson ImmunoResearch , Baltimore , USA ) . Post-staining was done with 2% aqueous uranyl acetate and 5% triple lead and observed at 120 kV on a Philips CM120 BioTWIN Transmission Electron Microscope .
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The malaria parasite invades red blood cells by binding to proteins on the surface of this host cell . A family of proteins called P . falciparum reticulocyte binding-like homologue ( PfRh ) proteins are important for recognition of the red blood cell and activation of the invasion process . An important member of the PfRh family is PfRh5 . We have identified a novel cysteine-rich protein we have called P . falciparum Rh5 interacting protein ( PfRipr ) , which forms a complex with PfRh5 in merozoites . PfRipr has 10 epidermal growth factor-like domains and is expressed in mature schizont stages where it is processed into two polypeptides that associate and form a complex with PfRh5 . The PfRipr protein localises to the apical end of the merozoites in micronemes whilst PfRh5 is contained within rhoptries and both are released during invasion when they form a complex that is released into the culture supernatant . Antibodies to PfRipr1 can potently inhibit merozoite attachment and invasion into human red blood cells consistent with this complex playing an essential role in this process .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology"
] |
2011
|
An EGF-like Protein Forms a Complex with PfRh5 and Is Required for Invasion of Human Erythrocytes by Plasmodium falciparum
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To cause disease , Clostridioides ( Clostridium ) difficile must resist killing by innate immune effectors in the intestine , including the host antimicrobial peptide , cathelicidin ( LL-37 ) . The mechanisms that enable C . difficile to adapt to the intestine in the presence of antimicrobial peptides are unknown . Expression analyses revealed an operon , CD630_16170-CD630_16190 ( clnRAB ) , which is highly induced by LL-37 and is not expressed in response to other cell-surface active antimicrobials . This operon encodes a predicted transcriptional regulator ( ClnR ) and an ABC transporter system ( ClnAB ) , all of which are required for function . Analyses of a clnR mutant indicate that ClnR is a pleiotropic regulator that directly binds to LL-37 and controls expression of numerous genes , including many involved in metabolism , cellular transport , signaling , gene regulation , and pathogenesis . The data suggest that ClnRAB is a novel regulatory mechanism that senses LL-37 as a host signal and regulates gene expression to adapt to the host intestinal environment during infection .
Clostridioides difficile ( formerly Clostridium difficile ) poses a serious , ongoing , public health threat . C . difficile infection ( CDI ) results in mild to severe diarrhea and leads to approximately 29 , 000 deaths each year in the United States [1] . Patients are typically infected after treatment with antibiotics , which disrupt the intestinal microbiota that provide colonization resistance against CDI by competition and release of antimicrobial peptides ( AMPs ) [1 , 2] . The host innate immune system also plays an important role in the prevention of infections . A critical feature of this defense is the production of AMPs , including defensins , cathelicidin ( LL-37 ) , and lysozyme [3 , 4] . LL-37 , a cationic AMP , is of particular importance in CDI because it is not only produced constitutively in the colon by the colonic epithelium , but it is also released in high levels from neutrophils , which are a key component of the initial immune response to CDI [5] . LL-37 is stored in neutrophil granules at a concentration of ~6 μg/ml and has been reported to reach levels of 15 μg/ml in the lungs of cystic fibrosis patients and ~5 μg per gram of feces during shigellosis [6–8] . LL-37 forms an amphipathic alpha-helical structure that can insert into bacterial membranes and cause bacterial cell death [9–11] . Common bacterial resistance mechanisms to LL-37 include cell surface modifications that prevent LL-37 access to the bacterial surface , efflux pumps that eliminate LL-37 that enters the bacteria , secreted proteases that degrade LL-37 , and modulation of host production of LL-37 [5] . C . difficile demonstrates inducible resistance to LL-37 , and current epidemic ribotypes have higher levels of resistance to LL-37 than other ribotypes [12] . Although C . difficile resistance to LL-37 is documented , no clear homologs of known resistance mechanisms are apparent in the genome and no additional LL-37 resistance mechanisms have been identified . Moreover , the mechanisms by which C . difficile responds to this and other innate immune factors are poorly understood . We hypothesized that C . difficile recognizes and responds to LL-37 and that this response occurs , at least in part , at the level of transcription . In this study , we determined the transcriptional response of C . difficile to the host peptide , LL-37 . We identified an operon , CD630_16170-CD630_16190 ( herein named clnRAB ) , that was highly induced by LL-37 and was not expressed in response to other cell-surface active antimicrobials . This operon encodes a predicted GntR-family transcriptional regulator ( ClnR ) and an ABC transporter system ( ClnAB ) . We determined that the ClnR regulator represses clnRAB expression and yet is also necessary for LL-37-dependent induction of clnRAB transcription . Transcriptional analyses of a clnR mutant indicated that ClnR is a global regulator that controls the expression of numerous genes including toxins , alternative metabolism pathways , transporters , and transcriptional regulators . Growth analyses revealed that exposure to LL-37 modifies the metabolism of C . difficile and that this response occurs through ClnR . In addition , we observed that both a clnR and a clnAB mutant are more virulent in the hamster model of infection and a clnR mutant is defective at colonization in the mouse model of infection . In vitro analyses confirmed that ClnR is a DNA-binding transcriptional regulator that directly controls expression of the cln operon and other ClnR-regulated genes . Further , we observed specific binding of ClnR to LL-37 , verifying the direct interaction of these factors . Based on these data , we propose that LL-37 acts as a host signal that is transmitted through ClnRAB , enabling C . difficile to regulate global gene expression to adapt to the intestinal environment during infection .
To test the hypothesis that C . difficile responds to LL-37 through changes in gene transcription , we performed RNA-seq analysis on bacteria grown with or without sub-MIC levels of LL-37 to determine which genes were differentially expressed . RNA-seq analysis revealed 228 genes that were differentially expressed at least 2-fold and with p < 0 . 05 , including 107 genes that were induced and 121 genes that were down-regulated in the presence of LL-37 ( S1 Table ) . Genes differentially expressed in LL-37 include loci predicted to encode metabolic pathway components , nutrient acquisition mechanisms , transcriptional regulators , multidrug transporters , antibiotic resistance factors , conjugation-associated proteins , and genes of unknown function . Notably , several of these loci were previously investigated in C . difficile and found to contribute to growth , antimicrobial resistance or virulence , including genes involved in succinate , glucose , fructose , mannitol , ethanolamine , butyrate , acetyl-CoA and amino acid metabolism , oligopeptide permeases , elongation factor ( EF-G ) , ferredoxin oxidoreductase , and ECF sigma factors [13–23] . Of the differentially regulated genes identified , the most highly induced by LL-37 were three genes comprising an apparent operon: CD630_16170–16190 . These genes encode a putative GntR-family transcriptional regulator ( CD630_16170 , clnR ) and a downstream ABC transporter system composed of an ATP-binding component ( CD630_16180 , clnA ) and a permease ( CD630_16190 , clnB ) . The results of the RNA-seq analyses for clnRAB expression were verified by qRT-PCR in the 630Δerm strain and for the epidemic 027 ribotype strain , R20291 ( Fig 1 ) . Based on the substantial induction of these genes and their resemblance to antimicrobial response systems , we pursued the function of this operon further . Transcriptional analysis of strains grown in increasing concentrations of LL-37 demonstrated that expression of each of these genes increased in a dose-dependent manner for both strains , illustrating that these genes are similarly expressed and regulated in diverse C . difficile isolates ( Fig 1 ) . Using nested PCR from C . difficile cDNA templates , we also confirmed that the CD630_16170–16190 genes are transcribed as an operon ( S1 Fig ) . Given the high level of induction of the clnRAB operon in LL-37 , and because transporters are common antimicrobial resistance mechanisms , we hypothesized that ClnAB may confer resistance to LL-37 . To test this , we generated insertional disruptions in the CD630_16170 ( clnR::erm; herein clnR null mutant ) and CD630_16180 ( clnA::erm , herein clnAB null mutant ) coding sequences ( S2A Fig ) , and analyzed the ability of the mutants to grow in the presence of LL-37 ( Fig 2 ) . Although the clnR mutant has a minor growth defect when grown in BHIS alone , both the clnR and clnA mutants grew slightly better than the parent strain in 2 . 5 μg/ml LL-37 . Evaluation of the minimum inhibitory concentrations ( MICs ) and minimum bactericidal concentrations ( MBCs ) for LL-37 in these strains revealed no observable differences in either the MIC or MBC for the clnR or clnAB null mutant in comparison to the parent strain ( S2 Table ) . Considering that some antimicrobial transporter mechanisms are activated by and confer resistance to multiple classes of antimicrobials , we investigated the resistance of both mutants to other cell-surface acting compounds . Neither the clnR nor the clnAB mutant had altered MIC values for any other cell surface-active antimicrobial tested ( S3 Table ) . These findings indicate that the cln operon does not play a significant role in resistance to LL-37 or other tested cell-surface active antimicrobials . The induction of the cln operon suggested that this locus was responsive to LL-37; however , antimicrobials may induce changes in bacterial gene expression as a general stress response or due to disruptions in cellular processes [24 , 25] . To determine whether the induction of this operon is specific to LL-37 or a general response to cellular stress , we evaluated the expression of clnR in the presence of a variety of other antimicrobial compounds ( Table 1 ) . Transcription of clnR was also induced when C . difficile was exposed to the mouse cathelicidin , mCRAMP , but clnR was not induced in the presence of sequence-scrambled LL-37 or the sheep cathelicidin , SMAP-29 , which is less similar to LL-37 than mCRAMP ( S3 Fig ) [26–28] . Similarly , none of the other cell-surface-active antimicrobials tested ( lysozyme , ampicillin , vancomycin , nisin , or polymyxin B ) induced clnR expression ( Table 1 ) . These results indicate that induction of the clnRAB operon is dependent on the specific sequence of LL-37 and is not caused by antimicrobial-induced cell-surface stress . Accordingly , we named this operon clnRAB to reflect the specificity of the induction in response to LL-37 and similar Cathelicidins . As antimicrobial resistance did not explain the changes in growth for the clnR and clnAB null mutants in LL-37 , we hypothesized that there were changes in the expression of genes other than clnRAB in the cln mutants . To test this , we examined gene expression by RNA-seq for the clnR mutant grown with and without LL-37 , compared to that of the parent strain ( S4 Table ) . This analysis revealed that 178 genes were differentially expressed at least 2-fold in the clnR mutant ( p < 0 . 05 ) . Notably , the clnR mutant demonstrated negative and positive changes in transcription , with many genes exhibiting additional conditional regulation by LL-37 . In the absence of LL-37 , the clnR mutant exhibited increased expression of 14 genes and decreased expression of 32 genes . Disruption of clnR had an even greater impact on expression in the presence of LL-37 , resulting in increased expression of 29 genes and decreased expression of 103 genes . Of the 228 genes differentially regulated by LL-37 ( S1 Table ) , 56 were also influenced by clnR ( S5 Table ) . These results indicate that ClnR acts as both a repressor and inducer of gene expression , and that this regulatory potential is largely dependent on LL-37 . The 178 genes regulated by ClnR fell into many different functional classes , with the most common ClnR-dependent genes encoding proteins with predicted metabolic functions ( Fig 3 ) . These results support the premise that ClnR acts as a global transcriptional regulator in response to LL-37 . RNA-seq results from the clnR mutant and parent strain were validated by qRT-PCR for several apparent ClnR-dependent genes , as well as analysis of expression in the clnAB mutant and complemented strains ( S6 Table ) . Comparisons of clnR and clnAB mutant expression revealed that the regulator and transporter disruptions resulted in disparate effects on the transcription of some genes ( vanZ1 , cdd4 and iorA ) ( S6 Table ) . These disparate effects are most prominent in the presence of LL-37 , highlighting that ClnR activity is dependent on LL-37 . In some cases , the clnR and clnAB mutants had similar levels of expression , suggesting that the ClnAB transporter function is important for the activation of ClnR , whereas in other cases ( vanZ1 , cdd4 and iorA ) expression diverged in the clnR and clnA mutants , suggesting a role for the ClnAB transporter in the regulation of some LL-37-dependent genes independent of ClnR . Complementation of clnR and clnAB was performed by restoring the entire clnRAB operon in trans , as restoration of expression was not possible using only the disrupted clnR or clnAB , respectively ( S2B Fig ) . As a result , the clnR complemented strain expresses the acquired copy of clnRAB and the native clnAB . Similarly , the clnAB complement expresses the native clnR and the acquired clnRAB . The altered ratios of components in these complement strains ( S2B Fig ) may account for some discrepancies noted in these strains , further highlighting the complicated relationship between ClnR and ClnAB in regulation . These complex gene regulatory patterns suggest that multiple factors are involved in the transcription of some ClnR-regulated genes , and that ClnR has both direct and indirect effects on the expression of some loci . To understand the molecular mechanism of ClnR function , we further explored regulation of the clnRAB locus . ClnR is annotated as a GntR-family transcriptional regulator , and protein sequence comparisons suggest that it is a member of the YtrA sub-family of GntR regulators ( S4 Fig ) . GntR-family regulators are most common among bacteria that inhabit complex environmental niches [29] . The YtrA sub-family regulators are often found in conjunction with ABC-transporters and are typically autoregulatory [30] . We examined the impact of clnR and clnAB disruption on expression of the clnRAB operon to determine if ClnR regulates expression of itself and clnAB . qRT-PCR analysis using primers located downstream of the clnR and clnA insertional disruptions revealed that transcription of clnR and clnA are increased in the clnR mutant grown without LL-37 ( Fig 4 ) , suggesting that ClnR auto-represses the cln operon . Note that in the clnR insertional mutant , the clnR transcript cannot be translated into functional protein because of the insertional disruption , but clnA and clnB transcripts are produced and are expected to be translated . These results indicate that the disruption of clnR is not polar on expression of clnA ( or clnB ) , but clnAB expression is disregulated in the clnR strain . In the presence of LL-37 , clnR and clnA expression are no longer induced in the clnR mutant , demonstrating that ClnR activates the cln operon in response to LL-37 . Conversely , the clnAB mutant displays lower clnR and clnA expression during growth with or without LL-37 ( Fig 4 ) . These results provide further evidence that the ClnAB transporter contributes to regulation of the cln operon and the ability of ClnR to respond to LL-37 . Based on the evident changes in metabolic gene expression in the clnR mutant and the impact of LL-37 on ClnR-dependent transcription , we investigated the effect of ClnR and LL-37 on the growth of C . difficile with relevant metabolites . To this end , we grew the clnR mutant and the parent strain in minimal medium supplemented with glucose , fructose , mannose , mannitol , N-acetylglucosamine , or ethanolamine , with and without LL-37 ( Fig 5 ) . We observed that for the first 2–3 h , growth of the cln mutants and the parent strain were indistinguishable in the presence or absence of LL-37 , regardless of the supplemented carbon source ( Fig 5 ) . Other groups have also observed preferential utilization of peptides by C . difficile , as amino acids are a preferred energy source for this bacterium [31–33] . As anticipated , the addition of each of the examined carbon sources to minimal medium ( MM ) resulted in shorter doubling times ( i . e . , faster growth ) for the parent strain cultures ( 630Δerm ) than in the base minimal medium , with the exception of ethanolamine supplementation ( Fig 5 , S7 Table , column one ) . The clnR mutant grew less well than other strains in MM , MM with glucose , and MM with NAG , suggesting that ClnR is important for the utilization of peptides , glucose and NAG ( Fig 5 ) . When a low concentration of LL-37 ( 0 . 5 μg/ml; 1/30 MIC ) was added to the growth medium , the clnR and clnAB mutants grew better than the parent strain in base MM , MM with mannitol , MM with NAG , and MM with EA supplementation ( Fig 5 ) , suggesting that ClnRAB is important for growth in a variety of nutrients in the presence of LL-37 . These results indicate that the changes in growth observed with low levels of LL-37 are due to changes in ClnR-dependent bacterial metabolism , rather than the antimicrobial activity of LL-37 . Moreover , the data strongly suggest that the growth and metabolism delays observed in LL-37 are mediated by ClnR through repression and activation of metabolic gene expression ( S4 and S5 Tables ) . As LL-37 is a host-produced peptide and C . difficile inhabits the gastrointestinal tract , the natural consequences of ClnR-LL-37–dependent gene regulation would appear during the growth of the pathogen in the host intestine . To examine the effects of cln mutants in vivo , we used the hamster and mouse models of CDI . Like humans , hamsters and mice are sensitive to colonization by C . difficile , especially after receiving antibiotics , and both produce cathelicidins similar to LL-37 ( S3 Fig ) [34–36] . Syrian golden hamsters are acutely susceptible to infection by C . difficile , with as few as 1 to 10 CFU needed to produce fulminant disease [37] . Mice are naturally not as susceptible to CDI as hamsters and require 104−107 CFU to achieve colonization , usually with low morbidity [38] . For these reasons , the hamster model is most useful for examining early colonization and virulence , while mice allow for assessment of long-term C . difficile colonization [39] . Hamsters were infected with spores of 630Δerm , clnR , or clnAB strains and monitored for symptoms of infection as described in the Methods . Hamsters infected with either the clnR or clnAB mutant strains succumbed to infection more rapidly than animals inoculated with the parent strain , indicating that the clnR and clnAB mutants are more virulent ( mean time to morbidity: 46 . 0 ± 12 . 2 h for 630Δerm , 32 . 5 ± 5 . 8 h for clnR , 35 . 2 ± 6 . 1 h for clnAB; Fig 6A ) . As the hamster model uses clindamycin to induce host susceptibility , it is conceivable that differences in strain outcomes were due to an advantage for growth provided by the ermB cassette in the clnR and clnAB mutants . To test this possibility , additional controls were performed using isogenic strains with and without the ermB cassette , as well as a range of clindamycin doses . No differences in infectivity were observed with or without the ermB cassette , nor did the dose of clindamycin impact infection in the 630 strain background ( S5 Fig ) . C . difficile disease is mediated by the two primary toxins , TcdA and TcdB . To determine whether the increased virulence of the cln strains was related to increased toxin levels , we extracted RNA from cecal samples collected from animals at the time of morbidity and performed digital droplet PCR for absolute quantification of tcdA , tcdB , and clnR expression ( S6 Fig ) . No significant differences in toxin expression were apparent at the time of morbidity . Because only one timepoint could be assessed , the results do not resolve whether the clnR and clnA mutants had altered toxin expression during the course of infection . But , the time from infection to morbidity for clnR and clnAB infections imply that these mutants produce toxin earlier in the course of infection , resulting in earlier symptoms of disease and morbidity . To assess C . difficile colonization by the different strains , hamster fecal samples were taken at 12 h post-infection and plated onto selective medium . C . difficile was recovered from fecal samples in significantly more animals infected with the clnR strain than in the 630Δerm-infected group , suggesting that the clnR mutant colonizes the hamster intestine more rapidly ( Fig 6B ) . In addition , the clnR mutant reached a higher bacterial burden at the time of morbidity ( 1 . 2 x 107 CFU/ml for 630Δerm , 2 . 7 x 107 CFU/ml for clnR , p < 0 . 05; Fig 6C ) . These results illustrate that the clnRAB operon plays a significant role in the colonization and virulence dynamics of C . difficile hamster infections . The colonization results in hamsters suggested a role for ClnRAB in colonization dynamics , which was further examined in the mouse model . Mice were infected with spores of either 630Δerm , clnR , clnAB , clnR Tn::clnRAB , or clnAB Tn::clnRAB strains and monitored for colonization and disease as described in the Methods . Mice infected with clnR lost less weight and recovered more quickly , while mice infected with the clnAB mutant lost weight faster than those infected with 630Δerm ( Fig 7A ) . Additionally , mice infected with clnR cleared the bacteria more quickly , with fewer animals having detectable CFU in their feces ( Fig 7B; S7 Fig ) . While the impact on short-term colonization and virulence in hamsters and longer-term colonization and persistence in mice are contrasting , the results from both animal models strongly suggest that ClnR contributes to the ability of C . difficile to initiate colonization , cause disease , and persist in the intestinal environment . Considering that differences in either sporulation or germination rates can also influence virulence and bacterial burden in vivo , we assessed sporulation and germination for the clnR and clnAB mutants for defects in either process . No significant difference in sporulation or germination rates was observed for either mutant ( S8 Fig ) . Since toxin production is the primary virulence factor leading to C . difficile symptoms , we further investigated the effects of LL-37 and ClnRAB on toxin production under more controlled conditions in vitro . qRT-PCR analysis of tcdA and tcdB transcription was assessed for the cln mutants and parent strain during logarithmic phase growth in BHIS medium , with or without added LL-37 . As shown in S9A and S9B Fig , LL-37 exposure resulted in increased expression of tcdA ( 4 . 6-fold ) and tcdB ( 2 . 2-fold ) in wild-type cells ( p < 0 . 05 ) . In contrast , the clnR and clnAB mutants demonstrated lower expression of toxins in LL-37 , suggesting that ClnRAB is partially responsible for LL-37-dependent regulation of toxin expression in vitro . Toxin expression is known to be controlled by several regulatory factors , many of which respond to low nutrient availability and/or the transition to stationary phase growth [40] . To determine which of the toxin regulators may be influenced by LL-37 , we examined expression of regulators and regulator-dependent factors , including tcdR , sigD , ilcV ( as an indicator of CodY activity ) , and CD0341 ( as an indicator of CcpA activity ) ( S8 Table ) . Of these , only ilvC expression is statistically altered in LL-37; however , the increase in ilvC expression is far more modest ( 2 . 2-fold increase; p < 0 . 05 ) than would be expected with robust CodY activation [18 , 19] . Because toxin production is typically low at mid-logarithmic phase in BHIS medium , we also examined toxin protein levels after 24 h growth in TY medium . Western blot analysis indicated that TcdA levels were significantly lower in the clnR mutant in TY medium , relative to the parent strain . When cells were grown in medium supplemented with LL-37 , final TcdA levels decreased about 3-fold in the parent strain ( S9C Fig ) . In contrast , TcdA levels did not change for the clnR and clnAB mutants in LL-37 . While these findings contradict the induction of tcdA expression observed at log-phase in BHIS medium , the data support the observation that LL-37 and ClnRAB influence toxin expression , and that the outcome of this regulation on toxin production is dependent on growth conditions . These observations provide further evidence that the ClnRAB system is involved in toxin production and that this system is necessary for the influence of LL-37 on toxin production . As a predicted GntR-family transcriptional regulator , we hypothesized that ClnR binds DNA . Because expression results suggested that ClnR is autoregulatory ( Fig 4 ) , we initially tested whether ClnR directly regulates its own promoter . We produced recombinant His-tagged ClnR and performed gel shifts with fluorescein-labeled DNA of the 84 bp upstream of the predicted clnR transcriptional start site . This DNA fragment was selected because it encompasses a predicted σA-dependent promoter with -10 ( at -52 to -47 bp ) and -35 ( at -73 to -68 bp ) consensus sequences and a tandem repeat sequence ( at -46 to -16 bp ) that includes a possible ClnR-binding site ( S10 Fig ) . Incubation of His-ClnR with this DNA fragment resulted in a shift visible after electrophoresis , both with and without LL-37 ( Fig 8A ) . The apparent Kd value for this interaction was calculated to be 118 nM ( ± 40 nM ) without LL-37 and 85 nM ( ± 7 nM ) with LL-37 , indicating that the affinity of ClnR for this DNA sequence does not change significantly in the presence of LL-37 in these conditions . Additional ClnR-regulated promoters were examined for direct binding , including predicted upstream promoter elements for the metabolic operons grd ( CD630_23540 ) , mtl ( CD630_23340 ) , and ior ( CD630_23810 ) ; other transcriptional regulators , including sigU ( csfU , CD630_18870 ) and CD630_16060 , as well as the uncharacterized vanZ ortholog ( CD630_12400 ) . ClnR bound to all of these promoter sequences but exhibited specificity for PvanZ , PCD1606 , and PsigU , with or without LL-37 ( Figs 8B–5D ) . Binding was less specific for Pgrd , Pmtl and Pior under the conditions tested ( S11 Fig ) . The calculated apparent Kd for PvanZ was 141 nM ( ± 59 nM ) without LL-37 and 139 nM ( ± 33 nM ) with LL-37 , the apparent Kd for PCD630_16060 was 1 . 9 μM ( ± 0 . 2 μM ) without LL-37 and 2 . 6 μM ( ± 0 . 5 μM ) with LL-37 , and the apparent Kd for PsigU was 2 . 5 μM ( ± 0 . 2 μM ) without LL-37 and 4 . 2 μM ( ± 3 . 4 μM ) with LL-37 ( Fig 8 ) . Because the apparent Kd values for these targets are similar both with and without LL-37 , it does not appear that LL-37 influences ClnR binding of these targets in these in vitro conditions . Although the EMSA did not uncover differences in ClnR-DNA binding in the absence or presence of LL-37 in vitro for the DNA targets examined , our expression and growth data suggest that ClnR regulates transcription in response to LL-37 . Based on clnR mutant phenotypes for cells grown with and without LL-37 , we hypothesized that ClnR directly binds LL-37 to regulate ClnR activity . To test this hypothesis , we performed surface plasmon resonance ( SPR ) to examine the binding kinetics of ClnR with LL-37 . These experiments showed that His-ClnR interacts with LL-37 with a Kd of 83 ± 14 nM ( Fig 9A ) . This Kd value is similar to the apparent Kd values that were calculated for the affinity of His-ClnR for Pcln DNA . This result suggests that the concentrations of ClnR needed to interact with both LL-37 and Pcln DNA are similar , and that interactions of these three components could occur simultaneously . Furthermore , the interaction between His-ClnR and LL-37 is specific , as SPR using scrambled LL-37 found no apparent interaction between these molecules ( Fig 9B ) .
Many bacteria encode signaling systems for detecting conditions within the host environment , allowing for activation of genes that are necessary for survival within the host . LL-37 acts as a signal for many pathogens to adapt to the host , though most of the mechanisms that have been investigated are implicated in bacterial virulence and antimicrobial resistance [41–47] . Little is known about the molecular mechanisms that C . difficile uses to adapt and survive in the host intestinal environment . Our results have revealed that LL-37 alters global gene expression in C . difficile through the previously unknown regulator and ABC-transporter system , ClnRAB . Moreover , the activation of ClnRAB is specific to LL-37 and independent of the antimicrobial effects of this host peptide . Many of the genes regulated by LL-37 and ClnRAB function in metabolism and energy production ( S4 Table , Fig 3 ) . The regulation of metabolic pathways in response to LL-37 was previously observed in other pathogens , including S . pyogenes , E . coli , P . aeruginosa , and S . pneumoniae , but their role in the bacterial response to LL-37 has not been clear [43 , 46–48] . Our data indicate that the regulation of genes by LL-37/ClnRAB in vivo has notable effects on C . difficile colonization and virulence ( Fig 6 , Fig 7 ) . Results from the mouse infection model suggest that disruption of ClnRAB results in dysregulation of metabolism that significantly hinders the ability of C . difficile to colonize; in contrast , in the exquisitely toxin-sensitive hamster model , the metabolism defects in cln mutants quickly progress to nutrient deprivation and toxin production . These effects are not unexpected , given that nutrient deprivation is demonstrably the primary factor driving C . difficile toxin expression [20 , 32 , 40 , 49–54] . C . difficile possesses an unusual metabolic repertoire for energy generation , including solventogenic fermentation [55 , 56] , Stickland ( amino acid ) fermentation [31] , and autotrophic growth via the Wood-Ljungdahl pathway [57] . However , the importance of most of these individual metabolic pathways for growth and virulence in vivo has not been determined . Because ClnR is a global regulator that negatively and positively influences the expression of multiple metabolic pathways , many of which are constitutively expressed in a clnR mutant , we cannot infer which of these pathways are most influential for host pathogenesis . Determining how and which ClnR-controlled pathways and mechanisms influence disease could expose potential vulnerabilities of C . difficile that may be exploited to prevent infections . Overall , our results indicate that ClnRAB responds directly and specifically to LL-37 without conferring LL-37 resistance and suggest that ClnR responds to LL-37 as an indicator of the host environment , conferring a colonization advantage . The clnRAB locus is highly conserved in C . difficile , with representation at ≥ 99% amino acid sequence identity in over 500 strains at the time of this publication ( NCBI , BLASTp ) . The data strongly suggest that ClnR acts as a pleiotropic regulator in C . difficile that controls the expression of genes involved in metabolism and virulence , in response to the host peptide , LL-37 . Further study of the activation and downstream impacts of this regulatory pathway will contribute greatly to our understanding of how C . difficile adapts to the host environment and causes disease .
S8 Table lists the bacterial strains and plasmids used in this study . Escherichia coli was grown aerobically in LB medium ( Teknova ) at 37˚C [58] . As needed , cultures were supplemented with 20 μg chloramphenicol ml-1 ( Sigma-Aldrich ) or 100 μg ampicillin ml-1 ( Cayman Chemical Company ) . C . difficile was grown at 37°C in an anaerobic chamber containing 10% H2 , 5% CO2 and 85% N2 ( Coy Laboratory Products ) in brain heart infusion medium supplemented with 2% yeast extract ( BHIS; Becton , Dickinson , and Company ) , TY broth [51] , or 70:30 sporulation agar [59] , as previously described [60] . As needed , C . difficile cultures were supplemented with LL-37 ( Anaspec ) at the concentrations stated in the text , or 2 μg thiamphenicol ml-1 ( Sigma-Aldrich ) for plasmid selection . Plasmids and strains used in this study are listed in S9 Table . S10 Table lists the oligonucleotides used in this study . C . difficile strain 630 ( GenBank accession NC_009089 . 1 ) served as the reference for primer design and cloning . PCR amplification was performed using genomic DNA from strain 630Δerm as a template . PCR , cloning , and plasmid DNA isolation were performed according to standard protocols [60–62] . Plasmids were confirmed by sequencing ( Eurofins MWG Operon ) . Null mutations in C . difficile genes were introduced by re-targeting the group II intron of pCE240 using the primers listed in S10 Table , as previously described [16 , 61] . Plasmids were introduced into E . coli strain HB101 pRK24 via transformation . E . coli strains were then conjugated with C . difficile for plasmid transfer . Transconjugants were exposed to 50 μg kanamycin ml-1 to select against E . coli , 10 μg thiamphenicol ml-1 to select for plasmids , and subsequently , 5 μg erythromycin ml-1 to select for insertion of the group II intron into the chromosome . Insertion of the group II intron into erythromycin resistant clones was confirmed by PCR using the primers listed in S10 Table . The clnRAB coding sequence and apparent promoter ( pMC649 ) were introduced into the Tn916 transposon of BS49 as MC951 [20] . MC951 was then mated with C . difficile strains MC885 and MC935 to generate MC950 and MC953 , respectively . Transconjugants were exposed to 50 μg kanamycin ml-1 to select against B . subtilis and 5 μg erythromycin ml-1 to select for integration of the transposon . Insertion of the genes was confirmed by PCR using the primers listed in S10 Table . Complete information on plasmid construction is available in the supplemental materials ( S11 Table ) . Active cultures of 630Δerm or the clnR mutant were diluted to approximately OD600 0 . 05 in BHIS alone or with 2 μg LL-37 ml-1 and grown to OD600 0 . 5 for harvesting . RNA was extracted and DNase I treated as previously described [18 , 63] . rRNA was depleted from the total RNA using the Bacterial Ribo-Zero rRNA Removal Kit ( EpiCentre , Madison , USA ) following the manufacturer's instructions . cDNA libraries were prepared with the ScriptSeq v2 RNA-Seq library preparation kit ( Epicentre , Madison , USA ) . Briefly , the rRNA depleted sample was fragmented using an RNA fragmentation solution prior to cDNA synthesis . The fragmented RNA was further reverse transcribed using random hexamer primers containing a tagging sequence at their 5′ ends , 3′ tagging was accomplished using the Terminal-Tagging Oligo ( TTO ) . The di-tagged cDNA was purified using the AMPure XP ( Agencourt , Beckmann-Coulter , USA ) . The di-tagged cDNA was further PCR amplified to add index and sequencing adapters , the amplified final library was purified using AmpureXP beads . The final pooled libraries were sequenced on the Illumina HiSeq3000 system in a Single-end ( SE ) 150 cycle format , each sample was sequenced to approximate depth of 8–12 million reads . Sequenced reads were aligned to the CD630Derm ( GenBank Accession GCA_000953275 . 1 ) genome reference for the 630Derm strain of C . difficile using the STAR Aligner ( version 2 . 4 . 0g1; [64] ) . Counts of reads that uniquely map to genes in the reference genome annotation were accumulated using htseq-count ( HTSeq 0 . 6 . 1p1; [65] ) . Samples from two independent experiments were library size normalized separately in DESeq2 [66] and the resulting normalized gene read counts were used as the gene abundance estimation and imported into Excel for gene expression comparisons . Gene abundances from the two experiments were averaged , and data were analyzed using the Student’s two-tailed t-test . Cluster of orthologous genes ( COG ) designations were assigned according to the NCBI COG database ( 2014 updated version ) [67] . Sample preparation and analyses were performed by the Yerkes Nonhuman Primate Genomics Core ( Emory University ) . Raw data files are available in the NCBI Gene Expression Omnibus ( GEO ) database under accession number ( GSE115638 ) . Active cultures were diluted to an OD600 of approximately 0 . 05 in BHIS alone or BHIS with antimicrobials . The antimicrobials used included: LL-37 ( Anaspec ) , scrambled LL-37 ( Anaspec ) , mCRAMP ( Anaspec ) , SMAP-29 ( Anaspec ) , ampicillin ( Cayman Chemicals ) , vancomycin ( Sigma Aldrich ) , nisin ( MP Biomedicals ) , or polymyxin B ( Sigma Aldrich ) . Cultures were harvested at an OD600 of 0 . 5 , mixed with 1:1 ethanol:acetone on ice and stored at -80°C . RNA was extracted , DNase I treated , and used to generate cDNA as described above for RNA sequencing . qRT-PCR reactions were performed using the Bioline Sensi-Fast SYBR and Fluroescein kit on a Roche LightCycler 96 instrument . Primers were designed with the assistance of the IDT PrimerQuest tool ( Integrated DNA Technologies ) and are listed in S10 Table . Each qRT-PCR reaction was performed as technical triplicates for at least three biological replicates . The ΔΔCt method was used to normalize expression to rpoC , an internal control transcript , for relative quantification [68] . Statistical analysis of the results was performed using GraphPad Prism version 7 for Macintosh ( GraphPad Software , La Jolla , GA ) to perform either one- or two-way analysis of variance ( ANOVA ) with Dunnett’s or Sidak’s multiple-comparison test , as indicated . RNA was extracted from cecal samples as previously described [22] . RNA was subsequently DNase I treated and used to generate cDNA as described above for qRT-PCR . cDNA was diluted to a final concentration of 5 ng/μl RNA equivalent . Samples were prepared in duplicate with 1 . 25 ng/μl cDNA , 70 nM each of forward and reverse primers ( as listed in S10 Table ) , and 1x QX200 ddPCR EvaGreen Supermix ( Bio-Rad ) . 20 μl of each sample was loaded into a Bio-rad DG8 cartridge for droplet generation in a Bio-Rad QX200 Droplet Generator with 70 μl Droplet Generation Oil for EvaGreen ( Bio-Rad ) per sample . Droplets were transferred to an Eppendorf Twin-Tech 96-well plate , which was sealed with foil prior to PCR on a C1000 Touch thermal cycler with the following reaction parameters: 5 min at 95°C , 40 rounds of 30 s at 95°C and 1 min at 53°C , 5 min at 4°C , 5 min at 90°C ( all steps with 2°C/s ramp ) . Droplets were then read on the Bio-Rad QX200 Droplet Reader . Samples without reverse transcriptase were run as a negative control and were used as reference to manually set the threshold values for positive calls in the QuantaSoft analysis software . Samples were only analyzed for tcdA , tcdB , and clnR expression if rpoC transcripts were detected ( as a housekeeping gene , the detection of rpoC indicates sufficient C . difficile genomic material was present in the sample ) . Statistical analysis of the results using GraphPad Prism version 7 for Macintosh ( GraphPad Software , La Jolla , GA ) to perform two-way ANOVA with Dunnett’s multiple-comparison test . Minimum inhibitory concentrations ( MIC ) were determined as previously described [69] . MICs were determined for LL-37 ( Anaspec ) , ampicillin ( Cayman Chemicals ) , vancomycin ( Sigma Aldrich ) , nisin ( MP Biomedicals ) , and polymyxin B ( Sigma Aldrich ) . Briefly , overnight cultures of C . difficile strains were diluted 1:50 in Mueller-Hinton Broth ( Difco ) and grown to OD600 of 0 . 45 ( ~5 x 107 CFU/ml ) . Cultures were then diluted 1:10 in MHB and seeded at a further 1:10 dilution in a round-bottom 96-well plate prepared with serial dilutions of antimicrobials for a starting concentration of ~5 x 105 CFU/ml . Plates were incubated for 24 h at 37°C in the anaerobic chamber . The MIC was determined as the lowest concentration of antimicrobial at which no growth was visible after 24 h . For MBC determination , the full volume of wells at concentrations at and above the MIC were transferred as a 1:10 dilution into BHIS and incubated for 24 h at 37°C in the anaerobic chamber . The minimum bactericidal concentration ( MBC ) was determined as the lowest concentration of antimicrobial at which no growth was visible after 24 h . C . difficile strains were grown in BHIS medium containing 0 . 2% fructose and 0 . 1% taurocholate , as previously described [20] . Cultures were diluted into BHIS medium and grown to OD600 of ~0 . 5 , then diluted 1:10 into TY medium with or without 2 μg LL-37 ml-1 and grown for 24 h at 37°C . Cells were harvested by centrifugation , resuspended in SDS-PAGE loading buffer ( without dye ) and mechanically disrupted as previously described [19 , 70] . Protein concentrations were assessed using a micro BCA assay ( Thermo Scientific ) and 6 μg of whole cell protein was loaded onto a 12% polyacrylamide gel ( Bio-Rad ) . Proteins were subsequently transferred from the SDS-PAGE gel onto nitrocellulose membranes ( 0 . 45 μM; Bio-Rad ) , and probed with mouse anti-TcdA antibody ( Novus Biologicals ) . Membranes were then washed and probed with goat anti-mouse secondary Alexa Fluor 488 antibody ( Life Technologies ) . Imaging and densitometry analyses were performed using a ChemiDoc MP and Image Lab Software ( Bio-Rad ) . Density of the TcdA band was normalized to total protein density . Three biological replicates were analyzed for each strain and condition . Statistical analyses were performed using either a Student’s t test with Holm-Sidak correction or a one-way ANOVA , followed by a Dunnett’s multiple comparisons test . Recombinant N-terminally His-tagged ClnR was produced by GenScript ( Piscataway , NJ ) . Gene transcription in the clnR mutant complemented with His-tagged ClnR confirmed the functionality of this protein ( S12 Table ) . 5’-fluorescein-labeled DNA ( 10 ng per reaction; purified by extraction from a 4–20% TGX polyacrylamide gel ) was incubated for 30 min at 37°C with His-ClnR ( 0–8 μM ) with 10 mM Tris , pH 7 . 4 , 10 mM MgCl2 , 100 mM KCl , 7 . 5% glycerol , and 2mM DTT . 50 ng of salmon sperm DNA was added to each reaction as a noncompetitive inhibitor . In competition experiments , either 100 ng ( 10x ) or 1 μg ( 100x ) unlabeled target DNA ( specific ) or unlabeled Pspo0A ( nonspecific ) DNA was incubated with His-ClnR ( 125 nM for Pcln reactions , 8 μM for other targets ) for 20 min at 37°C prior to the addition of labeled PclnR DNA for a further 10 min incubation . Reactions were loaded onto a pre-run 4–20% TGX polyacrylamide gel ( Bio-Rad ) and imaged on a Typhoon phosphoimager ( GE Lifesciences ) using the 520 BP fluorescence channel . Images from at least three replicates were analyzed in ImageLab ( Bio-Rad ) to determine the density of signal in bound and unbound fractions . Using GraphPad Prism , apparent Kd values were calculated by non-linear regression using an equation for cooperative binding of Y = Fmax* ( ( x/Kd ) n ) / ( 1+ ( x/Kd ) n ) , where Y = the fraction of bound DNA , x = the concentration of ClnR , Fmax = the saturation level of bound DNA , Kd = the concentration of ClnR when half of the DNA is bound , and n = cooperativity coefficient [71] . Growth curves were performed using a minimal medium based on a previously described complete defined minimal media ( CDMM ) , but lacking D-glucose as used by Cartman et al . and adjusted to pH 7 . 4 [33 , 72] . The base medium was supplemented with 30 mM D-glucose ( Sigma-Aldrich ) , 30 mM D-fructose ( Fisher ) , 30 mM D-mannose ( BD Difco ) , 30 mM D-mannitol ( Amresco ) , 30 mM N-acetylglucosamine ( Chem-Impex ) , or 30 mM ethanolamine-HCl ( Sigma-Aldrich ) , as noted . Growth curves in minimal medium ( MM ) were carried out as follows: log-phase cultures were grown to an OD600 of 0 . 5 in BHIS medium , then diluted 5-fold into MM . Diluted cultures were then used to inoculate minimal medium broth for growth assays at a starting OD600 of ~0 . 01 ( 2 ml into 23 ml of MM ) . Growth curve data were analyzed by two-way ANOVA with Dunnett’s test for multiple comparisons , comparing each strain to 630Δerm at each time point . Doubling times were analyzed by one-way ANOVA with Dunnett’s test for multiple comparisons or by Student’s t test , as indicated . LL-37 ( 10 μm in acetate buffer , pH 5 . 0; Anaspec ) was immobilized onto a CM5 sensor chip ( GE Healthcare ) using standard amine-coupling at 25°C and targeted at 2000 RU ( actual RU 2028 ) [73] . sLL37 ( Anaspec ) was immobilized on a separate CM5 sensor chip using the same procedure ( final signal RU 1726 ) . SPR analyses were performed using a Biacore X100 instrument ( GE Healthcare ) . Running buffer I was HBS-P buffer ( GE Healthcare ) . Running buffer II was composed of 113 mM NaCl , 24 mM NaHCO3 , 3 . 9 mM KCl , 1 . 3 mM CaCl2 , 0 . 6 mM MgCl2 , 0 . 005% surfactant P20 , pH 7 . 3 [74] . His-ClnR ( as described above for EMSAs ) was dialyzed into running buffer II , then diluted into running buffer II for two-fold serial dilutions from 10 μM to 0 . 625 μM . His-ClnR was injected over immobilized LL-37 or sLL-37 for 180 s association time and 300 s dissociation time . The chip surfaces were regenerated by injecting 1 M NaCl in 50 mM NaOH for 90 s , then the surface was stabilized for 300 s prior to the next cycle run . The flow rate used was 10 μl/min . Binding to the reference cell was subtracted and the data were evaluated by Baicore X100 evaluation software ( V2 . 0 . 1 ) . Experiments were performed a minimum of two times . Male and female Syrian golden hamsters ( Mesocricetus auratus; Charles River Laboratories ) were housed individually in sterile cages within a biosafety level 2 facility in the Emory University Division of Animal Resources . Sterile water and rodent feed pellets were available for the animals to consume ad libitum . Hamsters were administered 30–60 mg/kg body weight clindamycin as indicated ( Hospira ) by oral gavage 7 days prior to inoculation with C . difficile , to promote susceptibility to infection [19 , 70] . Spores were prepared as previously described [35 , 36] and stored in phosphate-buffered saline ( PBS ) with 1% bovine serum albumin . Spores were heated for 20 minutes at 60°C and cooled to room temperature prior to inoculating hamsters . Hamsters were administered spores of strains 630Δerm , clnR ( MC885 ) , or clnAB ( MC935 ) by oral gavage as indicated and monitored for signs of disease . Hamsters were considered moribund after ≥ 15% weight loss from maximum body weight or when lethargic , with or without concurrent diarrhea and wet tail . Hamsters were euthanized once reaching either of these criteria . Fecal samples were collected daily , and cecal samples were collected post-mortem at the time of morbidity . Colony forming units ( CFU ) in fecal and cecal samples were plated on TCCFA medium as described previously [20] . Differences in CFU counts were analyzed using one-way ANOVA with Dunnett’s multiple-comparison test , and differences in survival were analyzed using log-rank regression . Fisher’s exact test was performed to examine differences in the numbers of animals with detectable CFU at 12 h . p . i . These statistical analyses were performed using GraphPad Prism version 7 for Macintosh ( GraphPad Software , La Jolla , CA ) . Workspace surfaces were treated with Clorox Healthcare Bleach Germicidal Cleaner to disinfect and prevent spore cross-contamination . For the murine studies , similar methods were used with the following exceptions . Male and female C57BL/6 mice ( Mus musculus; Charles River Laboratories ) were co-housed , with two to five animals per cage , by treatment group . Instead of clindamycin treatment , mice were provided cefoperazone ( 0 . 5 mg/ml; Sigma Aldrich ) in drinking water for six days , beginning 8 days prior to inoculation with C . difficile [75] . Cefoperazone-containing water was exchanged every other day to maintain antibiotic potency . Two days prior to inoculation with C . difficile , animals were returned to antibiotic-free sterile drinking water . Spores were prepared as detailed above , and administered as a dose of approximately 1 x 105 spores by oral gavage . Weight loss of ≥ 20% maximum body weight qualified animals as moribund . Differences in CFU counts were analyzed using one-way ANOVA with Dunnett’s multiple-comparison test at each time point , and differences in weight were analyzed using two-way ANOVA with Dunnett’s multiple comparisons test . These statistical analyses were performed using GraphPad Prism version 7 for Macintosh ( GraphPad Software , La Jolla , CA ) . All animal experimentation was performed under the guidance of veterinarians and trained animal technicians within the Emory University Division of Animal Resources ( DAR ) . Animal experiments were performed with prior approval from the Emory University Institutional Animal Care and Use Committee ( IACUC ) under protocol #DAR-2001737-052415BA . Animals considered moribund were euthanized by CO2 asphyxiation followed by thoracotomy in accordance with the Panel on Euthanasia of the American Veterinary Medical Association . The University is in compliance with state and federal Animal Welfare Acts , the standards and policies of the Public Health Service , including documents entitled "Guide for the Care and Use of Laboratory Animals"—National Academy Press , 2011 , "Public Health Service Policy on Humane Care and Use of Laboratory Animals"—September 1986 , and Public Law 89–544 with subsequent amendments . Emory University is registered with the United States Department of Agriculture ( 57-R-003 ) and has filed an Assurance of Compliance statement with the Office of Laboratory Animal Welfare of the National Institutes of Health ( A3180-01 ) . Spores were purified as described previously , with some modifications [19 , 76] . C . difficile strains were grown on 70:30 sporulation agar plates for 72 h to induce spore formation and allow for vegetative cell lysis . Cells were then scraped from the agar plates , resuspended in sterile water , briefly frozen at -80°C , thawed at 37°C , and left overnight at room temperature . Spore preparations were pelleted at 3200 x g for 20 min , washed in 10 ml of spore stock solution ( 1x PBS , 1% BSA ) , pelleted , and resuspended in 1 ml of spore stock solution . The spore suspension was then applied to a 12 ml , 50% sucrose solution and centrifuged at 3200 x g for 20 min . Following centrifugation , the supernatant was decanted and the spore pellet was checked by phase contrast microscopy to verify the elimination of vegetative cells . Sucrose purification was repeated , if necessary , to achieve >95% spore purity . Purified spores were diluted in spore stock solution to a stock concentration of OD600 = 3 . 0 . Spores were heat activated for 30 min at 60°C immediately prior to germination assessments . Activated spores were then diluted 1:10 into 800 μl BHIS with either 100 μl of 50 mM taurocholic acid or 100 μl dH2O as a negative control , and the OD600 was then recorded every 2 min for 20 min . Assays were carried out at room temperature . The percentage decrease in optical density was determined based on the starting OD600 for each sample . Assays were performed with spores from three independent spore preparations . Data from the three replicates was averaged and analyzed by a one-way ANOVA for each time point . Sporulation efficiency was assessed as previously described [77] . Briefly , mid-log C . difficile cultures at OD600 = 0 . 05 were plated on 70:30 agar and incubated anaerobically at 37°C for 24 hours . Cells were scraped from the plate , resuspended in BHIS , and imaged on a Nikon Eclipse Ci-L microscope with an X100 Ph3 oil-immersion objective . At least 1 , 000 cells from at least 2 fields of view were assessed per strain and experiment . The percentage of spores was calculated as the number of spores divided by the total number of cells , multiplied by 100 . The mean percentage of spores and the standard error of the mean were calculated from three independent experiments and analyzed by two-way ANOVA . C . difficile strain 630 ( GenBank accession NC_009089 . 1 ) ; C . difficile strain R20291 ( NC_013316 . 1 ) . The locus tags for individual genes mentioned in the text are listed in S10 Table .
|
C . difficile is a major nosocomial pathogen that causes severe diarrheal disease . Though C . difficile is known to inhabit the human gastrointestinal tract , the mechanisms that allow this pathogen to adapt to the intestine and survive host defenses are not known . In this work , we investigated the response of C . difficile to the host defense peptide , LL-37 , to determine the mechanisms underlying host adaptation and survival . Expression analyses revealed a previously unknown locus , which we named clnRAB , that is highly induced by LL-37 and acts as a global regulator of gene expression in C . difficile . Mutant analyses indicate that ClnRAB is a novel regulatory system that senses LL-37 as a host signal to regulate adaptation to the intestinal environment .
|
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2018
|
The C. difficile clnRAB operon initiates adaptations to the host environment in response to LL-37
|
The flipping-out of a DNA base from the double helical structure is a key step of many cellular processes , such as DNA replication , modification and repair . Base pair opening is the first step of base flipping and the exact mechanism is still not well understood . We investigate sequence effects on base pair opening using extensive classical molecular dynamics simulations targeting the opening of 11 different canonical base pairs in two DNA sequences . Two popular biomolecular force fields are applied . To enhance sampling and calculate free energies , we bias the simulation along a simple distance coordinate using a newly developed adaptive sampling algorithm . The simulation is guided back and forth along the coordinate , allowing for multiple opening pathways . We compare the calculated free energies with those from an NMR study and check assumptions of the model used for interpreting the NMR data . Our results further show that the neighboring sequence is an important factor for the opening free energy , but also indicates that other sequence effects may play a role . All base pairs are observed to have a propensity for opening toward the major groove . The preferred opening base is cytosine for GC base pairs , while for AT there is sequence dependent competition between the two bases . For AT opening , we identify two non-canonical base pair interactions contributing to a local minimum in the free energy profile . For both AT and CG we observe long-lived interactions with water and with sodium ions at specific sites on the open base pair .
DNA base pair opening , or base breathing , is the process of breaking the hydrogen bonds of a base pair . Opening is the first critical step of base flipping in which either base moves away from the DNA double helix . Fundamental biological processes such as DNA replication , modification and repair [1–3] rely on this mechanism for accessing the functional groups of the bases . Experimentally , X-ray crystallography has revealed how enzymes directly operate on a flipped-out DNA base by binding it into the enzyme active site [1] . Furthermore , nuclear magnetic resonance ( NMR ) experiments have quantified base pair opening in terms of base pair lifetimes and free energies by measuring imino proton , i . e . H1 in guanine and H3 in thymine , exchange rates with solvent [4 , 5] . The free energy of the open , proton exchanging state relative to the closed state is calculated by relying on a two-state model . Most importantly , these NMR studies have proven that base pair opening occurs spontaneously and without help from an enzyme on a timescale of milliseconds [6] . Recently , the dynamics of base flipping has also been studied using fluorescence correlation spectroscopy ( FCS ) [7] . The exact mechanism for base flipping is still not well understood on an atomistic level [8] . In the presence of an enzyme , protein-DNA interactions may be an important first step in the process [9] . Alternatively , spontaneous base pair opening could be the trigger of further enzyme interactions [10] . In either case , characterizing base pair opening in terms of DNA only is necessary for fully understanding the more complex scenario of DNA in an enzyme environment . Specifically , the question of how DNA base pair sequence and helical conformation affect the propensity for base pair opening remains largely unanswered . Early studies [11] agree that the characteristics of opening is primarily determined by the base pair type . The lifetimes of the canonical Watson-Crick ( WC ) base pairs AT and GC have been observed to be 1–5 and 10–50 ms , respectively . However , the nucleic acid sequence and helical conformation are also important factors [12 , 13] . For example , AT base pairs in so called A-tracts have closed state lifetimes of up to 100 ms [13 , 14] . Because of the short lifetimes of the open states , experimental studies have limited resolution . The problem of mapping laboratory measurements to the underlying molecular event is highlighted by a recent FCS study [7] on DNA mismatches in which the observed base pair lifetimes were significantly longer than those measured by NMR . The straightforward conclusion is that NMR and FCS measurements are not directly comparable; while FCS is sensitive only to extrahelical flipping , NMR can detect any opening angle large enough for solvent to gain access to the bases . To gain further atom-level insight into the flipping process , a substantial amount of computational effort has been put into characterizing the opening pathway in recent decades [8] . Because of the high free energy barriers involved in the base pair opening , biased simulation algorithms are required for efficient sampling . The method of choice has typically been molecular dynamics ( MD ) combined with umbrella sampling along a reaction coordinate , i . e . a function of the coordinates . More recently , adaptive biasing methods have been combined with principal component analysis [15] and path optimization methods [16] . In many cases the results of these studies are in qualitative but not quantitative agreement with each other [8] . In addition , making direct comparisons or drawing an overall conclusion about the source of a discrepancy is in practice very difficult because typically different authors have studied different DNA sequences and base pairs using different or modified force fields , reaction coordinates and sampling methods . Furthermore , since the first papers on this topic were published , new simulation methods have appeared , force fields have been developed further and computational power has continued to grow . Indeed , recent very long MD simulations indicate that the structure and dynamics of the DNA duplex requires μs of simulation time to fully converge [17] . In summary , there is still a great need for more systematic and extensive computational studies . Here , we calculate free energies and structurally characterize the opening of canonical DNA base pairs . We use a newly developed sampling algorithm , the accelerated weight histogram method ( AWH ) [18] . In contrast to umbrella sampling , AWH samples multiple transition pathways within one simulation which is a good test for hysteresis effects . To probe sequence dependency , we present results for 11 target base pairs of type GC and AT located in different positions in either of two DNA sequences . As a test of existing force fields and of the simulation method we use two different force fields , CHARMM27 [19] and parmbsc1 [20] , and compare calculated free energies to experimental values obtained from NMR .
As a first step in studying base pair opening we need a reaction coordinate that parameterizes the opening pathway . Our use of the reaction coordinate is two-fold . First , we apply a bias along this degree of freedom to promote opening and escape the WC state , i . e . conformations where the WC hydrogen bonds are intact . Second , we use it to analyze and understand the resulting data . In most previous work , authors have used geometrically intuitive measures . Early work [21] used the distance between the N1 atom of the purine ( base A or G ) to the N3 atom of the pyrimidine ( base T or C ) defining the central WC hydrogen bond of the opening base pair , see Fig 1A . We denote it dN1N3 . More recently , different variants of dihedral angles describing the position of the opening base with respect to the helical axis have been the most popular choice [22–24] mainly because they distinguish between which base flips into which groove of the DNA helix , major or minor . We have chosen one such dihedral angle [22] for analysis purposes in this work . We further shift this angle by the average angle in the WC state such the closed state has angles of approximately zero , the major groove has positive angles and the minor groove negative angles . We denote the resulting dihedral angle θ , see Fig 1A . In this study we prefer to bias along dN1N3 rather than a dihedral angle . Our primary aims are to investigate sequence dependency of the opening free energy and to be able to compare our results to NMR data . NMR experiments measure the rate of imino proton exchange with solvent , a rare event which can occur as soon as the central WC hydrogen bond is broken and the imino proton is exposed to the surrounding solvent . A straightforward way of sampling such configurations in simulations is to bias sampling toward larger values of dN1N3 . Values of dN1N3 ≾ 3 Å characterize the WC state , whereas the value of a dihedral angle is not as specific in mapping to this state [23] . Indeed , high “sensitivity” to dN1N3 has even been used as a measure of a “good” dihedral reaction coordinate [24] . In addition , pathway optimization studies [16] indicate that dN1N3 “elongation” is an important , previously overlooked , component in the early stages of opening . Here , we wish to sample the most probable pathways of the opening process and would rather not add the complication of specifying which base should go in which direction . In addition , forcing each base separately into both the major and the minor groove is likely computationally less efficient when there is a preference for one base and/or direction . Thus , choosing dN1N3 as a biasing coordinate allows us to connect simulations to experiments while making minimal assumptions on the preferred pathway . In previous work [15 , 23 , 25] , the focus has typically been on characterizing the full extent of base flipping . Although the base is very likely to proton exchange for large flipping angles , the free energies of such states are expected to be higher than proton exchanging states with smaller angles and are thus not expected to contribute significantly to NMR measurements . For efficiency reasons we therefore sample distances large enough to expose the imino proton but small enough to avoid irrelevant , high free energy states . In the popular umbrella sampling method [26] sampling along the reaction coordinate pathway is ensured by running multiple simulations and harmonically restraining each simulation to sample around a particular reaction coordinate value , λ . The unbiased free energy profile is subsequently calculated by reweighting and combining samples from all simulations using the weighted histogram analysis method ( WHAM ) [27] . The main drawback of umbrella sampling is that the umbrella restraints may introduce ergodicity problems . Specifically , the results can be very sensitive to the starting configurations since sampling is essentially restrained to a single pathway . Thus , if applied naively , the free energy profiles from umbrella sampling may seem converged while in fact important pathways have effectively been excluded . AWH [18 , 28] is similar to umbrella sampling in that it applies harmonic restraints along a reaction coordinate . There are fundamental differences however . First of all , instead of each simulation being assigned a single λ value , one AWH simulation samples all λ values . Fig 1B shows an example trajectory where λ corresponds to dN1N3 . This mitigates ergodicity problems by allowing one trajectory to explore multiple pathways . Again this is examplified by the figure , which shows one trajectory that samples both pathways where A opens toward the major groove and T toward minor , as well as the the other way around . Second , with umbrella sampling the bias of each simulation is constant and the overall bias is implicitly set by the mapping of simulations to λ values . With AWH on the other hand , the bias is explicitly included in each simulation as a time-dependent function of λ . More specifically , AWH samples an extended , time-dependent ensemble P ( x , λ; t ) , where the probability of each λ is set and tuned by a bias function g ( λ; t ) , P ( x , λ; t ) ∼eg ( λ;t ) . Configurations x are sampled using MD and λ is sampled using a Gibbs sampler , i . e . λ is regularly drawn from P ( λ|x ) . The bias function is updated at regular intervals by increasing the bias in undersampled regions according to the following formula Δ g ( λ ; t ) = - ln W ref ( λ ; t ) + W sampled ( λ ; t ) W ref ( λ ; t ) + W target ( λ ; t ) , where Wsampled ( λ;t ) = ∑t′′<t′<t P ( λ|x ( t′ ) ) is the sum of probability weights sampled since the last update at time t′′ Wtarget ( λ; t ) is a user-defined target distribution function with ∑λ Wtarget ( λ; t ) = ∑λ Wsampled ( λ; t ) ( here chosen uniform ) ; and Wref ( λ; t ) is a reference weight histogram representing all prior sampling history . As samples accumulate over time , Wref ( λ; t ) grows and in the long time limit , Δg ( λ; t ) ∼1/Wref ( λ; t ) ∼1/t . Thus , the fluctuations in the bias closely connects to the current amount of sampling . The time-dependent bias alters the dynamics of the simulations . Initially , when Wref is relatively small , large bias updates push the system out of local free energy minima , promoting exploration along the reaction coordinate . Later , when Wref has grown , the bias changes slower and the dynamics becomes increasingly diffusive as the bias converges . Clearly then , the growth rate of Wref is key for the efficiency and convergence of the method . Initially , the bias is still far from optimal and sampling is statistically inefficient , i . e . correlation between samples is high . Thus , for sake of robustness and efficiency of the method it is necessary to restrict the histogram growth initially . Here we follow the scheme motivated and demonstrated in previous work [18] . Briefly , the AWH simulation is divided into two stages: an initial stage where Wref grows exponentially but slower than the real sampling rate , and a final stage , t > texit , where Wref grows linearly according to the sampling rate . The transition from initial to final stage is defined such that in the final stage the size of Wref equals the actual number of collected samples . The slower growth of Wref in the initial stage corresponds to continuously scaling down the histogram , effectively assigning more weight to later samples than earlier ones . In the final stage each sample is given equal weight . When analyzing data from AWH simulation we propose to weigh samples in the same way . Here , we simply ignore initial stage data and perform all free energy calculations on final stage data only , for the sake of simplicity and because initial stage samples may anyway be far from equilibrium . This is not critical since in this work most of the data in each run is in any case sampled from the final stage , see Fig 1B . From data biased along a reaction coordinate ξ we would like to extract an estimate of the unbiased free energy Φ ( u ) for any observable u ( including the case u = ξ ) . This is easily done post-simulation by reweighting the biased samples as ( see S1 Appendix for details ) e - Φ ^ ( u ) = ∑ i , t 1 u ( u t i ) e - b t i ( ξ t i ) ∫ d ξ ′ e - Φ ^ ( ξ ′ ) + b t i ( ξ ′ ) , ( 1 ) where Φ ^ ( u ) , Φ ^ ( ξ ) are free energy estimates along u and ξ , respectively . The index i runs over independent AWH simulations with observables u t i at time t , each with its own effective bias b t i ( ξ ) applied . 1u is shorthand for the required binning procedure: 1 u ( u t i ) = 1 if u t i falls into the bin labeled by u and 0 otherwise . The unbiasing is taken care of by the factor e - b t i ( ξ t i ) . This factor is further properly normalized by the integral over ξ ( the partition function of the extended ensemble ) . This expression is exact for equilibrium sampling which is a reasonable approximation in the AWH final stage . In this work , in order to simplify the analysis slightly , we have further applied the approximation that the bias is constant in the final stage , i . e . b t i ≈ b t final i . When u = ξ , Eq ( 1 ) needs to be solved self-consistently since now the sought-for variable occurs on both sides of the equation . However , our AWH implementation already calculates and outputs an estimate Φ ^ i ( ξ ) for each simulation i on the fly [18] . This is convenient because no post-processing is needed and does not require frequently writing to disk . Data from multiple simulations is therefore combined by self-consistently solving the following equation for the combined estimate Φ ^ ( ξ ) , e - Φ ^ ( ξ ) = ∑ i N i e - Φ ^ i ( ξ ) ∫ d ξ ′ e - Φ ^ ( ξ ′ ) + b i ( ξ ′ ) ∫ d ξ ′ e - Φ ^ i ( ξ ′ ) + b i ( ξ ′ ) , ( 2 ) where Ni are the number of samples collected in simulation i and bi ( ξ ) has been evaluated at t = tfinal . We estimate the standard deviation of our free energy averages , obtained either by Eqs ( 1 ) or ( 2 ) , using jackknifing ( details in S1 Appendix ) . From the biased simulations we obtain the free energy profile along the reaction coordinate . In contrast , the free energy obtained from NMR data is that of an “open” state relative to a “closed” state . The open state is comprised of configurations that expose the imino proton to solvent , thus contributing to the measured proton exchange rate . In order to calculate a comparable free energy from simulations we therefore need an observable of solvent accessibility . The free energy corresponding to this observable is then calculated using the reweighting procedure of Eq ( 1 ) . In the past there has only been a limited number of direct comparisons of free energies from experiment and simulations [23 , 29] . Typically , the approach has been to use the solvent accessible surface area ( SASA ) of the imino proton as the observable and labeling configurations with SASA larger than a chosen cutoff area as “open” and the rest as “closed” . A drawback of using SASA is that it strongly depends on the chosen cutoff area and is fairly slow to calculate for large data sets . In our simulations we consider the imino proton to be solvent accessible if it is hydrogen bonded to a water molecule . The hydrogen bond is defined using an acceptor-donor distance cutoff of 3 . 5 Å and an acceptor-donor-hydrogen angle cutoff of 30° . Here , we refer to configurations with such a hydrogen bond as “open” and the corresponding free energy as the “opening free energy” , or the “calculated opening free energy” to distinguish it from the opening free energy obtained experimentally . We have verified that this opening free energy is very similar to the free energy obtained using SASA with a small cutoff area ( ∼0 . 1 Å2 ) . It is important to note that our solvent accessibility criterion is not expected to exactly capture the open state detected by NMR . Indeed , an exact mapping of a given configuration to “open” or “closed” as inferred from NMR data does not exist . The NMR open state is only implicitly defined by a two-state model parameterized by experimentally determined average rates . Notably , it is assumed that the open state has approximately the same proton transfer rate as a free nucleotide [6] while realistically there could e . g . be partially open states which behave differently . Furthermore , the experimental free nucleotide reference state includes configurations that are not hydrogen bonded which our accessibility criterion will exclude . This overestimates the free energy by up to −ln 0 . 8 = 0 . 2 kB T relative to the NMR free energy since we observe hydrogen bonding 80-90% of the time in the free nucleotide and maximally open configurations . An additional complication is that the approximations made in the NMR modelling could be more or less valid for different sequences and base pairs . We have simulated two sequences , denoted L and M , that have previously been experimentally characterized using NMR experiments [6] . For each sequence we investigated the opening of several target base pairs located in the region where the sequences differ , see Fig 2 . We added an extra GC base pair at the ends of each system which was restrained as described below to avoid ends effects . Simulations were performed using a version of the GROMACS [30] master branch code [31] , extended by an implementation of AWH [32] . We used the force fields CHARMM27 [19] , CHARMM36 [33] and parmbsc1 [20] . CHARMM27 is available in the GROMACS package [34] . GROMACS compatible force field files for CHARMM36 were downloaded from the authors’ website [35] . Parmbsc1 for GROMACS was implemented by us [36] . Together with the CHARMM force fields we used CHARMM-modified TIP3P water [37] and sodium ions . With parmbsc1 we used SPC/E water [38] and sodium ions [39] . We list the main MD settings here but refer to the template GROMACS parameter input file in S1 File for details . Note that in this section we use GROMACS compatible units , e . g . 1 nm = 10 Å . The MD time step was 0 . 002 ps . Bonds involving hydrogens were constrained using LINCS [40] . The temperature was kept at 300 K using the v-rescale thermostat [41] and the pressure at 1 bar using Parrinello-Rahman pressure coupling [42 , 43] . Long-range electrostatics were calculated using particle mesh Ewald [44] . For CHARMM , Lennard-Jones interactions beyond the cutoff were calculated by switching the force to zero . For parmbsc1 the force was shifted to zero and dispersion-correction was applied for energy and pressure . The DNA starting structures were generated in the double strand B-form using the 3DNA software [45] . Each system was solvated in explicit water and neutralized by adding sodium ions . The rhombic dodecahedron simulation box had dimensions of approximately 8 . 7 nm , fitting ∼14800 water molecules . Base pairs at both ends of the DNA helix were restrained by 0 . 5 ⋅ k ( dN1N3 − 0 . 3 nm ) 2 , where k = 1000 kJ/ ( mol ⋅ nm2 ) . The solvated system was equilibrated by first energy minimizing ( until the maximum force < 1000 kJ/mol ) , then adding 50 ps of NVT MD , and finally NPT equilibrating for 50 ns before the production AWH runs . The AWH reaction coordinate sampling interval was defined by [λmin , λmax] = [0 . 25 , 0 . 65] nm . The force constant at each λ was 32 000 kJ/ ( mol ⋅ nm2 ) . The AWH code automatically determines the λ spacing base on the force constant; here Δλ = 3 ⋅ 10 − 3 nm . As described in previous work [18] , the initial bias update size is set as a function of two input parameters: an estimate of the diffusion along the biased coordinate and an estimated initial error . Here we estimated the diffusion to 5 ⋅ 10 − 5 nm2/ps and the initial error to 5 kB T . These estimates can be very rough since the efficiency of AWH is quite robust to their values [18] . In our initial biased CHARMM27 simulations we encountered sampling problems due to configurations in which the two partner bases on opposite strands stack on top of each other . For sampling efficiency reasons we therefore added to all our simulations a bias potential designed to avoid sampling such configurations . Explicitly , we added the following potential acting on the distance between the center of mass of the six-member ring of each base: Vring = 0 . 5 ⋅ k ( dring − 0 . 48 nm ) 2 for dring < 0 . 48 nm and Vring = 0 , otherwise , where k = 32 000 kJ/ ( mol ⋅ nm2 ) . The ring distance cutoff was set by first calculating the free energy landscape for the two-dimensional coordinate ( dN1N3 , dring ) and then determining the lowest value of the cutoff that would still exclude base pair stacked regions of phase space . See section Base pair stacked state for further details and discussion . The AWH simulations were 100 ns and 200 ns long for CHARMM and Parmbsc1 , respectively . The simulation lengths were chosen differently because we observed slower transitions along the biased coordinate for Parmbsc1 compared to CHARMM27 . Each target system , i . e . combination of force field , DNA sequence and base pair to open , was replicated 16 times using different AWH and thermostat seeds , starting from the same equilibrated structure . Free energy averages and error estimates were obtained by combining these independent simulations ( see S1 Appendix ) . Two sets of simulations were extended further in order to reach comparable free energy accuracies for all target systems: Parmbsc1 L:TA10 ( 576 ns ) and CHARMM27 M:CG11 ( 222 ns ) . The reweighting procedures of Eqs ( 1 ) and ( 2 ) can be sensitive to the amount of sampling due to the exponential of the reweighting factor . Therefore one can obtain higher accuracy of the free energies by excluding individual runs that have few transitions across the sampling region . Here we require at least one back-and-forth transition from dN1N3 ≤ 0 . 27 nm to ≥ 0 . 5 nm , or vice versa . This criterion excluded in total three runs one of each CHARMM27 L:TA10 ( +0 . 03 kB T ) and M:TA15 ( +0 . 5 kB T ) ; and one Parmbsc1 L:TA9 ( +0 . 07 kB T ) , where the values in parenthesis show how excluding the run affected the calculated opening free energy . In both excluded CHARMM27 runs we observed base pair stacking tendencies of the target base pair and/or of neighboring base pairs .
Fig 3 shows free energies as a function of the reaction coordinate dN1N3 for CHARMM27 and Parmbsc1 . Distances where the solvent accessibility reaches 20% are marked with a circle on each curve ( roughly at 4 . 2 Å for AT and 4 . 8 Å for GC ) . This indicates the extent of opening that NMR experiments are sensitive to and which configurations we label as “open” or “closed” when calculating the opening free energies . In Fig 4 , we show representative configurations for different values of dN1N3 to aid the reader in visualizing the opening structures in different regions along the reaction coordinate . For AT pair opening there are two distinct free energy minima: the global minimum at 2 . 9 Å ( WC state ) and a local minimum at dmin ∼ 5 . 5 Å which is up to 2 kB T deep for CHARMM27 and 3 kB T for Parmbsc1 . In order to characterize the local minimum we analyzed the interactions between A and T of the target base pair by counting minimum distance atom pairs for frames with dN1N3 ∈ dmin ± 0 . 3 Å . We found two clearly dominating base pair interactions which account for 80–90% of the configurations . First , for A opening into the major groove and T shifted toward minor the most frequent interaction was that of the A:C2 hydrogen and the major groove carbonyl oxygen O4 of T , see Fig 4C . Alternatively , for T opening toward major and A perturbed into minor , the most frequent base pair interaction was a non-WC hydrogen bond between the hydrogen of A:N6 and the minor groove carbonyl oxygen O2 of T , see Fig 4D . Structures with the latter interaction have been suggested based on molecular mechanics simulations to be responsible for proton exchange from AT pairs [46] . The same interaction has been observed also in an earlier MD study [47] , but without directly connecting it to a local minimum in the free energy profile as we do here . Interestingly , we have not found previous mention of the first interaction . In our simulations however it is clearly a relevant interaction , occurring 60–80% of the local minimum time for CHARMM27 and 50–60% of the time for certain Parmbsc1 target base pairs . For GC pair opening the free energy increases monotonously up until dN1N3 ∼ 5 . 5 Å where it levels off . To investigate what happens beyond the sampled interval , in particular for GC opening , we ran simulations of Parmbsc1 L:TA11 and M:CG11 but extending the interval from 6 . 5 Å to 8 . 0 Å ( and extending the simulation time to obtain comparable statistical accuracies ) . In both cases the average free energy profile rises 2–3 kB T in the added interval indicating that we are not missing important states from the extended region . We note that excluding the weight of these states will lead to a systematic ( positive ) error in our calculated opening free energies ( presented below ) . From these simulations we estimate this error to be ∼0 . 3 kB T . To detect the presence of next-to-nearest-neighbor sequence effects we included several target base pairs with identical nearest neighbors but different neighbors beyond that . Such related profiles have the same color but are either solid or dashed in Fig 3 . In the figure it is clear that targets with the same nearest neighbors in general are more similar than those that have different direct neighbors which shows that the nearest-neighbor sequence is a dominating factor for the free energy . However , for Parmbsc1 L:TA15 vs M:TA15 the free energy profiles are clearly not overlapping and for CHARMM27 L:AT8 vs L:AT12 the profiles have similar barrier but different depths of the local minimum . This indicates the existence of other effects competing with nearest-neighbor effects . In Fig 5 we show the calculated opening free energies for each base pair . The color coding is analogous to Fig 3 ( with half-filled markers corresponding to dashed lines ) . NMR free energies at the simulated temperature were obtained by interpolating between experimental values [6] at the two nearest temperatures . Error bars in all cases are 0 . 1–0 . 3 kB T . In the figure , we have grouped base pairs based on the nearest neighbors sequence , i . e . a triplet , in direction 5′ to 3′ on the strand containing the target pyrimidine . In terms of these triplets , both force fields predict a free energy trend TTT > ATT > TTA ≥ ATA . For GC base pairs the trend is TCT > ACA . The results are not as clear when T is flanked by either G or C , i . e . for L:TA15 , M:TA15 and M:TA10 . In these cases , CHARMM27 in general gives roughly 2–3 kB T higher values than Parmbsc1 . We do not report results for CHARMM36 since for this force field we obtained variations in the free energy profiles of several kB T across runs ( see S1 Fig ) . In several runs with convergence problems we saw that backbone was elongated and deformed , often in combination with stacking of the target base pair ( see section Base pair stacked state ) . Sometimes also neighboring base pairs were affected . Presumably , the difference between CHARMM27 and CHARMM36 is due to the increased backbone flexibility of the latter . Interestingly , a seemingly small change in force field parameters can have great impact on the sampling , especially when it is biased towards high free energy regions as in our case . Similar backbone deformations and stacking structures have also been observed in a free , 90 μs long CHARMM36 simulation [17] . Sampling such structures properly would likely require a more complex reaction coordinate and an amount of sampling beyond the scope of this study . To further structurally characterize the opening , we analyzed the data in terms of local base-pair parameters , i . e . three translations: shear , stretch , stagger , and three rotations: buckle , propeller and opening , using the 3DNA software [45] . Note that the local base-pair “opening” parameter is different from the dihedral angle θ introduced earlier ( Fig 1A ) . They are both angles but for instance θ is defined for each base while the opening parameter is defined for one base pair . We plot histograms of opening , shear and stretch as a function of dN1N3 in Fig 6 , again using Parmbsc1 L:AT12 and M:CG11 as illustrative examples . See S2 Fig for histograms of remaining parameters and target base pairs , for both force fields . These types of histograms help us understand what the sampled configurations are , but one should remember they have no dynamic information . For instance a “sudden jump” in the parameter value at a given dN1N3 value simply corresponds to a free energy barrier along the parameter for that distance . Thus , it does not imply any kind of discontinuity in the trajectories generating the histograms . For instance , major or minor groove opening corresponds to separate pathways which in our simulations would be sampled in a continuous manner by traversing the dN1N3 sampling interval more than once . Generally both force fields have similar parameter profiles for small opening distances where the WC hydrogen bonds are still not fully broken while at larger distances the results tend to diverge . For AT opening ( Fig 6A ) and distances below ∼4 Å , both force fields display increasingly negative opening values , which corresponds to both bases opening toward the minor groove ( structure not shown ) . This allows the major groove WC hydrogen bond to remain intact . This trend is further accompanied by increasingly positive stretch and negative stagger and propeller ( see S2 Fig ) . In the barrier region around dN1N3 ∼ 4 Å the AT opening mode switches as the major groove hydrogen bond breaks , enabling positive opening in this direction . In a transition region 0 . 4–0 . 5 Å , typically both bases contribute to the positive opening value by cooperatively swinging toward the major groove , see Fig 4B . Decreased distances between the hydrogen of A:C2 and T:O2 further help stabilize such configurations . For larger dN1N3 only one of the bases flips out further while its partner contributes negatively to the opening parameter by shifting to the minor groove instead of following into the major groove , which leads to a drop in the opening parameter value . In the same region shearing increases , negatively or positively depending on which base swings in which direction , enabling the non-WC base pair interactions shown in Fig 4C and 4D . For GC pairs , opening occurs toward the major groove , i . e . with positive values of the opening parameter ( Fig 6B ) . The major groove hydrogen bond breaks already for dN1N3 <3 . 5 Å enabling the rotation of the bases relative to each other . Increasing shear enables the hydrogen acceptor C:O2 to be “shared” between the two donors G:N1 and G:N2 for dN1N3∼ 4–5 Å ( Fig 4F ) . Likely , these interactions help explain the plateau seen in the corresponding free energy profiles of parmbsc1 ( Fig 3 ) . The minor groove hydrogen bond between G:N2 and C:O2 remains intact up until dN1N3 ∼5 . 5 Å , acting as a “hinge” for the rotation ( Fig 4G ) . After the last WC hydrogen bond breaks , the base pairs stop directly interacting ( Fig 4H ) . Opening and stretch values drop and larger opening distances are obtained by further increase in shearing and opening values . The local base-pair parameters are only sensitive to the orientation of one base relative to its partner but do not give information about their orientations relative to the helix backbone . In order to determine which base tends to flip more and into which groove we instead use the dihedral angle θ ( see Fig 1A ) . To get an idea of the opening motion in terms of θ it is instructive to analyze the histograms θ ( dN1N3 ) . We refer the reader to S3 Fig for these histograms . These figures confirm our previous observations that in a transition region typically both bases open up toward the major groove ( as in Fig 4B and 4G ) , while for larger base separation , one base tends to “fall back” into the helix ( θ ≈ 0° ) . To gain more quantitative knowledge about the modes of opening in terms of θ however , we decomposed the opening probability into disjoint opening modes . An “opening mode” is here defined by the opening direction for each base , i . e . is θ in the major , minor or neither groove ? Here , we define the minor and major groove as the intervals θ < −Δθ and θ > +Δθ , respectively , where Δθ = 30° . Thus , we can map each open configuration into either of 9 opening modes ( 3 opening directions to the power of 2 bases in a pair ) . It is clear that the “preferred” mode is sensitive to the choice of Δθ . If it is set to zero , all positive angles will be labeled as major and all negative angles as minor and there will be no distinction between the base being flipped out or not . If it is set too large , there will also be no separation since no configuration will be considered flipped . The free energy of each mode was calculated by the same reweighting technique as the opening free energy . In Table 1 , we report the most probable opening modes for each target base pair , listed in order of preference . We only list modes that are within 1 kB T of the minimum free energy mode . The table shows that when at least one base has |θ| > 30° , the clearly dominant modes are those where only one base opens toward the major groove . The preference for the major groove is consistent with previous simulation studies of C flipping [25] while it does not support that both grooves are accessible for pyrimidines as has been proposed in the past [23 , 47] . The neither-groove mode ‘–’ is also frequently listed in the table . For parmbsc1 it is even in most cases ranked as the most important mode . This means that even opening at a small angle is sufficient for significantly exposing the imino proton to solvent . In addition , there are clear sequence effects , especially for parmbsc1 . In case of the GC base pairs , the force fields agree on C being the opening base in agreement with evidence that purine bases stack better than pyrimidine base . Obviously , we cannot exclude the possibility that G is preferred for GC in other sequence contexts . For AT base pairs , the picture is more complex presumably since T is the most hydrophobic base . Both force fields agree that for T flanked by T , i . e . for L:TA10 , A is the preferred base to open . More generally however , for AT targets the force fields give different results which is surprising considering the consistent free energy trend we observed for AT base pairs flanked by A or T ( Fig 5 ) . In particular , opening base T is more often preferred for parmbsc1 than for CHARMM27 . This may be due to underestimation of the hydrophobicity of T from the force field [48] . Possibly it is for the same reason that parmbsc1 displays more sequence sensitivity for these base pairs . We expect interactions with water molecules to play an important role in the opening mechanism . For instance , when studying the distributions of waters around the open base pair we typically see well-defined clusters of waters molecules hydrogen bonding to atoms of both bases in the open pair ( see Fig 4 for examples ) . Such water bridges have previously been observed in both free simulations [47] and umbrella sampling simulations [49] where they were associated with long-lived opening events and increased water residence times , respectively . From ∼ 200 free simulations of parmbsc1 L:TA10 and M:CG11 starting from the sampled open configurations we estimated the hydrogen bond lifetimes of water with each donor/acceptor atom on both DNA bases , see atom labels in Fig 4 , by counting the number of different hydrogen bonding water molecules . Each simulation was continued at least until the open base pair closed , here meaning dN1N3 < 3 Å . Since we expect different solvation patterns for different states , we split each trajectory into one part with times before closing and another with the remaining times and analyzed them separately . In the case of AT , we further divided the simulations in two groups based on the starting configuration having either A or T more swung out into the major groove , c . f . Fig 4C and 4D . For the closed state , we find that the water hydrogen bond lifetime at donor/acceptor sites facing the major groove are on the order of 10-100 ps while minor groove facing sites have longer timescales , on the order of ∼ 100 ps . For the central sites A:N1 and T:N3 , which become hydrated in the open AT pair , we see a dramatic effect on the water hydrogen bonding lifetimes depending on which groove they face . When A:N1 faces major ( Fig 4C ) the estimated lifetime is 70 ps . When it turns toward minor ( Fig 4D ) it increases to 1000 ps . Often , only one or a few waters have time to interact before the base pair closes , which happens on average after 6 ns . Analogous results hold for T:N3 . We expect these long-lived interactions to be due to minor groove bridging waters as shown in Fig 4C and 4D . Indeed , in these simulations there is a water molecule within 3 . 5 Å of both the central site facing the minor groove and its partnering site ∼ 80% of the open time . For the CG central sites , we obtain water hydrogen bond lifetimes of 90 ps for C:N3 . For G:N1 we obtain 400 ps , which is larger than what we typically observe toward the major groove or in bulk water but less than the longest times observed in the case of AT . We note that the closing time limits the maximum hydrogen bonding time we can measure from these simulations , but in the case of CG the average closing time of 3 ns is still an order of magnitude larger . A fundamental difference between the AT and CG pair is that for CG the central and minor groove sites are either both donors ( G ) or both acceptors ( C ) while for AT the bases have alternating acceptor/donor sites . Thus , when C is open toward the major groove and a water bridge has formed between the pair , a small rotation or translation of the water molecule could be enough to instead form another water bridge with the neighboring site . This may increase the mobility of the bridging water molecule . For AT on the other hand , for one water bridge to transform into another would require rearrangements also of the DNA . In addition , ion-DNA interactions ( see analysis below ) could be competing with bridging waters . A more detailed analysis of the water hydrogen-bonding network around the open base pair would be necessary to fully understand these effects . Binding of Na+ to the major groove has been associated with base pair opening in μs long free simulations [17 , 49] . In general , the ion composition could change the free energy landscape , as NMR experiments indicate [50] . To investigate such effects , we have analyzed ion , here Na+ , interactions with the DNA bases for the same set of simulations as for the water analysis . Contact frequencies were obtained using a distance cutoff of 3 . 5 Å . Ion residence times were calculated as the time interval between the first and last time of contact . In certain cases and at specific sites , marked by a ’+’ in Fig 4 , the contact probability is substantially larger in the open state than after closing . For CG , all three acceptor sites involved in WC bonds ( G:O6 , C:N3 , C:O2 ) are in contact with Na+ 40–50% of the time in the open state . After closing , the major groove acceptor atoms on G ( G:O6 and G:N7 ) were in contact with Na+ roughly 15% of the time while the remaing sites have a fraction of less than 3% . The average residence times were generally on the order of ∼ 1000 ps . These observations strongly suggests that ions could play a role in the base pair opening mechanism: destabilizing WC bonds enabled by a fairly common and long-lived interaction with major groove acceptor sites on the base , and preventing closing by occupying WC bonding sites . Also for the open AT base pair in the case where A opens toward major ( Fig 4C ) , the acceptor T:O2 is in contact with Na+ 48% of the time . By visual inspection we have verified that this ion is also often in contact with the oxygen of a water bridging from T:N3 to A:N3 ( also shown in the figure ) . On the other hand , when T is open toward the major groove ( Fig 4D ) we do not observe contact frequencies of the same magnitude . Here we address the question: can our simulations reproduce the experimental trend of opening free energies ? As was discussed in detail in section Estimating the opening free energy , our calculated free energies are not expected to exactly match NMR values . We would however expect to see the same trends , at least in clear-cut cases . Our statistical accuracy is typically ∼0 . 2 kB T which is sufficient to distinguish the experimental free energy trend . Turning again to Fig 5 , we see that certain experimental trends are roughly reproduced by simulations . In particular , both force fields assign a relatively high free energy to the GC targets and to L:TA10 . However , there are clear discrepancies . For instance , the free energy differences between GC and AT targets are several kB T smaller in the simulations than expected from experiments . Also , in general the experimental free energies are higher than the calculated ones . In the cases of L:TA15 and M:TA15 , which have the lowest free energies according to NMR , the calculated free energies are relatively high for both force fields . One may speculate that there are long timescales especially affecting the ends of base pairs which are beyond our simulation time-scale and that the added end restraints makes it more difficult to open these pairs . To investigate such effects further we calculated the free energy for parmbsc1 M:TA15 with free ends . Intuitively , we would expect this to lead to a lower free energy since the purpose of the end restraints are to stabilize the system . However , freeing the ends resulted in ∼0 . 3 kB T higher value and an error of the same magnitude . Thus , if there are important long timescale end effects we would need even longer simulation times to probe them . In the NMR experiments the free energy is derived from average imino proton exchange rates under the assumption of a two-state model , thus assuming a single open state . With MD simulations we can directly measure closing rates . We have done this by choosing a representative set of 165–200 open conformations for two cases , removing the dN1N3 restraint and measuring the time it takes for the central WC hydrogen bond to reform . In a two-state system the distribution of the open state lifetimes is exponential . In contrast , by applying multi-exponential fitting to our data we obtain two lifetimes of 4 and 20 ns for L:TA10 and of 2 and 7 ns for M:CG11 . If there are indeed two lifetimes that differ by factor of 5 , there is a possibility that NMR measurements are effectively not detecting all states . In NMR experiments the proton exchange rate is measured as a function of increasing exchange catalyst concentrations until the rate levels off . If there are multiple states with different exchange kinetics , there will be several plateaus in the exchange rate profile . When the concentration is not increased beyond the first plateau , the total closing rate will be underestimated and the free energy overestimated . A final remark concerns differences in the exchange kinetics of an accessible imino proton in the helix versus a free nucleotide . In the experimental study [6] we are comparing with here , these are considered to be same . Other works assume a difference of a factor 1 . 5 [51] which accounts for the fact that a base in the helix does not diffuse , unlike a free nucleotide . The simulations reveal an additional difference which is not considered in the experimental studies of proton exchange . In the free nucleotide the imino proton has a hydrogen bond lifetime with water of 10 ps . For AT pairs in the helix with the proton accessible we measured water hydrogen bond lifetime of 100–150 ps , which is an order of magnitude larger . Similar times have been observed for a bridging water in simulations [47] and water in the major groove in NMR experiments [52] . Under the assumption of a two-state model , the result of lower exchange rates would be a lower closing rate as well as a lower opening free energy to account for the same NMR data , but it is unclear how much lower they would be . In our trial CHARMM27 AWH-biased simulations we repeatedly observed configurations having the target base pair partners shifted along the helical axis relative to each other and stacked on top of each other . Often the whole dN1N3 interval was traversed several times before discovering the base pair stacked state after which the system appeared to get “stuck” , sometimes for the remaining simulation time . Our conclusion is that there are free energy barriers associated with transitioning to/from the base pair stacked state which are not being flattened efficiently by the applied bias along the reaction coordinate . In order to characterize the stacked state further we performed additional AWH simulations on CHARMM27 L:TA10 , this time biased along two dimensions: dN1N3 and the distance between the six-member ring of each base in the pair . Small ring distances can only be obtained by tilting the plane of one ring relative to the other or stacking of the rings . Thus , by biasing toward small ring distances we can enhance sampling of base pair stacked configurations . The two-dimensional sampling region was implicitly defined by setting a maximum free energy difference as has been described previously [18] . The resulting free energy is shown in Fig 7 , an average of 8 independent 290 ns long simulations . The landscape clearly separates three regions of interest: the WC global minimum at ( dN1N3 , dring ) ∼ ( 2 . 9 , 5 . 5 ) Å , a local minimum at ∼ ( 5 . 5 , 6 . 7 ) Å , and a relatively populated region dring ≤ 4 . 6 Å corresponding to the base pair stacked state . The stacked state significantly contributes to the opening free energy . Excluding such configurations , defined as dring < 4 . 6 Å , from the free energy calculations raises the opening free energy by roughly 1 kB T in this case . As a further reference , we ran a set of umbrella sampling simulations for the same base pair using the previously well-studied CPDb dihedral angle [24] as a reaction coordinate ( see S1 Text for simulation details ) . Interestingly , also with this reaction coordinate and sampling method we observed base pair stacking in three of the simulations . This proves that the stacked state is not specific to our choice of reaction coordinate or sampling method . It also demonstrates that when using umbrella sampling , looking at the obtained free energy profile and its error bars ( see Fig in S1 Text ) is generally not sufficient for detecting sampling issues of this sort . To our surprise , we have not been able to find previous reports on similar observations of base pair stacking for CHARMM27 . One possibility is that such configurations have been sampled but not detected . Alternatively , previous authors may have been “lucky” enough to not sample the stacked state , for instance because of less extensive sampling than in the present study . Without further analysis we cannot assess whether the base pair stacked state is real or the result of the force field , CHARMM in particular , being poorly parameterized in this high free energy region of phase space . In this work we have for sampling reasons effectively assumed the latter by adding to all our simulations a bias potential designed to prevent the system from sampling below a certain value of dring . The potential was added for both CHARMM and parmbsc1 runs . For parmbsc1 simulations however dring rarely goes below the cutoff and so the added bias is effectively not applied . We have investigated the sequence dependency of DNA base pair opening using atomistic MD simulations . Both GC and AT opening have been targeted in two different sequences and using two force fields: CHARMM27 and parmbsc1 . We have focused sampling on small base pair openings , including only the most probable regions where the imino proton is exposed to solvent . In this region , the distance dN1N3 has been demonstrated to be an effective biasing reaction coordinate which makes minimal assumptions about the opening pathway . We have obtained free energy profiles along dN1N3 with a statistical accuracy of 0 . 2–0 . 3 kB T using a robust adaptive biasing method , AWH . For opening of AT base pairs we have characterized two important interactions between the opening bases contributing to a local minima along the free energy profile . In addition , in the case of one GC and one AT base pair , we have shown that the open base pair provides specific sites in the major groove where water bridges between the bases can form and where the water-DNA hydrogen bonding time is on the 1000 ps and 100 ps timescale for AT and GC , respectively . For the same base pairs we have shown that certain acceptor atoms are very likely to be in contact with Na+ in the open configurations . We have also calculated opening free energies , where “open” here means that the imino proton is hydrogen bonded with water . By determining the free energy contribution of different opening modes , we have shown that opening typically occurs by flipping one base >30° into the major groove while the other base remains within the helix ( <30° ) . The opening pathway is however not as simple as one base flipping independently of its WC partner . For example , for AT opening it is likely to find both bases slightly perturbed towards the major groove in the free energy barrier region . In addition , configurations where both bases open less than 30° contribute significantly to the opening free energy . Furthermore , we have shown that both the free energy and the preferred opening mode are sequence dependent . In particular , the nearest neighbor sequence is a dominant factor . However , we have also observed differences for base pairs with the same nearest neighbors indicating the presence of more distant sequence effects . The two force fields reproduce the same free energy trend for most of the target base pairs . However , for opening of AT pairs parmbsc1 tends to favor flipping of T more than CHARMM27 . Compared to free energies obtained from NMR experiments , the values calculated here are generally lower . The interpretation of NMR proton exchange experiments has , necessarily , relied on simple models such as two-state kinetics . The simulations show that this is probably an overly simplified picture of reality . Depending on the sequence there can be multiple open states and , as has been shown previously , water molecules can bridge the opening base pairs which can affect free energies and proton exchange . The extensive amount of sampling together with the use of a sampling method allowing for multiple pathways has revealed the existence of a base pair stacked state in which the WC partners stack their six-membered rings on top of each other . We observe base pair stacking for both force fields , but to a very different extent; for CHARMM27 it is relatively low in free energy and thus complicates sampling , whereas for parmbsc1 it only occurs rarely and transiently . Thus , we cannot exclude the possibility that this is a force field issue . Alternatively , if it is physically relevant , our simulations show that base pair stacking could contribute significantly to NMR opening free energies . The present work has demonstrated the potential of studying base pair opening with advanced simulation methods . Our results show how sequence affects the free energy , modes and mechanism of base pair opening , only the first of which is accessible by NMR experiments . This information is essential for understanding how molecules interact with DNA .
|
The DNA double helix , a molecule that stores biological information , has become an iconic image of biomedical research . In order to use or repair the information it carries , the bases that are stacked in the helix need to be chemically exposed . This can happen either by separating the two strands in the helix or by flipping out individual bases . Here , we focus on the latter process . Usually proteins are involved in interactions with bases , but it is still unclear if bases are pulled out actively by proteins or if they act on spontaneously flipped bases . Although experiments can detect base pair opening , it is difficult to detect which base moves in which direction . Here , we present results from molecular dynamics simulations using a recently developed sampling method which improves the statistics in the simulations by enhancing the probability of the base pair opening event . We observe differences in probability , modes and mechanism of opening that depend not only on the types of the bases in the pair , but also strongly on their neighbors . This provides essential information for understanding how DNA functions .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"discussion"
] |
[
"protons",
"chemical",
"bonding",
"molecular",
"dynamics",
"geometry",
"simulation",
"and",
"modeling",
"mathematics",
"dna",
"dna",
"structure",
"thermodynamics",
"hydrogen",
"bonding",
"physical",
"chemistry",
"research",
"and",
"analysis",
"methods",
"chemistry",
"nucleons",
"molecular",
"biology",
"free",
"energy",
"dihedral",
"angles",
"physics",
"biochemistry",
"biochemical",
"simulations",
"computational",
"chemistry",
"nuclear",
"physics",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
] |
2017
|
Sequence dependency of canonical base pair opening in the DNA double helix
|
We contribute a new methodological approach to the ongoing efforts towards evaluating public health surveillance . Specifically , we apply a descriptive framework , grounded in prospect theory ( PT ) , for the evaluation of decisions on disease surveillance deployment . We focus on two attributes of any surveillance system: timeliness , and false positive rate ( FPR ) . In a sample of 69 health professionals from a number of health related networks polled online , we elicited PT preferences , specifically respondents’ attitudes towards gains , losses and probabilities ( i . e . , if they overweight or underweight extreme probabilities ) by means of a series of lotteries for either timeliness or FPR . Moreover , we estimated willingness to pay ( WTP ) for improvements in the two surveillance attributes . For contextualization , we apply our framework to rabies surveillance . Our data reveal considerable probability weighting , both for gains and losses . In other words , respondents underestimate their chances of getting a good outcome in uncertain situations , and they overestimate their chances of bad outcomes . Moreover , there is convex utility for losses and loss aversion , that is , losses loom larger than gains of the same absolute magnitude to the respondents . We find no differences between the estimated parameters for timeliness and FPR . The median WTP is $7 , 250 per day gained in detection time and $30 per 1/10 , 000 reduction in FPR . Our results indicate that the biases described by PT are present among public health professionals , which highlights the need to incorporate a PT framework when eliciting their preferences for surveillance systems .
Disease surveillance system ( DSS ) evaluation is a topical issue that has led to the development of multiple frameworks and methods . While differences remain among these methods , most contain a number of criteria or attributes to capture the multi-dimensional nature of the challenge and to qualify the performance of the DSS [1] . Sensitivity ( the capacity of the DSS to detect and quantify the event of interest ) , timeliness ( the capacity to detect the event within pre-defined times ) , and false positive rate ( FPR ) ( related to the capacity of the DSS to truly detect the events of interest; in other words informing the efficiency of the DSS ) are common criteria across many frameworks [2] . The last two are the target of this work . Improvement ( deterioration ) in timeliness and FPR deliver some level of utility ( disutility ) , understood as the satisfaction or usefulness derived from the materialization of those attributes , to the relevant surveillance stakeholders . The level of utility ( disutility ) delivered may vary between stakeholders , and even for the same stakeholders facing different contexts . For example , in a gains setting context , where increased surveillance investments may lead to improved timeliness and FPR vs . a loss setting context with surveillance de-investments possibly leading to inferior timeliness and FPR . Assessment of this utility ( disutility ) , adjusted for known biases ( see below ) , should be an integral part of any surveillance evaluation . Rabies is a neglected disease , fatal in most cases once clinical signs appear , and the alleged cause of more than 50 , 000 deaths/year [3] . Rabies is also entirely preventable with the administration of timely and adequate post-exposure-prophylaxis ( PEP ) . Despite the significant death toll , rabies surveillance remains under-developed and lacks systematic approaches to its assessment [4] . The need to address human rabies surveillance limitations becomes urgent given the 2030 global elimination goal , and the specific requirement of an “effective surveillance” for validation and certification of disease freedom [5] . Of critical relevance for an effective rabies surveillance is the timely detection of exposed cases to allow immediate implementation of PEP , in an efficient manner , i . e . with as few false positive exposures as possible in order to limit PEP wastage ( that is , a low FPR ) . PEP is an expensive resource and its availability limited , most so in developing settings where rabies remains endemic [6 , 7] . A number of works have reported the excessive prescription of PEP in different settings [8] and the need to optimize its administration to facilitate the sustainability of rabies programs . This work aims to improve the understanding of the underlying attitudes and motivations for/against the investment of resources in enhanced surveillance approaches . We focus on enhancements leading to improvements in rabies surveillance timeliness and false positive rate . Our work applies a valid descriptive framework , grounded in prospect theory ( PT ) [9] , to quantify the effects of several cognitive biases , namely , loss aversion and probability weighting , and the relevance of the reference point ( a proxy for stakeholders’ context ) for the utility of surveillance timeliness and false positive rate . Briefly , PT is a descriptive theory that explains decisions under risk and describes several cognitive biases . First , people tend to form reference points ( RP’s ) and regard outcomes as deviations from this RP . Hence , people are sensitive to changes in outcomes rather than to final outcomes . The relevance of RP to properly frame interventions to increase their acceptability and uptake is well established in healthcare settings [10] . Second , people make a distinction between outcomes above the RP ( gains ) and outcomes below it ( losses ) . They perceive losses to loom much larger than gains of the same absolute magnitude , which results in a higher weight being attached to losses than to gains . This phenomenon is known as loss aversion and can influence the uptake of interventions . Third , people have difficulties to process probabilities , which they transform nonlinearly into decision weights . This behavior is called probability weighting and often causes small probabilities to be overweighted , and large probabilities to be underweighted . The purpose of the study is to evaluate the presence of these cognitive biases in surveillance investment decisions and to quantify their magnitude , in order to disentangle the different components of risk aversion . This is critical to compute unbiased utilities associated with surveillance or disease control interventions , which inform strategic settings . To the best of our knowledge , the application of PT in a surveillance setting is novel .
No ethical approval was required for our study . All study participants gave informed consent . We polled members of different public health and animal health networks/communities to inform the aforementioned PT parameters . S1 Raw data were collected by means of an online questionnaire , which was programmed in Qualtrics . The full questionnaire can be found in the supplementary materials . For both the timeliness and FPR attributes , the survey consisted of four different parts . The first three parts were used to elicit the utility of gains , the utility of losses , and loss aversion ( detailed below ) , respectively . The last part of the questionnaire aimed to elicit respondents’ willingness to pay ( WTP ) for an improvement in timeliness and FPR , respectively . In addition , we elicited the respondents’ RPs . Each part was introduced with a brief description of the choice situation , followed by a practice question . The different parts were presented in randomized order . We apply ( cumulative ) prospect theory with specific functional forms for utility and probability weighting [11–13] . For the utility functions for gains and losses , we assume the commonly used power function , and for probability weighting we estimate Prelec’s [14] one-parameter model . Loss aversion is defined as the ratio of the slopes of the loss and gain utility functions [15] , and preferences are elicited by means of comparing risky options to certain options . Indifferences are obtained by the use of choice lists , which is the most common technique to elicit risk preferences in economics [16 , 17] . We consider preferences over two-outcome lotteries ( x , p;y , 1-p ) , which give outcome x with probability p ( 0<p<1 ) and outcome y with probability 1-p . Furthermore , we assume preferences are reference-dependent with respect to a reference point r . Gains are outcomes that are strictly preferred to r and losses are outcomes strictly less preferred to r . A gain prospect involves no losses , a loss prospect involves no gains , and a mixed prospect involves both a gain and a loss . In this study , we assume subjects behave according to prospect theory . In particular , we assume two common parametric shapes of the utility function and the probability weighting function . For utility , we assume the usual power function: U ( x ) = xα for gains and: U ( x ) =− ( −x ) βforlosses . ( 1 ) With U ( x ) the utility of outcome x , α , β>0 , and α , β<1 implying a concave [convex] utility for gains [losses] . Similarly , α , β>1 implies a convex [concave] utility for gains [losses] . α , β = 1 implies linear utility . Probability weighting is modeled according to Prelec’s [14] one-parameter function: wi ( p ) =exp{− ( −ln ( p ) ) j} , ( 2 ) with wi ( p ) representing the decision weight given to probability p , i = + , - ( i . e . we have separate weighting functions for gains and losses ) , and j = γ for gains and j = δ for losses . Whenever 0<j<1 , this function has the familiar inverse S-shape , with overweighting of small probabilities and underweighting of large probabilities . In the case of surveillance , it means that health professionals would give too much weight to a small probability of improved timeliness and FPR , too little weight to higher probabilities , and that they are not sensitive enough to changes in this probability in the middle of the probability spectrum . This function also causes insensitivity to probabilities in the middle , and extreme sensitivity to changes from impossible to possible ( e . g . a slight change from p = 0 to p = 0 . 01 ) and from possible to certain ( e . g . from p = 0 . 99 to p = 1 ) . Expected utility theory ( i . e . no probability weighting ) is a special case of this function when j = 1 . Finally , loss aversion is modeled by multiplying the utility of losses by a factor λ . A decision maker is said to be loss averse if λ>1 , gain seeking if λ<1 , and loss neutral if λ = 1 . In our context , loss aversion implies that health professionals would give too much weight to deteriorations in timeliness and the FPR compared to improvements in them . Appendix A gives a derivation of the regression equations that result from the aforementioned assumptions . For the FPR attribute , the four parts were similar to those of the timeliness attribute . Respondents were asked to imagine a rabies surveillance system with an average annual FPR of 4 , 000 out of 10 , 000 suspect exposures . The FPR condition also consisted of eight choice lists in the gain part , eight choice lists in the loss part and one choice list in the mixed part . This prospect had to be traded off against the status quo , which was set to be 4 , 000/10 , 000 . The three parts were presented in a random order . Next we elicited the respondents’ WTP . The respondents were instructed that the current surveillance system had an FPR of 4 , 000/10 , 000 and costed $50 , 000 . The alternative system had an FPR of 3 , 000/10 , 000 and costed $X . Finally , we asked respondents for their own RPs . Respondents chose from options representing ranges of FPR that would best match the FPR of the rabies surveillance system of their own countries/areas . If they did not know the FPR of their rabies surveillance system , they were instructed to choose the option for which they felt neutral , i . e . it was neither a gain nor a loss . The stimuli of the FPR attribute are listed in Table 2 . The parameters of Eqs 1 and 2 were estimated by nonlinear regression [23] . The gain parameters α and γ were estimated simultaneously using the responses to questions 9–16 ( from Tables 1 and 2 ) . The same was done for the loss parameters β and δ with the responses to questions 1–8 ( from Tables 1 and 2 ) . Finally , the loss aversion coefficient λ was assessed by means of the indifference value obtained from the responses in the mixed prospect together with the other parameters obtained . Appendix A gives a derivation . WTP values were estimated by plugging the indifference values of the WTP question into the following equations , for timeliness and FPR , respectively: Timeliness:$X−$50 , 00020−10;FPR:$Y−$50 , 0004 , 000−3 , 000 . ( 3 ) Where the first one gives the WTP per day gained in detection and the second gives the WTP per 1/10 , 000 reduction in FPR . In order to test the reliability of the data , we computed the number of violations of extended monotonicity . That is , because several lotteries were dominating others , the corresponding certainty equivalent should be higher for the dominating lotteries than for the dominated lotteries . Otherwise , a respondent would violate transitivity . For example , if we compare Lotteries 5 and 7 of Table 1 , we can see that Lottery 7 will always result in a higher loss than Lottery 5 . Hence , according to monotonicity , respondents will have to give a higher certainty equivalent ( i . e . less negative ) to Lottery 5 . Considering monotonicity to be a fair criterion most subjects would like to satisfy , we have a measure of reliability of our data by counting the number of opposite cases .
The median WTP for one day improvement ( or a 5% out of the 20-day baseline ) in timeliness is $7 , 250 ( IQR $3 , 000-$10 , 000 ) and the median WTP for is $30 for a 0 . 01% improvement in FPR ( IQR $18 . 75-$86 . 25 ) . Hence , when assuming WTP per 0 . 01 percentage point improvement in FPR is constant irrespective of the amount of FPR , the median WTP for a 500 improvement ( a 5% out of 10 , 000 ) in the FPR is 500*$30 = $15 , 000 ( IQR $9 , 375-$43 , 125 ) . In other words , respondents were 2 . 1 times more willing to pay for improvements in FPR than for those in timeliness . Figs 7 and 8 show the distribution of the respondent’s own reference points . Taking the middle point of the five timeliness classes ( 1 to 10 days , 11 to 20 , etc . ) , the average timeliness was 11 . 94 days . For FPR , the average from 28 respondents was 2 , 321/10 , 000 ( setting a value of 7 , 500 for the respondent checking the >7000 box ) .
The objective of the work was to evaluate the presence of PT cognitive biases in surveillance investment decisions . In other words , our results inform strategic settings . They do not aim to change or inform the clinical management of rabies cases and hence our target are not frontline health care staff , but budget and portfolio managers who need to assess the utility of different surveillance alternatives , for rabies in this instance . Rabies surveillance is fairly unrefined . Only recently there have been efforts to evaluate the efficiency of its different forms , for example , of the integrated bite case management [25] . This form of surveillance entails active contact tracing and application of PEP to suspect exposures . Our results could expand on the economic evaluation of different rabies surveillance alternatives , e . g . passive surveillance vs . active surveillance , by plotting their incremental cost effectiveness ratio ( ICER ) against our elicited process-specific WTP . At this point , we note that our outcomes , timeliness and FPR , are process-related and diverge from the outcomes , e . g . quality-adjusted life years ( QALYs ) , deaths averted , normally targeted in health economic evaluations . A further application of our results is in the extension of probabilistic sensitivity analyses to include our computed risk aversion parameter . Briefly , the specification of the utility function , for each surveillance alternative and outcome in our case , as a net benefit assumes a risk-neutral decision maker . Adjustments to include risk aversion are possible [26] , and desirable , that would return unbiased estimates of the comparators of interest ( e . g . expected incremental benefit ) between the surveillance alternatives . Given the little appetite for inefficiencies in the last mile of disease control and elimination programs [4] , fine-tuned comparisons between the multiple interventions , taking into account the behavioral biases here elicited , is a must . We could not implement the RPs of the respondents to inform our lotteries . This would have required dynamic adjustment of the lotteries as the respondents completed the questionnaire . Given the online nature of the exercise , this was not feasible . The results of the reference point estimation suggest that the reference points we implemented in our lottery questions were too high ( i . e . 20 days vs . an average of 11 . 75 days for timeliness and 4 , 000/10 , 000 vs . 2 , 321/10 , 000 ) . This implies that , if respondents took their own estimate as reference point instead of the one given in the instructions to the lottery questions , they were on average more likely to consider outcomes as losses than we assumed . However , our findings of concave utility for gains and convex utility for losses ( as seen from the reference points used in the S1 Instructions questionnaire ) , as well as the evidence of loss aversion , indicate that these induced reference points were indeed adopted in our choice tasks . Such a finding was also reported by related studies [27 , 28] . No validation workshop was possible given the widespread location of our respondents . We focused on timeliness and FPR . Other surveillance attributes could have been targeted , such as sensitivity . However , given the cognitive burden on respondents , and the a priori uncertain number of them , we decided to restrict our scope to two attributes . Our target population was made of public health and animal health professionals . In other words , a homogenous highly educated group that should lead to no differences between their responses as seen in related studies assessing risks [29] due to variations in numeracy . We however recognize that lottery questions do present a significant cognitive burden on respondents ( this was raised by some of our respondents who expressed the difficulty in addressing the questions ) . Still , the dominance violation rate of 4 . 5% may be considered fairly low , given that we should allow for response errors and imprecise preferences . Although our analyses did not find any difference within our cohort , it may be possible that greater probability weighing biases may occur in populations with lower numerical skills [29] . Our choice of networks and approach to polling them was a convenient one . We did not seek to poll rabies experts but a cohort of knowledgeable health professionals to test whether they might display PT-specific cognitive biases around the selection of surveillance alternatives . Although one third of the participants had not worked on rabies , we considered that their experience on DSS and One Health issues qualified them to understand the purpose of the experiment and aptly complete the questionnaire . We stress that our results should be interpreted cautiously , and we recommend further research on this topic using a larger sample size to be able to test for differences in results between different demographic groups .
|
In this paper we contribute a new methodological approach to the ongoing efforts towards evaluating public health surveillance . Specifically , we apply a descriptive framework for the evaluation of decisions on disease surveillance deployment . We focus on two attributes of any surveillance system: timeliness and false positive rates . In a sample of 69 health professionals from a number of health related networks polled online , we elicited preferences , specifically respondents’ attitudes towards gains , losses and probabilities ( i . e . , if they overweight or underweight extreme probabilities ) by means of a series of lotteries . For contextualization , we apply our framework to rabies surveillance . Our data reveal that respondents underestimate their chances of getting a good outcome in uncertain situations , and they overestimate their chances of bad outcomes . Moreover , losses loom larger than gains of the same absolute magnitude to the respondents . We find no differences between the estimated parameters for timeliness and false positive rates . Our results indicate that the biases described are present among public health professionals , and highlight the need to adjust for them when eliciting their preferences for surveillance systems .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"decision",
"making",
"tropical",
"diseases",
"social",
"sciences",
"neuroscience",
"arithmetic",
"research",
"design",
"rabies",
"cognitive",
"psychology",
"mathematics",
"cognition",
"neglected",
"tropical",
"diseases",
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"research",
"and",
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"and",
"occupational",
"health",
"infectious",
"diseases",
"zoonoses",
"epidemiology",
"probability",
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"disease",
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"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"physical",
"sciences",
"cognitive",
"science"
] |
2019
|
Unbiased assessment of disease surveillance utilities: A prospect theory application
|
Current understanding of adaptive immune , particularly T cell , responses to human rhinoviruses ( RV ) is limited . Memory T cells are thought to be of a primarily T helper 1 type , but both T helper 1 and T helper 2 memory cells have been described , and heightened T helper 2/ lessened T helper 1 responses have been associated with increased RV-induced asthma exacerbation severity . We examined the contribution of T helper 1 cells to RV-induced airways inflammation using mice deficient in the transcription factor T-Box Expressed In T Cells ( Tbet ) , a critical controller of T helper 1 cell differentiation . Using flow cytometry we showed that Tbet deficient mice lacked the T helper 1 response of wild type mice and instead developed mixed T helper 2/T helper 17 responses to RV infection , evidenced by increased numbers of GATA binding protein 3 ( GATA-3 ) and RAR-related orphan receptor gamma t ( RORγt ) , and interleukin-13 and interleukin-17A expressing CD4+ T cells in the lung . Forkhead box P3 ( FOXP3 ) and interleukin-10 expressing T cell numbers were unaffected . Tbet deficient mice also displayed deficiencies in lung Natural Killer , Natural Killer T cell and γδT cell responses , and serum neutralising antibody responses . Tbet deficient mice exhibited pronounced airways eosinophilia and mucus production in response to RV infection that , by utilising a CD4+ cell depleting antibody , were found to be T helper cell dependent . RV induction of T helper 2 and T helper 17 responses may therefore have an important role in directly driving features of allergic airways disease such as eosinophilia and mucus hypersecretion during asthma exacerbations .
Human rhinovirus ( RV ) infections cause the common cold and are associated with two thirds of asthma and one third of chronic obstructive pulmonary disease ( COPD ) exacerbations [1 , 2] . There are currently no specific licensed therapies or vaccines available for RV infections . Current understanding of adaptive immune responses to RV is very limited . Almost all studies have focused on the role of antibodies , showing that neutralising antibodies generated in response to infection can be protective against symptoms , but because of the antigenic heterogeneity amongst the >150 RVs people continue to suffer infections throughout life [3 , 4] . It is unknown what , if any , contribution conventional T cells make to virus control or to the severity of RV-induced colds . Similarly , whilst T helper cell responses have been associated with disease outcomes in asthma exacerbations , the contribution of RV-specific T cells has not specifically been studied . Studies of memory cells in humans have suggested that RV-specific T cells in tonsil and peripheral blood are primarily CD4+ helper cells which express the Th1 cytokine interferon ( IFN ) -γ upon re-stimulation with RV [5–7] . However , production of the Th2 cytokines interleukin ( IL ) -4 , IL-5 and IL-13 by RV-specific memory cells has also been described and IL-4 and IL-13 have been detected in supernatants of RV-exposed peripheral blood mononuclear cells ( PBMC ) from asthmatics , suggesting that Th2 responses to RV develop in some individuals [5 , 6 , 8] . RV infection in fact induces both Th1 promoting factors such as C-X-C motif ligand ( CXCL ) 10 and IL-12 , and Th2 promoting factors C-C motif ligand ( CCL ) 17 , CCL22 , IL-33 and IL-25 in vivo and/or in vitro [9–13] . RVs therefore appear to have the capacity to induce both Th1 and Th2 orientated responses . For other respiratory viruses such as respiratory syncytial virus ( RSV ) for which T cell immunity is better understood , T cells have complex roles , both aiding virus clearance but also causing immunopathology , with alterations in the balance between type 1 and type 2 T cell responses having been linked to severity of virus associated immunopathology [14] . The limited available evidence for RV suggests that Th1 responses are desirable because we have shown in the mouse that enhancing the memory Th1 response by means of vaccination is associated with enhanced neutralising antibody responses and in humans , that greater Th1 and lessened Th2 number following polyclonal stimulation are associated with improved disease outcome during experimental RV-induced asthma exacerbations[15 , 16] . It remains to be fully established however what type of response is most desirable in terms of limiting virus replication and potentially reducing disease , and what exactly the implications of an aberrant T helper response might be . We therefore addressed the question of whether Th1 responses to RV are required for virus control and what influence they have on other aspects of airway disease by employing the mouse model of infection and mice deficient in T-Box Expressed In T Cells ( Tbet ) , a transcription factor which promotes Th1 and suppresses both Th2 and Th17 differentiation . We showed that whilst wild type mice developed Th1 responses to RV , Tbet deficient mice instead developed a mixed Th2/Th17 response . This altered response was associated with T helper cell-dependent airways eosinophilia and mucus secretion reminiscent of asthmatic airways inflammation . RV-specific Th2/Th17 responses , if developed in asthmatics as has been proposed , could therefore have an important role in enhancing disease during RV-induced disease exacerbations .
To confirm that Tbet-/- mice lacked Tbet expression in helper T cells following RV infection , we performed flow cytometry staining of lung T cells harvested from Tbet-/- and wild type ( w/t ) control mice 2 and 7 days after infection . RV infected w/t mice displayed an increase in the number of CD3+CD4+ T cells expressing Tbet compared to phosphate buffered saline ( PBS ) challenged w/t animals on day 7 post-infection ( Fig 1A ) . As expected , Tbet-/- mice lacked Tbet expressing CD3+CD4+ cells . Instead , RV infected Tbet-/- mice had increases in lung CD3+CD4+ T cells expressing both GATA binding protein 3 ( GATA-3 ) ( Fig 1B ) , the Th2 associated transcription factor , and RAR-related orphan receptor gamma t ( RORγt ) ( Fig 1C ) , the Th17 associated transcription factor , compared to both RV infected w/t and PBS challenged Tbet-/- mice on day 7 post-infection . We also enumerated forkhead box P3 ( FOXP3 ) expressing CD4+ T cells as a measure of Treg responses . The number of FOXP3 expressing lung CD3+CD4+ T cells was increased at 7 days post-infection in both w/t and Tbet-/- mice , but was not different between the 2 mouse strains ( Fig 1D ) . To confirm this T helper phenotype , we performed intracellular cytokine staining ( ICS ) for Th1 ( IFN-γ ) , Th2 ( IL-13 ) , Th17 ( IL-17A ) and Treg ( IL-10 ) associated cytokines in polyclonally stimulated lung leukocytes on day 7 post-infection . Consistent with the transcription factor staining , IFN-γ expressing CD4+ T cell number was increased in RV infected vs . PBS challenged w/t , but not Tbet-/- mice ( Fig 1E ) , whereas IL-13 ( Fig 1F ) and IL-17A ( Fig 1G ) expressing cell number was increased in RV infected Tbet-/- mice compared to both RV infected w/t mice and PBS challenged Tbet-/- mice . IL-10 expressing cell number was not significantly increased following RV infection in either mouse strain ( Fig 1H ) . Differences in Th1 , Th2 and Th17 cytokine expression between RV infected w/t and Tbet-/- mice were supported by changes in lung tissue cytokine mRNA expression ( Fig 1I–1K ) . Il10 mRNA levels were increased in RV infected w/t mice compared to PBS challenged w/t mice and compared to RV infected Tbet-/- mice ( Fig 1L ) . These findings are consistent with a mixed Th2/Th17 helper T cell response to RV in Th1 deficient Tbet-/- mice . In addition to T cell transcription factor and cytokine expression , we also measured airway T cell chemoattractant levels in Tbet-/- vs . w/t mice . On days 1 and 2 post-infection the Th1 associated C-X-C motif chemokine receptor ( CXCR ) 3 ligand CXCL10/IP-10 ( Fig 2A ) and the Th2 associated C-C motif chemokine receptor ( CCR ) 4 ligands CCL22/MDC ( Fig 2B ) and CCL17/TARC ( Fig 2C ) in bronchoalveolar lavage ( BAL ) were similarly induced in RV infected Tbet-/- and w/t mice compared to the respective PBS challenged controls . Levels of CCL17/TARC in BAL were however significantly elevated in RV infected Tbet-/- vs . w/t control mice on day 7 post-infection . Changes in T cell response might be expected to have effects on antibody responses due to the roles of T cell cytokines on antibody class switching . Consistent with this , we found that RV-specific serum immunoglobulin ( Ig ) G2c , a Th1 associated antibody isotype , was detectable in RV infected w/t mice but completely absent in RV infected Tbet-/- mice ( Fig 3A ) . Despite the increase in Th2 responses however , levels of RV-specific IgG1 , a Th2 associated antibody isotype , were comparable in Tbet deficient and w/t mice following infection ( Fig 3B ) . These changes in RV binding antibodies in Tbet-/- mice were associated with a complete lack of serum neutralising antibody in RV infected Tbet-/- mice on day 14 post-infection , in contrast to RV infected w/t mice ( Fig 3C ) . We next determined what effect the altered T helper cell response to RV in Tbet-/- mice had on other aspects of airways inflammation . The cellular airways response to RV in the mouse is typically characterised by early neutrophilia followed by an increase in lymphocyte number peaking around days 4–7 post-infection [17] . Total numbers of cells in BAL were not different between RV infected Tbet-/- and RV infected w/t mice at any timepoint studied ( Fig 4A ) . Similarly , the neutrophil response was unaltered by Tbet deficiency , with increases in neutrophils in BAL evident in both RV infected w/t and RV infected Tbet-/- mice on days 1 and 2 post-infection compared to their respective PBS challenged controls ( Fig 4B ) . Lymphocyte number in BAL was significantly increased in RV vs . PBS challenged w/t , but not Tbet-/- mice on days 2 and 7 post-challenge ( Fig 4C ) . Lymphocyte numbers however did not significantly differ between RV infected Tbet-/- and w/t groups . Macrophage number in BAL was unaffected by Tbet deficiency ( Fig 4D ) . RV infected Tbet-/- mice did however develop significant eosinophilia in the airways at 7 days post-infection , a finding that was not observed in any other treatment group ( Fig 4E ) . Similarly to the association of increased CCR4 ligand levels with Th2 cells , the eosinophilic airways response to RV in Tbet-/- mice was associated with an increase in the eosinophil chemoattractant CCL24/Eotaxin 2 in the airways compared to PBS challenged Tbet-/- and RV infected w/t mice on day 7 post-infection ( Fig 4F ) . Mucin production is a characteristic feature of RV infected epithelial cells and mucus production represents a symptom of RV infection and asthma in humans [18] . We therefore measured airway mucin producing cells via Periodic Acid Schiff ( PAS ) staining of lung sections and mucin 5AC ( MUC5AC ) protein levels in BAL . There was no evidence of PAS positive cells in the airways of any treatment group on day 1 post-challenge , but by day 7 post-infection there was strong PAS staining in RV infected Tbet-/- mice that was absent in infected w/t mouse lungs ( Fig 4G and 4H ) . This finding was supported by a significant increase in BAL MUC5AC protein levels in RV infected Tbet-/- vs . w/t mice 7 days after infection ( Fig 4I ) . Low powered images of PAS stained lungs are shown in S1 FIg . Given both our understanding of the role of T cell subsets in other respiratory virus infections and the fact that the limited studies of RV antigen-specific memory T cells in humans show a strong Th1 bias , we hypothesised that Th1 cells might directly or indirectly have an antiviral role . Measurement of viral RNA in lung tissue however showed that lung virus levels on both day 1 and day 7 post-challenge were not increased by Tbet deficiency ( Fig 5A ) . In fact , RV RNA levels were significantly lower on day 1 post-infection in Tbet-/- vs . w/t mice . This difference in lung virus load was not due to differences in induction of innate antiviral mediators , because Ifnb and Ifnl2/3 ( interferon-λ2/3 ) mRNA levels in lung tissue , and interferon-λ protein levels in BAL were no different in RV infected Tbet-/- and w/t mice ( Fig 5B–5D ) . Tbet is also expressed in a number of other cells of the immune system , most notably Natural Killer ( NK ) cells and NK T cells . We therefore measured NK and NKT , as well as γδT cell responses to RV infection to determine if deficiencies in these innate cells were present that could contribute to the inflammatory phenotype found in Tbet-/- mice . Innate responses were measured around their peak at 2 days after infection [19 , 20] . PBS challenged Tbet-/- mice had similar total lung cell counts ( Fig 6A ) but fewer NK ( Fig 6B ) , IFN-γ expressing NK ( Fig 6C ) , NKT ( Fig 6D ) and IFN-γ expressing NKT cells ( Fig 6E ) compared to PBS challenged w/t mice , indicating a basal deficiency in these cells . Infection did not overcome this deficiency because RV infection significantly increased NK , NKT and IFN-γ producing NK and NKT number in the lungs in w/t mice , but not Tbet-/- mice . Lung γδT cell ( Fig 6F ) and IFN-γ producing γδT cell ( Fig 6G ) number was not significantly different in PBS challenged Tbet-/- vs . w/t mice , but again failed to increase following infection in Tbet-/- mice , such that total and IFN-γ+ γδT cell number was higher in RV infected w/t vs . RV infected Tbet-/- mice . Given that Tbet-/- mice displayed deficiencies in innate cellular responses to RV , we next systemically depleted CD4+ T cells in Tbet deficient mice to determine if the altered inflammatory phenotype in Tbet-/- mice was dependent upon T helper cells . Administration of a single dose of a well characterised CD4+ cell depleting monoclonal antibody on day 0 , 3 hours prior to infection , led to a complete absence of detectable CD4+ T cells in the lungs of both RV and PBS challenged Tbet-/- mice at 7 days post-challenge ( Fig 7A ) . Whilst T helper cells could obviously not be measured in antibody treated mice , an absence of induction of Il4 ( Fig 7B ) , Il13 ( Fig 7C ) and Il17a ( Fig 7D ) mRNAs in the lung following infection supported an absence of Th2 and Th17 responses in CD4+ cell depleted mice . Total BAL cell number ( Fig 7E ) was not significantly affected by CD4+ cell depletion . BAL eosinophils ( Fig 7F ) were however significantly reduced in RV-infected anti-CD4 vs . RV-infected isotype control treated mice . As shown in Fig 4 , RV-infected Tbet-/- mice had high levels of MUC5AC in BAL ( Fig 7G ) and large amounts of PAS staining in the airway epithelium ( Fig 7H ) 7 days after infection . Both mucin protein induction and PAS staining were almost completely abolished in RV infected mice treated with anti-CD4 antibody .
We utilised a Tbet deficient mouse strain to examine the role of Th1 cell responses in RV infection . We found that in the absence of Tbet , Th2 and Th17 responses developed in the airways and that this altered T cell response was associated with an impaired antibody response and drove an inflammatory phenotype characterised by airways eosinophilia and enhanced mucus production . The Th2 and Th17 responses that developed in the RV infected mouse lung can be explained by the well-established role of Tbet in both facilitating Th1 differentiation and at the same time supressing Th2 and Th17 differentiation . Tbet directly induces IFN-γ gene expression and therefore effector functions of Th1 cells , as well as up-regulating expression of the Th1 cell homing receptor CXCR3 [21 , 22] . Tbet also inhibits Th2 differentiation by repressing expression of the Th2 differentiation promoting transcription factor GATA-3 , binding of the IL4 silencer and suppression of IL5 and IL13 expression via sequestration of GATA-3 away from Th2 genes[22–25] . Tbet also inhibits Th17 differentiation via a number of possible mechanisms such as blocking Runt-related transcription factor ( RUNX ) 1 activation of Rorc , the gene encoding the Th17 associated transcription factor RORγt [26] . Tbet is also expressed in some regulatory T cells ( Treg ) , although its function in these cells is not well understood [27] . We saw an increase in FOXP3+ CD4+ T cells in the lungs of RV infected mice , but neither this nor the proportion of CD4+ T cells expressing IL-10 was affected by Tbet deficiency . Tbet deficient mice did however display a reduction in lung Il10 mRNA levels after infection , suggesting a possible deficiency in IL-10 production from a non-helper T cell source . RV induces a number of factors in mice that are associated with Th2 responses , such as IL-25 and CCR4 ligands [11 , 13] . In RV infected Tbet deficient mice we observed an increase in airway levels of the chemokine CCL17 , a ligand for CCR4 which is expressed on Th2 cells , on day 7 post-infection . This suggests that Th2 trafficking in addition to differentiation might be enhanced in Tbet deficient mice . Given that CCL17 was expressed in RV infected wild type mice on days 1 and 2 post-infection , but did not result in Th2 responses , it would appear however that a lack of Tbet mediated suppression of Th2 differentiation in lymphoid tissue must precede chemokine expression for a Th2 response to occur . We also did not show that increased CCL17 expression in RV infected Tbet deficient mice preceded Th2 recruitment , as one might expect , due to a lack of intermediate timepoints between days 2 and 7 to our studies . This is true also of the association of increased CCL24 levels in the airways of RV infected Tbet deficient mice with airways eosinophilia and further work is therefore required to determine whether chemokines play a role in the Th2 and eosinophil responses observed in Tbet deficient mice . Th2 inducing factors are also expressed following RV infection in otherwise healthy humans without there being measureable Th2 responses . RV infection of asthmatics does however enhance airway Th2 responses and it is assumed in this case that RV infection is primarily enhancing allergen-specific Th2 cell responses in the asthmatic airways [12] . However , to date nobody has assessed the antigen specificity of Th2 cells during RV-induced asthma exacerbations and some investigators have described RV-specific Th2 memory based on analysis of peripheral blood cells [5 , 8] . If RV-specific Th2 responses occur in the human airways during infection , then our finding that RV-specific Th2 or Th17 cells can drive airways eosinophilia and mucus hypersecretion could have important implications in terms of understanding RV-induced asthma disease mechanisms . Th1 ( Tbet ) deficiency also had significant effects on antibody responses to RV , causing an absence of IgG2c and no difference or a small increase in the IgG1 response . This is very much consistent with the roles of T cell derived cytokines in B cell class switching , whereby IFN-γ is associated with IgG2a switching ( IgG2c in C57BL/6 mice is analogous to IgG2a in humans and other mouse strains ) and Th2 cytokines IL-4 and IL-5 with switching to IgG1 . Tbet is also expressed in B cells where it has been associated with class switching to IgG2a , even in a T cell independent system [28 , 29] . More interesting however is the finding that Tbet deficiency completely abrogated the neutralising antibody response , suggesting that Tbet/Th1 cells are absolutely required for neutralising antibody generation and that neutralising antibodies might be exclusively IgG2a . This is consistent with a previous finding that TLR-9 agonist administration enhances neutralising antibody responses to RV in mice , but no other study has examined antibody isotypes or type of T cell help required for generation of neutralising antibody to RV [30] . We only measured lung virus loads in terms of viral RNA in lung tissue , not viable virus in the airways , but the lack of neutralising antibody response did not increase virus loads in the lung by this measure and in fact , viral RNA levels in the lung were greater in Tbet-/- mice 24hrs after infection . The reason for the higher viral RNA levels in Tbet-/- mice soon after infection are not known , but it is perhaps not surprising that lung virus loads were not increased despite an absence of neutralising antibody given that neutralising antibodies are not measureable in the mouse until at least a week after infection , when virus has largely been cleared from the lung . In humans however virus tends to persist longer , with viral RNA levels peaking in the nose around 3 to 4 days after infection and persisting until at least 10 days post-infection in experimental infection studies [12 , 16] . Diminished neutralising antibody responses could therefore have negative consequences in terms of virus clearance in man . This is also an important consideration in terms of generation of protective immunity in humans , suggesting that asthmatics with greater Th2 responses might be impaired in their ability to develop protective neutralising antibody . In terms of future vaccine design , our findings suggest that Th1 promoting adjuvants will likely be required for developing protective antibody responses after vaccination . An array of immune cells other than helper T cells also express Tbet , including dendritic cells ( DC ) , CD8+ T cells , Innate Lymphoid Cells , Natural Killer cells , Natural Killer T cells and γδT cells [31] . We found NK , NKT and γδT cell responses to be deficient in the lungs of Tbet-/- mice following infection . NK and NKT cells were in fact deficient in the lungs even without virus challenge , consistent with the role of Tbet , along with Eomes , in their development and survival [32 , 33] . With the exception perhaps of DC , all of the cell types listed above have effector functions which can overlap to a lesser or greater degree with Th1 cells . We have in fact shown previously that NK cells are an early source of IFN-γ in the mouse RV infection model , with a possible role in virus control [19] . We therefore depleted CD4+ cells in Tbet-/- deficient mice to show that the inflammatory phenotype exhibited was dependent on the aberrant T helper cell response . Depletion of CD4+ T cells resulted in a near complete reversal of RV induced eosinophilia and mucus production , suggesting that these features were indeed dependent on CD4+ T cells . Though this finding shows a CD4+ T cell dependence , it does not establish that CD4+ T cells were sufficient to drive this inflammatory phenotype , and a number of other cells may contribute . For example , Tbet deficiency in DCs is associated with an impaired ability to prime Th1 responses [34] . Nevertheless , the T helper cell dependence is consistent with the many reports that Th2 and Th17 associated cytokines can facilitate airway mucus cell metaplasia and/or eosinophilia [35–37] . More specifically , Tbet deficient T cells have been shown to drive airway IL-4 production and hyperreactivity in an asthma model and both IL-13 and IL-17A have been shown to be able to drive allergic airways disease in Tbet deficient mice [38–40] . In summary , we have shown that deficiencies in Th1 responses led to the development of Th2 and Th17 responses to RV . These altered T helper responses were associated with development of some features of asthmatic airways inflammation in mice and may therefore contribute to asthma exacerbations in humans . New treatments which enhance Th1 responses or replace deficient Th1 promoting/Th2 supressing factors in RV infections , such as the recombinant type I/III interferons which are currently in development , could therefore be beneficial in limiting such Th2 and Th17 mediated RV disease .
All of the animal studies described were carried out under the authority of the UK Home Office ( Animals ( Scientific Procedures ) Act 1986 ) project licence number PPL 70/7234 . Intranasal dosing was performed under light , transient anaesthesia with isoflurane . Animals were culled by overdose of pentobarbitone . Homozygous Tbet knockout ( Tbet-/- ) ( B6 . 129S6-Tbx21tm1Glm/J ) and wild type ( w/t ) , female C57BL/6J mice were obtained from Charles River Laboratories UK and were housed in individually ventilated cages . Mice were 6–8 weeks old at the commencement of studies . All studies were conducted according to the UK Animals ( Scientific Procedures ) Act 1986 under the authority of project licence number PPL 70/7234 . Mice were infected with 5x106 TCID50 RV1B , in 50uL volume , intranasally under anaesthesia with isoflurane . CD4 expressing cells were depleted in the indicated studies by intraperitoneal administration of 0 . 2mg anti-mouse CD4 antibody clone GK1 . 5 ( BioXCell , New Hampshire , USA ) in 150μL PBS , 3hrs prior to infection . Control mice were administered 0 . 2mg isotype control rat IgG2b clone LTF-2 ( BioXCell ) . Depletion efficacy was assessed by flow cytometry staining of lung cells . Depletion was effective by 24hrs post-administration and was assessed in the studies shown on day 7 post-infection . Where depletion was not successful ( <99% reduction in lung CD3+CD4+ cells; n = 3 of 15 RV-anti-CD4 , n = 3 of 15 PBS anti-CD4 ) , mice were excluded from all other analyses . Lung tissue was processed for flow cytometry by homogenisation with the gentleMACS tissue dissociator ( Miltenyi Biotech GmbH , Germany ) and digestion in medium containing 1mg/mL collagenase A from Clostridium histolyticum ( Roche Diagnostics , West Sussex , UK ) and 80 units/mL Deoxyribonuclease I from bovine pancreas ( Sigma-Aldrich , Dorset , UK ) . Red cells were lysed with ACK buffer . Lungs were lavaged 4 times via the trachea with 1 . 5mL of PBS containing 55mM EDTA . Lavage cells were separated by centrifugation and spun onto cytospin slides for subsequent differential staining . Blood was collected from the carotid arteries and serum separated by centrifugation using Microtainer SST tubes ( BD Biosciences , Oxford , UK ) . The apical lobe of the right lung was harvested from mice after lavage was performed and was stored in RNAlater buffer ( Qiagen , Manchester , UK ) for subsequent RNA analysis . RNA was extracted using the RNeasy mini kit ( Qiagen ) and cDNA generated using the Omniscript RT kit ( Qiagen ) and random primers . RV serotype 1B was propagated in H1 HeLa cells ( American Type Tissue Culture Collection ( ATCC ) ref CRL-1958 ) and purified using previously described methods [17] . Virus was titrated on Ohio HeLa cells ( European Collection of Authenticated Cell Cultures ) and TCID50 was calculated using the Spearman-Karber formula . Purified , uninfected H1 HeLa cell lysate was generated in the same manner for use as a control in virus-specific antibody assays . Flow cytometry was performed on lung leukocytes obtained by collagenase digestion as described . For intracellular cytokine staining , cells were stimulated for 4hrs in medium containing 50ng/mL Ionomycin , 500ng/mL Phorbol myristate acetate ( PMA ) ( both Sigma Aldrich ) and golgi transport inhibitor ( Golgi Stop; BD Biosciences ) before staining . In brief , 1x106 cells were stained with live/dead fixable dead cell stain ( Life Technologies , Paisley , UK ) , Fc receptors were blocked with anti-CD16/CD32 ( BD Biosciences ) and cells were then incubated with fluorochrome-conjugated antibody cocktails . Antibodies used were anti-CD4 clone RM4-5 ( BD Biosciences ) , anti-CD3 clone eBio500A2 ( eBioscience , San Diego , CA , USA ) , anti-NK1 . 1 clone PK136 , anti-γδTCR clone GL3 , anti-IL-17a clone TC11-18H10 ( all BD Biosciences ) , anti-IFN-γ clone XMG1 . 2 ( Biolegend , San Diego , CA , USA ) , anti-IL-13 clone eBio13A , anti-IL-10 clone JES5-16E3 ( both ebioscience ) , anti-Tbet clone 04–46 ( BD Bioscience ) , anti-FOXP3 clone FJK-16s , anti-RORγT clone B2D ( both eBioscience ) and anti-GATA3 clone L50-823 ( BD Bioscience ) . Intracellular staining was performed using the Foxp3/Transcription Factor Staining Buffer Set ( eBioscience ) . Flow cytometry data was acquired with a BD LSR Fortessa cytometer and analysed using FlowJo software vX0 . 6 ( FLOJO LLC , OR , USA ) . T helper cells were defined as live/dead marker negative , single cells , forward scatter/side scatter low , CD3 vs . CD4 double positive . CD4 was further plotted against intracellular transcription factors or cytokines for Fig 1 . Cytokine and chemokine proteins in BAL were assayed using protocols and reagents from Duoset ELISA kits ( R&D systems , Minneapolis , MN , USA ) . Assay ranges were as follows . CXCL10 62 . 5–4 , 000 pg/mL , CCL22 7 . 8–500 pg/mL , CCL17 31 . 2–2 , 000 pg/mL , CCL24 15 . 6–1 , 000 pg/mL . RV-specific IgGs were measured using in-house assays . 96 well plates were coated with purified RV1B inoculum or HeLa lysate control overnight and blocked with PBS 5% skimmed milk . Treatment group sera were pooled and diluted in PBS 5% milk before being added to plates . Detection antibodies were biotinylated rat anti-mouse IgG1 ( clone A85-1 ) and IgG2a/c ( clone R19-15 ) ( both BD biosciences ) diluted in PBS 1% BSA . Plates were developed by incubation with streptavidin- horseradish peroxidase and TMB substrate ( both Sigma Aldrich ) . Values generated by antibody binding to a HeLa lysate control wells were subtracted from that of RV1B coated wells during analysis . MUC5AC was measured using an in-house , semi-quantitative , direct ELISA assay . Plates were coated with BAL fluid , diluted 1 in 10 in PBS , or cell culture supernatant standard and allowed to dry . After washing and blocking , biotinylated detection antibody ( anti-MUC5AC clone 45M1 , Fisher Scientific , Loughborough , UK ) was applied . Plates were developed by standard methods . Serum neutralisation of RV was measured in Ohio HeLa cells . Pooled sera were heat inactivated at 56°C for 45minutes , then diluted and incubated with RV1B before addition of HeLa cells . A reference anti-RV1B anti-sera ( American Type Culture Collection ) was used as a positive control . After 48-72hrs at 37°C cytopathic effect ( CPE ) was measured by staining with crystal violet . After drying , crystal violet was re-dissolved in 1% SDS and absorbance was measured at 560nm . Cytokine/interferon mRNAs and viral RNA in lung tissue were measured by Taqman qPCR using Quantitect probe PCR mastermix ( Qiagen ) and were normalised to 18S ribosomal RNA . Primer and probe sequences for Il13 [41] , RV , Ifnβ , Ifnl2/3 and 18s rRNA [42] have been described previously . Il10 mRNA was measured using a commercial Taqman gene expression assay ( Mm00439616_m1 , Life Technologies ) . Primer sequences ( 5’-3’ ) for other assays are as follows . Ifng forward TCA AGT GGC ATA GAT GTG GAA GAA , Ifng reverse TGG CTC TGC AGG ATT TTC ATG , Ifng probe FAM-TCA CCA TCC TTT TGC CAG TT-TAMRA; Il4 forward ACA GGA GAA GGG ACG CCA T , Il4 reverse GAA GCC CTA CAG ACG AGC TCA , Il4 probe FAM-TCC TCA CAG CAA CGA AGA-TAMRA; Il17a forward TCAGACTACCTCAACCGTTCCA , Il17a reverse AGCTTCCCAGATCACAGAGGG , Il17a probe TCACCCTGGACTCTCCACCGCA . For histology , lungs were inflated with 4% paraformaldehyde , removed from the thorax and immersed in 4% paraformaldehyde . Lungs were paraffin wax embedded , cut into 5μM sections and stained with periodic acid-Schiff ( PAS ) to detect mucus producing cells . PAS staining was scored using a previously described system adapted from Semitekolou et al [43] , in which 0 = <5% of airway PAS positive , 1 = 5–25% of airway PAS positive , 2 = 25–50% of airway PAS positive , 3 = 50–75% of airway PAS positive , 4 = >75% of airway PAS positive . 15 airways were counted per section and averaged . Scoring was performed blind . Images were acquired using a Zeiss AxioScope A1 microscope with A-plan 10x ( 0 . 25 aperture ) ( Figs 4 and 7 ) or 2 . 5x ( S1 Fig ) objective , Zeiss Axiocam ERc5s camera and Zen2012 Blue Edition version 1 . 1 . 1 . 0 software ( Carl Zeiss Ltd , Cambridge , UK ) . Graphical data is expressed as mean +/- SEM . Differences were assessed by 1 or 2-way ANOVA followed by Bonferroni post-test to pinpoint specific differences . Differences were considered significant if p<0 . 05 . Analyses were performed using Prism software v6 ( GraphPad Software , La Jolla , CA , USA ) .
|
Rhinovirus infections cause the common cold and a high proportion of exacerbations of asthma and chronic obstructive airway disease . Little is understood of the roles of T cells in causing or limiting rhinovirus disease . Rhinovirus-specific memory cells are primarily of a T helper 1 type in healthy people , but rhinovirus-specific T helper 2 cells have been detected in blood of asthmatics . We examined the role of T helper 1 cells in rhinovirus disease using a mouse strain deficient in a key T helper 1 associated transcription factor . We show that mice deficient in T helper 1 cells instead develop a mixed T helper 2 and T helper 17 response to rhinovirus that was associated with a lack of neutralising antibody response and development of airway eosinophilia and mucus secretion , features of allergic asthmatic airways inflammation . Our study therefore provides new insight into how the nature of the rhinovirus-specific T helper cell response could influence rhinovirus-induced airways disease , especially in asthma , as well as generation of protective immunity .
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2016
|
Tbet Deficiency Causes T Helper Cell Dependent Airways Eosinophilia and Mucus Hypersecretion in Response to Rhinovirus Infection
|
We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation , either experimentally or using simulations of biophysically detailed models . The dimensionality of the data is first reduced to the first principal component , and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation , which describes the motion of a Brownian particle in a potential . The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived . We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal . During deep anesthesia , intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a one-dimensional diffusion equation . Under lighter anesthesia , however , the distributions of the times spent in the up and down states were better fitted by such a model , suggesting a role for noise in determining the time spent in a particular state .
Deciphering the fundamental mechanisms that underlie brain function requires an explicit description of the dynamics of the neuronal and synaptic substrate . Explicit neurodynamical models can describe the complex dynamics arising from the involved neuronal networks [1] , [2] . Traditionally , theoretical neuroscience follows an ab initio approach consisting of two main steps: 1 ) construction and simulation of models based on detailed descriptions of the neuronal and synaptic operations with a large number of neurons in a specified ( hypothesized ) network architecture , and 2 ) reduction of the hypothesized models such that an in-depth analytical study is feasible , and a systematic relation between structure ( parameters ) , dynamics , and functional behavior can be solidly established . Models of neurons such as integrate-and-fire [3] are frequently used . The advantage of this type of models is that the simulation of biologically realistic networks allows the study of the neural correlates of brain function , for comparison with experimental data . On the other hand , the model is simple enough so that it is possible to obtain a reduced description based on mean-field techniques [4] , [5] . The mean-field reduction simplifies the analysis of networks of spiking neurons , by partitioning the network into populations of neurons that share the same statistical properties . Using some plausible approximations , the stationary firing rate of each population can be expressed as a function of the firing rates of all the populations in the network . The set of stationary , self-reproducing rates for the different populations in the network can then be found solving a set of coupled self-consistency equations ( see e . g . [4] ) . The method allows to characterize the activity of the network as a function of the neuronal and synaptic parameters . For this ab initio approach to be applicable , however , one needs an explicit representation of the dynamics at the microscopic level . Even when such representation is actually available , it may not be possible or easy to come up with a low-dimensional description of the original system . Here we introduce an alternative methodology that allows for an effective reduction of dimensionality . The method is data-driven , in the sense that it does not require any knowledge of the dynamics at the microscopic level . The basic idea is is to fit the underlying dynamics of the data using a stochastic , nonlinear differential equation . In general , fitting a model to data from a nonlinear stochastic system is a difficult problem because of the high dimensionality of the space of available models . Without some prior knowledge to guide model selection , the likelihood of picking the “correct” model for some data set is slim . Here we describe a method that can be applied to data from systems that are ( a ) stationary , ( b ) driven by additive white noise , and ( c ) whose deterministic motion is governed by an effective one-dimensional energy function . In such type of systems , the stationary distribution of the variable can be straightforwardly related with the underlying energy function . When neurodynamical data is high-dimensional , the dimensionality of the system can be first reduced using principal curves or principal components analysis . To model data from such systems we proceed in two steps . We estimate first the energy function and then the intensity of the noise . The energy function is uniquely determined by the stationary distribution , so to accomplish the first step we estimate this distribution from data . In particular we assume that we have access to samples from this distribution and that the underlying potential can be fit by a piecewise quadratic polynomial . To fit the intensity of the noise we need a measure that is dependent on this parameter in a known way . In this work we use the mean first-passage time through a particular boundary , for which there are closed-form expressions . That is , given samples of the first-passage times of the system under study , we find the noise intensity that yields the same mean first-passage time in the system described by the fitted energy function . We first apply the method to simulated data from a one-dimensional rate equation . In this case , the assumptions of the method are fulfilled and we can recover the original model with high accuracy if the number of data points is sufficiently high . Next we show that the method is applicable also to data from high-dimensional neuronal models . In particular , we use the method to obtain the effective dynamics of a network of spiking neurons operating near a bifurcation . Finally , we apply the method to real data from intracellular recordings from cortical neurons in vivo .
Here we show how the method works for a one-dimensional rate model described by a Langevin equation , ( 1 ) where is a nonlinear function and is Gaussian white noise with standard deviation . The method described in this article provides an effective description of the system of exactly the same type as Equation ( 1 ) , given a sample of states . Since the system is one-dimensional , an energy function satisfying can be trivially defined without resorting to any approximation method . Thus , we do not gain much insight in applying this method to such a simple system . Our aim in this section is rather to check that the piecewise approximation of the probability density described in Methods recovers correctly the energy of the system , which is well-defined in this particular example . We also study the sensitivity of the estimation to the number of subintervals used in the piecewise quadratic approximation . As a second example of our reduction method , we consider a large-scale network of spiking neurons exhibiting bistability . The network we use is the binary decision network introduced by Wang [9] , with identical architecture and parameters ( see Figure 3 and Text S1 for the details ) . In short , the model consists of a fully connected network of integrate-and-fire neurons with synaptic dynamics mediated by excitatory AMPA and NMDA receptors , and by inhibitory GABA receptors [4] . Excitatory neurons are structured into two subpopulations . Due to the strong recurrent connections between cells within each population and to the shared inhibitory feedback , the two subpopulations compete with each other for higher activity . This competition eventually culminates with the network settling into an attractor where the activation of one population suppresses the activity of the other . There are two such attractors , called asymmetric attractors , associated with the two possible outcomes of the competition . Apart from recurrent currents , all cells receive AMPA-mediated synaptic currents from external neurons that emit spikes following Poisson statistics . For a wide range of external inputs and connection weights , the network operates as a winner-take-all , and is therefore able to sustain either one of the two asymmetric stable states . As in the rate model analyzed in the previous section , noise induces transitions between states that are simultaneously stable , giving rise to a bimodal distribution in the rate variables when the system is observed long enough . This bimodality can be seen in Figure 4A , which shows the two-dimensional histograms of the population-averaged activities of both populations , for different levels of external input . Note that the strength of the method is not in reducing the dimensionality of the system , but in extracting effectively the underlying stochastic dynamics in the form of a diffusion equation . Thus a prerequisite for applying the method is to select a range of parameters where the dynamics of the system can be reduced to one-dimensional dynamics . In this type of system , this is the case in the neighborhood of a bifurcation ( see , e . g . , [10] ) . Given the reduced first principal component of the original data , we apply the procedure detailed in Methods to extract the effective energy function associated with a one-dimensional Langevin equation ( see Equations ( 6 ) and ( 9 ) ) . We show in Figures 4B and 4D the effective energy function for the symmetric and asymmetric case , respectively . The figures also show the stationary distribution of the reduced variable capturing the essential part of the dynamics , as well as the maximum likelihood fit of , Equation ( 10 ) . By using Equation ( 10 ) we can easily extract the effective energy function . We then estimated the noise intensity along the same lines of the previous section . In brief , we generated a large set of sample paths , starting out at one the attractors , and computed the first-passage time through some prescribed boundary . In our case , the boundary was halfway to the main barrier separating the two attractors . Choosing the boundary this way , the first-passage times were considerably shorter than the transition times between attractors , allowing for larger samples and thus better numerical estimation . We could then estimate from Eq . ( 13 ) the noise intensity , using the sample mean of the first-passage times and the effective potential . For the symmetric case ( Figures 4B , C ) , we initialized the system at and set the boundary at . For the asymmetric case ( Figure 4D–F ) , the system was initialized at and the barrier was at . Using the estimates of the noise intensity and the effective energy function , we checked the approximation by comparing the residence times in each of the attractors ( Figures 4C and 4E , F for the symmetric and asymmetric case , respectively ) . The agreement between the distribution of residence times for the one-dimensional Langevin and for the original data is remarkable . Note that with this method we can easily estimate the transition times between attractors , using just the one-dimensional reduced system . This is particularly useful when the transition times are long , of the order of seconds , for which a reliable estimation requires simulations of the high-dimensional system , defined in our case by a system of several thousands of nonlinear differential equations . The reduction can be done without such computational effort , since it requires only a good estimate of the stationary distribution , to extract the underlying energy , as well as an estimate of the escape times , in order to get the estimate of the noise intensity . The effective data-driven reduction allows us to extract explicitly the underlying form of the energy function associated with the bistable behavior , the level of fluctuations , and consequently allow us to calculate the characteristic escape times in a much more efficient way , due to the fact that with the reduced system they can be calculated semi-analytically . We next analyzed experimental data from intracellular recordings in the auditory cortex of anesthetized rats . During anesthesia with ketamine-xylazine , the cerebral cortex exhibits robust slow oscillatory activity , as it has been previously described in the cat [11] , [12] , ferret [13] , and rat [14] , [15] . Up and down states were recorded both by means of local field potential ( not shown ) and intracellularly ( Figure 5 ) during periods of lighter and deeper anesthesia . Anesthesia levels were deeper after the injection of supplemental doses ( see Methods ) . During periods of light anesthesia , without reaching the transition to the awake state ( for a complete transition from sleep to awake , see [16] ) , cortical activity still shows up and down states , but their distribution appears to be more random ( Figure 5A ) . Given the normalized and centered membrane potential of the recorded data , we applied the procedure detailed in Methods to extract the effective energy function associated with a reduced Langevin equation , ( Figure 5B ) . Using Equation ( 10 ) we can easily extract the underlying energy or potential function . In both cases we shifted the variable to be in the positive range , and scaled the energy function by a factor 1/100 to facilitate its visualization . We then estimated the underlying noise by using Equation ( 13 ) and the estimate of the escape time from a meta-stable state . In our case , we took the escape time that the system initialized in the down state ( ) need to cross a barrier at . We found that the time spent in the down-state could be well fitted with our model , whereas the time spent in the up-state was less well described ( Figure 5C and D ) . As a quantitative measure of how well the reduced model can describe the distribution of transition times we used the Kolmogorov-Smirnov test . This is a non-parametric test of the hypothesis that two sets of observations are sampled from the same probability distribution . We applied this test to the dwell times in the down and up-states respectively ( e . g . the data shown in Figure 5C and D ) . We can not reject the hypothesis that the dwell times in the data have the same distribution as those in the reduced model ( ) . However , the distributions of the dwell-times in the upstate are significantly different ( ) . The Kolmogorov-Smirnov test hence reinforce the conclusions drawn by looking at Figure 5C and D . During periods of deep anesthesia , up and down states generated in the cortex were quite regular , both in their amplitude and time intervals between up states ( Figure 5E ) . Next , we will see how well this data can be described by our reduction . In this case , to estimate the noise intensity we considered the mean escape time needed for the system initialized in the down state ( ) to cross a barrier at . In this case , the stationary distribution can be of course fitted , but the distributions of the residence time in the down and up states cannot be captured by our model ( Figures 5G and H ) . An application of the Kolmogorov-Smirnov test in this case confirms that dwell-times in both the up- and down-state were significantly different between the data and the reduced model ( for both ) . In summary , when the same procedure is carried out , we no longer get a good fit of the distribution of dwell times . Note the strong regularity in the data as evidenced by the peak in the probability distribution of the life time of the experimental data . This result is nevertheless relevant because tells us that the data is not purely noise driven . In fact , previous studies have shown that these data evidenced a strong adaptation effect [17]–[20] , which plays a crucial role in the transitions . The method is therefore also useful to reject the hypothesis of a pure noise driven transition .
We have introduced a novel methodology for extracting , in a data-driven fashion , the stochastic dynamics underlying experimental or simulated neuronal data . The main idea of the method is to test the hypothesis that the underlying dynamics is consistent with a Langevin equation , which describes the motion of a Brownian particle in a potential . This is done by extracting an effective potential consistent with the asymptotic stationary distribution of the data and subsequently estimating the intensity of the underlying fluctuations from the average escape time from a specific region . The initial hypothesis can then be tested by checking how well the escape-time distribution of the data can be fitted by the reduced model . If this fit is reasonably good then we can affirm that the observed dynamics are consistent with an underlying stochastic process of the Langevin type . Note that the test could be extended to all possible escape times , i . e . , considering different escape boundaries , allowing for a sharper test of the original hypothesis . If the distribution of escape times is not well fitted by the reduced model , we can reject the hypothesis that the system is described by a one-dimensional Langevin equation . We applied the method to data from models of simulated neuronal activity as well as recordings from real cortical neurons . The method is however applicable to data from any system that obeys the assumptions of the dynamics and the noise . Indeed , our method generalizes a similar approach suggested in the context of laser dynamics [21] . In this work we propose an efficient and semi-analytical way of estimating the noise through the estimation of  transition times . After extracting a parametric form of the underlying energy function , we can express the transition times by a closed form expression ( Equation ( 13 ) ) , which can be used to estimate the noise intensity . Furthermore , we use a more robust maximum-likelihood-based method for the estimation of the underlying effective energy by decomposing the stationary distribution of the main variable as a mixture of Gaussians . The method is directly applicable to data from one-dimensional systems but we also demonstrated how it could be applied to data originating from a higher dimensional system . This implied first reducing the dimensionality by projecting the data onto the first principal component , and then provide an effective model for the reduced one-dimensional data . This approach will work whenever the dynamics of the original system is confined to a one-dimensional manifold which is approximately the case for the spiking network that we studied [10] , [22] . We have applied the method to data from bistable systems but it is equally straightforward to apply the method to multistable systems . As long as the dynamics can be described approximately as a diffusion in an energy landscape our method is applicable . The fact of transitions between up and down states in the cerebral cortex is a neural network phenomenon that has aroused great interest , since the mechanisms involved may be critical for persistent activity , memory or attention . However , the cellular and network mechanisms involved in the initiation , maintenance and termination of up states are still a matter of debate . Different mechanisms of initiation of up states have been proposed , either appealing to stochastic or alternatively deterministic processes . The cortical network in vivo generates slow rhythmic activity in complex interaction with other rhythms in the thalamocortical network rhythmic activity [11] , [12] . A role for thalamic inputs has been proposed [11] , [23] , and both intracortical or thalamocortical synaptic inputs can eventually start up states [15] , [23] , [24] . However , it is known that the thalamus is not required for the rhythm to occur , since it persists after thalamic lesions and it can be recorded in isolated cortical slabs in vivo [25] and in cortical slices in vitro [18] . In the isolated cortex , it has been proposed that up states start by spontaneous spikes that activate the recurrent cortical circuitry , bringing the network to an up state where activity reverberates [19] . This model relies on strong cortical recurrence plus activity-dependent hyperpolarizing currents that terminate the up states and maintain down states . Alternative proposed mechanisms are the initiation of up states by summation of spike-independent stochastic releases of neurotransmitters or noise producing random transition between up and down states [26] . Those mechanisms would determine the initiation of up states given that they overcome the ones believed to start , and to maintain , down states such as potassium currents [17]–[19] , metabolically modulated currents [20] , or cortical disfacilitation [27] . The study presented here suggests that some of those seemingly mutually exclusive mechanisms regulating up and down states could indeed coexist . The analysis of intracellularly recorded up and down transitions by means of a reduced Langevin equation reveals that the stochasticity of up state occurrence varies with the dynamic state of the in vivo network . While in deep anesthesia , the occurrence of up states is not well fitted by the Langevin equation . Given that the Langevin equation describes the stochastic dynamics of a network , the bad fit to the data in deep anesthesia suggests that the process is not stochastic but deterministic and therefore controlled by non-random processes . However , in vivo during lighter anesthesia the time spent in the up and down states was better described by a one-dimensional model . In particular , the time spent in the down state was reasonably described by the model . This could indicate a role for random fluctuations in shaping the transitions from the down to the up-state . It is important to notice however that there are several aspects of the intracellular data that are not well described by the model ( nor intended to be well described ) . There are for example high frequency oscillations in the upstate not captured by the model . Our findings suggest that different network mechanisms inducing up states that have been proposed by different authors and appeared to be incompatible , could indeed be simultaneously participating but in different functional states of the network . Thus , transitional states between sleep and awake ( or light anesthesia ) would be dominated by mechanisms involving stochasticity while deep sleep would be dominated by deterministic mechanisms . Another possible scenario is that in which the same mechanisms of initiation of up states would trigger more or less regular waves . This possibility has been achieved in a computer model by varying the cortical synaptic strength [28] .
Rats were cared for and treated in accordance with the EU guidelines on protection of vertebrates used for experimentation ( Strasbourg 3/18/1986 ) as well as local ethical guidelines and regulations . In this section , we describe how to extract the effective energy function from a data set like , for example , the temporal sequence of firing activity in a network of spiking neurons . We also show how to estimate the intensity of the concomitant noise . By doing this , we will able to write the stochastic neurodynamical equation describing the generation of the data set . We assume that the system is stationary . Recordings from primary auditory cortex A1 were obtained from adult wistar rats ( 230–350 g ) . Anesthesia was induced by intraperitoneal injection of ketamine ( 100 mg/kg ) and xylacine ( 8–10 mg/kg ) . The animals were not paralyzed . Supplemental doses by intramuscular injection of ketamine were 75 mg/ ( kg h ) and were given with intervals of 30–60 min . The depth of anesthesia was monitored by the recording of low-frequency electroencephalogram ( EEG ) and the absence of reflexes . The anesthesia level was deeper after a new dose and would progressive lightened during the interval ( see Results ) . Rectal temperature was maintained at 37 , heart rate ( 250–300 bpm ) and blood concentration ( 95% ) . Once in the stereotaxic apparatus , a craniotomy ( mm ) was made at coordinates AP −3 . 5 to 5 . 5 mm from bregma , L 7 mm . After opening the dura , intracellular recordings were obtained with borosilicate glass capillaries 1 mm O . D . 0 . 5 I . D . ( Harvard Apparatus ) filled with potassium acetate ( resistances 50–80 ) . For stability , and to avoid desiccation , agar ( 4% ) was used to cover the area . Data was acquired with a CED commercial acquisition board ( Cambridge Electronic Design , UK ) and its commercial software Spike 2 . Further details of the procedure can be found in [15] .
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We introduce a novel methodology that allows for an effective description of a neurodynamical system in a data-driven fashion . In particular , no knowledge of the dynamics operating at the neuronal or synaptic level is required . The idea is to fit the underlying dynamics of the data using a stochastic differential equation . We use a Langevin equation that describes the stochastic dynamics of the system with the assumption that there exists an underlying potential , or energy function . The advantage of this description is the fact that , for one-dimensional systems , the stationary distribution of the variable can be straightforwardly related to the underlying energy function . In cases where the dataset is high-dimensional we reduce the dimensionality with techniques like principal curves or principal components analysis . The methodology we propose is particularly relevant for cases where an ab initio approach cannot be applied like , for example , when an explicit description of the dynamics at the neuronal and synaptic levels is not available .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"neuroscience/theoretical",
"neuroscience"
] |
2009
|
Effective Reduced Diffusion-Models: A Data Driven Approach to the Analysis of Neuronal Dynamics
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Trypanosoma ( T . ) evansi is a dyskinetoplastic variant of T . brucei that has gained the ability to be transmitted by all sorts of biting flies . T . evansi can be divided into type A , which is the most abundant and found in Africa , Asia and Latin America and type B , which has so far been isolated only from Kenyan dromedary camels . This study aimed at the isolation and the genetic and phenotypic characterisation of type A and B T . evansi stocks from camels in Northern Ethiopia . T . evansi was isolated in mice by inoculation with the cryopreserved buffy coat of parasitologically confirmed animals . Fourteen stocks were thus isolated and subject to genotyping with PCRs targeting type-specific variant surface glycoprotein genes , mitochondrial minicircles and maxicircles , minisatellite markers and the F1-ATP synthase γ subunit gene . Nine stocks corresponded to type A , two stocks were type B and three stocks represented mixed infections between A and B , but not hybrids . One T . evansi type A stock was completely akinetoplastic . Five stocks were adapted to in vitro culture and subjected to a drug sensitivity assay with melarsomine dihydrochloride , diminazene diaceturate , isometamidium chloride and suramin . In vitro adaptation induced some loss of kinetoplasts within 60 days . No correlation between drug sensitivity and absence of the kinetoplast was observed . Sequencing the full coding sequence of the F1-ATP synthase γ subunit revealed new type-specific single nucleotide polymorphisms and deletions . This study addresses some limitations of current molecular markers for T . evansi genotyping . Polymorphism within the F1-ATP synthase γ subunit gene may provide new markers to identify the T . evansi type that do not rely on variant surface glycoprotein genes or kinetoplast DNA .
Surra , a wasting disease caused by Trypanosoma ( T . ) evansi , is one of the non tsetse-transmitted Animal African Trypanosomoses ( AAT ) occurring in Ethiopia . The disease imposes significant financial losses due to reduced fertility and mortality and is prohibiting the import of highly productive yet trypanosusceptible cattle breeds [1–3] . T . evansi belongs to the subgenus Trypanozoon , that also comprises T . brucei and T . equiperdum [4–6] . In terms of geographical distribution , Trypanosoma equiperdum and T . evansi , causing respectively dourine in horses and surra in livestock in Africa , Asia , and South America , have been far more successful than T . brucei , a parasite confined to sub-Saharan Africa where its vector , the tsetse fly , is present [7] . Recent phylogenetic studies suggest that T . evansi and T . equiperdum evolved from T . brucei on several occasions and from genetically distinct T . brucei strains and therefore could be considered as subspecies of T . brucei [8 , 9] . Trypanosomes are characterised by the presence of a structure called kinetoplast that corresponds with the DNA ( kDNA ) of their unique mitochondrion . T . brucei kDNA contains 20–50 copies of maxicircles ( about 23 kb ) and a highly diverse set of thousands of minicircles ( about 1 kb ) . Maxicircles contain rRNA coding regions and genes coding for subunits of the respiratory chain complexes while minicircles code for guide RNAs required for editing [10] . T . equiperdum and T . evansi are dyskinetoplastic ( kDNA- ) since they lack part of the kDNA [8–11] . T . equiperdum typically has retained maxicircles , in some cases with substantial deletions , but has lost its minicircle diversity . T . evansi does not have maxicircles and either shows minicircle homogeneity or are akinetoplastic ( kDNA ) [10 , 12–14] . Based on their minicircle restriction digestion profile , T . evansi can be divided into type A and type B [15 , 16] . T . evansi type A is the most abundant and is found in Africa , South America and Asia . It is characterised by the presence of the gene for the variant surface glycoprotein ( VSG ) RoTat 1 . 2 . This RoTat 1 . 2 VSG is expressed early during infections resulting in the detectability of anti-RoTat 1 . 2 antibodies in animals infected with T . evansi type A [17 , 18] . In contrast , T . evansi type B is far less common and has so far been isolated only from camels in Kenya [16 , 19] . More recently , serological and molecular evidence for the presence of T . evansi type B in Sudan , Ethiopia and Chad was published [20–24] . T . evansi type B lacks the RoTat 1 . 2 gene and as a consequence , infections with this type are not detected with serological and molecular tests based on RoTat 1 . 2 VSG , such as the CATT/T . evansi and RoTat 1 . 2 PCR [15 , 18 , 19 , 25] . So far , three molecular tests have been developed for the identification of T . evansi type B: the EVAB PCR , targeting a type B-specific minicircle DNA sequence , and a PCR and a LAMP targeting a type B-specific VSG JN 2118Hu [15 , 19 , 26] . T . equiperdum is the least known parasite of the Trypanozoon group , with very few isolates available for research , albeit new stocks were isolated from Ethiopian and Venezuelan horses recently [24 , 27] . Unlike T . brucei , T . evansi and T . equiperdum cannot develop in tsetse flies due to their inability to transform into the procyclic life stage . They can only survive in a mammalian host where they produce ATP exclusively through glycolysis . In contrast to bloodstream forms , ATP production in procyclic trypanosomes relies on oxidative phosphorylation and , therefore , on the capacity to express the full set of corresponding mitochondrial genes , including some which are encoded by the kDNA [10 , 28] . Bloodstream forms of T . evansi , T . equiperdum and laboratory-generated T . brucei strains that have lost all or critical parts of their kDNA , can survive without kDNA due to specific single amino acid mutations in the gamma ( γ ) subunit of the mitochondrial F1-ATP synthase [28] . Interestingly , the specific mutations/deletions in the C-terminal region of F1-ATP synthase γ subunit enable differentiation among the Trypanozoon strains [8] . Furthermore , when the F1-ATP synthase γ subunits of T . evansi type A ( A281del ) , T . equiperdum ( A273P ) and the laboratory-generated T . brucei ( L262P ) strains are overexpressed in a T . brucei γ subunit knock out strain , the latter can survive after loss of its kinetoplast after treatment with DNA intercalating drugs such as acriflavin or ethidium bromide [28 , 29] . Once the genetically modified T . brucei are independent from kDNA maintenance and expression , they become multidrug resistant to the diamidine and phenanthridine class of drugs [30] . In T . evansi , drug resistance has been reported in several type A strains originating from Africa , Asia and Latin America [31–34] . Some Chinese strains appear to be innately resistant to the phenanthridine class of drugs [35] . In contrast , nothing is known on the drug susceptibly of the T . evansi type B strains . In a previous study , we reported that T . evansi infections are very common in camels , equines , cattle and small ruminants in Tigray and Afar provinces in Northern Ethiopia [20] . We also provided molecular and serological evidence that both T . evansi type A and type B occur in these provinces . In that study , of those dromedary camels that were parasitologically positive , buffy coat samples were collected and cryopreserved in liquid nitrogen for later isolation of the parasite . We here report on the isolation , adaptation to in vitro culture , genetic and phenotypic characterisation and in vitro drug sensitivity of T . evansi type A and B from Northern Ethiopia .
The Animal Experimentation Ethics Committee ( AEEC ) of the Institute of Tropical Medicine ( ITM ) advised on the protocol for collection of blood samples from dromedary camels ( EXT2012-1 ) and for the isolation of trypanosomes via inoculation of mice ( EXT2012-2 ) at the College of Veterinary Medicine , Mekelle University . The study protocol for in vivo expansion of trypanosomes at ITM was approved by the AEEC ( BM2013-1 ) . Collecting blood from camels and experiments on mice were conducted according to the national guidelines of the Ethiopian Ministry of Livestock and Fishery Development and the Institutional Review Board of the Ministry of Science and Technology . Details on the collection and cryopreservation of buffy coat samples from dromedary camels that were parasitologically confirmed in the micro haematocrit centrifugation technique have been fully described elsewhere [20] . Two hundred μl of thawed buffy coat were inoculated intraperitoneally ( IP ) in two 25–30 g Swiss albino mice that were immunosuppressed with 0 . 16 μg kg-1 body weight dexamethasone ( Shanghai Central Pharmaceutical , China ) one day prior to inoculation [36] . Parasitaemia was checked in 5 μl of tail blood using the matching method [37] , starting from day 7 post-infection and subsequently on every third day . As soon as trypanosomes were detected in at least one mouse , the animal was anaesthetised ( the other kept as a backup ) , its blood was collected on heparin by heart puncture , diluted in an equal volume of phosphate buffered saline glucose ( PSG; 7 . 5 g/l Na2HPO42H2O , 0 . 34 g/l NaH2 PO4H2O , 2 . 12 g/l NaCl , 10 g/l D-glucose , pH 8 ) and subinoculated into four naïve mice ( 200 μl each ) which were monitored for parasitaemia as described above . Mice used as backup were euthanised when the newly infected mice became positive . When parasitemia reached about ± 107 . 8 cells ml−1 of blood , two of these parasitaemic mice were euthanised ( the other two were kept as back up ) and blood was taken for subinoculation into four other naïve mice . This protocol was repeated until the parasitaemia reached about 108 . 4 cells ml−1 . At this stage the stock was considered in vivo adapted . All four mice were anaesthetised and exsanguinated by heart puncture in an equal volume of Triladyl-egg yolk-phosphate buffered saline glucose ( TEP ) cryomedium [38] for cryopreservation in 1 ml aliquots . Cryostabilates were thawed in a water bath at 37°C and diluted in PSG to 1 trypanosome per field ( ± 105 . 7 cells ml−1 ) . Two-hundred μl volumes were injected IP in two naïve 20–30 g female OF-1 mice ( Charles River , Belgium ) . Starting from three days post infection ( DPI ) , parasitaemia was monitored daily and harvested at first peak parasitemia , typically at day 4 to 5 post-infection , as described above . Volumes of 0 . 5 ml of the blood were run over a mini Anion Exchange Centrifugation Technique ( mAECT ) column to separate the trypanosomes from the blood [39] . The trypanosomes eluted from the column were washed twice with 5 ml ice-cold PSG by centrifugation at 1500 g for 15 min . After the last centrifugation , the supernatant PSG was discarded and the trypanosome sediment was re-suspended in 100 μl of PSG . Part of this suspension was used for in vitro culture adaptation . The remainder was centrifuged at 1500 g for 5 min and the sediment was frozen at -80°C until DNA extraction . The isolates used for in vivo isolation and expansion and the corresponding T . evansi type A and B specific PCR result on their corresponding buffy coat DNA are indicated in Table 1 . The highly concentrated trypanosome suspension in PSG was diluted to 2 x 105 cells ml−1 in Hirumi’s modified Iscove’s medium 9 ( HMI-9 ) , complemented with 15% ( v/v ) heat-inactivated foetal bovine serum ( Gibco , Belgium ) and 5% ( v/v ) heat-inactivated horse serum ( Gibco , Belgium ) ( abbreviated as HMI-9 ( HS ) ) [40 , 41] . Parasites were seeded at 2 x 104 , 2 x 103 and 2 x 102 cells ml−1 , in a total volume of 500 μl in a 48-well plate ( Nunc , Denmark ) and incubated at 37°C and 5% CO2 . After 72 hours , a well , where trypanosome density had increased above 2 x 105 cells ml−1 , was used for further subpassage in 500 μl of HMI-9 ( HS ) . The well with the highest density of viable parasites was then further maintained in HMI-9 without horse serum [40] . When possible , log phase growing in vitro cultures were scaled up in flasks ( Nunc , Denmark ) to obtain larger numbers of parasites for cryostabilisation , DNA extraction and in vitro drug sensitivity testing [42] . The in vitro growth curves of the different stocks were generated by seeding cells at 1 x 104 cells ml−1 in 500 μl of HMI-9 in three replicate wells that were counted every 24 h . The doubling times ( Td ) were calculated from the exponential part of the curve using non-linear regression fitted with an exponential equation in GraphPad Prism 6 ( GraphPad , version 6 , USA ) . DNA extraction of trypanosome sediments prepared from the in vivo expanded and the in vitro adapted populations was performed with DNA Isolation Kit ( Roche Diagnostics , Germany ) following the protocol recommended for isolation of DNA from mammalian tissue . From T . b . brucei AnTat 1 . 1E , T . b . gambiense LiTat 1 . 3 , T . b . gambiense type II ABBA and T . equiperdum Dodola 940 , DNA was extracted using the Maxwell 16 Tissue DNA Purification kit on a Maxwell 16 instrument according to the manufacturer's instructions ( Promega , Belgium ) . DNA concentrations were measured using the Nanodrop ND-1000 UV-Vis spectrophotometer ( NanoDrop Technologies , USA ) and adjusted to 10 ng μl-1 . A set of PCRs targeting VSG genes ( RoTat 1 . 2 and JN 2118Hu ) , maxicircle genes ( ND4 , ND5 , ND7 and A6 ) , class A minicircles ( miniA PCR ) and class B minicircles ( EVAB PCR ) minisatellites ( MORF-2REP ) , P2 adenosine transporter ( AT1 ) and the F1-ATP synthase γ subunit were adopted to characterise the studied parasite populations [4 , 15 , 19 , 28 , 43–45] . Where applicable , the published PCR protocols were adjusted to the requirements of the HotStarTaq Plus DNA polymerase ( Qiagen , Germany ) . Primer sequences , reaction mixture contents , cycling conditions and expected amplicon size are described and referenced in Table 2 . All PCR amplifications were carried out in 200 μl thin-wall PCR tubes ( ABgene , UK ) in a T3 thermocycler 48 ( Biometra , Germany ) . Ten μl of amplified products were electrophoresed in 1 to 2% agarose gel at 135 V for 30 min and afterwards stained with ethidium bromide for visualization under UV light . For direct sequencing , PCR was performed in 50–100 μl volumes and amplicons were cleaned up and concentrated using a PCR cleanup kit ( QIAquick PCR Purification Kit , Qiagen , Germany ) and sent out for bidirectional direct sequencing at the Genetic Sequencing Facility ( VIB , Belgium ) using the described PCR primers . The full length sequence of the F1-ATP synthase γ subunit was cloned into a BamHI and HindIII double digested pHD309 vector using the In-Fusion Cloning kit ( Clontech , Japan ) . Primers contained a F1-ATP synthase γ subunit specific sequence based on the T . evansi sequence of STIB 810 ( EU185797 ) and a 5′ extension of 15 bp specific to the place of integration in pHD309 , containing the restriction sites and sequence overlap with the vector , as required for the In-Fusion Cloning reaction . Proofreading-PCR was performed using the Clone-Amp HiFi PCR premix ( Clontech , Japan ) . Amplicons were cleaned up ( QIAquick PCR Purification Kit , Qiagen , Germany ) before use in the In-Fusion protocol . The reaction products were transformed in Stellar competent cells according to the manufacturer's recommendations ( Clontech , Japan ) . Transformant clones were checked for the presence of insert using colony PCR , cultured in LB medium , plasmid purified ( QIAprep Spin Miniprep Kit , Qiagen , Germany ) and at least 7 to 12 clones per transformation were bidirectionally sequenced at the Genetic Sequencing Facility ( VIB , Belgium ) using primers binding to pHD309 . Melarsomine dihydrochloride ( Cymelarsan , Sanofi Aventis , France ) and isometamidium hydrochloride ( Veridium , Ceva Santé Animale , Belgium ) were prepared as 10 mg ml−1 stock solutions in distilled water . Dophanil powder ( Dophanil , Docpharma , Belgium ) , containing 445 mg diminazene diaceturate and 555 mg antipyrine per gram , was concentrated to a 10 mg ml−1 diminazene diaceturate solution in DMSO ( Sigma , Belgium ) . Suramin ( Germanin , Bayer , Germany ) was prepared as a 100 mg ml−1 in DMSO . A method to measure the IC50 values of compounds in 96-well plates was performed as described elsewhere [46] . Briefly , 2 × 104 cells ml−1 from in vitro adapted stocks were exposed to seven threefold drug dilutions , ranging from 5000 to 7 ng ml−1 for suramin , 500 to 0 . 7 ng ml−1 for diminazene diaceturate and from 250 to 0 . 35 ng ml−1 for melarsomine dihydrochloride and isometamidium hydrochloride , in a total volume of 200 μl of HMI-9 medium . Next , the plate was incubated for 72 hours at 37°C with 5% CO2 followed by addition of 20 μl of resazurin ( Sigma , Belgium; 12 . 5 mg in 100 ml PBS ) for measuring trypanosomes viability . After a further 24 h incubation at 37°C and 5% CO2 , fluorescence was measured ( excitation λ = 560 nm; emission λ = 590 nm ) with a VictorX3 multimodal plate reader using top reading ( Perkin Elmer , Belgium ) [42] . The results were expressed as the percent reduction in parasite viability compared to the parasite viability in control wells without drugs . The 50% inhibitory concentration ( IC50 ) was calculated using non-linear regression fitted with a ( log ) inhibitor versus normalised response ( variable slope ) equation ( GraphPad , version 6 , USA ) . The IC50 values obtained from day 30 and day 60 in vitro cultures were compared using t-tests corrected for multiple testing according to the Holm-Sidak method ( α = 0 . 05 ) ( GraphPad , version 6 , USA ) . Trypanosome populations at different stages of in vivo and in vitro expansion were examined for the presence of the kinetoplast using 4' , 6-diamidino-2-phenylindole ( DAPI ) staining . Briefly , live trypanosomes in PSG or in vitro culture medium were washed in PBS by centrifugation , deposited onto microscope slides , air dried and fixed with methanol for 30 min . Subsequently , the slides were rehydrated in PBS and mounted in 87% glycerol containing 1 μg ml-1 DAPI ( Sigma , Belgium ) [28] . Images were captured with an epifluorescence microscope ( Olympus BX41 , Olympus , Japan ) equipped with a NU fluorescent cube ( excitation: 360–370 nm and emission > 420 nm ) ) and Cell˄D software ( Olympus , Japan ) . DAPI stained trypanosomes were grouped according to the number of kinetoplasts ( K ) and nuclei ( N ) present within each cell . The percentage of kinetoplastic cells in a DAPI stained slide was calculated on the basis of on average 300 examined trypanosomes , by dividing the sum of 1K1N + 2K1N + 2K2N cells by the sum of 1K1N + 2K1N + 2K2N + 0K1N + 0K2N cells . A two-tailed Spearman correlation matrix ( using a confidence interval of 95% ) was used to find the correlation between the percentage of kinetoplastic cells at day 30 and day 60 of in vitro culture and the respective IC50 value for a particular drug ( GraphPad , version 6 , USA ) . To check the in vivo infectivity of trypanosome populations that were cryostabilised after continuous propagation in vitro for 60 days , 5 x 106 cells in 300 μl were inoculated in a single OF-1 mouse where after parasitaemia was checked as described above .
Thirty cryopreserved buffy coat specimens from parasitologically positive dromedary camels were inoculated in immunosuppressed Swiss albino mice . In total , 22 parasite stocks originating from 22 different animals could be isolated and cryopreserved after 2 to 5 subpassages in mice . They were labelled as MCAM/ET/2013/MU/01 to MCAM/ET/2013/MU/22 . Based on positivity in RoTat 1 . 2 PCR and EVAB PCR of the corresponding cryopreserved buffy coats , 20 of these stocks are T . evansi type A and 2 are T . evansi type B ( Table 1 ) [20] . Copy cryovials of these primary isolates were brought to ITM , Antwerp and 14 were selected for further expansion in mice . The selection was based on their geographical origin and subtype: 12 type A stocks originated from different sampling stations in Afar and Tigray ( MCAM/ET/2013/MU/01 , 02 , 04 , 05 , 06 , 07 , 08 , 09 , 11 , 13 , 15 , 17 ) and two type B stocks ( MCAM/ET/2013/MU/10 and 14 ) were from Awash Fentale in Afar . At peak parasitaemia , between 4 to 7 DPI , parasites were harvested , purified from blood using a mAECT column , washed with PSG and pelleted for DNA extraction and for in vitro culture adaptation . DNA extracts of in vivo expanded stocks were subjected to RoTat 1 . 2 PCR and JN 2118Hu PCR to identify the T . evansi type based on type-specific VSG sequences . In addition , the specificity of these PCRs was tested on DNA of other Trypanozoon strains ( T . b . brucei AnTat 1 . 1E , T . b . gambiense LiTat 1 . 3 , T . b . gambiense type II ABBA , T . evansi type A RoTat 1 . 2 , T . evansi type B KETRI 2479 and T . equiperdum Dodola 940 ) . Results are represented in Table 3 . All the in vivo expanded stocks that originated from RoTat 1 . 2 PCR positive buffy coats , were also positive in RoTat 1 . 2 PCR ( MCAM/ET/2013/MU/01 , 02 , 04 , 05 , 06 , 07 , 08 , 09 , 11 , 13 , 15 and 17 ) . Direct sequencing of the 488 bp amplicons from these putative T . evansi type A stocks and the T . evansi RoTat 1 . 2 strain revealed 100% identity ( in a 350 bp sequenced fragment ) with the published RoTat 1 . 2 VSG sequence ( AF317914 ) , thus identifying them as T . evansi type A . Only one synonymous polymorphism ( C699A ) was found in MCAM/2013/ET/MU/04 . The gel with the RoTat 1 . 2 PCR products from the purified trypanosomes showed a faint band of about 400 bp amplified in T . evansi KETRI 2479 and in MCAM/ET/2013/MU/10 and 14 . Direct sequencing of these 400 bp amplicons failed . The PCR targeting the T . evansi type B specific VSG JN 2118Hu generated the expected amplicon in T . evansi type B KETRI 2479 and in MCAM/ET/2013/MU/10 and 14 . Additionally , an amplicon was generated from MCAM/ET/2013/MU/15 . Also for T . b . brucei AnTat 1 . 1E and T . b . gambiense type II ABBA , amplicons of 273 bp were produced in the JN 2118Hu PCR . Direct sequencing of these amplicons revealed that the Ethiopian T . evansi type B MCAM/ET/2013/MU/10 and 14 , T . evansi type B KETRI 2479 and T . b . brucei AnTat 1 . 1E were 100% identical ( in a 190 bp sequenced fragment ) to the corresponding sequence of JN 2118Hu VSG ( AJ870486 ) . In T . b . gambiense type II ABBA , one synonymous mutation ( G300A ) was found . Four PCRs that target maxicircle DNAs , of which three NADH-dehydrogenase subunits ( ND4 , ND5 , ND7 ) and the ATPase subunit 6 ( A6 ) , and two PCRs that target class-specific minicircle sequences ( miniA PCR and EVAB PCR ) were run on DNA extracts of the purified trypanosomes ( Table 3 ) . All Ethiopian T . evansi stocks and T . evansi type A RoTat 1 . 2 and T . evansi type B KETRI 2479 were negative for all four maxicircle genes , while T . b . brucei AnTat 1 . 1E , T . b . gambiense LiTat 1 . 3 , T . b . gambiense type II ABBA and T . equiperdum Dodola 940 were positive for all four maxicircle genes . All stocks that contain RoTat 1 . 2 VSG , except MCAM/ET/2013/MU/09 , were positive in miniA PCR . Additionally , weak amplification was seen in T . b . brucei AnTat 1 . 1E . MCAM/ET/2013/MU/10 and 14 were positive in EVAB PCR , confirming their identification as T . evansi type B as observed on their corresponding buffy coat specimens ( Table 1 ) . Additionally , EVAB PCR amplicons were detected in 3 stocks that were also positive for RoTat 1 . 2 VSG PCR suggesting a mixed infection with type A and B: a strong amplification was present in MCAM/ET/2013/MU/15 , while a weak amplification was visible in MCAM/ET/2013/MU/11 and 17 . The presence of kinetoplasts in the trypanosome cells was demonstrated using fluorescence microscopy with DAPI staining on ex vivo isolated trypanosomes ( Table 3 ) . T . evansi RoTat 1 . 2 , T . evansi KETRI 2479 and all but one Ethiopian T . evansi stocks show a kinetoplast in > 96% of the cells . Stock MCAM/ET/2013/MU/09 was found to be akinetoplastic since only the nucleus of the trypanosomes was visible with DAPI . In T . evansi RoTat 1 . 2 , the MORF2-REP locus consists of 4 and 6 repeats , while in T . evansi KETRI 2479 , 3 and 5 repeats were found ( Table 3 ) . In vivo expanded Ethiopian stocks of type A had either 1 allele ( 7 repeats ) or 2 alleles ( 6 and 7 repeats ) , thus displaying a different pattern than T . evansi type A RoTat 1 . 2 . The Ethiopian type B stocks MCAM/ET/2013/MU/10 and 14 contain 3 and 4 repeats , and thus have a pattern different from T . evansi type B KETRI 2479 . MCAM/ET/2013/MU/15 showed a clear pattern of the Ethiopian type B ( 3 and 4 repeats ) , and double allele pattern of the Ethiopian type A ( 6 and 7 repeats ) . The other presumed mixed type A and type B stocks MCAM/ET/2013/MU/11 and 17 showed only the Ethiopian type A T . evansi pattern ( Fig 1 ) . DNA extracted from the buffy coats revealed similar MORF2-REP patterns as the in vivo expanded trypanosomes except for the buffy coat of MCAM/ET/2013/MU/15 that revealed only the Ethiopian type A MORF2-REP pattern . The other Trypanozoon strains showed the following patterns: T . b . gambiense LiTat 1 . 3 had 7 and 11 repeats , T . b . gambiense type II ABBA had 3 repeats , T . equiperdum Dodola 940 had 11 repeats , while no amplicons were generated from T . b . brucei AnTat 1 . 1E under the giving PCR conditions . Sequence analysis of in total 136 clones of the full length F1-ATP synthase γ subunit , amplified from DNA of the in vivo expanded Ethiopian stocks MCAM/ET/2013/MU/04 , 06 , 09 , 10 , 11 , 13 , 14 , 15 and of T . b . brucei AnTat 1 . 1E , T . b . gambiense LiTat 1 . 3 , T . evansi RoTat 1 . 2 , T . evansi KETRI 2479 , T . b . gambiense type II ABBA and T . equiperdum Dodola 940 revealed diverse homozygous and heterozygous nucleotide polymorphisms spread over the entire coding sequence ( Table 4 ) . The F1-ATP synthase γ subunit of T . b . gambiense LiTat 1 . 3 ( KT934830 ) appeared homozygous and identical to the T . b . gambiense DAL972 sequence ( Tbg972 . 10 . 90 ) . T . b . gambiense type II ABBA ( KT934831 ) appeared homozygous and differed in only 2 SNPs ( G801T and A882G ) from the T . b . gambiense sequence . T . evansi RoTat 1 . 2 and the Ethiopian stocks MCAM/ET/2013/MU/04 , 06 , 09 , 11 and 13 were heterozygous and revealed in one allele ( KT934833 ) , identical to the published full length T . evansi STIB 810 ( EU185798 ) sequence , the deletion of nucleotides A841-843del . The second allele contained a C142T polymorphism ( KT934832 ) , that is not present in the wild-type T . evansi STIB 810 sequence ( EU185797 ) , but that could be identified in the genome sequence of the Chinese akinetoplastic T . evansi STIB 805 strain [9] . For T . evansi KETRI 2479 and the Ethiopian stocks MCAM/ET/2013/MU/10 and 14 we obtained heterozygous alleles , different from the partial sequence of T . evansi KETRI 2479 ( EU185794 ) . The first allele had the unique A844T polymorphism ( KT934835 ) , and differed from the second allele in 3 additional SNPs ( T321C , T807C , T867G ) that were also found in some T . b . brucei and T . equiperdum . Interestingly , the in vivo expanded stock of MCAM/ET/2013/MU/15 revealed alleles that belonged to T . evansi type A and type B . In contrast , when the original buffy coat of this stock was tested , only alleles of T . evansi type A were found . Finally , T . equiperdum Dodola 940 ( KT934836 ) appeared homozygous and its single allele was identical to one of the two alleles found in T . b . brucei AnTat 1 . 1E ( KT934837 ) , but differed in 5 SNPs with the sequence from T . equiperdum BoTat 1 . 1 ( EU185793 ) and in 6 SNPs with T . equiperdum STIB 841 ( EU185792 ) . However , for the T . equiperdum STIB 841 strain , 5 of the 6 SNPs were ambiguous polymorphisms that do not rule out similarity to T . equiperdum Dodola 940 . Fourteen Ethiopian T . evansi stocks , T . evansi RoTat 1 . 2 and T . evansi KETRI 2479 were expanded in mice and purified from blood at peak parasitaemia to initiate primary in vitro cultures in HMI-9 ( HS ) medium . After 96 hours , the initial 2x104 cells ml−1 inoculum reached concentrations above 2x105 cells ml−1 for all the different stocks . These cells were used for further in vitro propagation by subpassage in fresh medium . Over the next 72 hours , only MCAM/ET/2013/MU/09 , 14 and 15 , and T . evansi RoTat 1 . 2 and T . evansi KETRI 2479 showed proliferation . In contrast , slightly increased cell densities were observed for MCAM/ET/2013/MU/01 , 04 , 06 and 10 . For all other strains not a single inoculum proliferated and longer incubation led to growth cessation . Because the HMI-9 ( HS ) medium did not support sufficient in vitro culture growth for most of the Ethiopian T . evansi stocks , it was abandoned and replaced with HMI-9 without horse serum . In vitro adapted strains of T . b . brucei AnTat 1 . 1E and T . b . gambiense LiTat 1 . 3 were cultured in HMI-9 in parallel . In vitro cultures were only considered adapted to HMI-9 medium when it was possible to maintain the parasites in continuous proliferation . To this extent , dense parasite cultures , containing 2–5 x 105 cells ml−1 , were subpassaged into new wells using serial fivefold dilutions in fresh medium . When these subpassages reached densities above 2 x 105 cells ml−1 within a 48–96 hours period , the stock was considered adapted . The five stocks that already grew well in the HMI-9 ( HS ) medium continued proliferating when inoculated from the dense cultures at serial fivefold dilutions in HMI-9 . These five stocks were considered to be in vitro adapted after 15 days of in vitro culture . Out of the four remaining stocks , only MCAM/ET/2013/MU/04 and 10 slowly regained the ability to proliferate in HMI-9 at a reduced subpassaging scheme using serial twofold dilutions . MCAM/ET/2013/MU/04 required 25 days to adapt , while MCAM/ET/2013/MU/10 was only fully adapted after day 35 of in vitro culture . Gradually increasing the culture volume allowed to obtain sufficient parasites from the adapted cultures for in vitro drug testing , DNA extraction , and cryostabilisation at day 30 ( all , except MCAM/ET/2013/MU/10 ) and at day 60 of in vitro culture ( all stocks ) . DNA of the in vitro adapted stocks was subjected to RoTat 1 . 2 PCR , EVAB PCR and MORF2-REP PCR . All in vitro stocks had similar molecular profiles as their corresponding in vivo expanded parental stocks , except MCAM/ET/2013/MU/15 . While the in vivo expanded stock of the latter was identified as a mixed infection of T . evansi type A and type B , the in vitro adapted stock ( at day 30 and day 60 in vitro culture ) was identified as pure T . evansi type B with the above mentioned PCRs and confirmed by cloning and sequencing of the F1-ATP synthase γ subunit . Thus , beside T . evansi RoTat 1 . 2 and T . evansi KETRI 2479 , we achieved the in vitro adaptation of 2 Ethiopian type A stocks , 2 Ethiopian type B stocks and additionally ended up with a pure T . evansi type B in vitro adapted stock originating from a mixed type A and type B in vivo adapted stock . Growth curves were generated for T . b . brucei AnTat 1 . 1E and all seven in vitro adapted stocks ( Fig 2 ) . T . b . brucei AnTat 1 . 1E and T . evansi RoTat 1 . 2 had the shortest Td , 7 . 5 ± 0 . 3 h-1 and 7 . 7 ± 0 . 2 h-1 respectively , and reached the highest maximum population density ( MPD ) of ± 3–4 x 106 cells ml-1 , while T . evansi KETRI 2479 had a longer Td , 10 . 8 ± 0 . 2 h-1 , and a lower MPD of ± 1 x 106 cells ml-1 . The Ethiopian type A stocks MCAM/ET/2013/MU04 and MU09 had a Td of 11 . 2 ± 0 . 4 and 11 . 3 ± 0 . 4 respectively , and a MPD of ± 1 x 106 cells ml-1 . Similarly , the Ethiopian type B stocks MCAM/ET/2013/MU10 , 14 and 15 had a Td of 12 . 9 ± 0 . 5 , 11 . 3 0 . 5 and 12 . 1 ± 0 . 6 respectively , and a MPD of ± 0 . 7–1 x 106 cells ml-1 ( Fig 2 ) . After day 30 and 60 of in vitro culture , IC50 values were determined for melarsomine dihydrochloride ( Cymelarsan ) ( Fig 3A ) , isometamidium hydrochloride ( Veridium ) ( Fig 3B ) , diminazene diaceturate ( Dophanil ) ( Fig 3C ) and suramin ( Germanin ) ( Fig 3D ) . In general , non-significant differences ( p > 0 . 05 ) were found between IC50 values recorded at day 30 and day 60 of in vitro culture , except for the melarsomine dihydrochloride IC50 values of T . evansi RoTat 1 . 2 and T . evansi MCAM/ET/2013/MU/14 and for the isometamidium hydrochloride IC50 values of T . evansi KETRI 2479 and T . evansi MCAM/ET/2013/MU/09 ( p < 0 . 05 ) . For comparison between the different stocks , the IC50 values of day 30 and day 60 of in vitro cultures were averaged . All Ethiopian T . evansi stocks had IC50 values for melarsomine dihydrochloride ( IC50 1 . 9–3 . 3 ng ml-1 ) that were similar to those of T . b . gambiense LiTat 1 . 3 ( IC50 4 . 3 ng ml-1 ) , T . b . brucei AnTat 1 . 1E ( IC50 6 . 8 ng ml-1 ) , T . evansi RoTat 1 . 2 ( IC50 3 . 0 ng ml-1 ) and T . evansi KETRI 2479 ( IC50 4 . 1 ng ml-1 ) . For isometamidium hydrochloride , the IC50 values of the Ethiopian T . evansi ( IC50 0 . 6–6 . 2 ng ml-1 ) fall within the range of T . b . gambiense LiTat 1 . 3 ( IC50 0 . 1 ng ml-1 ) , T . b . brucei AnTat 1 . 1E ( IC50 7 . 3 ng ml-1 ) , T . evansi RoTat 1 . 2 ( IC50 7 . 1 ng ml-1 ) and T . evansi KETRI 2479 ( IC50 5 . 5 ng ml-1 ) . However , the two Ethiopian T . evansi type A stocks ( IC50 4 . 3–6 . 2 ng ml-1 ) appear to be threefold less sensitive that the three type B stocks ( IC50 0 . 6–1 . 9 ng ml-1 ) . For suramin , large differences in IC50 values were found among the Ethiopian T . evansi ( IC50 15 . 9–261 . 5 ng ml-1 ) stocks and among the other strains: T . b . brucei AnTat 1 . 1E ( IC50 39 . 5 ng ml-1 ) and T . evansi RoTat 1 . 2 ( IC50 35 . 8 ng ml-1 ) appear highly susceptible , while T . b . gambiense LiTat 1 . 3 ( IC50 134 . 0 ng ml-1 ) and T . evansi KETRI 2479 ( IC50 222 . 4 ng ml-1 ) are less susceptible . The two Ethiopian T . evansi type A ( IC50 153 . 5–261 . 5 ng ml-1 ) appear to be tenfold less sensitive than the three type B ( IC50 15 . 9–27 . 6 ng ml-1 ) . For diminazene diaceturate , the IC50 values of all Ethiopian T . evansi ( IC50 17 . 5–48 . 5 ng ml-1 ) are higher than those of T . b . gambiense LiTat 1 . 3 ( IC50 5 . 2 ng ml-1 ) and T . evansi RoTat 1 . 2 ( IC50 13 . 8 ng ml-1 ) , but similar to T . b . brucei AnTat 1 . 1E ( IC50 39 . 6 ng ml-1 ) and T . evansi KETRI 2479 ( IC50 24 . 0 ng ml-1 ) . The two Ethiopian T . evansi type A ( IC50 37 . 4–48 . 5 ng ml-1 ) appear to be twofold less sensitive than the three type B ( IC50 17 . 5–25 . 9 ng ml-1 ) . Direct sequencing of the full length TeAT1 PCR amplicons of MCAM/ET/2013/MU/04 , 09 , 10 , 14 , and 15 , T . evansi type A RoTat 1 . 2 and T . evansi Type B KETRI 2479 revealed no polmorphisms to the wild-type TeAT1 sequence ( AB124588 ) . DAPI staining was performed on in vivo and in vitro propagated stocks ( Fig 4 ) . In vitro culture did not change the percentage of kinetoplastic cells in T . b . gambiense LiTat 1 . 3 ( 99% ) , T . b . brucei AnTat 1 . 1E ( 99% ) and MCAM/ET/2013/MU/09 ( 0% ) . On the other hand , already after 30 days in vitro culture a decrease in the percentage of kinetoplastic cells was observed in T . evansi RoTat 1 . 2 ( 89% ) , T . evansi KETRI 2479 ( 81% ) , MCAM/ET/2013/MU/04 ( 97% ) , 14 ( 93% ) and 15 ( 94% ) compared to non-in vitro adapted trypanosomes . After 60 days of in vitro culture , the percentage of kinetoplastic cells dropped even further for T . evansi KETRI 2479 ( 64% ) , MCAM/ET/2013/MU/04 ( 89% ) and 10 ( 35% ) . No significant correlation was observed between the percentage of kinetoplastid cells of all in vitro adapted T . evansi stocks ( including day 30 and day 60 ) and their IC50 values for melarsomine dihydrochloride ( ρ = -0 . 13 , p = 0 . 67 ) , isometamidium hydrochloride ( ρ = -0 . 324 , p = 0 . 278 ) , suramin ( ρ = -0 . 097 , p = 0 . 752 ) and diminazene diacetureate ( ρ = -0 . 355 , p = 0 . 233 ) . These data suggest that among the in vitro adapted Ethiopian T . evansi stocks there is no relation between the drug sensitivity and the presence of kinetoplast DNA . Furthermore , their loss of kDNA does not seem to influence rodent infectivity since all cryostabilates made from day 60 in vitro cultures remained infective for mice with detectable parasitaemia at 4–5 DPI .
Previous molecular and serological studies revealed that trypanosome infections in camels from Northern Ethiopia are caused by either RoTat 1 . 2 PCR or EVAB PCR positive parasites . In some instances amplicons of both PCRs were detected within the same buffy coat extract , suggesting the occurrence of mixed infections [20] . The present study was undertaken to isolate the trypanosomes from camels carrying apparent single infections through inoculation of their buffy coats in immunosuppressed mice . The in vivo inoculation led to the successful isolation of 22 stocks , out of which 14 were selected on the basis of their geographical origins for further investigations ( 5 stocks from Tigray and 9 stocks from Afar ) . Next , we performed an in-depth comparative molecular analysis on DNA extracts from the isolated parasite stocks using diverse PCRs . Furthermore , we analysed the specificity of each of these PCRs on a collection of Trypanozoon strains . The RoTat 1 . 2 VSG sequence can be used to characterise T . evansi type A [25 , 43] . In our collection , all buffy coats positive in RoTat 1 . 2 PCR yielded in vivo isolated stocks that were RoTat 1 . 2 PCR positive but that were negative in the maxicircle gene targeting PCRs . Furthermore , with the exception of the akinetoplastic stock MCAM/ET/2013/MU/09 , all these strains had type A minicircles . MCAM/ET/2013/MU/09 may be naturally akinetoplastic since the DNA extracted from the original buffy coat was negative in all PCRs targeting kinetoplast DNA . The occurrence of naturally akinetoplastic strains was previously documented in Latin America and China [12–14 , 47] . One stock ( MCAM/ET/2013/MU/04 ) contained a SNP in its RoTat 1 . 2 VSG PCR amplicon . SNPs in RoTat 1 . 2 amplicons were previously reported in Egypt but do not necessarily lead to a negative result in RoTat 1 . 2 based antibody detection tests . This was also the case for the camel from which MCAM/ET/2013/MU/04 was isolated [48 , 49] . Initially defined by minicircle class B , identification of T . evansi type B is possible with EVAB PCR that amplifies a fragment of this minicircle [15] . Additionally , it was proposed that the VSG JN 2118Hu , first described in a Kenyan T . evansi strain , is a specific marker for T . evansi type B [19] . In our collection , 2 buffy coat extracts that were positive in EVAB PCR yielded in vivo isolated stocks that were EVAB PCR positive as well . Interestingly , an EVAB PCR amplicon was also detected in three additional in vivo expanded stocks that were RoTat 1 . 2 PCR positive but for which the corresponding buffy coats were EVAB PCR negative . These three stocks might be mixed infections . JN 2118Hu VSG PCR appeared to be less sensitive because it detected only 3 out of 5 EVAB PCR positive isolated stocks . Furthermore , the JN 2118Hu VSG PCR appeared to be less specific since T . b . brucei AnTat 1 . 1E and T . b . gambiense type II ABBA were also positive in this PCR . None of the EVAB PCR positive isolated stocks contained maxicircle DNA and they were all negative in miniA PCR , except for the three mixed infections . Therefore , we conclude that we isolated at least two “pure” T . evansi type B stocks from Ethiopian camels , decades after the initial isolation of T . evansi type B from camels in Kenya [15] . We used the minisatellite locus MORF2-REP to verify whether both putative mixed stocks , that were positive in RoTat 1 . 2 PCR and EVAB PCR , were real mixed infections or hybrids between T . evansi type A and B . The Ethiopian isolates clustered in two classes of T . evansi type A , of which one with a previously described heterozygous profile ( 6 and 7 repeats ) and one with a homozygous profile ( 7 repeats ) . The Ethiopian T . evansi type B stocks had a heterozygous profile ( 3 and 4 repeats ) differing from the only known profile described for Kenyan type B isolates ( 3 and 5 repeats ) [50] . In one of the mixed infections we observed a profile that can be interpreted as a mixture of Ethiopian type A and type B , while the others only revealed the Ethiopian type A pattern . These results prove that we are dealing with mixed infections and not with hybrids between T . evansi type A and type B . To exclude that these apparent mixed infections represent cross-contamination with genetic material , we attempted in vitro cultivation of the in vivo expanded stocks . Previously we have shown that addition of 1 , 1% methylcellulose to HMI-9 greatly helps the in vitro adaptation of Trypanozoon strains , including T . b . gambiense and T . evansi RoTat 1 . 2 [40] . However , to avoid the use of this highly viscous medium we preferred the use of horse serum to adapt T . evansi stocks as is suggested in previous reports [51–53] . While this approach proved to be successful for all type B stocks , only two out of nine Ethiopian T . evansi type A could be adapted . Interestingly , in the case of mixed stock MCAM/ET/2013/MU/15 , this medium selected T . evansi type B out of the mixed population . While only the type A infection was detected in the buffy coat DNA extract , both types could be detected in the in vivo expanded stock DNA , but eventually only type B was detected in the in vitro adapted stock . Gillingwater and colleagues reported on the drug sensitivity profiles of a panel of T . evansi and T . equiperdum strains where they considered T . evansi STIB 806K to be a reference sensitive strain for suramin ( IC50 70 . 4 ng ml-1 ) , diminazene diaceturate ( IC50 4 . 5 ng ml-1 ) and melarsomine dihydrochloride ( IC50 1 . 4 ng ml-1 ) . They reported drug resistance in two T . evansi stocks with an IC50 for suramin > 10000 ng ml-1 ( STIB 780 and STIB 781 ) , and in the T . equiperdum OVI strain , with an IC50 for diminazene diaceturate of 302 ng ml-1 and an IC50 for melarsomine dihydrochloride of 17 . 6 ng ml-1 [46] . The only strain that is shared between their panel and our collection is T . evansi RoTat 1 . 2 , which despite different approaches in the experimental testing , yielded corresponding IC50 values , especially for diminazene diaceturate and melarsomine dihydrochloride , thus facilitating comparison between both studies . In our Ethiopian T . evansi collection , no resistance against melarsomine dihydrochloride was found . However , some stocks appeared to have raised IC50 values for suramin ( > 200 ng ml-1 ) and diminazene diaceturate ( > 50 ng ml-1 ) . The IC50 values that we observe for T . b . gambiense LiTat 1 . 3 and the Ethiopian T . evansi type B are similar to the in vitro IC50 value of 0 . 82 ng ml-1 found by Sahin and coworkers for T . congolense IL3000 which is sensitive to isometamidium ( Veridium ) in vivo [54] . In the same study , an in vitro IC50 of 11 . 06 ng ml-1 is reported for T . b . brucei AnTat 1 . 1 strain , which is slightly higher than the value that we obtained in experiments with our T . b . brucei AnTat 1 . 1 strain and the other T . evansi stocks [54] . Nevertheless , defining our T . evansi stocks as either sensitive or resistant based solely on the in vitro drug sensitivity results may be too audacious , given the fact that IC50 values were determined in only one assay , the resazurin viability assay [55–57] . Therefore , an in vivo drug sensitivity profile of all our Trypanozoon strains against the commonly used trypanocides remains to be elucidated . Interestingly , both Ethiopian T . evansi type A stocks appear to be less susceptible to suramin , diminazene diaceturate and isometamidium hydrochloride than the three type B stocks . In T . b . brucei , resistance against suramin and isometamidum hydrochloride has been linked to several proteins [58 , 59] , while resistance to diamidine and melaminophenyl classes of drugs is attributed to the transporter protein TbAT1 and the aquaporin AQP2 [60–62] . The lower sensitivity to diminazene diaceturate was not caused by mutations in the T . evansi TeAT1 [63] . Interestingly , DAPI staining of the trypanosomes indicated slight to severe loss of the kDNA in all in vitro adapted T . evansi stocks , when compared to in vivo adapted stocks . The loss of kDNA in in vitro cultured T . evansi is a phenomenon that has been known for a long time [10 , 15 , 55 , 64] . Non-vital loss of the kinetoplast is made possible by mutations in the F1-ATP synthase γ subunit of T . evansi allowing to uncouple from the Fo subunit and effectively circumventing the requirement for mitochondrial gene expression [65] . Furthermore , it has been shown that the expression of certain T . evansi F1-ATP synthase γ subunit coding sequences in T . brucei allows this species to survive loss of its kDNA after chemical treatment [28] . Moreover , in such genetically modified T . brucei , independence of kDNA maintenance and expression is associated with multidrug resistance [30] . In our collection of T . evansi stocks we did not observe differences in drug sensitivity between populations that were partially or completely akinetoplast confirming earlier evidence that the presence or absence of kDNA is irrelevant within this context [30 , 55] . Recently , Carnes et al . showed that SNPs in the F1- ATP γ subunit could be used to genotypically support the multiple origins of at least 4 dyskinetoplastic T . evansi/T . equiperdum lineages: one major group of RoTat 1 . 2 VSG positive T . evansi/T . equiperdum type A , and three very small groups each represented by only a single strain: T . evansi type B KETRI 2479 , T . equiperdum BoTat and T . equiperdum OVI [9] . All Ethiopian T . evansi type A had the corresponding mutation of the type A group . The Ethiopian type B T . evansi shared a similar profile as KETRI 2479 . Finally , the Ethiopian T . equiperdum strain Dodola , which had some maxicircle genes but was negative for both type A and type B markers revealed an F1-ATP synthase sequence similar to T . b . brucei AnTat 1 . 1E strain , thus likely belongs to the same dyskinetoplastic group as T . equiperdum OVI [9 , 28] . In conclusion , our study shows that the apparent T . evansi type that is detected in a buffy coat of an infected camel does not necessarily represent the full diversity that is present in the infected animal . Moreover , the fact that 5 out of 22 new T . evansi isolates from camel in Ethiopia contain T . evansi type B may be an indication that is more widespread than currently known . The inoculation of the trypanosomes in immunosuppressed mice may allow the propagation of mixed populations . In contrast , in vitro cultivation seems to reduce the diversity by selecting for only one particular type , in our study T . evansi type B . Secondly , our study addresses some drawbacks of current molecular markers for T . evansi genotyping . To rely solely on VSG markers or kDNA markers for the molecular identification of T . evansi may be misleading due to possible recombinations occurring in VSG genes and to the presence of akinetoplastic T . evansi stocks . In this regard , we confirm that the F1-ATP synthase γ subunit gene , that is not related to the VSG repertoire nor to the presence of kDNA , may become an interesting target for genotyping T . evansi stocks in areas where both types overlap and where mixed infections can occur . Nevertheless , it is not possible to separate the Ethiopian T . equiperdum from T . brucei on the basis of this target gene . Thirdly , no evidence of in vitro drug resistance was found in our collection of T . evansi type A and type B stocks . The presence or partial absence of kDNA in the in vitro adapted T . evansi stocks did not correspond with the drug sensitivity phenotype .
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Trypanosoma ( T . ) evansi causes surra in various animal species in Africa , Latin America and Asia . Despite inducing important animal suffering , economic losses and being a World Animal Health Organisation ( OIE ) notifiable disease , surra is severely neglected in terms of awareness , control interventions and research into improved control tools . Most serological tests can only detect T . evansi type A , while molecular tests rely on detection of highly variable genes or on fragile kinetoplast DNA . Even more , the obscure T . evansi type B , first isolated decades ago in Kenya , totally escapes surveillance due to absence of reliable diagnostic tools . In the present study we isolated new type B stocks from Ethiopia , thus suggesting that this type of T . evansi is probably more widely distributed than previously thought . We further report on an alternative molecular marker for both types of T . evansi and present data on the drug sensitivity of the Ethiopian isolates .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"vertebrates",
"cloning",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"mammals",
"animals",
"protozoans",
"molecular",
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"techniques",
"cellular",
"structures",
"and",
"organelles",
"extraction",
"techniques",
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"and",
"analysis",
"methods",
"kinetoplasts",
"camels",
"artificial",
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"molecular",
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"dna",
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] |
2016
|
New Trypanosoma evansi Type B Isolates from Ethiopian Dromedary Camels
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Mass drug administration ( MDA ) , targeted at school-aged children ( SAC ) is the method recommended by the World Health Organization for the control of morbidity induced by soil-transmitted helminth ( STH ) infection in endemic countries . However , MDA does not prevent reinfection between treatment rounds and research suggests that only treating SAC will not be sufficient to bring prevalence to low levels and possibly interrupt transmission of STH . In countries with endemic infection , such as Myanmar , the coverage , who is targeted , and rates of reinfection will determine how effective MDA is in suppressing transmission in the long-term . In this paper , data from an epidemiological study on STH , comprising three surveys conducted between June 2015 and June 2016 in the delta region of Myanmar , are analysed to determine how STH prevalence and intensity in the study community changes over the course of a year , including reinfection after two MDA rounds in which the whole study sample ( all age groups , n = 523 ) were treated with albendazole . Prevalence in the first survey ( August 2015 ) was 27 . 92% for any STH , 5 . 54% for Ascaris lumbricoides , 17 . 02% for Trichuris trichiura and 9 . 75% for hookworm . Over the year ( survey one to survey three ) , prevalence of any STH decreased by 8 . 99% ( P < 0 . 001 ) and mean EPG significantly decreased for T . trichiura ( P < 0 . 01 ) and hookworm ( P < 0 . 001 ) . Risk ratios ( RRs ) for a four-month reinfection period ( August to December ) were statistically significant and were below one , indicating that STH prevalence had not bounced back to the prevalence levels recorded immediately prior to the last round of treatment ( any STH RR = 0 . 67 , 95% CI 0 . 56–0 . 81; A . lumbricoides RR = 0 . 31 , 95% CI 0 . 16–0 . 59; T . trichiura RR = 0 . 70 , 95% CI 0 . 55–0 . 88; hookworm RR = 0 . 69 , 95% CI 0 . 50–0 . 95 ) . The only statistically significant RR for the six-month reinfection period ( December to June ) was for A . lumbricoides infection in SAC ( RR = 2 . 67 , 95% CI 1 . 37–5 . 21 ) . All six-month RRs were significantly higher than four-month RRs ( P < 0 . 05 ) . Evidence of predisposition to infection ( low and high ) , as measured by the Kendall Tau-b statistic , was found for all species overall and within most age groups stratifications , except for hookworm infection in preschool-aged children . This study demonstrates that , for certain demographic groups , a six-month gap between MDA in these communities is enough time for STH infection to return to STH prevalence levels recorded immediately before the previous MDA round , and that on average the same individuals are being consistently infected between MDA rounds .
Soil-transmitted helminth infections ( STHs ) are classified by the World Health Organization ( WHO ) as neglected tropical diseases ( NTDs ) . Approximately 1 . 4 billion people worldwide are estimated to be infected with at least one of the main STHs ( Ascaris lumbricoides , Trichuris trichiura , Ancylostoma duodenale , Necator americanus ) [1] . Endemic countries carry out mass drug administration ( MDA ) campaigns to control STH infections with the goals of reducing STH prevalence and intensity of infection to a level where there is a low risk of morbidity in children [2 , 3] . The WHO recommends that MDA is carried out annually or biannually , targeting school-aged children ( SAC , 5–14 years old ) as they are at the highest risk of morbidity [3 , 4] . A goal set by the WHO for STH control is to decrease the prevalence of medium and high intensity infections ( MHII ) in SAC to below 1% at which point STH morbidity has been eliminated as a public health problem in the area [2] . In 2017 , this guideline was updated to include treatment of young children ( 12 to 23 months old ) , preschool-aged children ( pre-SAC , 2–4 year olds ) , adolescent girls ( 10–19 year olds ) and women of reproductive age ( WRA , 15–45 year olds ) [3] . Whilst MDA that targets pre-SAC and SAC can reduce morbidity in these groups , research indicates that MDA targeting all age groups ( community-wide ) is more effective at reducing STH prevalence and intensity in all groups , especially in hookworm-endemic areas [5 , 6] , and to interrupt transmission [7–9] . Myanmar , in Southeast Asia , is endemic for STH . A WHO-led survey conducted in 2002–2003 found that 69 . 7% of the SAC sampled were infected with at least one STH ( A . lumbricoides = 48 . 5% , T . trichiura = 57 . 5% and hookworm = 6 . 5% ) and 18 . 2% of SAC had a medium/high intensity infection [10] . MDA programmes targeting STH and lymphatic filariasis ( LF ) have been conducted in Myanmar since 2003 . The LF MDA programme is conducted under the Global Programme to Eliminate Lymphatic Filariasis ( GPELF ) and treats all age groups annually with albendazole and diethylcarbamazine ( DEC ) in December or January [11] . The STH MDA programme treats pre-SAC and 5–9 year olds ( primary school children ) annually , eight months after GPELF ( August ) , with albendazole . A follow-up WHO survey , conducted in 2012 , found that , after seven years of MDA , STH prevalence in SAC had decreased to 20 . 9% [12] . Coverage of the STH programme has been consistently high since 2006 , national coverage of SAC was reported as 97 . 49% in 2016 [12 , 13] . There are few recent epidemiological studies on STH in Myanmar . In 2015 , Htoon et al . reported 18 . 6% prevalence of T . trichiura in urban 6–8 year olds [14] . Anthelminthic drugs kill helminths within hosts , but do not prevent reinfection between MDA rounds [15 , 16] . Research into drug efficacy also suggests that a single dose of albendazole , as given in most countries’ MDA programmes , will not clear intestinal helminths , especially T . trichiura infections [17–20] . Therefore , as well as individuals gaining new infections after MDA , they may also be harbouring old infections not killed by previous treatment . Reinfection , or the change in STH prevalence and intensity over time , depends on multiple factors: the efficacy of the anthelminthics and coverage of MDA to effectively clear STH infections from all infected individuals in the population , the level of environmental contamination with eggs and larvae and an individual’s exposure to environmental contamination ( behavioural and social factors ) [21] . Those who are consistently reinfected with STH after clearing their infections with treatment are considered to be “predisposed” to infection [22] . Research is ongoing to determine what the underlying factors of predisposition are . They are likely to be a combination of genetic , immunological , environmental and behavioural factors [23–25] . Predisposition is usually defined as individuals who consistently reacquire infection to the same intensity class of infection ( i . e . low , medium , high ) as prior to treatment [26] . However , in low STH intensity populations , such as those that have undergone multiple years of MDA , the definition of predisposition could be widened to include all those who consistently harbour STH infections between MDA rounds . Being able to prioritise the groups of people that are consistently infected , despite MDA , would be highly beneficial to STH control programmes that are in the final years of MDA and are targeting the interruption of transmission [9 , 27] . A change in treatment policy may be desirable to target those predisposed to infection if they can easily be identified . In this paper we describe and analyse the infection status of study participants in a longitudinal epidemiological study of STH in Myanmar that was conducted between June 2015 and June 2016 . The aim of this analysis is to determine how the prevalence and intensity of STH infection changes over the course of a year under the influence of the STH and LF MDA programmes in Myanmar , both of which aim to significantly reduce STH prevalence and intensity ( although this is a secondary aim for the LF MDA programme ) . We also investigate if there is evidence for predisposition to infection in the study sample when stratified by a variety of confounding variables including age and sex .
Data were collected in an STH epidemiological study that has been detailed in a previous publication [28] . The study received ethical approval from the Imperial College Research Ethics Committee , Imperial College London , UK ( ICREC– 15IC2667 ) and the Department of Medical Research Ethics Review Committee , Ministry of Health and Sports , Myanmar . The study was explained to each potential participant and consent was sought at this time . Adults ( 18 years and above ) provided consent for themselves and for any children ( under 18 years old ) under their care ( parents or guardians ) . Children aged 10–17 years old also signed assent forms . All participants in the study were offered anthelminthics regardless of their disease status and non-participants in the study were treated as usual by the governmental MDA programmes ( Fig 1 ) . Treatment was instructed to be directly observed , although this could not be guaranteed in all cases . All participants provided their own answers to questionnaires except children from 2–4 years old , for whom we received answers from a parent/guardian . Data collection took place between June 2015 and June 2016 in Udo village , Taikkyi township , Yangon Region and Kyee Kan Theik village , Nyaung Don township , Ayeyarwaddy Region . Further details on the study sites , including environmental and socioeconomic information , are provided in a previous publication [28] . In June 2015 , a demographic survey and census were completed in the two study villages . Participants that fulfilled the inclusion criteria ( permanent residents of the village , provided informed written consent , above two years old , not pregnant/breastfeeding ) were recruited for the study by random selection of households . For each randomly selected household , the number of eligible household residents were tallied , and selection of households continued until the required sample size had been reached . Participants completed questionnaires collecting data on participants’ socioeconomic status , household structure and access to water , sanitation and hygiene ( WaSH ) facilities . The study comprised three parasitology surveys in August 2015 ( first survey–S1 ) , December 2015 ( second survey–S2 ) and June 2016 ( third survey–S3 ) ( Fig 1 ) . Stool samples were collected from the participants in each parasitology survey and were assessed for STH infection by trained laboratory technicians using the Kato-Katz method [29] . Egg counts were multiplied by 24 to give eggs per gram of faeces ( EPG ) [30] . The participants and their stool samples were assigned unique identification ( ID ) codes to maintain confidentiality and to link results over all surveys . All study participants were treated with 400mg albendazole at each survey and were also treated with 6mg/kg DEC at S2 . Since the study included participants from all age groups above two years old , this meant that participants over the age of nine years received two additional albendazole treatments ( in S1 and S3 ) compared to the regular treatment schedule ( Fig 1 ) . Data for the following analyses were from all participants who had a recorded Kato-Katz result from all three surveys . Overall , 523 participants ( 53 . 5% of total recruited participants , 19 . 0% of village populations ) from 211 households ( 67 . 6% of total recruited households , 35 . 6% of total households in the villages ) had Kato-Katz data from all three surveys ( S1 Fig ) . Table 1 presents the age and sex breakdown of the study participants . Data from both villages were merged and analysed as one dataset . RStudio ( R version 3 . 0 . 1 , Vienna , Austria ) was used for the following statistical analyses and to create the figures . Participants were grouped into age groups as defined by the WHO: preschool-aged children ( pre-SAC ) are 2–4 year olds , school-aged children ( SAC ) are 5–14 year olds and adults are 15+ year olds [4] . Exact confidence intervals ( 95% two-sided ) for mean prevalence were calculated using the Clopper-Pearson method [31] . Non-parametric mean EPG adjusted percentiles ( 95% two-sided , bias-corrected and accelerated—BCa ) were calculated using bootstrapping methodology with the “boot” package . Risk ratios ( RRs ) were calculated by dividing the prevalence of infection in the later survey by the prevalence of infection in the earlier survey ( e . g . RR = prevalence of A . lumbricoides in S2 / prevalence of A . lumbricoides in S1 ) . RR confidence intervals were calculated by multiplying the standard error of the natural logarithm of the RR by the z-score , and adding or subtracting the value from the log RR [32] . The significance test for the differences between RRs was derived from a formula published by Altman and Bland , 2003 [33] . The WHO recommended intensity cut-offs were used to group individual EPG into low , medium and high intensity infections [2] . We used McNemar’s test to assess the differences in prevalence and Wilcoxon signed rank test to assess the differences in intensity over the surveys , the significance level was set at P ≤ 0 . 05 . We used the Kendall rank correlation coefficient test to analyse the correlation between egg counts in different surveys , indirectly assessing predisposition to infection . The Kendall Tau-b value was chosen as it adjusts for tied ranks and the significance level set at P ≤ 0 . 05 . Data from all participants were included in Kendall-Tau analysis , including those who were uninfected in multiple surveys . Kendall Tau-b values range between minus one ( all pairs are discordant ) and one ( all pairs are concordant ) , a higher Tau-b value indicates more concordant than discordant pairs of individual egg counts and therefore higher overall correlation [34] . The study took place over the course of a year . Therefore , all participants will have aged one year during the study . Whilst there is a well-established relationship between age and STH infection , for simplicity we maintained the recorded ages for all participants at the age recorded in the first survey . This is assuming that age-related exposure did not drastically change over the course of a year . Also , we have maintained the usual WHO definition of SAC ( 5–14 years old ) , despite the fact that there is a different treatment frequency for 5–9 and 10–14 year olds in Myanmar . We have done this to align with how the WHO expects STH outcomes to be reported regarding infection prevalence and intensity by age grouping . We cannot guarantee that all infections were cleared after MDA . Therefore , “reinfection” in the case of our analysis does not necessarily refer to new infections picked up between MDA rounds , but rather changes in the number and proportion of infections between surveys .
Baseline ( S1 ) prevalence was 27 . 92% for any STH , 5 . 54% for A . lumbricoides , 17 . 02% for T . trichiura and 9 . 75% for hookworm ( Table 2 ) . From S1 to S2 prevalence of any STH fell by 8 . 99% and the reduction was statistically significant ( P < 0 . 001 ) , there was no change in prevalence from S1 to S2 ( not statistically significant ) and therefore the change in prevalence over the study year ( S1 to S3 ) was also significant ( P < 0 . 001 ) . The reductions in prevalence of each STH separately , were statistically significant from S1 to S2 and from S1 to S3 ( P < 0 . 05 ) . There were no statistically significant changes in prevalence from S2 to S3 . In the final survey ( S3 ) , prevalence of infection with any STH was 28 . 07% in SAC . Risk ratios ( RRs ) for reinfection differed between STH species and reinfection period ( Fig 2 and S1 Table ) . Four months post-MDA , the risk of infection was lower than in the preceding survey for all STHs ( any STH RR = 0 . 67 , 95% CI 0 . 56–0 . 81; A . lumbricoides RR = 0 . 31 , 95% CI 0 . 16–0 . 59; T . trichiura RR = 0 . 70 , 95% CI 0 . 55–0 . 88; hookworm RR = 0 . 69 , 95% CI 0 . 50–0 . 95 ) . The only statistically significant six-month RR was for A . lumbricoides infection in SAC ( RR = 2 . 67 , 95% CI 1 . 37–5 . 21 ) . However , the six-month RRs were significantly higher than the four-month RRs for infection with all STH species ( P < 0 . 05 ) except hookworm ( P = 0 . 44 ) . Six-month RRs were also statistically significantly higher than four-month RRs in SAC for infection with any STH , A . lumbricoides and T . trichiura , but were significantly lower for hookworm ( P < 0 . 05 ) . Six-month RRs were significantly higher in males for any STH and T . trichiura ( P < 0 . 01 ) but higher in females for A . lumbricoides ( P < 0 . 001 ) . From S1 to S2 , 86 . 2% of A . lumbricoides infections , 52 . 8% of T . trichiura infections and 66 . 7% of hookworm infections were “cured” ( Table 3 ) . This decreased to 44 . 4% for A . lumbricoides , 50 . 0% for T . trichiura and 62 . 9% for hookworm infections from S2 to S3 . Whereas , from S1 to S2 , 1 . 0% , 4 . 6% and 3 . 8% of previously uninfected people became infected with A . lumbricoides , T . trichiura and hookworm infections , respectively . From S2 to S3 , 1 . 6% , 6 . 7% and 3 . 3% of previously uninfected people became infected with A . lumbricoides , T . trichiura and hookworm infections , respectively . Mean EPG significantly decreased for A . lumbricoides ( P < 0 . 01 ) and T . trichiura ( P < 0 . 0001 ) from S1 to S2 , and significantly increased for hookworm ( P < 0 . 01 ) . No changes in EPG from S2 to S3 were statistically significant . Over the year ( S1 to S3 ) , mean EPG significantly decreased for T . trichiura ( P < 0 . 01 ) and hookworm ( P < 0 . 001 ) . The increase in A . lumbricoides EPG was not statistically significant ( Table 2 ) . A majority of the STH infections in all three surveys were low intensity infections for T . trichiura and hookworm ( 88 . 7–93 . 5% and 97 . 1–100% , respectively ) , whereas the majority of A . lumbricoides infections were low intensity in S2 ( 55 . 6% ) and medium intensity in S1 ( 51 . 7% ) and S3 ( 53 . 8% ) . At the final survey ( S3 ) there were no medium/high intensity hookworm infection in SAC but 6 . 14% and 5 . 26% A . lumbricoides and T . trichiura infections , respectively . Therefore , the WHO target of less than 1% MHII in SAC has not been achieved in the study villages . When mean EPG change was stratified by age group ( Fig 3 and S2 Table ) mean A . lumbricoides and T . trichiura EPG decreased from S1 to S2 and increased from S2 to S3 for all age groups except the 25–39 year olds for A . lumbricoides for which the opposite occurred . The mean change in EPG was not homogenous between all age groups . There was minimal change in mean EPG for both A . lumbricoides and T . trichiura in the youngest and oldest age groups . For hookworm , the increase and decrease in mean EPG was driven by the change in 5–14 year olds . A particularly high hookworm EPG result in the SAC group ( 158 , 136 EPG ) for S2 may have skewed these results upwards . A total of 38 ( 7 . 27% ) participants had STH infections in all three surveys and 67 ( 12 . 81% ) had infections for any two of the three surveys . Correlation coefficients ( Kendall Tau-b ) of individual participants’ egg count results between surveys were statistically significant for all species of STH ( Table 4 ) . Most of the correlations remained significant when stratified by sex or age group . Trichuris trichiura egg counts had the strongest concordance between surveys especially for males , SAC and pre-SAC . The strongest concordance was for hookworm egg counts in pre-SAC between the first and second surveys . However , the Kendall’s Tau-b value may have been inflated due to the small number of pre-SAC infected with hookworm ( two in survey one , three in survey two ) . Non-significant and low Tau values were calculated for A . lumbricoides infection in males and hookworm infection in SAC , denoting little evidence for predisposition in these groups .
MDA programmes have been ongoing in the delta region of since 2003 for STH , and since 2013 for LF [10 , 12] . Whilst STH prevalence has dropped significantly since the initiation of MDA , the prevalence target set by WHO to discontinue MDA ( under 1% ) has not yet been reached in surveyed communities [14 , 28] . Currently , there is no monitoring and evaluation ( M&E ) of STH in Myanmar and no longitudinal studies have taken place since 1990 [35] . It is therefore important for longitudinal M&E studies to take place in the country so that the long-term impact of MDA can be evaluated . The results of this study show that overall STH prevalence was significantly reduced following two community-wide MDA rounds of the study sample ( 27 . 92 to 18 . 93% ) , and the intensities of infection ( measured by EPG ) of T . trichiura and hookworm were significantly reduced ( 73 . 56 to 41 . 07 EPG and 40 . 2 to 11 . 47 EPG , respectively ) . However , in the final survey , prevalence of STH was 28 . 07% in SAC , indicating that , according to the WHO guidelines , MDA should continue at the current frequency [4] . In this analysis , risk ratios were used to describe the patterns of infection over a four-month and six-month reinfection period . RRs for the six-month reinfection period were significantly higher than for the four-month reinfection period . This is not surprising since the extra two months allows more time for people to become newly infected and for surviving infections to re-establish . If we assume that the MDA rounds had cleared infection , then the data suggest that , in the study setting , four months is not enough time for STH to re-infect individuals to the prevalence levels pertaining before that particular round of treatment , but six months may be enough time . However , it is more likely that , due to sub-100% drug efficacy plus non-compliance to treatment , some infections were retained after MDA , and six months was enough time for the surviving helminths to release sufficient eggs to trigger the acquisition of new A . lumbricoides infections in SAC . Due to the low efficacy of albendazole against T . trichiura ( as low as 30 . 7% and 43 . 6% cure rate in meta-analyses by Moser et al . [36] and Keiser et al . [17] , respectively ) , it is possible that there was a greater magnitude of reestablishment of surviving worms for this species as proportionally fewer T . trichiura worms will have been affected by the initial treatment and thus more T . trichiura worms will have survived to reproduce . In the study sites included in this analysis , T . trichiura is the most prevalent STH in all age groups ( peak of 29 . 82% in SAC , S1 ) . Whilst shortening the time between MDA rounds may limit reestablishment of all STH species , if a single dose of albendazole is the treatment strategy in STH MDA programmes , the prevalence of T . trichiura will not be adequately reduced [37 , 38] . Research is ongoing into the efficacy of co-administrations with anthelminthics such as moxidectin , ivermectin or tribendimidine to target T . trichiura [39–41] . Helminth eggs are highly durable in the right environmental conditions . A . lumbricoides and T . trichiura eggs can remain viable and infective for several months [42] , so individuals can become reinfected from eggs persisting in the environment , without the need for the deposition of new infective stages [16 , 43] . There is also the possibility of a seasonal effect on transmission [44 , 45] . The first and third surveys both took place during the dry season , whereas the second survey took place during the rainy season . Infective stage survival is known to be increased during rainy seasons [46–48] . It should also be noted that , for logistical reasons , the third survey in this study ( June 2016 ) took place two months prior to the usual timing of the STH MDA round ( August ) ( Fig 1 ) . During the annual August MDA round , only children aged 2–9 years old are treated . As such , all 2–9 year olds not enrolled in the study will have had a further two months for reinfection and all other age groups not enrolled in the study will have a further six or seven months until the community-wide lymphatic filariasis ( LF ) MDA round ( through GPELF ) . Therefore , we can hypothesise that if six months is enough time for STH prevalence to re-establish to the levels immediately prior to the last MDA round , the eight months between treatments for SAC and the 12 months between treatments for all other age groups may also allow infection to fully re-establish . Prevalence is used as a key STH epidemiological metric , but intensity of infection is more important as a determinant of morbidity [2] . Whilst STH prevalence dropped significantly between the first and third surveys , the slight reductions in mean T . trichiura and hookworm EPG were not statistically significant . STH intensity at the beginning of the study was already at a very low level . Most participants infected with STH had low intensity infections . However , at S3 6 . 14% of SAC had MHII of A . lumbricoides and 5 . 26% with T . trichiura . The WHO target is to decrease MHII in SAC to below 1% , thereby indicating that STH in that country has been eliminated as a public health problem [2] . Unfortunately , this has not yet been achieved in the study villages . Prior work on the effect of long-term MDA programmes on STH have identified that substantial drops in STH prevalence and intensity in the first years of MDA may be followed by smaller reductions in subsequent years [49 , 50] . For example , an eight year MDA programme in Burundi reported significant drops in prevalence in the first four years and no further decrease in the last four years [50] . A monitoring survey in Kenya found that the largest reductions in A . lumbricoides and hookworm prevalence and intensity were recorded after the first MDA in comparison to the second MDA and whilst prevalence of T . trichiura was significantly reduced after three years of MDA , the proportion of medium to high T . trichiura infections was not [51] . The reasons for this may well be related to MDA coverage levels and individual compliance to treatment at multiple rounds of treatment [52] . Few studies to date have recorded individual compliance to treatment but persistent low prevalence may , in part , be due to persistent non-compliers to treatment [53] . The possibility of emerging resistance against anthelminthics should also continue to be considered and monitored , especially in mass treatment programmes that have been ongoing for an extended period of time [54 , 55] . Evidence of predisposition to STH infection has been found in several epidemiological studies [22 , 56 , 57] and the results of the Kendall’s Tau-b analysis indicates that predisposition to infection exists within the study sample . Concordance between egg counts indicates that the same individuals have reinfected with STH after treatment . Stronger concordance between survey egg counts , and therefore stronger evidence for predisposition , was found in males and the younger age groups for T . trichiura and hookworm infection but only in females for A . lumbricoides infection . This is in agreement with Holland et al . [58] , who found stronger evidence for A . lumbricoides predisposition in females , but in disagreement with Haswell-Elkins et al . [59] and Quinnell et al . [60] , with females more predisposed to hookworm infection . Whilst identifying individual people that are predisposed to infection would be costly and time-consuming , requiring diagnosis at several time points , building and collating evidence on groups of people ( e . g . based on sex , age or occupation ) that are predisposed to infection would allow them to be prioritised in MDA programmes when possibly targeting transmission interruption [61 , 62] . The reasons that underlie predisposition to infection ( genetic , immunological , behavioural or exposure-related ) are difficult to separate and quantify in epidemiological studies . The “susceptibility” factors of predisposition ( genetics and immunology ) are unlikely to change with public health interventions , but the “exposure” factors can be reduced through health education and WaSH initiatives . A limitation of this study related to the data collection study is the low sensitivity of the Kato-Katz technique as a diagnostic tool . It is highly possible that infections were missed due to its use [63] . Another limitation is that , for important ethical reasons , the whole study sample ( all ages ) had to be treated during the MDA rounds that immediately followed the surveys , instead of the usual targeted ages ( SAC only after the first and third surveys ) . The patterns of reinfection presented here therefore do not necessarily represent the patterns that will have occurred in previous years , during routine MDA . As we could not confirm clearance of infection after MDA , the results may not be viewed as true “reinfection” . However , the aim of individual treatment during MDA programmes is not to clear infection in hosts but to reduce the intensity of infection and subsequent morbidity [4 , 64] . Therefore , the “reinfection” we measure in this study is synonymous with the reinfection that is occurring between treatments in most endemic country MDA programmes . During data collection we attempted to ensure that treatment was taken via directly-observed therapy ( DOT ) where possible , but without data to confirm that infections were cleared we cannot assume this was always the case . Finally , this analysis focuses on the individuals that were present for all three surveys during the epidemiological study . It is possible that these individuals may have systematically different behaviour to those that dropped out or were lost to follow-up . The key epidemiological observation in this study is the persistence of infection despite frequent and community wide MDA ( through the STH MDA programme and GPELF ) , and the strong evidence for predisposition . Whilst the majority of infections were low intensity , the target of below 1% MHII in SAC has not been achieved and community-wide prevalence of STH at the end of the study was 18 . 93% . The prevalence of any STH in SAC at the end of the study ( S3 ) was 28 . 07% which exceeds the threshold that WHO recommends for stopping MDA against STH [4] . Another key finding , from risk ratio analysis , is that a six-month gap between MDA rounds may be sufficient for STH infection to re-establish to pre-treatment levels within the community . In the long term , if diagnosis can be made more precise with new tools such as qPCR , and the costs of such tests greatly reduced , then future STH control may need to be based on targeted treatment to those predisposed to infection in order to eliminate transmission [65 , 66] .
|
Mass drug administration ( MDA ) , treating either whole communities or targeted groups without a prior diagnosis , is used as a control strategy for many neglected tropical diseases , including soil-transmitted helminth ( STH ) infection . MDA takes place at set intervals , aiming to reduce morbidity caused by the target disease . Research and policy focus is also increasingly considering the potential of interrupting STH transmission , leading to elimination . In this study we measure STH infection in two villages in the delta region of Myanmar over the course of a year , both before and after MDA rounds , to quantify the effect of treatment on infection and to identify groups with persistent infections . We found that whilst overall prevalence of STH infection decreased over the year , intensity of infection , measured by eggs per gram of faeces , did not significantly decrease . We also found evidence to suggest that particular people are predisposed to STH infection . This is possibly due to non-compliance to MDA , or behavioural and social factors . The findings presented here will provide evidence to support continuing Myanmar’s MDA programme for STH control and using accurate diagnostics to identify and target “predisposed” people for sustained treatment .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2019
|
Soil-transmitted helminth reinfection four and six months after mass drug administration: results from the delta region of Myanmar
|
Interleukin 17 ( IL-17 ) –producing γδ T cells ( γδ17 T cells ) have been recently found to promote tumor growth and metastasis formation . How such γδ17 T-cell responses may be regulated in the tumor microenvironment remains , however , largely unknown . Here , we report that tumor-associated neutrophils can display an overt antitumor role by strongly suppressing γδ17 T cells . Tumor-associated neutrophils inhibited the proliferation of murine CD27− Vγ6+ γδ17 T cells via induction of oxidative stress , thereby preventing them from constituting the major source of pro-tumoral IL-17 in the tumor microenvironment . Mechanistically , we found that low expression of the antioxidant glutathione in CD27− γδ17 T cells renders them particularly susceptible to neutrophil-derived reactive oxygen species ( ROS ) . Consistently , superoxide deficiency , or the administration of a glutathione precursor , rescued CD27− Vγ6+ γδ17 T-cell proliferation in vivo . Moreover , human Vδ1+ γδ T cells , which contain most γδ17 T cells found in cancer patients , also displayed low glutathione levels and were potently inhibited by ROS . This work thus identifies an unanticipated , immunosuppressive yet antitumoral , neutrophil/ROS/γδ17 T-cell axis in the tumor microenvironment .
A hallmark of solid tumors is their infiltration by immune cells that can either inhibit or promote tumor cell growth . Amongst such immune populations , γδ T cells are known to contribute to protective responses because of their potent ability to kill tumor cells and to produce cytokines like interferon gamma ( IFN-γ ) and tumor necrosis factor alpha ( TNF-α ) [1–5] , which constitutes a solid basis for γδ T-cell–based cancer immunotherapy strategies [6] . In stark contrast , cumulative evidence indicates that interleukin 17 ( IL-17 ) –producing γδ ( γδ17 ) T cells promote tumor progression in several experimental models , including a genetic mouse model of pancreatic intraepithelial neoplasia [7]; transplantable models of subcutaneous fibrosarcoma , skin carcinoma , and colon cancer [8]; subcutaneous and intrahepatic hepatocellular carcinoma [9]; as well as intraperitoneal ovarian cancer [10] . In addition to contributing to primary tumor development and progression , recent reports revealed metastasis-promoting features of γδ17 T cells , both in a genetic mouse model of breast cancer metastasis [11] and in transplantable mouse models of lung metastasis [12] . Importantly , in human cancers , γδ17 T cells were also observed and associated with advanced stages of disease in colorectal and squamous cell skin tumors [13 , 14] and decreased survival of patients with gallbladder cancer [15] . Of note , the Vδ1+ subpopulation of human γδ T cells was reported to be a major source of IL-17 in colon cancer [13] and squamous cell skin cancer [14] patients and to promote inflammation-induced cancer progression [16] . The pro-tumoral function of γδ17 T cells was shown to result from either direct support of tumor cell survival , through the interleukin 6 ( IL-6 ) –signal transducer and activator of transcription 3 ( STAT3 ) axis [7 , 17] , or indirect establishment of a prosperous environment for the tumor , especially through promotion of angiogenesis [8 , 15] . Moreover , part of these pro-tumoral effects occurs via recruitment/activation of myeloid cells . For instance , we have shown that γδ17 T cells accumulate in a mouse model of ovarian cancer and that they induce the mobilization of small peritoneal macrophages that express pro-inflammatory and pro-angiogenic mediators [10] . Other pro-tumoral myeloid subsets mobilized by γδ17 T cells include neutrophils [11] as well as myeloid-derived suppressor cells of both monocytic [9] and polymorphonuclear [13 , 18] lineages , which converge in the suppression of antitumor CD8+ T-cell responses . Thus , γδ17 T cells have been extensively shown to interact with myeloid cells that counteract tumor immune surveillance , and instead promote cancer progression . This notwithstanding , a large-scale analysis of thousands of tumor samples from 39 cancer types indicated that γδ T cells are globally associated with a good prognosis [19] , which may suggest that γδ17 T-cell responses are often limited by as yet unknown mechanisms . In fact , very little is known about the regulatory pathways that may control γδ17 T cells in cancers . We thus undertook to determine the cellular and molecular mechanisms controlling γδ17 T-cell responses in the tumor microenvironment . In mice , γδ17 T cells are comprised in discrete thymic and peripheral CD27− γδ T-cell compartments [20] and can be further subdivided into two main subpopulations expressing either Vγ4+ or Vγ6+ T-cell receptors ( TCRs ) [21] . These subsets have distinct developmental requirements in the thymus [22 , 23] and different homeostasis and dynamics in peripheral tissues [21] . Namely , whereas Vγ4+ γδ17 T cells typically populate secondary lymphoid organs ( from which they can be mobilized upon challenge ) , their Vγ6+ counterparts leave the fetal thymus to become tissue-resident , long-lived , and self-renewing cells that respond in situ [24–26] . This is particularly relevant in tissues where Vγ6+ γδ17 T cells are abundant , such as the dermis , tongue , lung , liver , uterus , and peritoneal cavity [21] . Interestingly , CD27− Vγ6+ γδ17 T cells proliferated extensively in the peritoneal cavity following the transplantation of ID8 ovarian cancer cells , thereby constituting the major source of pro-inflammatory and pro-angiogenic IL-17 that promoted tumor cell growth [10] . Following that study , we have investigated the contribution of γδ17 T cells to different tumor types developing in the same environment . Unexpectedly , we found that pro-tumoral γδ17 T cells failed to respond to discrete tumor challenges due to neutrophil-mediated suppression , which therefore limited tumor growth . We went on to dissect the molecular mechanisms underlying this unanticipated neutrophil/γδ17 T-cell cross talk in experimental mouse models of cancer and found an exquisite sensitivity of γδ17 T cells to reactive oxygen species ( ROS ) -induced oxidative stress in the tumor microenvironment .
This study initiated with an unexpected finding upon implantation of the B16-F0 cell line in the peritoneal cavity . In stark contrast to our previous observations with ID8 tumors [10] , B16-F0 ( simplified to B16 ) challenge did not increase the frequency of total γδ T cells or γδ17 T cells in the peritoneal cavity when compared to tumor-free controls ( Fig 1A ) , while CD8+ and CD4+ T cells accumulated significantly ( Fig 1B ) . We thus considered the possibility of γδ17 T cells being selectively inhibited by another immune cell population and examined leukocyte subsets previously associated with T-cell suppression . Interestingly , we found striking amounts of neutrophils in the peritoneal cavity of B16-bearing but not ID8-bearing mice ( S1A Fig ) , thus segregating with the lack ( Fig 1A ) or presence [10] of γδ17 T-cell responses , respectively . In fact , upon B16 tumor challenge , both neutrophils ( CD11b+Ly6G+Ly6Cint ) and monocytes ( CD11b+Ly6G-Ly6C+ ) accumulated , respectively , 40- and 20-fold within the leukocyte infiltrate ( CD45+ cells ) ( Fig 1C ) . Although regulatory T ( Treg ) cells decreased in frequency ( Fig 1C ) , we nonetheless assessed their impact , in parallel with that of myeloid cells , on γδ17 T cells , through depletion strategies using anti-CD25 monoclonal antibody ( mAb ) that targets Treg cells , anti-Gr1 and anti-Ly6G mAbs that target neutrophils , or anti-CD115/clodronate-liposomes that target monocytes and macrophages . Of note , these approaches were very efficient at depleting the corresponding target leukocyte subsets ( S2A Fig ) . Critically , only neutrophil depletion resulted in an increased frequency of IL-17+ γδ T cells in tumor-bearing mice ( Fig 1D ) . Given that ID8 promoted the accumulation of IL-17+ γδ T cells in the peritoneal cavity [10] , whereas in B16-bearing mice , the mobilization of neutrophils inhibited γδ17 T-cell responses , we questioned what would happen in animals bearing both tumor types . We found that neutrophil depletion still led to a marked increase in IL-17+ γδ T cells in ID8+B16-bearing mice ( S1B Fig ) , thus suggesting that neutrophil-mediated inhibition is a dominant phenomenon . We then aimed to validate and extend our findings to an orthotopic tumor model , and selected a hepatocellular carcinoma model ( Hepa 1–6 ) in which tumor growth is increased in the presence of IL-17 [9] . We implanted the Hepa 1–6 cell line directly in the liver of C57BL/6 mice and analyzed the immune infiltrate . Similarly to the B16 model , the frequency of neutrophils increased significantly in the hepatic tumor within the hematopoietic infiltrate ( CD45+ cells ) compared to the tumor-free liver tissue ( Fig 1E ) . We next depleted neutrophils using the anti-Gr-1 mAb and also used a genetically neutropenic mouse strain , Genista , which , because of a point mutation in the transcription factor growth factor independence 1 ( Gfi1 ) , lacks mature neutrophils ( in the periphery and in the bone marrow ) without impacting on lymphopoiesis nor on T- and B-Cell functions [27 , 28] . Consistently , tumor-bearing homozygous Genista mice displayed low frequencies of neutrophils , and the few remaining tumor-associated neutrophils expressed lower levels of the maturation markers , Ly6G and CD11b , when compared to littermate heterozygous controls ( S2B Fig ) . Importantly , neutrophil depletion or deficiency also led to a robust increase in IL-17+ γδ T cells in the intrahepatic Hepa 1–6 model ( Fig 1F ) . Moreover , we observed a 5-fold increase in tumor load in the homozygous Genista mice compared to their littermate controls ( Fig 1G , left panel ) . This was in line with the reduced tumor growth of neutrophil-depleted Il17−/− mice compared to neutrophil-depleted wild-type ( WT ) mice ( Fig 1G , right panel ) and supported our hypothesis that neutrophils limit tumor growth at least in part by inhibiting IL-17 production in the tumor microenvironment . Along the same lines , the proportion of IL-17–producing cells ( within CD45+ leukocytes ) was increased upon neutrophil depletion/deficiency in both tumor models , while the frequency of IFN-γ–producing cells remained unchanged ( S3A Fig ) . Importantly , the contribution of γδ T cells to IL-17 producers upon neutrophil depletion clearly outcompeted that of CD4+ T cells , for there were around 3-fold more IL-17+ γδ than IL-17+ CD4+ T cells ( S3B Fig , left panels ) , and the IL-17 mean fluorescence intensity ( MFI ) was consistently higher in γδ compared to CD4+ T cells ( S3B Fig , right panels ) . Taken together , these data suggest that neutrophils suppress tumor growth by inhibiting the major IL-17–producing population in the tumor niche , γδ17 T cells . Given that the ablation of neutrophils led to an increase in IL-17–producing γδ T cells , we investigated which γδ17 T-cell subset was affected and how—i . e . , the cellular mechanism of suppression . In both tumor models , the absence of neutrophils provoked an increase in the total proportion of γδ T cells ( Fig 2A ) but had no effect on CD8+ or CD4+ T cells ( Fig 2B ) . Neutrophils particularly affected γδ T cells negative for both Vγ1 and Vγ4 TCR chains , because these became dominant upon neutrophil depletion/deficiency ( Fig 2C ) . By using the staining protocol that combines GL3 and 17D1 mAbs [10 , 29] , we confirmed that the majority of these cells expressed the Vγ6 TCR chain ( Fig 2D , left panel ) while also mostly displaying a CD27− CD44+ phenotype ( Fig 2D , middle and right panels ) that tightly associates with γδ17 T cells [20 , 30–32] . Importantly , we found that neutrophils dampened Vγ6+ T cells in vivo through inhibition of proliferation ( Fig 2E ) and not by inducing apoptosis or impairing their recruitment from secondary lymphoid organs ( S4 Fig ) . In particular , we observed substantially increased 5-bromodeoxycytidine ( BrdU ) incorporation and higher proportions of Ki67+ Vγ6+ T cells in both Genista and in neutrophil-depleted mice ( Fig 2E ) . These results indicate that neutrophils can selectively and potently inhibit CD27− Vγ6+ T-cell proliferation in in vivo tumor models . Next , we dissected the molecular mechanism by which neutrophils suppressed γδ17 T cells using the B16-F0 intraperitoneal mouse model , because it allowed efficient purification of significant numbers of neutrophils from the PEC of tumor-bearing mice . In addition , we employed in vitro co-cultures to assess the direct impact of neutrophils on γδ17 T cells , in the absence of other cell types . We co-cultured purified neutrophils and CD27− γδ T cells that were induced to proliferate in vitro via stimulation with anti-CD3 and anti-CD28 mAbs [33] . We found that the proliferation of CD27− γδ T cells was inhibited when cultured with tumor-associated neutrophils , but not with bone marrow–derived neutrophils from either tumor-bearing or tumor-free mice ( Fig 3A ) . These results show that the tumor microenvironment endows neutrophils with their suppressive phenotype and that tumor-associated neutrophils are sufficient to exert direct inhibition on CD27− γδ T-cell proliferation . Moreover , consistent with the fact that IFN-γ+ cells ( S3A Fig ) , CD4+ , and CD8+ T cells ( Fig 2B ) are not affected by neutrophil depletion in vivo , we found that neutrophils from tumor-bearing mice preferentially impacted the in vitro proliferation of CD27− γδ T cells when compared to CD27+ γδ , CD4+ , and CD8+ T cells ( S5A Fig ) . One mechanism employed by neutrophils for immunosuppression is the production of ROS [34] . We thus analyzed ROS in peritoneal cells of tumor-bearing mice depleted or not for neutrophils . Neutrophil depletion reduced the percentages of superoxide-positive cells ( as assessed by dihydroethidium staining ) as well as the levels of hydrogen peroxide ( Fig 3B ) , indicating that neutrophils were a major source of ROS in vivo . Moreover , γδ T cells from the peritoneal cavity of B16 tumor–bearing mice exhibited increased protein oxidation levels when compared to the same population in neutrophil-depleted B16 tumor–bearing mice ( Fig 3C ) , suggesting that these cells are under oxidative stress in the presence of neutrophils . Consistent with this , the expression of enzymes or regulator genes involved in ROS scavenging was higher in Vγ6+ T cells from neutrophil-sufficient compared to neutrophil-depleted tumor-bearing mice . This indicates that Vγ6+ T cells are actively responding to oxidative damage , unlike CD4+ and CD8+ T cells , which are largely unchanged by the presence of neutrophils in the tumor microenvironment ( Fig 3D ) . To directly test the role of ROS-induced oxidative stress in the inhibition of γδ17 T cells , we used a cytochrome B ( −245 ) , β subunit ( Cybb ) −/− mouse strain that lacks the enzyme NADPH oxidase 2 ( NOX2 ) , which catalyzes the conversion of molecular oxygen to superoxide . We purified neutrophils from the peritoneal cavity of tumor-bearing Cybb−/− or WT mice and co-cultured them with anti-CD3/CD28-stimulated CD27− γδ T cells . Whereas WT neutrophils drastically inhibited the proliferation of CD27− γδ T cells , the latter were able to divide in the presence of Cybb−/− neutrophils , albeit not as efficiently as in the complete absence of neutrophils ( Fig 3E ) . Notably , CD27− γδ T-cell proliferation was also restored in co-cultures with WT neutrophils when these were supplemented with catalase in a dose-dependent manner ( S5B Fig ) . Critically , we validated these findings in vivo upon establishment of B16 tumors in Cybb−/− ( or WT ) mice , as we found that Vγ6+ and IL-17+ γδ T cells accumulated to significantly higher levels in Cybb−/− than in control mice ( Fig 3F ) . As a corollary to our working model , we tested the impact of the in vivo administration of a well-established antioxidant , N-acetylcysteine ( NAC ) , as a potential gain-of-function approach . Indeed , NAC treatment was sufficient to lead to an accumulation of Vγ6+ and IL-17–producing γδ T cells in the peritoneal cavity of tumor-bearing mice ( Fig 3G ) . Taken together , these results demonstrate that tumor-associated neutrophils potently suppress the proliferation of CD27− Vγ6+ γδ17 T cells via ROS-mediated induction of oxidative stress . To understand why Vγ6+ CD27− γδ T cells were especially affected by neutrophil-derived ROS , we assessed the effect of increasing concentrations of hydrogen peroxide ( H2O2 ) and superoxide ( O2 . - ) ( generated by the xanthine/xanthine oxidase system ) on the proliferation of CD27− and CD27+ γδ T-cell subsets in vitro . Both hydrogen peroxide and superoxide inhibited γδ T-cell proliferation , but CD27− cells were clearly more susceptible than their CD27+ counterparts ( Fig 4A ) . These results led us to hypothesize that CD27− γδ17 T cells might have lower capacity to detoxify ROS than CD27+ γδ T cells ( or other T-cell subsets ) . Moreover , as γδ17 T cells expanded upon in vivo administration of NAC ( Fig 3G ) , and this acts as a precursor to glutathione , we analyzed this major intracellular antioxidant and found significantly reduced basal levels in CD27− γδ17 T cells when compared to CD27+ γδ T cells , as well as CD8+ and CD4+ T cells ( Fig 4B ) . This may explain why neutrophil-derived ROS selectively impacted on CD27− γδ17 T-cell proliferation ( Fig 1A and 1D ) compared to CD27+ γδ T cells ( S3A Fig ) , CD8+ or CD4+ T cells ( Fig 1B ) in neutrophil-rich tumor models . Consistent with this , we found that several enzymes or antioxidants involved in ROS detoxification ( Fig 4C ) were selectively down-regulated in IL-17+ γδ T cells compared to IFN-γ+ γδ T cells ( Fig 4D ) . For example , Gclm , the gene that encodes for one of the subunits glutamate-cysteine ligase ( the first rate limiting step of glutathione production ) , as well as Gss , the gene that encodes for glutathione synthetase , were expressed less in IL-17+ T cells , which may explain the low glutathione pool in CD27− γδ T cells . Most other antioxidants , such as thioredoxins and peroxiredoxines , were also lower in IL-17+ γδ T cells . Altogether , this supports that differences in redox metabolism make γδ17 T cells more sensitive to oxidative stress than γδIFN-γ T cells . Finally , we questioned whether this pattern of differential expression of glutathione and susceptibility to ROS also applied to human T-cell subsets . We found that Vδ1+ γδ T cells , the main γδ T-cell subset associated with IL-17 production in human tumors [13 , 14 , 16] , were also profoundly affected by the presence of H2O2 , in contrast with their Vδ2+ γδ , CD8+ , and CD4+ T-cell counterparts ( Fig 4E , left ) . Consistently , Vδ1+ T cells also expressed significantly lower levels of glutathione ( Fig 4E , right ) . Altogether , these data strongly suggest that murine CD27− γδ17 T and human Vδ1+ T cells are particularly susceptible to ROS-mediated suppression because of their low basal glutathione levels , thus providing novel cues on how to limit their cancer-promoting functions in the tumor microenvironment .
γδ17 T cells are known to enhance neutrophil mobilization in the context of several infections and also in response to tumors [6 , 21 , 35] . Moreover , a positive feedback loop between neutrophil-derived IL-1β and IL-17 responses [36] and γδ17 T cells [37] has been suggested . By contrast , here we show that neutrophils inhibit γδ17 T cells , thus revealing a dynamic and multifaceted cross talk between these cell types in the tumor microenvironment . While the circumstances that dictate positive versus negative interactions remain unclear , the latter have been documented in other immune contexts . For instance , neutrophil depletion in a protective model of pulmonary cryptococcosis [38] or in an experimental mouse model of human metapneumovirus infection [39] resulted in increased IL-17 production by γδ T cells , but underlying molecular mechanisms were not identified . Provocatively , neutrophils may even act as important “rheostat” of γδ17 T-cell homeostasis , because mice deficient for either C-X-C chemokine receptor type 2 ( Cxcr2 ) or integrin beta chain-2 ( CD18; Itgb2 ) , which are characterized by low neutrophil counts in tissues , show increased tissue-resident γδ17 T cells [40 , 41] . A dual role for neutrophils in cancer has been suggested [42–46] , and as a result , neutrophil depletion can either reduce [42 , 47–51] or increase [52–55] tumor burden . Within the tumor niche , neutrophils are often associated with cancer progression , namely through promotion of angiogenesis or suppression of antitumor effector lymphocytes . Thus , tumor-associated neutrophils can produce large amounts of matrix metallopeptidase 9 ( MMP-9 ) , which remodels the extracellular matrix; promotes the release of pro-angiogenic vascular endothelial growth factor ( VEGF ) [47 , 56]; and inhibits CD8+ T-cell functions via secretion of IL-10 [57] , arginase 1 ( which degrades extracellular arginine ) [58] , or reactive nitrogen species [11] . In fact , Coffelt and colleagues recently proposed , in a transplantable model K14cre;Cdh1F/F;Trp53F/F ( KEP ) of mammary tumor-bearing mice , a link between Vγ4+ γδ17 T cells and neutrophils that led to inducible nitric oxide synthase ( iNOS ) -dependent suppression of cytotoxic CD8+ T cells and promoted lung metastases [11] . By contrast , in the peritoneal B16 and intrahepatic Hepa 1–6 tumor models , neutrophils inhibited γδ17 T cells , but not CD8+ T cells , through NOX2-dependent ROS production . These discordant actions of neutrophils in different tumor models may rely on their relative ROS levels and differential impact on T-cell subsets . As suggested by our data , γδ17 T cells expressing low intracellular glutathione are particularly susceptible to oxidative suppression , whereas CD8+ T cells likely require greater ROS concentrations . Interestingly , Treg cells were also recently shown to be highly sensitive to oxidative stress in the tumor microenvironment , due in this case to a weak nuclear factor ( erythroid-derived 2 ) -like 2 ( NRF2 ) -associated antioxidant system [59] , which may explain our observation of reduced Treg accumulation ( Fig 1C ) . On the other hand , the pleiotropic roles of neutrophils may be associated with heterogeneous maturation and activation phenotypes in different tumor models as well as mouse backgrounds ( such as FVB versus C57BL/6 ) [46] . For example , KEP tumor-induced neutrophils were immature and expressed c-kit protein and S100 calcium-binding protein A8 ( s100a8 ) transcript , which are molecules associated with pro-metastatic features [11]; in contrast , peritoneal B16 tumor–induced neutrophils did not express c-kit or up-regulated s100a8 when compared to neutrophils from the bone marrow of tumor-free mice . Moreover , our data on Genista mice , which lack mature neutrophils , indicate that it is the mature neutrophils that suppress γδ17 T cells . Thus , we propose that neutrophils can be suppressive and yet antitumoral by targeting γδ17 T cells , which is in line with their protective role , linked to IL-17 inhibition , in the murine Lewis lung carcinoma model [60] . In humans , Vδ1+ T cells can be important IL-17 producers that favor cancer progression through induction of inflammation [16] and recruitment of immunosuppressive myeloid cells [13] . Consistent with our mouse data , we found that Vδ1+ T cells express low basal levels of glutathione and are highly susceptible to ROS . In line with this , human neutrophils from healthy donors have also been shown to impact circulating γδ T-cell activation and cytokine production and proliferation through production of ROS [61] . ROS are short-lived molecules that originate from molecular oxygen and include superoxide ( O2− ) , hydrogen peroxide ( H2O2 ) , hypochlorous acid ( HCl ) , and hydroxyl radical , among others . Superoxide and hydrogen peroxide are the most common ROS involved in biological processes . Superoxide is rapidly dismutated to hydrogen peroxide or immediately reacts with surrounding molecules; hydrogen peroxide is more stable and can diffuse in the microenvironment and across cell membranes [62] . As it is technically challenging to pinpoint which species acts on γδ17 T cells in vivo , we favor hydrogen peroxide but cannot exclude a role for other ROS species such as hypochlorous acid , which is produced from hydrogen peroxide by myeloperoxidase ( highly expressed by neutrophils ) [63] . In conclusion , our study identifies neutrophil-derived ROS as important regulators of pro-tumoral γδ17 T cells that express particularly low levels of the antioxidant glutathione , which may open new avenues for clinical translation . On the other hand , it challenges the widely accepted view of immunosuppressive myeloid cells solely as being detrimental in cancer progression . In fact , additional lines of evidence support antitumor functions of neutrophils [64] , including enhanced cytotoxic activity [52 , 60 , 65] . Importantly , neutrophils appear to contribute to the efficacy of rituximab and trastuzumab treatments [66–68] , Bovis bacillus Calmette-Guerin treatment in bladder cancer [69] , radiotherapy [70] , and chemotherapy [65] . Therefore , we strongly believe that the pleiotropic functions of neutrophils can be manipulated—in order to boost their protective activities—in future cancer immunotherapy approaches .
Buffy coats from healthy volunteers were obtained under the agreement ( 15 . 12 . 2003 ) between Instituto de Medicina Molecular ( iMM ) and Instituto Português do Sangue e da Transplantação and were approved by the local ethical committee ( Centro de Ética do Centro Hospitalar Lisboa Norte—Hospital de Santa Maria ) . All mouse experiments performed in this study were evaluated and approved by our institutional ethical committee ( iMM-Orbea ) and the national competent authority ( DGAV ) under the license number 019069 . Briefly , euthanasia was performed by CO2 inhalation . Anesthesia was performed by isofluorane inhalation or by intraperitoneal administration of ketamine and medetomidine , and reversed by administration of atipamezole . C57Bl/6J ( B6 ) WT mice and B6 . TCRα−/− and B6 . TCRδ−/− mice were purchased from Charles River Laboratories . B6 . Il17−/− mice were kindly provided by Fiona Powrie ( University of Oxford , Oxford , United Kingdom ) , with permission from Yoichiro Iwakura ( Tokyo University of Science , Chiba , Japan ) . Genista mice were imported from the Center of Immunology Marseille Luminy ( France ) and bred in house . Genista homozygous mice were used as a neutropenic model and were compared to their heterozygous littermate controls . Mice were maintained in specific pathogen-free facilities of iMM . Cybb−/− male mice and their respective C57Bl/6J controls were purchased from Jackson laboratories and maintained in specific pathogen-free facilities at the Francis Crick Institute . IFN-γ/IL-17 double-reporter mice , generated by crossing IFN-γ-YFP mice [71] with Il17a-GFP mice [72] , were used to sort IL-17+ and IFN-γ+ γδ T cells from lymph nodes . Animals were 5–13 weeks of age and aged-matched within 3 weeks , and no randomization or blinding was performed when mice were allocated into experimental groups . Mice that did not develop visible tumors were excluded from the analysis . The Hepa 1–6 murine hepatocellular carcinoma cell line and B16-F0 melanoma cell line were purchased from ATCC ( Manassas , VA ) . Cells were tested for mycoplasma contamination and maintained in Dulbecco’s Modified Eagle Medium ( DMEM ) with 10% ( vol/vol ) FCS ( Gibco; Thermo Fisher Scientific ) and 1% ( vol/vol ) penicillin/streptomycin ( Sigma/Merck ) . Lentiviral infection of Hepa 1–6 cells with luciferase reporter was performed as previously described [73] . For orthotopic hepatocellular carcinoma model , anesthetized mice received 1 × 106 Hepa 1–6 cells implanted intrahepatically in 20 μL PBS through surgical procedure . Mice were euthanized 2–3 weeks later , and tumors were extracted for subsequent analysis . We injected 5 × 104 B16-F0 tumor cells intraperitoneally in 100 μL of PBS . Tumor growth was evaluated in situ by bioluminescence imaging as previously described [73] . For proliferation analysis , mice received 1 . 5 mg of BrdU i . p . at day 4 post–tumor inoculation and then were fed daily with 0 . 8 mg/mL BrdU ( Sigma/Merck ) in drinking water until the indicated day of analysis . For the ID8 and B16 co-injection experiment , 1 × 106 ID8 cells were injected intraperitoneally . ID8 tumors were let to grow for 2 weeks , after which 5 × 104 B16 cells were inoculated i . p . One group of mice was injected with anti-Gr-1 as described below . Two weeks after B16 tumor inoculation ( and 4 weeks upon ID8 injection ) , mice were euthanized and peritoneal exudate cells analyzed by FACS . For in vivo antibody depletion , 70 μg anti-Gr1 ( B16 intraperitoneal model ) , 250 μg ( Hepa 1–6 intrahepatic model ) ( Bio X Cell , clone RB6-8C5 ) , 250 μg anti-Ly6G antibody ( Bio X Cell , clone 1A8 ) , 1 mg anti-CD25 ( clone PC-61 . 5 . 3 , kindly provided by Luis Graça [iMM] ) , 300 μg anti-CD115 ( Bio X Cell , clone AFS98 ) , 70 μg isotype control ( Bio X Cell , LTF-2 ) , or PBS was injected i . p . at days 4 , 8 , and 12 post–tumor inoculation . For monocyte/macrophage depletion , 100 μL of clodronate liposomes ( Liposoma B . V . ) were injected s . c . or i . v . at days 4 , 8 , and 12 post–tumor inoculation . NAC ( Sigma/Merck ) was resuspended in PBS ( pH = 7 ) and administrated i . p . every other day , from day 4 post–tumor injection , at a concentration of 15 mg/kg . Fingolimod ( FTY720 , Sigma/Merck ) was given in the drinking water ( 2 . 5 μg/mL ) from day 4 post–tumor inoculation . Blood leukocytes ( buffy coat cells ) were isolated by gradient centrifugation in Histopaque and each lymphocyte population was FACS-sorted in FACS Aria ( BD Biosciences ) . Hepa 1–6 tumors were harvested , finely chopped , and digested with 1 mg/mL collagenase Type I , 0 . 4 mg/mL collagenase Type IV ( Worthington ) , and 10 μg/mL DNase I ( Sigma/Merck ) for 30 minutes at 37 °C . Cell suspension was then filtered through a 100 μm nylon cell strainer ( Falcon/Corning ) . Peritoneal exudate cells were obtained from the lavage of the peritoneal cavity with 5 mL ice-cold DMEM with 10% ( vol/vol ) FCS . Erythrocytes were osmotically lysed using RBC Lysis Buffer ( Biolegend ) . For surface staining , cells were Fc blocked with anti-CD16/32 ( 93; eBioscience/Thermo Fisher Scientific ) and incubated for 45 minutes with antibodies and LIVE/DEAD Fixable Near-IR ( Thermo Fisher Scientific ) in complete RPMI medium . The following monoclonal antibodies were purchased from eBioscience/Thermo Fisher Scientific: anti-CD3ε ( clone; 145-2C11 ) , anti-CD4 ( RM4-5 ) , anti-CD11b ( M1/70 ) , anti-F4/80 ( BM8 ) , anti-MHC II ( M5/114 . 15 . 2 ) , anti-CD27 ( LG . 7F9 ) , and anti-TCRγ4 ( UC3-10A6 ) ; from Biolegend: anti-CD8α ( 53–6 . 7 ) , anti-CD45 ( 30-F11 ) , anti-TCRδ ( GL3 ) , anti-Ly6C ( HK1 . 4 ) , anti-Ly6G ( 1A8 ) , anti-NK1 . 1 ( PK136 ) , and anti-TCRγ1 ( 2 . 11 ) ; and from BD Pharmigen: anti-CD44 ( IM7 ) . For T-cell intracellular cytokine staining , cells from tumor , PEC , or spleen were stimulated with 50 ηg/mL phorbol 12-myristate 13-acetate ( PMA; Sigma/Merck ) and 1 μg/mL ionomycin ( Sigma/Merck ) for 3 hours at 37 °C in the presence of 10 μg/mL brefeldin-A ( Sigma/Merck ) and 2 μM monensin ( eBioscience/Thermo Fisher Scientific ) . Cells were fixed and permeabilized using the Foxp3/Transcription Factor Staining Buffer set ( eBioscience/Thermo Fisher Scientific ) , following the manufacturer’s instructions , and then incubated for 30 minutes at room temperature , with the following antibodies from eBioscience/Thermo Fisher Scientific: anti-IFN-γ ( XMG1 . 2 ) , anti-IL-17 ( TC11-18H10 . 1 ) , Foxp3 ( FJK-16s ) , and Ki67 ( 16A8 ) . For BrdU staining , FITC BrdU Flow Kit ( BD Pharmingen ) was used following manufacturer’s instructions . For TCRγ6 ( Vγ6 ) detection , staining with GL3 and 17D1 monoclonal antibodies ( kind gift from Prof . Adrian Hayday , The Francis Crick Institute , UK ) was performed as previously described [29] . For Annexin V staining , Annexin V Kit ( eBioscience/Thermo Fisher Scientific ) was used following manufacturer’s instructions . Cell Event Caspase 3/7 Green ( from Thermo Fisher Scientific ) was used according to manufacturer’s instructions . For superoxide detection , cells were stained with dihydroethidium ( Thermo Fisher Scientific ) at a final concentration of 100 μM in PBS for 45 minutes at 37 °C . Cells were acquired on a FACS Fortessa ( BD Biosciences ) or LSR II , sorted on FACS Aria , and data analyzed using FACS Diva or FlowJo software ( Tree Star ) . Lymphoid ( spleen and lymph nodes ) were harvested from C57Bl/6J or B6 . TCRα−/− mice . Cell suspensions were stained with anti-CD3ε ( 145-2C11 ) , anti-TCRδ ( GL3 ) , and anti-CD27 ( LG . 7F9 ) for 30 minutes at room temperature . CD27+ , CD27− γδ T cells , CD4 , and CD8 T cells were FACS-sorted and stained with 1 mM of Cell Trace Violet ( Thermo Fisher Scientific ) in PBS for 20 minutes at room temperature . Cells were incubated on plate-bound anti-CD3ε ( 145 . 2C11 ) ( 10 μg/mL ) plus anti-CD28 mAb ( 37 . 51 ) ( 5 μg/mL ) in the presence of IL-7 ( 50 ηg/mL ) for 72 hours . IL-7 was from Peprotech and the antibodies were from eBiosciences/Thermo Fisher Scientific or BioLegend . Neutrophils were isolated from the peritoneal exudates of B16 tumor–bearing WT or Cybb−/− mice or from the bone marrow of tumor-free or tumor-bearing WT mice . For neutrophil purification , cells were stained with αGr-1–PE ( RB6-8C5 ) at a concentration of 40 ηg/mL , and mouse anti-PE selection kit ( STEMCELL Technologies ) was used . Alternatively , cells were cultured with different concentrations of H2O2 , the superoxide-generating system xanthine/xanthine oxidase ( Sigma/Merck ) , or catalase ( Sigma/Merck ) . Human cells were cultured with soluble anti-CD3 ( clone HIT3a , 1 μg/mL ) and IL-2 ( 10 ηg/mL ) for 6 days in the presence of 100–300 μM of H2O2 . CTV dilution was assessed by FACS . mRNA was prepared from FACS-sorted cell populations using High Pure RNA Isolation kit ( Roche ) . Reverse transcription was performed with random oligonucleotides ( Invitrogen ) using Moloney murine leukemia virus reverse transcriptase ( Promega ) for 1 hour at 42 °C . Relative quantification of specific cDNA species to endogenous reference hprt or β2microglobulin was carried out using SYBR on ABI ViiA7 cycler ( Applied Biosystems ) . The CT for the target gene was subtracted from the CT for endogenous references , and the relative amount was calculated as 2−ΔCT . Primer sequences were the following: nfe2l2 forward , GCAGCCATGACTGATTTAAGC , nfe2l2 reverse , CAGCCAGCTGCTTGTTTTC , gclc forward , GGCTCTCTGCACCATCACTT , gclc reverse , GTTAGAGTACCGAAGCGGGG , gclm forward , AGGAGCTTCGGGACTGTATCC , gclm reverse , GGGACATGGTGCATTCCAAAA , gpx1 forward , CAATGTAAAATTGGGCTCGAA , gpx1 reverse , GTTTCCCGTGCAATCAGTTC , gpx4 forward , TAAGAACGGCTGCGTGGT , gpx4 reverse , GTAGGGGCACACACTTGTAGG , gsr forward , ATCGTGCATGAATTCCGAGT , gsr reverse , GGTGGTGGAGAGTCACAAGC , gss forward , CACTATCTCTGCCAGCTTTGG , gss reverse , TTATTCAGGACATTGAGAACGTG , mdh2 forward , TGACCTGTTCAACACCAACG , mdh2 reverse , GATGGGGATGGTGGAGTTC , pgd forward , ATGGCCCAAGCTGACATTG , pgd reverse , GCACAGACCACAAATCCATGAT , prdx1 forward , GTTGGCCGCTCTGTGGATGAGAT , prdx1 reverse , ATCACTGCCAGGTTTCCAGCCAGC , prdx2 forward , GTTCTCCGGCCTAGGGCTCTCTC , prdx2 reverse , GCCGGAGGCCATGACTGCGTG , prdx3 forward , GAACCTGTTTGACAGACATACTGTG , prdx3 reverse , GGGGTGTGGAAAGAGGAACT , prdx4 forward , CTCAAACTGACTGACTATCGTGG , prdx4 reverse , CGATCCCCAAAAGCGATGATTTC , sod1 forward , TACTGATGGACGTGGAACCC , sod1 reverse , GAACCATCCACTTCGAGCA , srxn1 forward , AGTAGTAGTCGCCACCCTGG , srxn1 reverse , AGAGCCTGGTGGACACGAT , txn1 forward , TGCTACGTGGTGTGGACCTTGC , txn1 reverse , TCTGCAGCAACATCCTGGCAGT , txn2 forward , CGACCTTTAACGTCCAGGATG , txn2 reverse , ACTGTGCATGAAAGTCCACAAC , txnrd1 forward , ATGGACAGTCCCATCCCGGGA , txnrd1 reverse , GCCCACGACACGTTCATCGTCT , txnrd3 forward , CCAAGAAATATGGCTGGGAGT , txnrd3 reverse , TGTAGCCCCAGTTCAAGGAG . H2O2 was measured using OxiSelect Hydrogen Peroxide/Peroxidase Assay Kit ( Cell Biolabs ) , following manufacturer’s instructions . Protein oxidation was measured by flow cytometry using the FlowCellect oxidative stress kit ( Sigma/Merck ) , following manufacturer’s instructions . For glutathione quantification , cells were FACS-sorted from spleen and LN of C57Bl/6J or B6 . TCRα−/− tumor-free mice and lysed in 5% metaphosphoric acid at a concentration of 2 × 106 cells per mL ( for smaller cell numbers , the volume was adjusted accordingly ) . Glutathione ( GSSG/GSH ) detection kit ( Enzo Life Sciences ) was used to quantify total glutathione according to manufacturer’s instructions . No statistical methods were used to predetermine sample size . Statistics were done using nonparametric two-tailed Mann-Whitney test or , if both groups followed a normal distribution ( tested by D’Agostino and Pearson normality test ) , using two-tailed unpaired Student t test with 95% confidence intervals for unrelated samples . For paired samples , Wilcoxon-matched-pairs test was used . When more than two groups were compared , two-way ANOVA followed by Tukey HSD post hoc test was performed . Unless otherwise indicated , individual values and mean are plotted , or mean ± SEM . *p < 0 . 05; **p < 0 . 01; ***p < 0 . 001 .
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Tumors are infiltrated by many immune cells that influence many aspects of cancer progression and outcome , including tumor growth , invasion of healthy surrounding tissues , formation of metastasis , and response to treatments . Among tumor-infiltrating lymphocytes , γδ T cells play dual functions in the tumor milieu; whereas those that produce the antitumor cytokine interferon-γ are protective , their counterparts that make interleukin 17 ( IL-17 ) support tumor growth . It is therefore critical to understand which mechanisms may limit IL-17–biased γδ T-cell responses . In this study , we unexpectedly found that IL-17+ γδ T cells express very low levels of the antioxidant , glutathione , and are very sensitive to reactive oxygen species ( ROS ) , thus revealing their Achilles’ heel . Indeed , as ROS-producing neutrophils accumulate within tumors , they inhibit IL-17+ γδ T-cell proliferation and thereby suppress their pro-tumoral activities . We extended these findings , obtained in mouse models of cancer , to human γδ T cells and therefore believe that the modulation of local levels of oxidative stress may have important therapeutic implications .
|
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2018
|
Tumor-associated neutrophils suppress pro-tumoral IL-17+ γδ T cells through induction of oxidative stress
|
Chromatin-modifying enzymes and ATP-dependent remodeling complexes have been intensely studied individually , yet how these activities are coordinated to ensure essential cell functions such as transcription , replication , and repair of damage is not well understood . In this study , we show that the critical loss of Sas3 and Gcn5 acetyltransferases in yeast can be functionally rescued by inactivation of ISWI remodelers . This genetic interaction depends on the ATPase activities of Isw1 and Isw2 , suggesting that it involves chromatin remodeling activities driven by the enzymes . Genetic dissection of the Isw1 complexes reveals that the antagonistic effects are mediated specifically by the Isw1a complex . Loss of Sas3 and Gcn5 correlates with defective RNA polymerase II ( RNAPII ) occupancy at actively transcribed genes , as well as a significant loss of H3K14 acetylation . Inactivation of the Isw1a complex in the acetyltransferase mutants restores RNAPII recruitment at active genes , indicating that transcriptional regulation may be the mechanism underlying suppression . Dosage studies and further genetic dissection reveal that the Isw1b complex may act in suppression through down-regulation of Isw1a . These studies highlight the importance of balanced chromatin modifying and remodeling activities for optimal transcription and cell growth .
Two major classes of enzymes regulate the architecture of chromatin and are thereby critical for DNA-templated processes such as transcription , replication , and repair of damage . Remodeling enzymes use the energy of ATP hydrolysis to alter the structure or position of nucleosomes ( reviewed in [1] , [2] ) , whereas chromatin modifying enzymes act post-translationally on multiple nuclear substrates . Prominent among these are the nucleosomal histones that are extensively modified on their N- and C-terminal tails ( reviewed in [3] ) . Covalent modifications of histones and other chromatin proteins are diverse , including at least six specific types of reversible and dynamic modifications that are catalyzed by multimeric enzyme complexes . Among the consequences resulting from histone modifications , two have been especially well characterized . The first is disruption of contacts between DNA and histones or between nucleosomes . In this case , lysine ε-acetylation can destabilize nucleosomal interactions since it neutralizes this otherwise charged residue . The second consequence involves recruitment of effector proteins that bind via conserved recognition domains . For example , histone acetylation can be recognized by bromodomains , whereas histone methylation is recognized by chromo-like-domains and PHD domains ( reviewed in [4] ) . These domains are found in many nuclear proteins , including chromatin modifying enzymes and remodeling complexes . The simultaneous existence of multiple different marks on histones has led to the recognition that crosstalk among modifications can be a critical determinant for regulation of gene expression [5] . In addition , cooperation between histone modifiers and ATP-dependent remodeling complexes can contribute to transcriptional regulation ( reviewed in [6]–[8] ) and DNA damage repair ( reviewed in [9] ) . For example , the histone acetyltransferase ( HAT ) Gcn5 and the SWI/SNF chromatin remodeling complex were proposed early on to have cooperative functions in transcriptional activation by working in concert to modify chromatin structure [10]–[12] . Indeed , histone acetylation mediated by the SAGA complex containing Gcn5 stabilizes the anchoring of SWI/SNF to nucleosomes at promoters , and thus is important for SWI/SNF-dependent nucleosome remodeling and transcriptional activation in vitro and in vivo [13] , [14] . The SAGA complex interacts with another chromatin remodeling factor , the chromodomain protein Chd1 [15] , [16] , which is a component of Gcn5-containing SAGA and SLIK/SALSA HAT complexes [17] . In addition , Gcn5 is functionally linked to the essential RSC chromatin remodeling complex: H3K14 acetylation is recognized by one essential tandem bromodomain of Rsc4 and contributes to RSC complex-dependent gene activation [18] , [19] . Acetylation of histone H3 at lysines 9 and 14 strongly correlates with transcriptional activity and peaks over start sites of active genes [20] . Gcn5 is the HAT responsible for the majority of this acetylation in vivo [21]–[23] , consistent with the observation that Gcn5 is generally recruited to promoters of active genes , as described for H3K9 and K14 acetylation marks [20] , [24] . Two other HATs also specifically target histone H3 at K9 and K14 in vivo: the MYST family HAT Sas3 ( reviewed in [25] ) , and Elp3 [21] , [23] . Their functions appear most critical in the absence of Gcn5 . In particular , although inactivation of Sas3 in otherwise wild-type cells does not elicit obvious phenotypes , diminished Sas3 activity in a gcn5Δ null mutant results in defects in cell cycle progression , and complete loss of activity leads to cell death [22] . Genome-wide mapping established that Sas3 and Gcn5 are recruited to many of the same actively transcribed genes [26] . Furthermore , the binding sites of these HATs correlates with the H3K14 acetylation mark [26] . These observations strongly suggest that Sas3 and Gcn5 acetyltransferases are critical for active transcription , although the molecular mechanisms underlying their regulation have not been fully elucidated . In particular , since mutations of the established target lysines in histone H3 result in only mild phenotypes [27] , [28] , the essential function revealed in gcn5Δ sas3 mutants most likely extends beyond acetylation of H3 . Functional links have not yet been established between Sas3 and chromatin remodeling , or the loss of viability that results when both Sas3 and Gcn5 activities are compromised . We describe here a critical interaction between Sas3 and Gcn5 acetyltransferases and ISWI chromatin remodelers . Strikingly , and in contrast with nucleosome disrupting remodelers such as SWI/SNF and Chd1 , inactivation of Isw1 or Isw2 relieved conditional lethality in a gcn5Δ sas3 mutant . Genetic dissection of the complexes through which Isw1 acts clearly reveals that the antagonistic effects are mediated through the Isw1a complex , and furthermore , that elimination of non-catalytic subunits of Isw1b can overcome this antagonism . At a molecular level , the effects on cell viability tightly correlate with the recruitment of RNA polymerase II ( RNAPII ) at active genes . Together these studies provide new evidence for functional distinctions between the families of chromatin remodeling activities and point to critical interactions between the Sas3 and Gcn5 acetyltransferases , ISWI remodeling machines , RNAPII recruitment , and chromatin compaction .
Among the major H3 acetyltransferases , either Gcn5 or Sas3 is required for cell viability: loss of both enzymes leads to death . To understand this loss of viability , we asked if other chromatin-modulating activities contributed to it , and in particular if there was a role for ATP-dependent chromatin remodeling activities . There are four distinct families of biochemically defined chromatin remodeling complexes: SWI-SNF , ISWI , CHD , and INO80 [1] . The RSC and INO80 catalytic ATPases Sth1 and Ino80 are essential , although the ino80Δ lethality appears restricted to the W303 genetic background [29] , [30] . Individual inactivation of the other catalytic ATPases , Snf2 , Isw1 , Isw2 or Chd1 does not trigger marked growth defects in otherwise wild-type cells [15] , [31] , [32] . However , earlier studies reported synthetic lethality between SWI-SNF components and members of the Gcn5-SAGA complex [11] . We began by evaluating the effects of ATP-dependent chromatin remodeling activities when Sas3 and Gcn5 activities were compromised , using the temperature-sensitive gcn5Δ sas3 conditional mutant described earlier [22] . We observed two types of functional interactions between chromatin remodelers and the Sas3 and Gcn5 acetyltransferases . First , the temperature-sensitive phenotype of the gcn5Δ sas3 double mutant was exacerbated upon deletion of CHD1 ( Figure 1A and Figure S1A ) . This suggested parallel functions , likely through overlap in transcriptional regulation , in agreement with previous studies [12] , [17] . Second , and in distinct contrast , deletion of ISW1 and to a lesser extent ISW2 , improved growth of the gcn5Δ sas3 cells ( Figure 1A ) . Deletion of ISW2 does not further restore growth of the gcn5Δ sas3 isw1Δ mutant , indicating that rescue is maximal upon inactivation of Isw1 ( Figure S1B ) . The Isw1 and Isw2 chromatin remodeling complexes use the energy of ATP hydrolysis to alter nucleosome positioning [32]–[35] . To determine if the ATP-dependent catalytic activity , and not some other property of the enzymes was responsible for suppression , we analyzed the isw1-K227R and isw2-K215R mutants that affect ATP-binding sites to inactivate the enzymes [32] . Figure 1A shows that both catalytic mutations rescued the temperature sensitivity of the gcn5Δ sas3 mutant , with isw1-K227R having the stronger effect . Thus , suppression is dependent on the catalytic activities of the ISWI ATPases . The suppression observed was unexpected since most reported interactions between chromatin modifying enzymes and chromatin remodelers describe parallel functions , often through recognition of modified nucleosomes by the remodelers [12] , [17] , [18] , [36]–[38] . Because inactivation of the ATPase function of Isw1 consistently resulted in a stronger rescue phenotype than that of Isw2 , we focused on dissecting the mechanism underlying the potential antagonism between ISW1 remodeling and acetyltransferase activities mediated by SAS3 and GCN5 . The Isw1 ATPase is the catalytic subunit of two distinct complexes ( Figure 1B and reviewed in [1] , [39] ) . The Isw1a complex includes the non-catalytic subunit Ioc3 , whereas the non-catalytic subunits of Isw1b are Ioc2 and Ioc4 [32] , [35] . The in vivo functions of these two complexes are not yet fully established . Microarray studies reveal that the Isw1a and Isw1b complexes have overlapping roles in transcriptional regulation at some genes , but distinct functions at others [35] . To determine whether one or both Isw1 complexes are involved in antagonizing Sas3 and Gcn5 function , we inactivated the complexes individually by deleting genes encoding their non-catalytic subunits . The ioc3Δ mutant , but neither ioc2Δ nor ioc4Δ strains , strongly rescued the gcn5Δ sas3 phenotype , demonstrating that inactivation of the Isw1a complex is primarily responsible for the suppression mediated by loss of Isw1 ( Figure 1C ) . Furthermore , deletion of IOC2 exacerbated loss of viability , whereas ioc4Δ had no significant reproducible effects . This suggests an additional relationship between the Isw1b complex and the acetyltransferases Sas3 and Gcn5 , and further supports the existence of distinct functions for the two Isw1 complexes . Since combined inactivation of both complexes through deletion of ISW1 rescued the temperature sensitivity , Isw1a appears to have a prominent role in antagonizing Sas3 and Gcn5 activities . Previous studies demonstrated interactions between the SWI-SNF remodelers and Gcn5 alone , independent of Sas3 [11] , [12] . To determine if the antagonism between Sas3 and Gcn5 and the Isw1a complex is Gcn5-specific or acts through shared Sas3 and Gcn5 functions , we asked if inactivation of the Isw1a complex rescued temperature sensitivity associated with the single gcn5Δ mutation . Deleting IOC3 or ISW1 did not rescue , demonstrating that these acetyltransferases counteract Isw1a through shared functions of both acetyltransferases ( Figure 1D ) . Isw1 contains a SANT domain ( reviewed in [40] ) that is critical for binding to chromatin at regulated genes in vivo [41] . Biochemical studies indicate that the SANT domains from Ada2 and SMRT preferentially bind unacetylated histone H3 tails [42] , [43] . To determine whether H3 acetylation antagonizes Isw1 recruitment to chromatin , we evaluated Ioc3-Myc occupancy in gcn5Δ sas3 cells by chromatin immunoprecipitation ( ChIP ) at transcriptionally active target genes ( Figure 2 ) . Indeed , Sas3 and Gcn5 acetyltransferases are recruited to a similar set of actively transcribed genes , which correlate with H3K14 acetylation [26] . We selected the PYK1 gene for analysis since H3 acetyltransferases and Isw1 are enriched at this locus [24] , [26] , [44] . For other candidate genes , a recent genome-wide study revealed that Sas3 is enriched at RPL10 whereas UBP7 and CDC25 are impaired for H3K14 acetylation in a sas3Δ strain [26] . We observed that Ioc3 occupancy is enriched in the coding region compared to the promoter at the PYK1 , RPL10 , UBP7 and CDC25 genes ( Figure 2B , Figure 2C and Figure 2E ) , as previously described for Isw1 at regulated genes [41] . The inactivation of Sas3 and Gcn5 modestly increased the recruitment of Ioc3 at promoter and coding regions of PYK1 ( Figure 2B ) , although the levels of H3K14 acetylation were severely decreased at this locus ( Figure 2E ) . Similarly , Ioc3 occupancy was moderately affected by loss of Sas3 and Gcn5 at the promoter of RPL10 and at coding regions of RPL10 , UBP7 and CDC25 ( Figure 2C and Figure 2D ) . Of note , nucleosome density increased in the coding region of PYK1 upon inactivation of Sas3 and Gcn5 , as revealed by H3 occupancy ( Figure 2F ) . As the combined loss of Sas3 and Gcn5 resulted in a dramatic loss of H3 acetylation , we asked if suppression mediated by inactivation of the Isw1a complex might rescue this defect . Although the chromatin remodeling ATPase Isw1 has not been reported to regulate histone H3 acetylation , we hypothesized that nucleosome repositioning resulting from Isw1a inactivation might rescue gcn5Δ sas3 defects . Since K14 is the major and common target of Sas3 and Gcn5 acetyltransferases in vitro and in vivo [22] , [26] , [45] , we assayed global levels of H3K14 acetylation in wild-type , gcn5Δ sas3 and gcn5Δ sas3 ioc3Δ cells by protein immunoblotting . As previously described [22] , H3K14 acetylation decreased in the gcn5Δ sas3 strain , however there was no significant difference in acetylation in the gcn5Δ sas3 ioc3Δ strain ( Figure S3 ) . Because global restoration of H3K14 acetylation did not occur , we tested the idea that locus-specific changes might be responsible for rescue of the gcn5Δ sas3 mutant by assaying the local levels of H3K14 acetylation at the Isw1-responsive PYK1 gene under suppressing conditions . H3K14 acetylation levels were impaired over the whole PYK1 gene ( promoter and coding regions ) in the gcn5Δ strain , and more dramatically in the gcn5Δ sas3 strain ( Figure 3B ) , demonstrating that Sas3 and Gcn5 are responsible for H3K14 acetylation at this locus . Further , as revealed by the levels of H3K14 acetylation in the gcn5Δ strain , Sas3 markedly contributed to H3K14 acetylation in the promoter and 3′ regions of the PYK1 gene ( Figure 3B ) . Yet , no further difference in H3K14 acetylation levels was observed upon deletion of IOC3 or ISW1 ( Figure 3B ) . Similarly , the elevated H3 occupancy observed in the gcn5Δ sas3 strain at the PYK1 gene remained unaffected by inactivation of Ioc3 ( Figure 2F ) . Because inactivation of Isw1a does not suppress Sas3 and Gcn5 defects by directly restoring K14 acetylation either globally or locally , or by decreasing nucleosome occupancy , suppression must occur through some other mechanism . We asked if the interaction between Isw1a and H3 HATs is related to transcription as reflected by RNAPII occupancy . Indeed , genome-wide analyses revealed that Sas3 and Gcn5 are recruited to actively transcribed genes and that their occupancies correlate with transcriptional rates [20] , [24] , [26] . Based on the fact that Isw1 also regulates transcriptional activation [35] , [44] , we assayed RNAPII occupancy at the PYK1 gene . In agreement with the proposed roles of Sas3 and Gcn5 in transcriptional activation , we observed that loss of these HATs resulted in defective RNAPII recruitment at PYK1 ( Figure 3C and Figure S4A ) . Loss of Gcn5 slightly impaired occupancy at the 3′ region yet did not affect the promoter and 5′ regions , highlighting a role for Sas3 in RNAPII recruitment and in transcriptional activation . Significantly , deletion of IOC3 and ISW1 in a gcn5Δ sas3 mutant partially rescued RNAPII occupancy at the promoter and coding region of PYK1 ( Figure 3C and Figure S4A ) . We evaluated RNAPII at the other active genes described above to determine how general the suppressive effects were on occupancy . For this study , we also included the additional Isw1 and Sas3 target genes PMA1 and RPS5 , respectively [26] , [44] . As shown for PYK1 , we found that Sas3 and Gcn5 contributed to the recruitment of RNAPII at PMA1 , RPL10 , RPS5 , CDC25 and UBP7 coding regions ( Figure 3D and Figure S4B ) . Furthermore , deletion of IOC3 improved RNAPII occupancy at these genes , more than the modest effects observed with isw1Δ . We asked if these differences in the recruitment of RNAPII influenced trancription ( Figure S4C ) . We assayed the steady state levels of PYK1 , PMA1 and RPL10 mRNAs by RT-qPCR , and observed no significant changes in the gcn5Δ sas3 and gcn5Δ sas3 ioc3Δ mutants when compared to the wild-type strain ( Figure S4C ) . Together these results demonstrate that Sas3 and Gcn5 acetyltransferases and the Isw1a complex have antagonistic roles in chromatin as reflected by recruitment of RNAPII . RNAPII function and regulated recruitment during transcription are critically dependent on chromatin architecture . Given that Sas3 and Gcn5 acetyltransferases and the Isw1a complex have opposing effects on RNAPII recruitment to transcriptional target genes , we asked whether they also antagonistically regulate nucleosomal occupancy at the PYK1 locus . It has been suggested that PYK1 chromatin structure is dependent on Isw1 since the ATPase is associated with this coding region [44] . We examined nucleosomal organization at PYK1 by comparing micrococcal nuclease ( MNase ) cleavage patterns of chromatin prepared from wild-type and mutant strains ( Figure S5 ) . We observed no significant differences in MNase cleavage patterns in gcn5Δ sas3 ioc3Δ cells when compared to gcn5Δ sas3 or wild-type cells ( Figure S5 ) . One possible explanation was that the MNase assay might not detect the Isw1 remodeling activities at the PYK1 gene . For example , the ISWI-dependent rescue may involve nucleosome repositioning at a level too subtle to be detected using the MNase mapping assay . Indeed , Isw1 and Isw2 remodeling activities increase genome-wide nucleosome occupancy at mid-coding regions and intergenic regions , respectively , to prevent cryptic transcription [46] , [47] . In order to map nucleosome location with high resolution at the PYK1 gene , we took advantage of nucleosome-scanning analysis [48] . This method couples isolation of mononucleosomal DNA by MNase digestion with qPCR analysis using a set of overlapping primer pairs spanning the region of interest [48] . Nucleosome scanning analysis of the PYK1 region revealed the presence of four positioned nucleosomes , one located in the promoter region and three others positioned in the 5′ coding region ( Figure 4 ) . Further , a 150 bp region highly sensitive to MNase digestion has been identified in the promoter region ( −350 to −200 from the start codon ) , indicating the presence of a nucleosome depleted region ( NDR ) ( Figure 4 ) . This organization is characteristic of “open” promoters which favor the binding of transcription factors at the expense of nucleosomes [49] . Open promoters are a common property of constitutive genes , such as the conditionally essential gene PYK1 . No major changes in nucleosome positioning were observed upon inactivation of Gcn5 and Sas3 , or further depletion of Ioc3 ( Figure 4 ) . Multiprotein complexes can be altered both by mutation and by changing subunit abundance through gene dosage . We took advantage of gene overexpression ( reviewed in [50]–[52] ) as an independent approach to probe the relationship of ISW1 to SAS3 and GCN5 . Increased gene dosage of IOC2 or IOC4 restored viability at elevated temperature , whereas overexpression of IOC3 exacerbated sickness of the gcn5Δ sas3 mutant ( Figure 5A ) . Increased ISW1 also interfered with growth , confirming its generally antagonistic function . Furthermore , increased gene dosage of IOC2 did not rescue thermosensitivity of the gcn5Δ single mutant ( Figure S6A ) , nor did it serve as a bypass suppressor of the gcn5Δ sas3Δ strain . Together these results suggest that increasing the stoichiometry of the Isw1b complex can ameliorate the negative effects of the Isw1a complex in the gcn5Δ sas3 mutant . To determine if the Isw1a and Isw1b complexes act in parallel or in the same pathway , we performed a series of analyses to dissect the relative contribution of each complex . We first evaluated increased gene dosage of the Isw1b complex components in a strain depleted for the Isw1a complex . Of note , deletion of IOC3 partially rescues the gcn5Δ sas3 temperature sensitivity . Therefore , although the gcn5Δ sas3 ioc3Δ strain is less sensitive than the gcn5Δ sas3 ( Figure 1C ) , this mutant is still somewhat temperature sensitive ( Figure 5B ) , and provides the possibility for a dynamic range in which growth enhancement or inhibition could be observed . We found that overexpression of IOC2 or IOC4 did not rescue the residual gcn5Δ sas3 temperature sensitivity in an ioc3Δ background , demonstrating that the Isw1a complex is required for Isw1b-mediated dosage suppression ( Figure 5B ) . We next evaluated whether Isw1a overexpression enhanced the gcn5Δ sas3 phenotype in a strain depleted for Isw1b by deleting IOC2 and IOC4 . Overexpression of IOC3 still exacerbated the gcn5Δ sas3 temperature sensitivity in a strain depleted for Isw1b ( Figure 5C and Figure 5D ) . These results support the idea that increasing the relative balance of the Isw1b complex suppresses the gcn5Δ sas3 phenotype by counteracting Isw1a function . In addition , we observed that deletion of ISW1 interfered with the phenotypes resulting from overexpression of the IOC genes in the gcn5Δ sas3 strain , underscoring the critical role for the Isw1 catalytic ATPase itself ( Figure S6B ) .
Since the acetyltransferase activities of Sas3 and Gcn5 were initially reported to be essential [22] , understanding the molecular mechanisms underlying this synthetic lethality has remained incomplete . Indeed , whereas disruption of these H3-specific acetyltransferases results in cell death , mutation of lysine residues in histone H3 that are known to be targeted by Sas3 and Gcn5 has only modest phenotypes [27] , [28] . This discrepancy and the growing number of validated non-histone acetylation targets [53] , [54] strongly suggest that the Sas3-Gcn5 essential function may reside in acetylation of histone and/or non-histone targets . Our previous work demonstrated that the Sas3 and Gcn5 acetyltransferases are jointly required for viability and are responsible for the majority of histone H3 acetylation at K9 and K14 in vivo [22] . Here we show that both HATs contribute to in vivo acetylation of H3K14 at an actively transcribed gene , and furthermore we correlate the dramatic decrease in H3K14 acetylation with defective recruitment of RNAPII to promoter and coding regions . This reinforces the view that H3K14 acetylation is an epigenetic mark associated with transcriptional activation [20] . Moreover , the joint contributions of Sas3 and Gcn5 provide a molecular explanation for previous results showing that loss of Gcn5 only modestly affects acetylation of H3K14 at various genes [23] , [55] . We found that inactivation of ISWI family remodelers alleviates gcn5Δ sas3 cell death . In deep contrast , inactivation of the Chd1 remodeler exacerbates the sickness of the gcn5Δ sas3 strain . Unlike other chromatin remodeler families , most ISWI complexes are required for formation of repressed structures [32] , [44] , [46] , [56] , [57] and chromosome compaction in vivo [58]–[60] . Furthermore , inactivation of the linker histone H1 , another critical player in chromatin condensation , also rescues the growth defect of a gcn5Δ sas3 mutant [28] . Thus , destabilizing chromatin compaction , repressed structures , or higher-order chromatin structures through inactivation of ISWI complexes or histone H1 partially relieves growth defects in gcn5Δ sas3 cells . This supports the view that Sas3 and Gcn5 acetyltransferase activities counterbalance negative effects of repressed structures and condensed chromatin . In agreement with this model , we observed that inactivation of the Isw1a complex rescues RNAPII recruitment at active genes in a gcn5Δ sas3 mutant , to levels similar to those observed in the gcn5Δ mutant . This correlates with the degree of rescue observed for cell viability , strongly suggesting that Isw1-dependent rescue is mediated at least partially through the recruitment of RNAPII at actively transcribed genes . Similarly , the H4 HAT Esa1 can overcome the repressing function of Isw1 on transcription [61] . Conditional inactivation of Esa1 leads to defects in RNAPII recruitment at the MET16 gene upon induction , as well as impaired accumulation of MET16 transcript . Deletion of ISW1 was also seen to restore MET16 RNA levels and RNAPII distribution , which is very similar to our observations with RNAPII recruitment at active genes in the gcn5Δ sas3 mutant . However , the outcomes of these genetic interactions are clearly different: whereas inactivation of Isw1 rescues the sickness of gcn5Δ sas3 cells , it exacerbates cell growth defects of esa1 mutants [62] , [63] . These differences are likely to reflect the distinct biological functions and substrates of the Sas3/Gcn5 and Esa1 acetyltransferases . In contrast with Sas3 and Gcn5 acetyltransferases , functional characterization of ISWI enzymes in transcriptional regulation reveals significant roles in gene repression [35] , [46] , [47] , [56] . Future studies should determine if Sas3 and Gcn5 acetyltransferases and ISWI family remodelers act in the same pathway . In Drosophila , H4K16 acetylation regulates chromatin compaction by reducing the ability of ISWI to bind chromatin [64] , [65] . Such a mechanism has not been described for H4 acetylation in yeast , and we show here that loss of the main H3 acetyltrasferases Sas3 and Gcn5 only modestly affects Isw1a recruitment to chromatin at some active genes ( Figure 2 ) . Thus it appears that Sas3 and Gcn5 acetyltransferase activities may counteract Isw1 function independently of chromatin binding . Such regulation has been reported for the Chd1 and Isw2 remodeling enzymes: H4 acetylation antagonizes nucleosome remodeling by lowering the catalytic turnover of ATP hydrolysis without affecting nucleosome binding [38] . Further , Sas3 and Gcn5 acetyltransferases might control Isw1 function directly through acetylation . In Drosophila , Gcn5 acetylates Isw1 at a single lysine in vitro and in vivo [66] . This acetylation occurs in a region similar to the N-terminal tail of H3 , at a residue corresponding to K14 . We also detected low levels of Isw1 acetylation in wild-type cells , with a two-fold decrease in the gcn5Δ sas3 mutant ( data not shown ) . Although loss of the ATP-dependent remodeling activity of Isw1 is required to restore cell viability , we observed no significant effects on nucleosome positioning upon inactivation of Isw1a at the PYK1 gene . Of note , nucleosome occupancy appears slightly reduced in the gcn5Δ sas3 mutant , at the predicted nucleosomes +1 and +3 ( Figure 4 ) . This appears in contrast with the increased H3 occupancy observed at the PYK1 promoter and coding region by ChIP ( Figure 2F ) . However , it should be noted that nucleosome occupancy assayed by the MNase scanning method was normalized to the PHO5 promoter while the ChIPs for H3 were not , which is likely to account for this discrepancy . Alternatively , these results may suggest a local change in nucleosome assembly or disassembly in gcn5Δ sas3 mutants that results in accumulation of incomplete nucleosomes . Addition and removal of the H3/H4 tetramer is the first and last step of nucleosome assembly and disassembly , respectively ( reviewed in [67] ) . Tetrasomes protect around 80 base pairs of DNA , below the resolution of our MNase-qPCR study , which may contribute to the loss of nucleosome signal in the MNase assay . This possibility can be explored in future studies by evaluating H2B occupancy at the 5′ region of PYK1 and by testing genetic interactions between gcn5Δ sas3 and histone chaperones . An additional possibility for the ISWI-dependent chromatin remodeling rescue in gcn5Δ sas3 mutants is that it may be mediated through alteration of higher order chromatin structures . Indeed , the Drosophila ISWI-containing remodeler ACF can assemble regularly spaced arrays of H1-containing nucleosomes and can further catalyze repositioning of chromatosomes ( nucleosome+H1 ) in chromatin fibers [68] , [69] . Furthermore , inactivation of histone H1 also rescues the growth defect of a gcn5Δ sas3 mutant [28] . Therefore , restoration of RNAPII upon inactivation of Isw1a complex might rely on different states of chromatin that differ in the periodicity of chromatosome arrays . An unexpected finding uncovered by our genetic analysis is the opposing functions of Isw1a and Isw1b complexes in relation to the H3 HATs . Specifically , we observed that rescue mediated by Ioc2 and Ioc4 requires Ioc3 , but this requirement is not reciprocal . This suggests that , at least in the context of gcn5Δ sas3 , the Isw1b complex antagonizes the function of the Isw1a complex . Early on , functional characterization of these two complexes revealed distinct roles [35] . Notably they differ in their biochemical activities to bind and space nucleosomes [35] , [70] , [71] , their nucleosome remodeling properties in vivo [46] , [47] , as well as their roles in transcriptional regulation [35] , [44] . Similarly in other eukaryotes , ISWI complexes that share the same catalytic subunit have distinct biological functions , specified by their associated proteins ( reviewed in [1] , [39] ) . The observations reported here bring new understanding toward defining their differences by showing that one ISWI complex may counteract the function of another ISWI complex . Together , the results from this study deepen understanding of the essential roles for H3 HATs . Not only do the HATs positively promote gene-specific transcriptional activation , as has been well established , they also have a critical role in balancing the activities of ATP-driven chromatin remodelers . The functional antagonism between Isw1a and the Sas3 and Gcn5 acetyltransferases further defines biological distinctions between the Isw1 enzyme's catalytic activities in its two structurally distinct complexes . Thus , in addition to interactions between histone modifications defining transcriptional output ( reviewed in [6]–[8] ) , it is likely that future studies will reveal increasingly diverse interactions between the modifying machines and the complexes that dynamically define chromatin architecture through its remodeling .
Strains used are listed in Table S1 and are in the W303 background . Gene deletions and other standard procedures were performed as described [72] . The gcn5Δ sas3 conditional mutant was constructed with a chromosomal allele of the sas3-C357Y , P375A double point mutation . As described for the plasmid conditional mutant [22] , the chromosomal version of the mutant grows well at 30°C , but dies at 37°C . All strains carrying isw1Δ::kanMX , ioc2Δ::kanMX , ioc3Δ::natMX , ioc4Δ::hphMX , isw1-K227R and isw2-K215R-3FLAG-kanMX alleles were derived from the strains YTT441 , YTT823 , YTT825 , YTT855 [35] , YTT1223 and YTT1996 respectively , generous gifts from T . Tsukiyama . The gcn5Δ::natMX allele was obtained by marker swapping using p4339 ( generous gift from C . Boone ) on gcn5Δ::kanMX . The strains expressing Ioc3-13Myc and Isw1-13Myc from their chromosomal loci were constructed as described in [73] . All plasmids were derived from Yep351 ( 2μ LEU2 ) . pLP645 was constructed by inserting a BamHI-SalI fragment containing SAS3 into Yep351 opened with SalI and BamHI ( J . Lowell ) . pLP1524 was obtained from a genomic library generously provided by S . Roeder [74] . It contains a 3 . 9 kb fragment encompassing GCN5 gene ( Chr . VII 995 , 188 to 998 , 784 bp ) . To construct Yep351-IOC2 ( pLP2234 ) , a HindIII fragment ( 4 . 6 kb ) containing IOC2 was subcloned from pLP2170 ( containing genomic fragment from Chr . XII 328 , 038 to 332 , 847 bp ) into Yep351 . To create Yep351-ISW1 ( pLP2256 ) , a BamHI-PstI fragment was subcloned from pRS416-ISW1 ( a generous gift from T . Tsukiyama ) into Yep351 . Plasmid Yep351-IOC4 ( pLP2260 ) was constructed by PCR amplification of IOC4 ( −395 bp from start codon to +412 bp from stop codon ) , and cloned into Yep351 using HindIII-PstI restriction sites . Plasmid Yep351-IOC3 ( pLP2266 ) was constructed by PCR amplification of IOC3 ( −700 bp from start codon to +400 bp from stop codon ) , and cloned into Yep351 using HindIII-PstI restriction sites . Integrity of the constructs was confirmed by DNA sequencing . Cultures were grown for 2 days in SC or appropriate selective medium at permissive temperature . Cells were diluted to an A600 of 1 and plated in fivefold serial dilutions onto SC or selective medium , supplemented with 1 M sorbitol where indicated , and incubated for 4 days at the indicated temperatures prior to data collection . Chromatin immunoprecipitation assays were performed as described previously [75] with minor modifications . Cultures were grown in SC medium at 34°C to A600 of 0 . 7–0 . 9 and cross-linked with 0 . 86% formaldehyde for 40 min . Immunoprecipitations ( IP ) were either pre-cleared with CL4B Sepharose beads ( Sigma ) for 1 hour at 4°C , then incubated overnight at 4°C with anti-RNAPII ( 8GW16 , Covance ) or directly incubated overnight at 4°C with antibodies against H3 ( 07-690 , Upstate/Millipore ) , acetylated H3K14 ( 07-353 , Upstate/Millipore ) , or the myc epitope ( 9E10 ) . DNA was purified using PCR purification columns ( Qiagen ) and analyzed by real-time PCR ( MJ Research Opticon2 system ) . Primer sequences are listed in Table S2 . For quantification of ChIP samples , standard curves were generated for each set of primers , and DNA from IP and input samples was assayed for each strain in triplicate real-time PCR reactions . Each IP sample was normalized to the control IP ( i . e . no epitope or no antibody ) by subtraction , divided by the input sample , and expressed as percent of input ( % IP/input ) . The % IP/input values for H3K14Ac were further normalized to % IP/input values for total H3 . The % IP/input values for Ioc3-Myc and RNAPII were normalized to telVI and rDNA 5S control regions , respectively . Data represent averages from two or more independent experiments . Extracts from MNase digestions were prepared as described [76] , [77] . Briefly , cultures were grown in SC medium at 34°C to an A600 of 0 . 7–0 . 9 . Then ∼2×109 cells were harvested , washed in 1 ml sorbitol 1 M , resuspended in 1 ml of zymolyase solution ( sorbitol 1 . 1 M , 20 mM KPO4 , pH7 , 0 . 5 mM CaCl2 , β-mercaptoethanol 0 . 5 mM , zymolyase 100T 1 mg/ml ) and incubated for 1 . 5 min at room temperature . Spheroplasts were then washed twice in 1 M sorbitol and gently resuspended in 1 . 6 ml of cold buffer A ( 1 M sorbitol , 50 mM NaCl , 10 mM Tris-HCl , pH 7 . 4 , 5 mM MgCl2 , 0 . 5 mM spermidine , 0 . 075% NP40 and 1 mM β-mercaptoethanol ) . The cell slurry was divided into 400 µl aliquots , and each was added to a microfuge tube containing the MNase ( 0 , 60 , 150 and 400 U/ml final concentrations ) and incubated at 37°C for 4 minutes . The reaction was stopped by addition of 40 µl of stop buffer ( 250 mM EDTA , 5% SDS ) . DNA purification was performed as described in [76] . MNase digested DNA was run out on a 1 . 5% agarose gel and mononucleosome sized fragments were excised and purified using Qiagen's Gel Extraction kit . Purified mononucleosomes were analyzed by real-time PCR using the MJ Research Opticon2 system . Primer sequences are listed in Table S2 . For quantification of MNase digested samples , standard curves were generated for each set of primers . Digested and input DNAs were assayed for each strain with each primer set in triplicate PCR reactions . MNase digested samples were divided by the input value for each primer set to generate percent of input . This was further normalized to the PHO5 TATA region [78] . The protein immunoblotting , mRNA quantification , and chromatin analysis techniques used in Figures S2 , S3 , S4 , S5 are described in Supporting Methods ( Text S1 ) .
|
In eukaryotes , essential processes such as transcription , replication , and repair of damage occur in the context of chromatin . The structure of chromatin is tightly regulated during the cell cycle by chromatin-modifying enzymes , including acetyltransferases , and ATP-dependent remodeling complexes . Although there has been extensive characterization of their individual functions , little is known about how their activities are coordinated to maintain cell viability . In this study , we show that the critical loss of Sas3 and Gcn5 acetyltransferases can be functionally rescued by inactivation of ISWI remodelers . At a molecular level , the effects on cell viability tightly correlate with the recruitment of RNA polymerase II ( RNAPII ) at active genes , suggesting that transcriptional regulation may be the mechanism underlying cell viability rescue . Our genetic analyses reveal distinct roles for the two Isw1a and Isw1b complexes; in particular , the antagonistic effects are mediated specifically by the Isw1a complex . These studies highlight the importance of balanced chromatin modifying and remodeling activities for optimal transcription and cell growth .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"expression",
"genetics",
"epigenetics",
"biology",
"chromatin",
"genetics",
"and",
"genomics",
"dna",
"transcription",
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] |
2012
|
Functional Antagonism between Sas3 and Gcn5 Acetyltransferases and ISWI Chromatin Remodelers
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Epidemics of meningococcal meningitis ( MM ) recurrently strike the African Meningitis Belt . This study aimed at investigating factors , still poorly understood , that influence annual incidence of MM serogroup A , the main etiologic agent over 2004–2010 , at a fine spatial scale in Niger . To take into account data dependencies over space and time and control for unobserved confounding factors , we developed an explanatory Bayesian hierarchical model over 2004–2010 at the health centre catchment area ( HCCA ) level . The multivariate model revealed that both climatic and non-climatic factors were important for explaining spatio-temporal variations in incidence: mean relative humidity during November–June over the study region ( posterior mean Incidence Rate Ratio ( IRR ) = 0 . 656 , 95% Credible Interval ( CI ) 0 . 405–0 . 949 ) and occurrence of early rains in March in a HCCA ( IRR = 0 . 353 , 95% CI 0 . 239–0 . 502 ) were protective factors; a higher risk was associated with the percentage of neighbouring HCCAs having at least one MM A case during the same year ( IRR = 2 . 365 , 95% CI 2 . 078–2 . 695 ) , the presence of a road crossing the HCCA ( IRR = 1 . 743 , 95% CI 1 . 173–2 . 474 ) and the occurrence of cases before 31 December in a HCCA ( IRR = 6 . 801 , 95% CI 4 . 004–10 . 910 ) . At the study region level , higher annual incidence correlated with greater geographic spread and , to a lesser extent , with higher intensity of localized outbreaks . Based on these findings , we hypothesize that spatio-temporal variability of MM A incidence between years and HCCAs result from variations in the intensity or duration of the dry season climatic effects on disease risk , and is further impacted by factors of spatial contacts , representing facilitated pathogen transmission . Additional unexplained factors may contribute to the observed incidence patterns and should be further investigated .
Meningococcal meningitis ( MM ) is caused by Neisseria meningitidis ( Nm ) , a commensal bacterium of the human nasopharynx transmitted by direct contact with respiratory droplets from carriers and causing meningitis after crossing the nasopharyngeal mucosa . Epidemics of meningococcal meningitis recurrently strike countries of the African Meningitis Belt [1] . In this sub-Saharan area , MM dynamics is characterized by seasonality and spatio-temporal heterogeneity: the disease is endemic all year round but every dry season , a hyper-endemic or epidemic increase in incidence is observed , the magnitude of which varies between years and regions [2] , [3] . Within a country , localized outbreaks are reported at sub-district scales [4]–[6] . Most epidemics have been caused by meningococci of serogroup A but C , W or X outbreaks have also been reported [7]–[9] . Niger , a landlocked country of the Belt , reported between 1000 and 13500 suspected meningitis cases annually during 2004–2010 , with case-fatality rates of 4–12% [10] . Over the study period ( 2004–2010 ) , the surveillance-based control strategy applied in Niger was to launch reactive vaccination campaigns with A/C or A/C/W polysaccharide vaccines once an outbreak has exceeded a threshold defined at the district level by the World Health Organization ( WHO ) [11] . More than 100 years after the first major epidemic reported in the Belt , the reasons for the peculiar epidemiology of MM in Africa are still poorly understood [12] . A combination of concomitant factors is probably necessary to trigger an epidemic in a particular place at a particular time , involving the organism ( e . g . strain virulence and transmissibility [13] ) , the host ( e . g . immune status and susceptibility [14] , [15] ) and the environment ( e . g . dry climate and dusty winds [16] ) . Previous statistical ecologic studies aiming at explaining the spatio-temporal dynamics of MM epidemics in the Belt were mainly focused on climatic risk factors . These studies sought for drivers of either the seasonality of epidemics ( i . e . when the onset/peak/end of the meningitis season occur ) or their intensity ( i . e . magnitude of incidence over a chosen time period ) at different spatial scales . According to most authors , the temporal dynamics of sub-Saharan climate is the major driver of MM seasonality in the Belt [2] , [3] , [17]–[19] . The suspected contribution of climatic factors to the intensity of epidemics is still under debate . At the country level , Yaka et al partly related annual incidence in Niger to the northern component of wind during November to December [20] . At the district level , annual incidence in four African countries was correlated to rainfall amount and dust load in the pre- , post- and epidemic season [21] and monthly incidence in one district of Ghana was modelled by a combination of various climatic and non-climatic variables [22] . However , to our best knowledge , none of the published statistical models tried to explain intensity of meningitis outbreaks at a finer spatial scale than the district , whereas recent studies in Niger and Burkina Faso demonstrated that outbreaks occur at sub-district scales in highly localized clusters [4]–[6] . Besides , whereas two neighbouring areas ( sharing similar climatic conditions ) can have different epidemic behaviours [4] , [6] , few models combined climatic factors with other types of risk factors suspected to interact , such as previous epidemics , vaccination campaigns , population density or proximity to infected regions . The objective of our paper was therefore to study the influence of climatic and non-climatic factors on the spatio-temporal variations of annual incidence of MM serogroup A , the main etiologic agent over the study period , at the health centre catchment area ( HCCA ) scale in Niger , using a database of laboratory-confirmed cases and developing an explanatory Bayesian hierarchical model from 2004 to 2010 at the HCCA-year level .
This study was approved by the Clinical Research Committee of Institut Pasteur and authorized by the National Consultative Ethics Committee of Niger and the two French data protection competent authorities: CCTIRS ( Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé ) and CNIL ( Commission Nationale de l'Informatique et des Libertés ) . The data collected involving patients were anonymized . Spatial analyses were based on the National Health Map of Niger , created by the Centre de Recherche Médicale et Sanitaire ( CERMES ) in 2008 , at the level of the HCCAs , areas which include all villages served by the same integrated health centre . We used the 2010 updated version of this National Health Map of 732 HCCAs , in the WGS84 geodetic system with UTM 32N projection . On average , a HCCA covers a 40×40 km2 area . The study region comprised the 669 HCCAs of the southern most populated part of Niger , located roughly to the south of the 16th parallel ( Figure 1 ) . It represents 96% of the national population of 17 138 707 inhabitants ( 2012 national census ) . The semi-arid tropical climate of this Sahelian region is characterized by a long dry season from October to May and a rainy season from June to September . In the North lies the Sahara desert , with less than one inhabitant per km2 . The CERMES is the national reference laboratory in charge of the microbiological surveillance of bacterial meningitis in Niger . Basically , cerebrospinal fluid ( CSF ) samples taken from suspected cases of meningitis by health care workers are routinely collected throughout Niger and the etiological diagnosis is carried out by Polymerase Chain Reaction ( PCR ) for all CSF . This enhanced surveillance system is active in the whole country since 2002 , and has been described in detail elsewhere [4] . We used the CERMES database for a retrospective study on confirmed MM A cases between 1 July 2003 and 30 June 2010 . We aggregated MM A cases by HCCA and epidemiological year , defined as running from 1 July of the year n–1 to 30 June of the year n , in order to cover an entire meningitis season . Health districts were contacted to obtain data on polysaccharide vaccines ( number of delivered vaccine doses and/or vaccine coverage ) at the HCCA level over the study period and the previous two years . Full vaccination records could be collected only for Tahoua region over 2002–2010 . Missing data in records from other regions did not enable us to use them in our analyses . We thus studied the effect of previous vaccination campaigns conducted in Tahoua region during the years n-1 and n-2 on MM incidence of year n . We considered different forms for the vaccination covariate: either the coverage rate ( as a continuous or categorized variable ) , the vaccination status ( vaccinated: yes/no ) , or the exceedance of several coverage thresholds ( above threshold: yes/no ) . The cumulative effect of successive vaccination campaigns could not be studied as only one HCCA was vaccinated two years in a row . The Institut National de la Statistique ( INS ) provided the number of inhabitants per village according to the 2001 national census . We aggregated the villages' populations at the HCCA level and applied a mean annual population growth rate of 3 . 3% ( provided by the INS ) . We computed the population density covariate as the number of inhabitants per HCCA divided by the HCCA surface area calculated in ArcGIS software ( version 10 . 0 , ESRI Inc . Redlands , CA ) . We retrieved a shapefile of primary roads from the HealthMapper application of the WHO . This shapefile was superimposed to the National Health Map in ArcGIS . For each HCCA , we computed its minimum distance to the closest primary road and expressed it as a binary covariate ( road versus no road ) or a categorical covariate ( classes of distance ) . The landcover classification for Niger was obtained at a 1 km2 resolution , from the Land Cover Map of Africa from the Global Land Cover 2000 Project [23] . The main vegetation types represented in our geographical subset were different classes of shrublands , grasslands and croplands . Gridded climate data from 2003 to 2010 were extracted from ERA-Interim reanalysis , produced by the European Centre for Medium-Range Weather Forecasts ( ECMWF ) [24] . We retrieved relative humidity , temperature , total precipitation , U ( west-east ) and V ( south-north ) wind components at a 0 . 75° spatial resolution at a daily time-step . To characterize the wind-blown mineral dust emission from the Sahara , we used the Absorbing Aerosol Index ( AAI ) , a dimensionless quantity which indicates the presence of ultraviolet-absorbing aerosols in the Earth's atmosphere [25] . The AAI used in this study is derived from the reflectances measured by SCIAMACHY ( Scanning Imaging Absorption Spectrometer for Atmospheric Chartography ) satellite instrument in the ultraviolet wavelength range [26] . We retrieved monthly gridded data ( 1 . 00°×1 . 25° latitude-longitude grid ) from 2003 to 2010 ( www . temis . nl/airpollution/absaai/ ) . As we were interested in how the climate of a given year or season can influence the annual epidemic magnitude , we calculated multi-monthly means of each climatic variable , averaged over periods relevant to the meningitis season or the seasonal cycles of each climatic variable , both for each HCCA and for the whole study region ( see Figure S1 and Text S1 for further details ) . Shuttle Radar Topography Mission ( SRTM ) elevation data were obtained from the processed CGIAR-CSI ( Consortium for Spatial Information ) SRTM 90 m Digital Elevation Dataset version 4 . 1 [27] , available as 5°×5° tiles at a 3 arc second resolution ( approximately 90 m ) . Six tiles were downloaded and combined in ArcGIS to cover the whole study region . Finally , we collated these multi-source and multi-format spatio-temporal datasets and reconciled data at the HCCA level ( i . e . cartographic , epidemiological , vaccination and demographic data ) and gridded data ( i . e . landcover , climate , AAI and altitude data ) by averaging the gridded values over each HCCA using the statistical computing software R ( version 2 . 15 . 3 , R Core Team , R Foundation for Statistical Computing , Vienna , Austria ) . Then , in addition to the covariates described above , we created supplementary variables to include in the statistical analyses . To take into account potential interactions with bordering countries , we calculated in ArcGIS the minimum distance of each HCCA to the closest border and expressed it as a binary variable ( border versus no border ) or a categorical variable ( classes of distance and classes of bordering countries ) . The five bordering countries of our geographical subset are shown in Figure 1 . To account for potential geographic disparities in accessibility to health centres , we computed for each HCCA the mean distance ( weighted by the villages' population ) from villages to their health centre . To represent the tendency of meningitis to occur in spatio-temporal clusters of neighbouring infected HCCAs , we computed “neighbourhood” variables , using various definitions for this spatio-temporal interaction ( presence/total number of MM A cases in neighbours , mean/maximum incidence and number/percentage of neighbours with MM A cases , over an epidemiological year ) . Neighbours were defined as adjacent HCCAs ( first order neighbours ) , since a previous analysis showed that the median size of spatial clusters was of two neighbouring HCCAs [4] . We also computed «historical» variables describing what happened the previous year in terms of presence/number of MM A cases and incidence , in each HCCA , in its neighbours and in its district ( upper administrative level ) as potential proxies for natural immunity . We computed similar variables for other Nm serogroups at the HCCA level in order to explore potential interactions between serogroups . Finally , we included in the analyses the presence of early cases in each HCCA , defined as cases occurring before 31 December following [20] , as an early start of the hyper-endemic increase could indicate a higher epidemic risk . First , for descriptive purposes , we explored whether the annual epidemic magnitude in the study region could be related to the annual and early geographical distribution of MM A cases and localized outbreaks , using Pearson correlation coefficient . We defined localized outbreaks as HCCAs exceeding an annual incidence threshold of 20/100000 , corresponding to the 95th percentile of incidence , following the primary reference used in [6] . Then , to investigate the spatio-temporal association of MM A annual incidence at the HCCA level with climatic and non-climatic factors , we developed a retrospective hierarchical model in Niger for 2004–2010 , over two geographical subsets: ( i ) over the whole study region of 669 HCCAs and ( ii ) over a subset of 95 HCCAs ( located in Tahoua region ) for which vaccination data were fully available . The modelling approach we adopted was a Bayesian negative binomial generalized linear mixed model ( GLMM ) . We assumed that the number of observed MM A cases in each HCCA i and year t followed a negative binomial distribution with an unknown scale parameter κ and mean μit . We modelled log ( μit ) as a function of covariates as described above and appropriate random effects . Basically , we included spatial random effects at the HCCA level , separated into a spatially unstructured component to capture the influence of unknown factors that are independent across areas and a spatially structured component to capture the influence of spatially correlated effects . The temporal structure was modelled by yearly random intercepts . We included the expected number of cases in each HCCA i and year t as an offset in the model to estimate the incidence rate ratios ( IRRs ) associated with a unit increase in exposure , by exponentiating the regression coefficients . A preliminary forward stepwise covariate selection was performed in R software , estimating parameters by maximum likelihood . The Bayesian multivariate model was subsequently developed in WinBUGS [28] , using Markov chain Monte Carlo ( MCMC ) simulation methods . Further details on the modelling approach are given in Text S2 .
In Niger , from 1 July 2003 to 30 June 2010 , 5512 cases of Nm were biologically confirmed . Other aetiologies included Streptococcus pneumoniae ( N = 850 ) and Haemophilus influenzae ( N = 277 ) . Serogroup A accounted for 72 . 4% ( N = 3988 ) of Nm cases and was largely predominant every year , except during 2006 and 2010 when serogroups X and W represented 48 . 9% and 71 . 6% of Nm cases , respectively . The median age of Nm A cases was 8 . 3 years ( interquartile range ( IQR ) 5–13 ) . Among all Nm A cases , 97 . 0% originated from our study region and 28 . 0% from our Tahoua subset ( Figure 1 ) . Nm A cases essentially occurred over a six-month period: 98 . 1% of them were observed between December and May , with a peak during February–April ( 80 . 4% ) . MM A temporal evolution during July 2003–June 2010 ( Figure 2 ) was characterized by considerable between-year variations ( 17-fold increase between the lowest annual incidence of 0 . 7 per 100000 in 2005 and the highest annual incidence of 11 . 3 per 100000 in 2009 ) . Among the seven years of the study period , the annual MM A incidence across the whole study region was correlated to the number of HCCAs having at least one MM A case ( r = 0 . 95 , p<0 . 01 ) , to the number of localized outbreaks ( r = 0 . 99 , p<0 . 01 ) , to the maximum annual incidence of the localized outbreaks ( r = 0 . 80 , p = 0 . 03 ) , to the number of HCCAs with at least one early case ( r = 0 . 96 , p<0 . 01 ) and to the early incidence across the study region ( r = 0 . 93 , p<0 . 01 ) . The corresponding graphs are displayed in Figure S2 . The median duration of the localized outbreaks ( time between first and last cases ) was 45 days ( IQR 24–75 ) . In the Bayesian multivariate model over the whole study region , the overdispersion parameter of the negative binomial ( κ−1 ) had a posterior mean value of 2 . 586 ( 95% CI = 2 . 223–2 . 998 ) ( Table 1 ) . This was significantly different from zero , which confirmed that the negative binomial formulation was necessary to account for extra-Poisson variation in the dataset . Regarding fixed effects , five covariates were significantly associated with MM A incidence ( the 95% CI of their associated IRR did not contain 1 ) . A reduced risk was associated with higher average relative humidity during the meningitis season ( November–June ) over the study region ( posterior mean IRR = 0 . 656 , 95% CI 0 . 405–0 . 949 ) . Early rains in March in an HCCA represented a protective spatio-temporal factor ( IRR = 0 . 353 , 95% CI 0 . 239–0 . 502 ) . The analyses identified three non-climatic factors; a positive association was found between disease incidence and percentage of neighbouring HCCAs having at least one MM A case during the same epidemiological year ( IRR = 2 . 365 , 95% CI 2 . 078–2 . 695 ) , as well as presence of a road crossing the HCCA ( IRR = 1 . 743 , 95% CI 1 . 173–2 . 474 ) and occurrence of early cases before 31 December in a HCCA ( IRR = 6 . 801 , 95% CI 4 . 004–10 . 910 ) . The variances of the spatially structured and unstructured random effects were respectively 0 . 174 ( 95% CI 0 . 010–0 . 488 ) and 2 . 579 ( 95% CI 1 . 974–3 . 294 ) ( Table 1 ) . The posterior mean estimate of the spatial fraction was 0 . 062 ( 95% CI 0 . 004–0 . 166 ) , meaning that most of the residual area-specific variability was spatially unstructured . Spatial correlation was almost totally captured by the multivariate model . The year-specific random effects also significantly contributed to the model ( Table 1 ) and the inclusion of covariates helped to decrease the temporal random effects variance compared to the null model . A scatter plot of the 4683 fitted posterior mean MM A cases versus the observed MM A cases shows the overall goodness of fit of the model ( Figure 3 . A ) . The inter-annual variations of incidence at the study region level were correctly captured by the model ( Figure 3 . B ) . In Tahoua subset during 2002–2010 , mass campaigns of A/C or A/C/W polysaccharide vaccination have been conducted in 53 HCCAs-years out of 665; the median reported vaccination coverage was 80 . 0% ( IQR 53 . 5–89 . 2% ) . The final multivariate model over Tahoua subset yielded similar results as the model over the whole study region ( see Table S1 ) . The same covariates were independently associated to disease incidence , except that early rains were no longer significant over this smaller geographical subset . No vaccination covariates were significant .
To our knowledge , this study is the first spatio-temporal statistical model in the African Meningitis Belt developed at a spatial scale as fine as the health centre catchment areas and using laboratory confirmed cases of meningococcal meningitis . Relying on advanced statistical methods , we demonstrated that both climatic and non-climatic factors ( occurrence of early rains , mean relative humidity , occurrence of early cases , presence of roads and spatial neighbourhood interactions ) are important for explaining spatio-temporal variations in MM A annual incidence at the HCCA level . Appropriate statistical methods are necessary to investigate the underlying drivers of observed patterns of count data in small areas with spatio-temporal correlations . Hierarchical regression models of the Bayesian family have proven useful to analyse the spatio-temporal dynamics of infectious diseases in different settings , such as dengue in Brazil [29] , soil-transmitted helminth infections in Kenya [30] or schistosomiasis in China [31] . The Bayesian formulation allows to acknowledge the uncertainty associated with all model ( hyper ) parameters ( fixed and random ) , to include a spatial correlation structure within a prior distribution [32] and leads to more robust estimates in particular when the geographical level is small and the disease rare [33] . Such models are still rarely applied to MM in Africa . The modelling approach we adopted was a negative binomial GLMM using Bayesian estimation , to control for unobserved confounding factors and take into account the dependencies over space and time encountered in our dataset , incorporating year-specific and area-specific random effects [32] , [34] . Ignoring these multiple correlations could lead to overestimate the significance of the covariates [31] . This model also accounted for extra-Poisson variation ( overdispersion ) in the count data via the negative binomial formulation , allowing the variance to be larger than the mean . Another noteworthy feature of our analysis lies in its spatio-temporal resolution , uncommon for a country of the Belt . Since outbreaks have been shown to occur in spatially localized clusters at a sub-district level [4]–[6] , we considered primordial to analyse MM A dynamics at a finer spatial scale than the more usual country or district levels [17]–[21] , [35] , [36] . From a public health perspective , the health centre catchment area used in this study is also a judicious choice . Indeed , the Nigerien health care system is based on these integrated health centres , which constitute the lowest health level ( sub-district ) and whose locations are chosen according to accessibility for populations . Regarding the time scale , to comply with our objective of explaining the overall annual burden of MM A in an area during each meningitis season , we chose to conduct analyses at the year level . We did not seek in this paper to model the seasonality of meningitis , which would have implied working at least at month ( like in [22] ) or week ( like in [17] , [18] ) level . Our approach did not allow explaining intra-seasonal temporal dynamics and diffusion patterns – which were partly described in a previous paper [4] . This would constitute a distinct research question that the one tackled in this paper and should be further investigated . The results of this study bring new insights into the epidemiology of MM in the Belt and the risk factors that play a role in the spatio-temporal variations of incidence . First , we observed that , at the study region level , higher annual incidence correlated with larger number of HCCAs having at least one MM A case , with larger number of localized outbreaks and , to a lesser extent , with higher intensity of these localized outbreaks . This brings support to Mueller and Gessner hypothesis that the magnitude of incidence during meningitis seasons in a region or country can increase if the geographical expansion and/or the intensity of localized epidemics increase [3] . The epidemiology thus changes from a regular year with a small number of localized epidemics in the region to an epidemic wave with many localized epidemics . We then sought to evaluate factors that could be associated with the occurrence of these localized incidence increases in a particular area during a particular year . Based on the factors that emerged from our model and that we discuss below , we hypothesize that spatio-temporal variations in MM A incidence between years and HCCAs result from variations in the intensity or duration of the dry season climatic effects on disease risk , and is further impacted by factors of spatial contacts , representing facilitated pathogen transmission . First , the presence of primary roads and neighbourhood effects in the multivariate model indicates that human contacts and movements are important contributing factors that we assume to likely play a role in the transmission of the meningococcus , and/or an epidemic co-factor ( e . g . respiratory virus [3] ) . HCCAs crossed by a road would be statistically more prone to re-infections from distant areas than isolated HCCAs outside the primary road network , and would experience higher transmission levels due to higher intensity of human movements and contacts . Yet , we cannot exclude that differences in accessibility to health centres contributed to bring out primary roads as a risk factor . One could also argue that health centres served by a road sent more CSF samples due to easier logistics . However , another study conducted in Niger and based on reported suspected cases ( not affected by a potential logistic bias ) also showed fewer absences and higher reappearance rates of meningitis cases in districts along primary roads [36] . The percentage of neighbours with cases , representing local spatio-temporal interactions , is not a surprising risk factor . Indeed , a previous study [4] showed that cases usually tended to be clustered in space and that these clusters most often encompassed a small number of HCCAs . Areas with more infected neighbours would be more likely to be infected by local spatial transmission . Then , the presence of climatic parameters in the multivariate model indicates that , beyond an influence on MM seasonality agreed by several authors [2] , [3] , [17]–[19] , climate can have a quantitative impact on inter-annual variations of incidence . The main physiopathological hypothesis for the role of climate is that dryness and dusty winds would damage the nasopharyngeal mucosa and increase the risk of bloodstream invasion by a colonizing meningococcus , and thus the case-to-carrier ratio [2] . Here , we found that annual incidence was negatively correlated to mean seasonal humidity over the study region . This factor was purely temporal ( equal IRR for all spatial units within the same year ) , suggesting that humidity did not have spatial , but only temporal effect . At the study region level , the seasons of highest MM A incidence were also the seasons of lowest mean humidity . The between-year variations in humidity were not large but the results suggest that even a small decrease in humidity , resulting in a small increase in the case-to-carrier ratio according to the physiopathological hypothesis , can have a significant impact on the global MM A risk in all HCCAs , as these dryer conditions start in October and persist over several months ( cumulative effect ) and over a large geographical region . Similarly , Yaka et al detected a quantitative effect of climate on inter-annual variations of meningitis at the country level but November and December northerly winds were their best predictors [20] . This difference might be explained by the fact that they only considered the climatic conditions over the early dry season and not over the whole meningitis season . Interestingly , a second climatic factor , the occurrence of early rains in March , has a significant effect at the HCCA level . It has been noticed that the meningitis season seemed to stop at the onset of the rainy season , again explained by a decrease in invasiveness possibly due to less irritating conditions for the pharyngeal mucosa [2] . Our results are in agreement with this observation and , more precisely , show that the local occurrence of first rains in March , i . e . before the real beginning of the rainy season in the country , is a protective factor . The rains would thus stop the harmful effect of dryness and prevent local outbreaks to further develop . The last and particularly strong factor that emerged from our model is the presence of early cases in a HCCA ( before 31 December ) . It can be interpreted as a risk factor in itself ( an outbreak would have more time to develop if it starts earlier ) , as an indicator of longer exposure to irritating climatic conditions of the dry season , or as a proxy of other factors responsible for the presence of the bacteria and higher levels of carriage and/or invasion . In any case , this parameter remains a strong determinant of high incidence in a HCCA . At the study region level , we also showed that the annual MM A incidence was correlated to the number of HCCAs with at least one early case and to the overall early incidence . Two other studies stressed the importance of early cases in the final size of the epidemic: an early onset was a good predictor of an epidemic at the district level in [37] and the number of cases during the peak months increased with the number of early cases occurring between October and December at the country level ( Niger ) in [20] . WHO also considers early cases in the season as a warning sign of large epidemic [11] . Surprisingly , vaccination the previous or the two previous year ( s ) was not found to be a protective factor in Tahoua subset . However , we cannot rule out the possibility that the low number of vaccinated HCCAs-years in our subset ( 8% ) may have induced a lack of power to show a true protective effect of vaccination . This result could also be partially due to the decline of polysaccharide vaccine efficacy to 87% and 70% at one and two years after vaccination , respectively [38] . It is also possible that the provided data lack representativeness and over-estimate the real coverage . Of note , we decided not to study the impact of year n vaccination on year n incidence within this model formulation , as reactive vaccination would be associated with larger outbreaks ( those which required vaccination ) and , considering delays in implementing vaccination campaigns , would artificially appear as a risk factor in the model [39] , [40] . Residual spatio-temporal variations that remained unexplained by the covariates included in our model suggest that other unknown or unmeasured factors contributed to the observed incidence . First , because our study concerned an ecologic investigation , suspected factors at the individual level ( e . g . age , immuno-depression , smoking… ) could not be accounted for . Then , the temporal variations at the country level could be suspected to be influenced by higher susceptibility due to waning pre-existing immunity [15] or emergence of a new variant that can escape herd immunity [13] , [41] . However , the length of the study period did not enable us to study these effects: molecular characterization of Nm A isolates showed that the same sequence type ( ST-7 ) was predominantly circulating in Niger during 2004–2010 [42] , [43] . At the spatial level , the residual purely spatial variation observed in our model was mainly unstructured . The covariates better explained the spatial correlation , which both reflects shared environmental conditions and true epidemic diffusion , than the unstructured spatial variations . This suggests that other factors specific to each HCCA , such as quality of local health services or local behavioural practices , could contribute to explain the between-area heterogeneity in MM A incidence . The difficulty to measure such factors made the inclusion of area-level random effects necessary . Finally , other unexplained factors , such as respiratory viral co-infections , might contribute to the residual spatio-temporal heterogeneity , via an effect on transmission , colonization and/or invasion [3] . Although difficult to collect retrospectively , these factors should be further investigated at the health centre level and at least properly accounted for in any modelling attempt . Mathematical models , still little developed on this topic [44] , could also help us to better understand the role of carriage and immunity in the epidemic dynamics . This study relied on a unique dataset which provided a very precise picture of MM A spatio-temporal dynamics in Niger over seven years , and has already been used in published spatio-temporal analyses [4] , [5] . The cases were all biologically confirmed by CERMES laboratory , which allowed us to exclude misclassified infectious agents that give similar clinical signs of meningitis . Databases commonly used by most statistical studies on MM in the Belt ( e . g . [17]–[21] ) gather clinically-suspected cases of meningitis , and thus include a mixture of different Nm serogroups and other bacteria such as S . pneumoniae and H . influenzae . In Niger , over our study period , 40% of positive CSFs were infected by another agent that Nm A . Relying only on suspected cases would therefore introduce a large number of misclassified cases . If etiological confirmation by conventional PCR may have led to under-estimation of positive cases [45] , this should have affected the data in a spatially and temporally consistent way , as biological testing was performed with the same PCR assay by the same laboratory all along the study period . Our system may also suffer from underreporting from areas where performing a lumbar puncture and shipping the samples may represent logistical difficulties . We therefore excluded from the analyses the remote northern regions where population is very sparse and mainly nomadic . We can also consider that the potential variability in reporting rates has been taken into account through the explicit inclusion of overdispersion and spatial heterogeneity in our model . Besides , this surveillance system was extended to the whole country in 2002 ( it was only effective in the capital city before ) and might have been unsteady during the first months following its implementation . To reduce this potential temporal bias , we started our analyses in 2004 , being confident that the system had thus reached a stable state . Finally , like in many other settings , the population affected by meningitis may not be entirely covered by the surveillance system . However , we can reasonably assume that most meningitis cases , because of their severity , end up reaching the health centres , with or without prior self-treatment or consultation of a tradi-practitioner [46] . Moreover , social and spatial disparities in care-seeking behaviours are probably reduced by free healthcare offered to all people suffering from meningitis in Niger . For all the reasons above , we are thus confident that the surveillance system is representative enough and that underreporting did not substantially affect the validity of our results , which are more likely to reflect the true underlying risk factors than the spatial disparities in the surveillance system efficiency . To conclude , this study brings new insights into the epidemiology of meningitis in the Belt and allowed us to disentangle the climatic and non-climatic risk factors that play a role in the spatio-temporal variations of annual incidence at the health centre level . Besides , in the light of our results , a potential predictive model could rely on factors such as early cases in an area and its neighbours and early climatic conditions , provided their predictive value is evaluated . This could aid the development of an early warning system at the beginning of the meningitis season , following other recent attempts [47] . Despite new hope brought by the introduction of a meningococcal A conjugate vaccine [48] , the ways in which the meningococcus will adapt to this changing situation are unknown and other serogroups such as W and X might replace A as the dominant serogroup . Such modelling could thus be tested on these serogroups , which would likely be influenced by most of the identified risk factors due to similar ways of transmission and invasion , and applied in other sub-Saharan countries sharing these peculiar epidemiological and climatic features .
|
Meningococcal meningitis ( MM ) is a severe infection of the meninges caused by a bacterium transmitted through respiratory droplets . During January–May , epidemics of MM recurrently strike sub-Saharan countries , including Niger . Understanding why epidemics occur in a particular place at a particular time would help public health authorities to develop more efficient prevention strategies . To date , factors that govern the occurrence of localized outbreaks are still poorly understood and epidemics remain unpredictable . In this retrospective study ( 2004–2010 ) , we developed a statistical model in order to investigate the influence of various factors ( climatic , demographic , epidemiologic , etc . ) on the annual incidence of MM serogroup A at a fine spatial scale ( the health centre catchment area ) in Niger . We found that mean relative humidity and occurrence of early rains were protective climatic factors and that a higher risk was associated with the presence of a road , the percentage of neighbouring areas having cases and the occurrence of early cases before January . These findings contribute to improve our understanding of MM epidemics in Africa and the associated factors , and might be used in the future for the subsequent development of an early warning system .
|
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2014
|
Spatio-Temporal Factors Associated with Meningococcal Meningitis Annual Incidence at the Health Centre Level in Niger, 2004–2010
|
Leishmania is a digenetic protozoan parasite causing leishmaniasis in humans . The different clinical forms of leishmaniasis are caused by more than twenty species of Leishmania that are transmitted by nearly thirty species of phlebotomine sand flies . Pentavalent antimonials ( such as Pentostam or Glucantime ) are the first line drugs for treating leishmaniasis . Recent studies suggest that pentavalent antimony ( Sb ( V ) ) acts as a pro-drug , which is converted to the more active trivalent form ( Sb ( III ) ) . However , sensitivity to trivalent antimony varies among different Leishmania species . In general , Leishmania species causing cutaneous leishmaniasis ( CL ) are more sensitive to Sb ( III ) than the species responsible for visceral leishmaniasis ( VL ) . Leishmania aquaglyceroporin ( AQP1 ) facilitates the adventitious passage of antimonite down a concentration gradient . In this study , we show that Leishmania species causing CL accumulate more antimonite , and therefore exhibit higher sensitivity to antimonials , than the species responsible for VL . This species-specific differential sensitivity to antimonite is directly proportional to the expression levels of AQP1 mRNA . We show that the stability of AQP1 mRNA in different Leishmania species is regulated by their respective 3’-untranslated regions . The differential regulation of AQP1 mRNA explains the distinct antimonial sensitivity of each species .
Leishmaniasis is a protozoan parasitic infection in humans and other mammals that is transmitted by the bites of sandflies . The infection is caused by more than 20 different Leishmania species . The clinical manifestations range from self-healing cutaneous leishmaniasis ( CL ) to a potentially life threatening mucocutaneous leishmaniasis ( MCL ) [1] to the lethal , if untreated , visceral lesihmaniasis ( VL ) [2] . The disease is endemic in parts of 88 countries in five continents—the majority of the affected countries are in the tropics and subtropics . Approximately two million new cases are estimated to occur annually , of which 1 . 5 million are categorized as CL and 500 , 000 as VL . The parasite exists in two distinct morphological forms . The promastigotes form resides in the insect gut and appears to have slipper-like bodies with long flagella . The vertebrate forms of the parasite , amastigotes , have spherical , oval-shaped , aflagelleted bodies that reside in the macrophages of mammalian hosts . The first line of treatment against all forms of leishmaniasis is the pentavalent antimony-containing drugs sodium stibogluconate ( Pentostam ) and meglumine antimonite ( Glucantime ) . However , drug resistance is a major impediment to the treatment of leishmaniasis . For example , approximately 60% of the patients in India do not respond to antimonial treatment due to acquired resistance [3] . Mechanisms of antimonial resistance in Leishmania have been explored extensively for several decades and are considered to be multifactorial [4] [5] . We have shown that laboratory-raised arsenic resistant L . tarentolae , which are cross resistant to antimonials , overproduced trypanothione ( T[SH]2 ) [6] , the major reduced thiol in Kinetoplastida [7] , and conferred resistance by providing excess Sb-[TS]2 conjugates for the efflux pump in the plasma membrane [8] . The Sb-[TS]2 conjugates were shown to be sequestered into small intracellular vesicles near the flagellar pocket [9] . These mechanisms also seemed to play an active role in the pathogenic Leishmania [4] and also in field isolates [10] [11] . Amastigote-specific pentavalent antimonial reducing capability has also been implicated in VL [12] . Variability in the frequency of incidence of clinical antimonial resistance among Leishmania species has been reported [13] . However , it is not known whether an intrinsic variation in antimonial sensitivity exists among different Leishmania species . We reported that L . major was 50–70 times more sensitive to antimonite when compared to L . infantum [14] . Sarkar et al ( 2012 ) reported that Leishmania strains causing self-healing CL exhibited greater susceptibility towards oxidative stress as a result of low thiol content [15] . However , a comprehensive species wide study of all antimonial resistance markers reported so far is absent , and hence , the mechanism ( s ) of species-specific antimonial sensitivity is unknown . We discovered the first aquaglyceroporin from Leishmania ( AQP1 ) and showed its direct relationship to antimonite [Sb ( III ) ] , the active component of Pentostam and Glucantime , uptake [14] and resistance [16] . Overexpression of AQP1 in Leishmania cells led to hypersensitivity to antimonite , and disruption of one of the two LmAQP1 alleles in L . major conferred a 10-fold increase in resistance to Sb ( III ) [14] . Later , these findings were corroborated in field isolates from India [17 , 18] and Nepal [19] . Besides the metalloids arsenite [As ( III ) ] and Sb ( III ) , the water conduction capacity of AQP1 is 65% of that of the classical water channel , human AQP1 . Unlike the Trypanosoma and Plasmodium AQPs , AQP1 is a mercurial independent water channel . It also conducts glycerol , glyceraldehyde , dihydroxyacetone and sugar alcohols . We have also identified AQP1’s key role in osmoregulation and osmotaxis , which play crucial functions during parasite transmission [20] . Also , AQP1 is the first aquaglyceroporin to be exclusively localized in the flagellum of any organism . In intracellular amastigotes , it is localized in the flagellar pocket , rudimentary flagellum , and contractile vacuoles [20] . We have shown the involvement of flexible loop C of AQP1 in determining the substrate specificity of the channel [21 , 22] . Additionally , we showed that AQP1 was positively regulated at the post-translational level by a mitogen activated protein kinase 2 [17] . Therefore , AQP1 plays a major role in Leishmania cellular physiology and drug response . During the course of our research with AQP1 , we noticed that the muccocutaneous and cutaneous species were much more sensitive to Sb ( III ) when compared to the visceral species . Since AQP1 is the sole facilitator of Sb ( III ) in Leishmania , we asked whether AQP1 is driving this species-specific antimony sensitivity . In the absence of RNA polymerase II promoters , Leishmania genes are constitutively transcribed from large gene clusters as polycistronic pre-mRNAs . Steady-state levels of mature monocistronic mRNAs are regulated post-transcriptionally primarily by trans-splicing and polyadenylation [23 , 24] . Several examples in Leishmania species support the notion that post-transcriptional regulation of developmentally expressed transcripts involves sequences present mainly in the 3’-UTR [25 , 26 , 27 , 28] , and more rarely in intergenic regions between tandemly repeated genes [29 , 30] . The 3’-UTR also regulates logarithmic-stationary phase gene regulation [31] . In this study , we mapped and cloned the 3’-UTRs of AQP1 mRNA from six different Leishmania species representative of different clinical pathologies and endemic regions . Each approximately 1 . 8-kb 3’-UTR is highly U-rich ( 30% ) with only 49% GC ( in a highly GC-rich [∼ 60%] genome ) , and contains several well-known instability elements described in higher eukaryotes . Although the AQP1 protein sequences among these six species are more than 80% homologous , 3’-UTRs of AQP1 mRNAs differ significantly . We show that the species-specific antimonial sensitivity in Leishmania is uniquely driven by AQP1 , and that it is mediated by post-transcriptional regulation through the respective distinct 3’-UTR of each species-specific AQP1 mRNA .
Work here was carried out on six Leishmania species: three cutaneous ( L . major , L . tropica and L . panamensis ) , one mucocutaneous ( L . braziliensis ) and two visceral ( L . infantum and L . donovani ) . Our rationale for choosing these species was to represent every endemic continent ( Asia [L . donovani , L . infantum , L . major and L . tropica] , Africa [same as Asia] , Europe [L . infantum , L . major and L . tropica] and Americas [L . infantum , L . braziliensis and L . panamensis] ) ; clinical manifestation ( VL- L . donovani , L . infantum; CL- rest of the species; MCL- L . braziliensis ) ; and mode of transmission ( anthroponotic- L . donovani and L . tropica; zoonotic- rest of the species ) . Intrinsic difference in Sb ( III ) sensitivity in the representative six selected species was determined by exposing the promastigotes at increasing concentrations of potassium antimonyl tartrate . L . infantum was the least sensitive species . The EC50 data showed that it was 1 . 4 times more resistant when compared to L . donovani and 46 , 15 , 20 and 7 times more resistant than the cutaneous species , namely , L . major , L . tropica , L . braziliensis and L . panamensis respectively ( Table 1 ) . However , the cutaneous species also differed in sensitivity to Sb ( III ) among themselves . L . tropica , L . braziliensis and L . panamensis were 3 , 2 . 3 and 6 times more resistant to Sb ( III ) , respectively , when compared to L . major ( Table 1 ) . To study whether this species-specific antimony sensitivity was also operational in amastigotes , we determined the EC50 of potassium hexahydroxy antimonate [Sb ( V ) ] using intra-macrophagial amastigote model . We did not use pentavalent organo-antimonials as contaminating Sb ( III ) levels can represent more than 30% of total Sb [32] . The EC50 data showed that L . infantum was the least sensitive species among the six we tested . It was 1 . 4 , 25 , 9 . 6 , 20 , and 8 . 6 times more resistant when compared to L . donovani , L . major , L . tropica , L . braziliensis , and L . panamensis , respectively ( Table 1 ) . Again there was a slight variation in sensitivity among the cutaneous species . L . tropica , L . braziliensis and L . panamensis were 2 . 6 , 1 . 3 and 3 times more resistant to SbV when compared to L . major ( Table 1 ) . In general , the CL species were much more sensitive to antimony compared to the VL species . To understand this differential intrinsic antimonial sensitivity among the species in greater detail , we first examined the time-dependent intracellular accumulation of Sb ( III ) in the promastigotes . L . braziliensis showed the fastest and highest accumulation of Sb ( III ) at any given time followed by L . major , L . panamensis and L . tropica ( Fig . 1A ) . The lowest rate and total accumulation of Sb ( III ) were observed in L . donovani and L . infantum ( Fig . 1A ) . Generally , the antimony sensitive CL species accumulated more total antimony when compared to the VL species . Did this signify lower uptake or increased efflux in the VL species and higher uptake or slower efflux in the CL species ? To answer this question , we prepared everted membrane vesicles from promastigotes of each species , and Sb ( TS ) 2 conjugate accumulation was measured in the presence of 10 mM ATP as an energy source . The rates of transport of Sb-[TS]2 conjugate in the everted membrane vesicles of the six species were not significantly different from each other ( Fig . 1B ) . These results indicate that differential sensitivity among different species is not due to a change in the efflux rate of antimony . Mechanisms of antimonial resistance in Leishmania have been proposed to be multifactorial . Four major components shown to be modulated in many laboratory-raised and clinical antimonial resistant Leishmania spp . are: ( i ) higher MRPA level for greater intracellular sequestration of Sb-[TS]2 conjugates; ( ii ) overproduction of total thiols , specifically T[SH]2; ( iii ) faster efflux of Sb-[TS]2 conjugates through an unknown efflux system in the plasma membrane; and ( iv ) downregulation of AQP1 . Thus , we examined the MRPA mRNA levels in all six species first . The CL species showed much more MRPA mRNA when compared to the VL species . L . panamensis had the highest level of MRPA mRNA , which was about 7 . 4 fold more compared to L . donovani ( Fig . 2A ) . L . donovani and L . infantum had similar levels of MRPA mRNA . The levels of MRPA mRNA in L . major , L . tropica , L . braziliensis and L . panamensis were approximately 2 . 3 , 5 . 4 , 4 . 5 and 7 . 4 fold respectively , compared to L . donovani ( Fig . 2A ) . Therefore , having more MRPA mRNA does not justify the CL species being more sensitive to the antimonials . More mRNA does not necessarily result in more protein . However , the difference at the MRPA protein level between the CL and VL species does not explain the difference in antimony sensitivity between the species . Next , we measured the total non-protein thiol levels among all the species . There was no correlation between total thiol levels and species-specific antimonial sensitivity ( Fig . 2B ) . For example , three CL species , L . major , L . braziliensis , and L . panamensis , and one VL species , L . donovani , showed similar levels of non-protein total thiols ( Fig . 2B ) . Another CL species , L . tropica , showed almost one-half of the thiol levels compared to other CL species , whereas L . infantum had approximately double that of L . donovani . Fig . 1B shows that the activity of the efflux pump was similar in all six species . Taken together , these data indicated that the levels of total Sb ( III ) accumulation can only be different if the rate of uptake differed among the species . Next , we measured the mRNA levels of AQP1—the only Sb ( III ) facilitator in Leishmania . Relative ( normalized against L . donovani ) AQP1 mRNA levels were calculated by the 2-ΔΔCt method . The AQP1 mRNA levels corroborated with our EC50 data in promastigotes and intracellular amastigotes . L . infantum showed the lowest level of AQP1 mRNA among the species tested . L . major , L . tropica , L . braziliensis and L . panamensis produced 37 , 13 , 56 and 52 fold more AQP1 mRNA , respectively , when compared to L . donovani ( Fig . 2C ) . Therefore , the CL species showed much more AQP1 mRNA accumulation than the VL species , which also corroborated our Sb ( III ) accumulation data ( Fig . 1A ) . We have shown that the physiological function of AQP1 in Leishmania is osmoregulation . The strategic flagellar localization of AQP1 helps the parasite sense the changing osmotic environments between the vector and the host [20] . We could not detect native AQP1 expression at the protein levels by our anti-peptide polyclonal anti-AQP1 antibody in wild type Leishmania promastigotes [20] . Thus , it was reasonable to determine the functionality of AQP1 in all six species by determining their osmoregulatory capacities under hypo-osmotic shock ( 50% reduction in extracellular osmolarity ) , as we previously showed that osmoregulatory capacity was directly proportional to the AQP1 protein levels [20] [17] in the membrane . We observed that the CL species osmoregulate more efficiently when compared to the VL species , suggesting that CL species express more functional AQP1 than the VL species ( Fig . 3 ) . This corroborated with their EC50 ( Table 1 ) , Sb ( III ) accumulation ( Fig . 1A ) and AQP1 mRNA levels ( Fig . 2C ) , i . e . , promastigotes with less AQP1 mRNA ( VL species ) accumulated less amount of Sb ( III ) , which resulted in higher resistance to antimonials when compared to promastigotes with more AQP1 mRNA ( CL species ) . L . donovani and L . infantum promastigotes swelled more rapidly ( drops in absorbance ) and recovered their volumes ( rises in absorbance ) slowly when compared to all CL species ( Fig . 3 ) . It is interesting to note that the CL species L . braziliensis and L . panamensis showed the highest levels of AQP1 mRNA ( Fig . 2C ) , and they were the most efficient osmoregulators ( Fig . 3 ) . Among the VL species , L . infantum showed the highest antimonial resistance and was the poorest osmoregulator . It swelled more than L . donovani and recovered even slower from the hypo-osmotic shock ( Fig . 3 ) . There is a very strong correlation between the antimony sensitivity among the species , the osmoregulatory capacity and AQP1 mRNA levels of the promastigotes . Since Leishmania does not have any transcriptional control , we investigated the stability of AQP1 mRNA in all six species to determine if any post-transcriptional regulation is active in lowering the mRNA levels in the VL species . To determine the turnover rate of AQP1 mRNA , the mid-log phase promastigotes of all species were treated with sinefungin ( to stop pre-mRNA processing ) [33] followed by actinomycin D ( to inhibit transcription ) , [34] and cells were harvested up to 130 minutes . The decay of AQP1 mRNA was determined by qPCR and normalized against the 0 minute ( time of addition of actinomycin D ) . The half-lives of AQP1 mRNA from VL species , i . e . , L donovani and L . infantum , were determined to be 40 min and 26 min , respectively; on the other hand , the half-lives of the AQP1 mRNA from CL species , such as L . tropica and L . panamensis , were 57 and 117 min , respectively . The half-lives of the AQP1 mRNA in L . braziliensis and L . major were estimated to be > 130 min ( Fig . 4 ) . Therefore , AQP1 mRNA from the CL species was much more stable than from the VL species , which follows a similar trend that was observed in their respective steady-state levels of AQP1 mRNA: L . braziliensis ≥ L . panamensis > L . major > L . tropica > L . donovani > L . infantum ( Fig . 2C ) . Since mRNA stability in Leishmania is mostly controlled by the 3’-UTR [23] , we cloned the individual 3’-UTRs of AQP1 mRNA from all six species . The length of the 3’-UTR of the AQP1 mRNA was approximately 1 . 8 kb in all species examined as mapped by 3’-RACE PCR . The protein sequence alignment of AQP1 from all six species showed that they were very close to each other , with an overall similarity of about 88% ( S1 Fig . ) . Additionally , the overall identity between the open reading frames ( ORF ) of AQP1 in all six species was about 66% ( S2 Fig . ) . However , 3’-UTRs from all six species were more divergent and showed an overall identity of about 24% ( S3 Fig . ) . A greater similarity was seen between the pairs . L . infantum and L . donovani 3’-UTRs were 99% identical ( S4 Fig . ) . The CL species 3’-UTRs can be divided into two groups . L . tropica and L . major were 78% identical ( S5 Fig . ) , while L . panamensis and L . braziliensis 3’-UTRs were 94% identical ( S6 Fig . ) . Thus , based on their 3’-UTR sequences , the six species we examined can be divided into three distinct groups: VL ( L . donovani and L . infantum ) , CL1 ( L . major and L . tropica ) and CL2 ( L . braziliensis and L . panamensis ) groups . Based on the data presented above , we concluded that the stability of AQP1 mRNA plays a significant role in dictating the species-specific antimonial sensitivity in Leishmania . Given that 3’-UTR sequences between VL and CL species are very divergent , we hypothesized that these sequences are responsible for the species-specific differential AQP1 mRNA stability . To determine the underlying mechanism , a series of chimeric constructs were made with the full length 3’-UTR ( ∼ 1 . 8 kb ) of the AQP1 mRNA from each of the six species by cloning them at the 3’ end of the luciferase ( LUC ) reporter gene ( Fig . 5A ) . To direct accurate 5’ and 3’ processing of the LUC chimeric transcripts , these cassettes were flanked by an upstream α-tubulin intergenic ( IR ) region and by a downstream IR region ( ∼ 200 bp ) of each species-specific 3’-UTR of AQP1 mRNA . In trypanosomatids , polyadenylation is often directed by trans-splicing signals that are located 100–400 nucleotides downstream of the polyadenylation site [28 , 35 , 36] . These chimeras were expressed from an episomal plasmid pSPYNEOαLUC with neomycin phosphotransferase ( NEO ) as a marker . The vector alone , where LUC expression was only regulated by the intergenic regions of α-tubulin , is referred to in the present work as pLUC . Each of the species-specific LUC-AQP1-3’-UTR constructs were named pLUC-Ld ( L . donovani ) , pLUC-Li ( L . infantum ) , pLUC-Lm ( L . major ) , pLUC-Lt ( L . tropica ) , pLUC-Lb ( L . braziliensis ) , and pLUC-Lp ( L . panamensis ) ( Fig . 5 ) . Six individual chimeric constructs and the vector alone were transfected into the six species generating 42 transfectants . We also evaluated the relative copy number of LUC–containing plasmids by qPCR using pteridine reductase 1 ( PTR1 ) as the housekeeping control , the levels of which are similar in all transfectants ( S1A Table ) . The proper processing of 3’ end of UTRs from all transfectants with chimeric plasmids was determined by 3’-RACE PCR and sequencing . They had identical processing when compared to the 3’-UTR ends of the native AQP1 mRNA . Therefore , it was reasonable to deduce that the LUC expression and activity in the transfectants would largely depend upon the steady-state levels of LUC mRNA dictated by their stability in each species . We thus tested the role of each of the six 3’-UTRs in regulating LUC mRNA steady-state levels , stability , LUC protein levels and LUC activity in a species-specific manner . L . donovani transfected with pLUC-Li produced the lowest levels of LUC mRNA when compared to pLUC control , whereas pLUC-Ld produced slightly more ( only 0 . 37 fold ) . However , accumulation of the LUC mRNA under the control of 3’-UTRs from the CL species was 3 to 8 fold higher than that of the pLUC control ( Fig . 5B ) . pLUC-Lt , pLUC-Lm , pLUC-Lb , and pLUC-Lp transfectants produced 3 . 7 , 5 . 7 , 8 . 4 and 6 . 6 fold more LUC mRNA , respectively , compared to pLUC alone ( Fig . 5B ) . These data corroborated with our LUC mRNA stability , LUC protein expression and activity , suggesting that regulation occurs at the level of AQP1 mRNA stability . Indeed , LUC mRNA was most unstable in pLUC-Li transfectants , with a half-life of 25 min ( Table 2 ) . pLUC-Ld extended that for 39 min , and for pLUC-Lt , the half-life was 83 min . pLUC-Lm , pLUC-Lb , pLUC-Lp transfectants gave rise to the most stable LUC mRNA , extending their half-lives to over 130 min ( Table 2 ) . Western blot with an anti-luciferase antibody and densitometric analysis using α-tubulin as a loading control revealed that luciferase protein expression was lowest in pLUC-Li transfectants , at only about 21% , which resulted in about 18% luciferase activity when compared to pLUC control ( Fig . 5C and D ) . pLUC-Ld , pLUC-Lm , pLUC-Lt , pLUC-Lb and pLUC-Lp transectants expressed 1 . 5 , 4 . 5 , 2 . 6 , 5 . 5 , and 4 . 7 times more luciferase , respectively , when compared to pLUC-Li . This is also comparable with LUC activity , which was increased in the following order in the transfectants: pLUC-Li < pLUC-Ld < pLUC-Lt < pLUC-Lb < pLUC-Lm < pLUC-Lp ( Fig . 5C ) . Similar results were obtained when transfecting the chimeric LUC constructs with the 3’-UTRs in L . infantum . The 3’-UTRs from the CL species generated 3 to 5 fold more LUC mRNA when compared to the VL species ( S7A Fig . ) . The CL 3’-UTRs also made LUC mRNA more stable ( higher half-life ) in L . infantum ( Table 2 ) . These results also corroborated with LUC expression and activity ( S7B Fig . ) . When transfected in L . major , pLUC-Ld and pLUC-Li constructs produced similar basal levels of LUC mRNA when compared to pLUC alone . However , the 3’-UTRs from the CL species generated 4–5 fold more LUC mRNA ( Fig . 6A ) . These data corroborated with our LUC mRNA stability , LUC protein expression and activity . LUC mRNA produced from all constructs in L . major were quite stable , and the half-life was determined to be >130 min ( Table 2 ) . Densitometric analysis revealed that LUC expression ( Fig . 6C ) is similar in all CL UTR constructs . pLUC-Li and pLUC-Ld transfectants express LUC at 77% and 93% of pLUC control ( Fig . 6B ) , respectively . The highest LUC activity was observed in cells expressing LUC from pLUC-Lm , followed by pLUC-Lp , pLUC-Lt and pLUC-Lb . Similar results were obtained when transfecting the chimeric LUC constructs with the 3’-UTRs in L . tropica . LUC mRNA produced from all constructs in L . tropica were quite stable , and the half-life was determined to be >130 min , except in pLUC-Li and pLUC-Ld , which were 87 min and 89 min , respectively ( S8A Fig . , Table 2 ) . Four CL 3’-UTRs regulated to produce 85–101% LUC protein compared to pLUC control , which resulted in 81–100% LUC activities in pLUC-Lm , pLUC-Lt , pLUC-Lb and pLUC-Lp transfectants ( S8B Fig . ) . Promastigotes of L . braziliensis transfected with pLUC-Ld construct produced the lowest levels LUC mRNA when compared to pLUC alone , whereas pLUC-Li produced a little more ( only 0 . 42 fold ) . However , the UTRs from the CL species generated 4 . 5–6 . 0 fold more LUC mRNA compared to pLUC cells ( Fig . 7A ) . pLUC-Lb transfectants produced 4 . 5 fold more LUC mRNA , whereas pLUC-Lm , pLUC-Lt , and pLUC-Lp transfectants generated about 6 fold more ( Fig . 7A ) compared to pLUC . This data corroborated with our LUC mRNA stability , LUC protein expression and activity . LUC mRNAs produced from pLUC-Lm , pLUC-Lt and pLUC-Lp constructs in L . braziliensis were quite stable , and the half-lives were determined to be >130 min . The half-lives of LUC mRNAs generated from pLUC-Ld , pLUC-Li and pLUC-Lb were 84 min , 80 min and 98 min , respectively ( Table 2 ) . Densitometric analysis of LUC expression showed 72–75% of pLUC control in pLUC-Ld and pLUC-Li transfectants , which resulted in 60–65% of pLUC control LUC activity in those cells ( Fig . 7B ) . The CL 3’-UTRs behaved in a similar manner , producing 101–122% luciferase protein compared to pLUC control , which resulted in 100–110% of pLUC control LUC activity in pLUC-Lm , pLUC-Lp and pLUC-Lt transfectants ( Fig . 7B and C ) . However , LUC activity from pLUC-Lb construct was 86% compared to pLUC alone . Similar results were obtained in L . panamensis transfectants . The CL 3’-UTRs generated more stable LUC mRNAs with longer half-lives compared to the VL species ( S9A Fig . , Table 2 ) . Higher LUC protein expression and activity were also observed in the CL species ( S9B Fig . ) . Lastly , we argued that if 3’-UTR was driving the species-specific antimony sensitivity , swapping the 3’-UTRs between Ld and Lm-AQP1 should lead to contrasting antimonial sensitivity of a visceral species . Thus we cloned LdAQP1 and LmAQP1 ORFs into pSP72-YHYG-αtubIR with their native 3’-UTRs and also chimeric constructs where the 3’-UTRs were swapped . The four constructs , namely , LdAQP1-Ld3’-UTR , LdAQP1-Lm3’-UTR , LmAQP1-Ld3’-UTR , and LmAQP1-Lm3’-UTR , along with the vector alone control , were transfected into the L . donovani strain LdBOB . We evaluated the relative copy number of AQP1-3’-UTR-containing plasmids by qPCR using hygromycin phosphotransferase as the target and PTR1 as the housekeeping control , the levels of which were similar in all transfectants ( S1B Table ) . Sb ( III ) sensitivity of the transfectants were measured in promastigotes ( Fig . 8A ) and intracellular amastigotes ( Fig . 8B ) . As expected , overexpression of AQP1 made the VL strain hypersensitive to Sb ( III ) when compared to the vector alone control albeit to a different degree depending on which type of 3’-UTR the ORF had at its 3’ end in both promastigotes and amastigotes . The VL species was 6–24 times more sensitive to Sb ( III ) in both stages of the parasite whenever Lm-3’-UTR was present at the 3’ end of AQP1 when compared to the Ld-3’-UTR constructs ( Fig . 8A and B ) . A similar trend was observed during intracellular accumulation of Sb ( III ) . L . donovani promastigotes accumulated significantly more Sb ( III ) overexpressing AQP1 with Lm-3’-UTR constructs ( Fig . 8C ) . It was interesting to note that the osmoregulatory capacity of the VL species improved considerably with Lm-3’-UTR at the 3’ end of AQP1 , whereas promastigotes overexpressing AQP1 with Ld-3’UTR were still poor osmoregulators , although better than the vector alone controls ( Fig . 8D ) . These data convincingly show that 3’-UTR of AQP1 plays a major role in determining the species-specific antimonial sensitivity of Leishmania and that the effect is not stage-specific .
Although antimonial drugs are still the first line of treatment against all types of leishmaniasis , treatment failure is often a major cause of concern . The issue of treatment in American CL ( ACL ) is even more complex because of the factors that often influence the efficacy of the drugs , including the intrinsic and acquired variation in the sensitivities of the different Leishmania species [37] . Pentavalent antimonial drugs are the most prescribed treatments for American CL and MCL . The WHO recommends treating ACL with pentavalent antimonials at a dose of 20 mg/kg daily for 28 days [38] . There is no single effective treatment for all species of Leishmania . The choice of treatment strategy is based on geographical location and the infecting species [39] . Because of regional and species variability in treatment , doses of antimonials cannot be standardized , and local physicians determine appropriate dosages based on experience [40] . Therefore , it is clinically recognized that species and even strain-specific ( regional ) antimonial sensitivities are prevalent , which creates major impediments to adopting a single dose strategy for all types of leishmaniasis . There is also molecular and phenotypic heterogeneities that emerged in a natural L . donovani population from Nepal under antimonial treatment pressure . It has been proposed that each genetically distinct population can develop an antimonial resistant phenotype with a different molecular basis [41] . However , no single mechanism was identified for L . donovani . On the other hand , Leishmania strains causing self-healing CL were proposed to have greater susceptibility towards oxidative stress because they produced less non-protein thiols when compared to the VL species from the Indian subcontinent [15] . Here , we address this controversial issue of species-specific antimonial sensitivity in Leishmania by examining four Old World Leishmania species such as L . donovani , L . infantum , L tropica and L . major , and two New World species , namely L . panamensis and L . braziliensis . Our goal was to examine all commonly used drug resistant markers and determine whether there are any direct correlations . It is interesting to note that when some L . infantum clinical isolates from the Middle East showed CL phenotype , they produced 3–4 fold more AQP1 mRNA [42] . Therefore , the results of our study can be utilized for a larger study with clinical isolates and strains , but outside the scope of the work presented here . The most commonly used antimonial resistance markers are: ( i ) MRPA[9]; ( ii ) thiols [6 , 11 , 41]; ( iii ) an unknown efflux system [8 , 43]; and ( iv ) AQP1 [16 , 19 , 44 , 45 , 46] . We examined carefully each of these factors in all six species . First , there is a correlation between the amount of MRPA mRNA and species-specificity ( Fig . 2A ) . However , this is in contrast to the experimental evidence that MRPA is generally overexpressed in drug resistant Leishmania isolates [47] . MRPA is known to transport drug-thiol conjugates in Leishmania [9] and higher eukaryotes , including mammals [48 , 49] . Therefore , if correlated to the species-specific antimonial sensitivity , the MRPA mRNA levels in the CL species should have been lower along with lower non-protein thiol levels compared to the VL species . However , there was no discrimination in non-protein thiol levels between the VL and CL species ( Fig . 2B ) , the second commonly used marker in antimonial resistance . Thus , we conclude that MRPA and non-protein thiol levels are not correlated to the species-specific antimonial sensitivity in Leishmania . The third factor is the Sb-[TS]2 efflux pump , but the rate of efflux is similar in all six species ( Fig . 1B ) . The fourth factor is downregulation of AQP1 , and this seems to be driving the species-specific antimonial sensitivity . The two VL species consistently showed less AQP1 mRNA ( Fig . 2C ) , Sb ( III ) accumulation ( Fig . 1A ) and osmoregulatory capacity ( Fig . 3 ) . The parameters we studied to determine AQP1 functionality at the protein level and 3’-UTR derived mechanism ( s ) were extremely difficult to achieve with intracellular amastigotes , the clinically relevant form of the parasite , which need to be generated by in vitro macrophage infections . Axenic amastigote was developed to overcome this limitation , but was not successful for virulent Leishmania and specifically the CL species [50] . Also , we showed a similar pattern of antimonial resistance in the intracellular amastigotes of the VL species when compared to the CL species ( Table 1 ) . Hence , we chose to work with promastigotes , the vector form of the parasite . In this study , we established that the species-specific antimonial sensitivity in Leishmania is being driven by the regulation of AQP1 at the mRNA level . Additionally , we showed that AQP1 mRNA is highly unstable in the VL species compared to the CL species ( Fig . 4 ) . Since , Leishmania does not have any transcriptional control , we determined the role of the AQP1 3’-UTRs in the species-specific stabilization of AQP1 mRNA using the luciferase reporter assay . As expected , the VL species AQP1 3’-UTR renders LUC mRNA unstable in all six species except in L . major ( Table 2 ) , which was corroborated by their basal LUC mRNA levels ( Figs . 5A , B , 7A and S7A–S9A ) and LUC activity ( Figs . 5C , 7B and S7B–S9B ) . Much less LUC mRNA accumulates under the control of the VL 3’-UTRs both in VL and CL Leishmania species ( Figs . 5B–7A and S7A–S9A ) , suggesting that there is no species-specific factor responsible for the regulation at the level of AQP1 mRNA stability , but rather it is the differences in sequences of the 3’-UTRs that make the trans-acting factor ( s ) more conducive to binding or not and to stabilizing or destabilizing the AQP1 mRNA . The correlation between LUC mRNA levels and LUC protein activity is not linear in L . major ( Fig . 6 ) , L . tropica ( S8 Fig . ) and L . panamensis ( S9 Fig . ) for the VL 3’-UTRs , suggesting that less mRNA is not necessarily associated with reduced protein levels . This suggests the presence of other factors ( or differential expression of these factors ) in some CL species that could increase translation rates , despite the fact that amounts of mRNA are low for certain genes including LUC . However , levels of AQP1 native mRNA ( Fig . 2C ) seem to be correlated linearly with protein expression as it corroborated with functional properties of AQP1 , such as osmoregulation ( Fig . 3 ) and Sb ( III ) accumulation ( 1A ) . The CL 3’-UTRs are similarly stable in VL species as they are in CL species , suggesting that there is no effect from any species-specific trans-acting factor . It was interesting to note that all 3’-UTRs of AQP1 provided LUC mRNA with the highest level of stability in L . major ( Fig . 7 ) , which is the most antimonial sensitive species under investigation . The most compelling evidence that the 3’-UTRs play a major role in AQP1 species-specific functionality comes from the fact that Lm-3’-UTR made Ld-AQP1 function three times more efficiently compared to its native 3’UTR ( Fig . 8 ) and vice-versa . Additionally , AQP1 3’-UTR of L . braziliensis stabilized LUC mRNA , resulting in more LUC activity and protein expression in the VL species than in its native environment ( Figs . 5 and 8 ) , leading us to propose that L . braziliensis might harbor unique factors that are specific for its own AQP1 3’-UTR . However , basal level and stability of AQP1 mRNA in L . braziliensis were comparable to other CL species , such as L . major ( Figs . 2C and 4 ) . This difference could be attributed to the presence of specific AQP1 ORF sequences upstream to the 3’-UTRs in the native mRNA . In this context , it is interesting to note that L . braziliensis and L . panamensis ORF sequences differ ( S2 and S6 Figs . ) significantly from the four other species . L . donovani , L . infantum , L . major and L . tropica ORF sequences are closer to each other , sharing 87% identity among them ( S10 and S11 Figs . ) , whereas the overall identity among all six ORFs is 66% ( S2 Fig . ) . Therefore , the role of AQP1 ORFs in determining the native mRNA stability warrants further research in this direction , which is in progress . The fundamental question is why there is a species-specific regulation of AQP1 in Leishmania . AQP1 is an adventitious facilitator of Sb ( III ) ; therefore , this species-specific antimonial resistance driven by AQP1 is a bonus that the VL species enjoy during treatment . Although acquired antimonial resistance in Leishmania is multifactorial , it is tempting to speculate that more antimonial resistant cases are observed in VL [13] due to down regulation of AQP1 , because it is easy to downregulate something in which the intrinsic trend of higher mRNA instability leading to less production is already in place because the VL species likely does not need efficient osmoregulation . On the other hand , the physiological function of AQP1 is osmoregulation , and we showed that the CL species are better osmoregulators ( Fig . 3 ) . Is it possible that the CL species face greater osmotic challenges during vector to host transmission , or vice-versa , and more AQP1 helps them to overcome that barrier ? AQPs are also implicated in a number of unrelated physiologic processes and functions , such as lipid metabolism , cell migration , epidermal biology , cell adhesion , and neural signal transduction [51] . Thus , it is also tempting to speculate that species-specific AQP1 expression may help the respective species to find their appropriate niches , resulting in tissue tropism . In VL and most CL cases , a standard single dose of 20mg/kg/day for 28 days of antimonials has been mandated by WHO since 1990 [52] . However , our data emphasize that treating all types of leishmaniasis with the same systemic dosage of antimony may not be a good practice . A lower dosage of antimonials as treatment for the visceral infection ( which may be the correct dose for the cutaneous species as they are more sensitive ) may have been the reason for the emergence of a more drug-resistant phenotype in that species . These are ambitious and yet intriguing and fundamental questions in Leishmania biology . Thus , our novel finding of 3’-UTR driven species-specific regulation of AQP1 is going to drive new approaches in that direction .
Wild type Leishmania donovani strain LdBob ( kind gift from Professor Stephen M . Beverley at the Washington University School of Medicine ) , L . infantum strain MHOM/MA/67/ITMAP-263 , L . major strain LV39 ( kind gift from Professor Marc Ouellette , Laval University , Quebec , Canada ) , L . braziliensis strain MHOM/BR/75/M2903 ( from ATCC ) , L . tropica strain MHOM/IL/67/JERICHO II ( from ATCC ) , and L . panamensis strain MHOM/PA/71/LS94 ( from ATCC ) were used in this study . Promastigotes were grown at 26°C as described before [17] . Promastigotes were also grown on blood-agar/ brain heart infusion ( BHI ) broth biphasic medium containing ( a ) solid phase of 1 . 7% agar , 3 . 7% BHI and defibrinated rabbit blood and ( b ) liquid phase of 3 . 7% BHI broth . Human leukemia monocyte cell line THP1 was purchased from ATCC and maintained according to supplier’s instruction . Antimony sensitivity of the promastigotes was determined as described previously [17] . Briefly , log phase promastigote cultures were diluted to 2 X106 cells ml−1 in a culture medium containing various concentrations of Sb ( III ) in the form of potassium antimonyl tartrate ( Sigma ) . Following 72 h incubation , cell growth was monitored from the absorbance at 600 nm using a microplate reader ( Spectramax 340 , Molecular Devices ) . Percentage survival was plotted against Sb ( III ) concentrations and EC50 was determined using SigmaPlot 11 . 0 . Each assay was performed at least three times in triplicates . Error bars were calculated from the mean ± SE . Antimony sensitivity of amastigotes inside macrophages was determined after infecting THP1 derived macrophages . Briefly , 5 X105 THP1 cells/well/200 μl of RPMI were seeded in 16 chamber LabTek tissue culture slides ( Nunc ) and treated with 5 ng/ml phorbol myristate acetate ( PMA ) for 48h to differentiate into macrophages . Macrophages were infected with stationary phase promastigotes harvested from blood-agar/BHI biphasic medium at a parasite-to-macrophage ratio of 20:1 for 6 hours at 37oC with 5% CO2 . Non-internalized promastigotes were washed away , and infected macrophages were treated with increasing concentrations of Sb ( V ) in the form of potassium hexahydroxoantimonate ( Sigma ) for 7 days . Medium was replaced every alternate day , and fresh drug was added . After 7 days , cells were stained with the Giemsa using Quick III Statpak kit ( Astral Diagnostics ) . Numbers of amastigotes per 100 macrophages were determined by light microscopy . EC50 was calculated as described for promastigotes . Each assay was performed at least two times in triplicates . Error bars were calculated from the mean ± SE . Log phase Leishmania promastigotes were washed twice with phosphate-buffered saline ( PBS ) , pH 7 . 4 ( Invitrogen ) and suspended in PBS at a density of 108 cells ml−1 . Promastigotes were then incubated with 10 μM Sb ( III ) , a 200- μl portion was filtered through a 0 . 22 μm nitrocellulose filter at different time points ( 1 , 5 , 10 , 20 and 30 min ) , and the filter washed once with 5 ml of ice-cold PBS . The filters were digested with 0 . 4 ml of concentrated HNO3 ( 69–70% ) ( EM Science ) for 1 h at 70oC , allowed to cool to room temperature , diluted with high pressure liquid chromatography grade water ( Sigma ) to produce a final concentration of HNO3 of approximately 3% , and then analyzed by a PerkinElmer SCIEX ELAN DRC-e inductively coupled plasma mass spectrometer . Standard solutions were prepared in the range of 0 . 5–10 p . p . b . in 3% HNO3 using antimony standards ( Ultra Scientific ) . Each transport experiment was repeated at least three times with duplicate samples . Error bars were calculated from the mean ± SE . Membrane vesicles were prepared from promastigotes of each species as described previously [6] . They were rapidly frozen in liquid nitrogen in small aliquots and stored at -80°C until use . The total protein content of the plasma membrane fractions was determined by a filter assay as described previously [53] . ATP dependent uptake of Sb ( TS ) 2 was measured in the presence of 10 mM ATP as energy source , as described previously , with a few changes [6] . Briefly , vesicles were added at 0 . 5 mg of membrane protein/ml and incubated with 0 . 1 mM of Sb ( TS ) 2 in a buffer containing 75 MM Hepes-KOH , pH 7 . 0/0 . 15 M KCl . Reaction was started by the addition of ATP at room temperature . At the indicated intervals , samples ( 0 . 1 ml ) were removed and filtered on wet 0 . 22 μm nitrocellulose filter , and the filter washed once with 5 ml of ice-cold PBS . The membranes were digested with 70% HNO3 , and total Sb content was measured by ICP-MS as described above . Each transport experiment was repeated at least two times with triplicate samples . Error bars were calculated from the mean ± SE Relative changes in cell volume following the induction of hypo-osmotic shock were measured as described earlier [54] . Briefly , log phase promastigotes were washed twice in PBS and re-suspended at a density of 109 cells ml−1 . One-hundred-microliter portions of the cell suspension were transferred to a microtiter plate . Hypo-osmotic shock was induced by dilution of the isotonic cell suspension with an equal volume of deionized water , and the absorbance at 550 nm was recorded every 15 sec for 3 min in a microplate reader ( Spectramax 340 , Molecular Devices ) . A decrease in absorbance corresponds to an increase in cell volume . Isosmotic control experiments consisted of dilution of cell suspensions with appropriate volumes of isosmotic buffer . All hypo-osmotic shock experiments were conducted at a final osmolarity of 150 mOsm ( 1:1 dilution of isosmotic buffer and water ) . Each experiment was repeated at least three times in triplicate . Error bars were calculated from the mean ± S . E . Genomic DNA was isolated using DNAzol reagent ( Life Technologies ) . Total RNA from Leishmania promastigotes was isolated using TRIZOL reagent ( Life Technologies ) according to the manufacturer’s protocol . DNA was removed from total RNA preparation using TURBO DNA-free Kit ( Ambion ) according to the manufacturer’s instructions . Integrity of total RNA preparations was confirmed by denaturing agarose gel electrophoresis . The AQP1 3’-UTR fragments from all six Leishmania species were mapped using 3’-RACE kit ( Invitrogen ) with 2 μg of total RNA as template according to the manufacturer’s protocol . AQP1 gene specific primers ( S2 Table ) were designed according to genome sequences in TriTrypDB . The amplified 3’-UTR fragments for all the species were cloned into pGEMT-Easy vector ( Promega ) according to the manufacturer’s instructions and sequenced ( Eton Biosciences ) . As the database sequence of AQP1 ORF of L . tropica is not available , we cloned its AQP1 ORF from L . tropica genomic DNA using primers ( sense 5’- GAATTC ATGAACTCTCCTACAACCATGCC-3’ and antisense 5’- GCGGCCGC CTAACAGCTGGGCGGAATGAT-3’ ) designed against L . major AQP1 ORF . This L . tropica AQP1 ORF sequence was used to design primers ( S2 Table ) for subsequent mapping of L . tropica AQP1 3’-UTR by 3’-RACE as described above . Previously described [28] luciferase ( LUC ) expression vector for Leishmania pSPYNEOαLUC is referred to as LUC-control or pLUC in this study . The full-length 3′UTR of AQP1 and 200 base pairs beyond the 3’ end of the poly ( A ) site was PCR amplified from genomic DNA of each species using AccuPrime Taq DNA Polymerase ( Invitrogen ) and primers ( S2 Table ) with BamHI or SalI restriction sites inserted at 5’ and 3’ ends . To clone a similar 200 base pair sequence from L . tropica , a 555 base pairs fragment downstream to the poly ( A ) site was cloned using L . tropica genomic DNA using the sense primer ( 5’- GATGAGTGCACACGGCGTACTTC-3’ ) designed against L . tropica AQP1 ORF and the antisense primer ( 5’- ATGGTCGTACCACGCAAAGTCACC-3’ ) designed against L . major dtatbase ( TriTrypDB ) sequence . The sequence of this fragment was used to design primers ( S2 Table ) for subsequent cloning of L . tropica full-length 3′UTR of AQP1 and 200 base pairs downstream to the 3’ end of the poly ( A ) site . PCR products were cloned ( cloning primers are described in S2 Table ) into the pGEMT-Easy vector ( Promega ) and sequenced ( Eton Bioscience ) as described above . Different LUC-chimeric constructs were generated by digesting the pGEMT-Easy clones with BamHI or SalI ( New England Biolab ) and subcloned into the BamHI or SalI sites of LUC gene ( 3’ end ) in the vector pSPYNEOαLUC , respectively . Directions of the cloned DNA segments were confirmed by sequencing . All plasmid constructs with forward orientation in respect to LUC open reading frame ( ORF ) were purified using QIAprep Spin Miniprep Kit ( Qiagen ) . Purified plasmid constructs were transfected into Leishmania by electroporation . Briefly , stationary phase promastigotes were washed and resuspended in ice cold electroporation buffer ( 21 mM HEPES , 150 μM NaCl , 5 mM MgCl2 , 120 mM KCl , 0 . 7 mM NaH2PO4 , 6 mM glucose ) at a density of 108 cells/ml . Three hundred microliters of resuspended cells were transferred to 0 . 2 cm electroporation cuvette ( Bio-rad ) with 10 μg of plasmid DNA . Cells were electroporated in Gene Pulser ( Bio-rad ) at 0 . 45 kV and 500 μF . All transfectants were selected and maintained in presence of 60 μg/ml geneticin ( G418 ) ( Invitrogen ) . The relative copy numbers of different LUC constructs in different transfectants were determined by qPCR using total genomic DNA from each transfectant . The relative abundance of target amplicons between samples was estimated by the 2−ΔΔCT method [55] using pteridine reductase 1 ( PTR 1 ) as loading control . The processing of 3’ end of each 3’-UTR expressing from episomal copy was reconfirmed by 3’-RACE PCR as described above with gene specific primers designed from LUC ORF ( S2 Table ) . The full-length ORF of AQP1 was PCR amplified from genomic DNA of L . donovani ( LdAQP1 ) and L . major ( LmAQP1 ) using AccuPrime Taq DNA Polymerase ( Invitrogen ) and primers ( S2 Table ) with BamHI and XbaI restriction sites inserted at 5’ and 3’ ends respectively . The full-length 3′-UTR of AQP1 and 200 base pairs beyond the 3’ end of the poly ( A ) site were similarly PCR amplified using genomic DNA of L . donovani ( Ld3’-UTR ) and L . major ( Lm3’-UTR ) and primers ( S2 Table ) with XbaI restriction site inserted at 5’ and 3’ ends . PCR products were cloned into the pGEMT-Easy vector and sequenced as described above . pGEMT-Easy clones containing LdAQP1 and LmAQP1 were digested with BamH1 and XbaI ( partial digestion with BamH1 for LdAQP1 ) and the resultant AQP1 ORF fragments were subcloned into pSP72-YHYG-αtubIR to generate LdAQP1-pSP72-YHYG-αtubIR and LmAQP1-pSP72-YHYG-αtubIR constructs . pGEMT-Easy clones containing Ld3’-UTR and Lm3’-UTR were digested with Xba1 and the resultant 3’UTR fragments were subcloned into both LdAQP1-pSP72-YHYG-αtubIR and LmAQP1-pSP72-YHYG-αtubIR constructs linearized with XbaI to generate LdAQP1-Ld3’-UTR-pSP72-YHYG-αtubIR ( pLd-Ld ) , LdAQP1-Lm3’-UTR-pSP72-YHYG-αtubIR ( pLd-Lm ) , LmAQP1-Ld3’-UTR-pSP72-YHYG-αtubIR ( pLm-Ld ) and LmAQP1-Lm3’-UTR-pSP72-YHYG-αtubIR ( pLm-Lm ) constructs ( AQP1-3’-UTR constructs ) . Directions and integrity of the cloned DNA segments in AQP1-3’-UTR constructs were confirmed by sequencing . pSP72-YHYG-αtubIR ( vector ) and all AQP1-3’-UTR constructs were purified and transfected into L . donovani and L . major promastigotes as described above . All transfectants were selected and maintained in the presence of 300 μg/ml hygromycin B ( Invitrogen ) . The relative copy numbers of different constructs in different transfectants were determined against hygromycin phosphotransferase gene by qPCR as described earlier . cDNA synthesis was carried out using 500 ng of total RNA and AccuScript High Fidelity 1st strand cDNA synthesis kit ( Agilent ) according to the manufacturer’s instructions . The first-strand cDNA reaction mix was treated with 0 . 25N NaOH at 650C for 30 minutes to degrade the template RNA molecules . The reaction mix was neutralized using equimolar hydrochloric acid and purified using Qiagen PCR purification kit according to the manufacturer’s instructions . For qPCR , 10 ng of genomic DNA and for qRT-PCR , 2 μl of diluted purified cDNA reaction corresponding to 6 ng of template RNA , were used in a 10 μl reaction containing forward and reverse primers for the target genes ( S2 Table ) and 1X iQSYBR Green supermix ( Bio-rad ) . The reactions were run on an Eppendorf Realplex2 PCR machine in the following thermal cycling conditions: initial denaturation at 950C for 3 minutes followed by 40 cycles of 950C for 15 sec and 650C for 20 sec . A final melting curve analysis was performed for each reaction to confirm that the PCR generated a single amplification product . Multiple primer sets against each target were designed using PrimerQuest software ( Intergrated DNA technologies; http://www . idtdna . com/Primerquest/Home/Index ? Display=SequenceEntry ) and tested for their efficiency using the afore-mentioned thermal cycling conditions; the set ( s ) of primers showing efficiency between 98% to 102% were included in the current study . The relative abundance of target amplicons between samples was estimated using glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) as loading control by the 2−ΔΔCT method [55] . Error bars were calculated from the mean ± SD of three independent experiments in triplicate . Similar expression levels of GAPDH in all six species were confirmed using β-tubulin as the loading control ( S3 Table ) . To determine the half-lives of AQP1 or LUC mRNA , mid-log phase promastigotes were treated with sinefungin ( 5 μM ) ( Sigma ) for 15 min followed by incubation with 10 μg/ml of actinomycin D ( Sigma ) to arrest trans-splicing and transcription , respectively . Cells were harvested just before adding actinomycin D and considered as the zero min time point . Subsequently , cells were harvested at 15 , 30 , 45 , 60 and 120 min; an additional eight min of processing time was added while presenting the data . Total RNA isolation and qRT-PCR analysis were performed as described above using AQP1 or LUC ORF specific primers ( S2 Table ) . Error bars were calculated from the mean ± SD of three independent experiments in triplicate . Mid-log phase promastigotes ( 5X106 ) were lysed with 50 μl of lysis buffer ( 62 . 5 mM Tris-phosphate pH 7 . 8 , 5 mM DTT , 2 . 5% Triton X-100 , 25% glycerol ) . A 10 μl of lysate was used to estimate the luciferase activity using Luc-Screen Extended-Glow Luciferase Reporter Gene Assay System ( Life Technologies ) according to the manufacturer’s instruction . Error bars were calculated from the mean ± SE of three independent experiments in triplicate . Whole cell lysates were prepared by lysing 1x107 promastigotes from each transfectant in 100 μl of 1x Laemmli’s buffer [56] . 10 μl lysate was used to fractionate proteins on 12% SDS-PAGE . Fractionated proteins were electroblotted on nitrocellulose membranes ( Whatman ) and probed sequentially with polyclonal goat anti-luciferase ( Promega ) and monoclonal mouse anti-α-tubulin ( Sigma ) . The labeling was visualized with horseradish peroxidase-conjugated mouse anti-goat ( Pierce ) and rabbit anti-mouse ( Abcam ) respectively using a Western Lightning Chemiluminescence Reagent Plus system ( PerkinElmer ) . Amount of luciferase expression relative to cells transfected with pSPYNEOαLUC was estimated by densitometric analysis using ImageJ software followed by normalization against the amount of α-tubulin of the respective cells . Error bars were calculated from the mean ± SE of two independent experiments . The level of total intracellular non-protein thiol was measured in deproteinized cell extracts as described previously [6] . Briefly , log phase promastigotes ( 6 x 108 ) were harvested , washed with PBS and suspended in 0 . 6 ml of 25% tricholoracetic acid . Cell debris and denatured protein were removed by centrifugation at 16 , 000g for 20 min at 4°C after 10 min incubation on ice . The thiol content of the supernatant solution was determined using 0 . 6 mM 5 , 5'-dithio-bis ( 2-nitrobenzoic acid ) ( DTNB ) in 0 . 2 M sodium phosphate buffer ( pH 8 . 0 ) . The concentration of 2-nitro-5-thiobenzoate ( TNB ) , derivatives of non-protein thiol-DTNB reaction , was estimated spectrophotometrically at 412 nm . The concentration of total thiols in the test supernatants was estimated against a standard curve of cysteine . Error bars were calculated from the mean ± SE of three independent experiments in triplicate .
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The degree of response to antimonial drugs varies widely between species and even among strains of the same species of the protozoan parasite Leishmania . However , the molecular mechanism ( s ) is unknown . In this study , we show that Leishmania aquaglyceroporin AQP1 drives this species-specific antimonial resistance . Aquaglyceroporins are channel proteins that facilitate the passage of small uncharged molecules , such as glycerol and water , across the biological membranes . AQP1 helps the parasite cope with the osmotic challenges it faces during its life cycle . Additionally , AQP1 is an adventitious facilitator of antimonite , the active form of pentavalent antimonial drugs . We show that AQP1 expression level is species-specific , and less AQP1 in visceral species compared to the cutaneous species results in increased resistance to antimonials . We also demonstrate that the 3’-untranslated regions ( 3’-UTR ) of the AQP1 mRNA is a major determining factor of species-specific regulation of AQP1 . Along with water homeostasis , aquaglyceroporins are also involved in directed cell migration . The variable levels of AQP1 in different Leishmania species may enable them to find their appropriate niches in vertebrate hosts and cope with the species-specific osmotic challenges during their life cycles .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Species-Specific Antimonial Sensitivity in Leishmania Is Driven by Post-Transcriptional Regulation of AQP1
|
Schmallenberg virus ( SBV ) is an emerging orthobunyavirus of ruminants associated with outbreaks of congenital malformations in aborted and stillborn animals . Since its discovery in November 2011 , SBV has spread very rapidly to many European countries . Here , we developed molecular and serological tools , and an experimental in vivo model as a platform to study SBV pathogenesis , tropism and virus-host cell interactions . Using a synthetic biology approach , we developed a reverse genetics system for the rapid rescue and genetic manipulation of SBV . We showed that SBV has a wide tropism in cell culture and “synthetic” SBV replicates in vitro as efficiently as wild type virus . We developed an experimental mouse model to study SBV infection and showed that this virus replicates abundantly in neurons where it causes cerebral malacia and vacuolation of the cerebral cortex . These virus-induced acute lesions are useful in understanding the progression from vacuolation to porencephaly and extensive tissue destruction , often observed in aborted lambs and calves in naturally occurring Schmallenberg cases . Indeed , we detected high levels of SBV antigens in the neurons of the gray matter of brain and spinal cord of naturally affected lambs and calves , suggesting that muscular hypoplasia observed in SBV-infected lambs is mostly secondary to central nervous system damage . Finally , we investigated the molecular determinants of SBV virulence . Interestingly , we found a biological SBV clone that after passage in cell culture displays increased virulence in mice . We also found that a SBV deletion mutant of the non-structural NSs protein ( SBVΔNSs ) is less virulent in mice than wild type SBV . Attenuation of SBV virulence depends on the inability of SBVΔNSs to block IFN synthesis in virus infected cells . In conclusion , this work provides a useful experimental framework to study the biology and pathogenesis of SBV .
Approximately 30 percent of all infectious diseases that emerged between 1990 and 2000 were caused by arthropod-borne viruses ( arbovirus ) [1] . This is probably the result of a combination of factors including a dramatic increase in travelling and commercial exchanges , climate and ecological changes and increased livestock production . In addition , changes in trading and commercial policies have created optimal conditions for the movement of infected vertebrate hosts and invertebrate vectors over wide geographical areas . Several European countries are currently experiencing the emergence of a previously uncharacterized arbovirus of domesticated ruminants , Schmallenberg virus ( SBV ) [2] , [3] . SBV infection causes a mild disease in adult cattle characterized by reduced milk production , pyrexia and diarrhea [4] . However , SBV infection of susceptible pregnant animals can be associated with musculoskeletal and central nervous system malformations in stillborn or newborn lambs and calves [3] . SBV was detected for the first time in November 2011 in plasma samples collected from cows displaying fever and diarrhea and farmed near the town of Schmallenberg , Germany [3] . The first acute infections associated with SBV were reported in August 2011 , while the first malformations in stillborn animals caused by this virus were detected in The Netherlands in December 2011 [5] . Since then , 9 countries have reported congenital malformations and stillbirth associated with the presence of SBV as of May 2012 [6] . In some areas , SBV cross-reacting antibodies have been detected in as high as to 100% of the cattle surveyed [7] , [8] , although the clinical and consequent economic impact of this infection is not completely clear as yet [9] . Phylogenetic analysis revealed that SBV belongs to the genus Orthobunyavirus within the Bunyaviridae , a large family comprising hundreds of viruses able to infect a broad range of vertebrate and invertebrate hosts . Bunyaviruses are significant pathogens both in humans and animals and cause a range of diseases including febrile illnesses ( Oropouche virus ) , encephalitis ( La Crosse virus ) and hemorrhagic fevers ( Rift Valley fever virus ) [10] . SBV clusters with viruses from the Simbu serogroup , in particular with viruses of the species Sathuperi virus including Sathuperi virus ( SATV ) , Douglas virus ( DOUV ) and Shamonda virus ( SHAV ) currently classified within the Shamonda virus species [11] . Viruses from Simbu serogroup have been associated with abortions , stillbirths and malformations ( arthrogryposis - hydranencephaly syndrome ) in ruminants in Asia , Africa and Oceania . Akabane virus ( AKAV ) is the most widely studied member of the Simbu serogroup [12]–[17] . In the literature , there is relatively little information on the diseases ( if any ) associated with SATV and SHAV infection . Viruses from the Simbu serogroup have not been detected in Europe before and given that SBV has not been found in archived samples so far , it is likely that the first introduction of SBV in Europe occurred in spring 2011 . Given the current available data , it is not possible to ascertain when and how SBV was introduced in Europe . With the exception of hantaviruses , all Bunyaviruses are transmitted by arthropod vectors [10] . SBV is also thought to be an arbovirus given its close relatedness to viruses of the Simbu serogroup ( all known to be transmitted by insects ) . In addition , SBV has also been detected in pools of Culicoides biting midges in Denmark [18] . Bunyaviruses are enveloped viruses and have a segmented single stranded RNA genome of negative or ambisense polarity . The viral genome comprises three RNA segments referred to as small ( S ) , medium ( M ) and large ( L ) which encode four structural proteins: the nucleocapsid protein ( N ) ; two glycoproteins ( Gn and Gc ) ; and the viral polymerase . Members of the Orthobunyavirus encode two additional non-structural proteins , NSs and NSm . NSs is encoded by an open reading frame in the S segment overlapping the N gene while NSm is encoded by the M segment as a polyprotein that is co-translationally cleaved into NSm , Gn and Gc [10] . The NSs protein of Bunyamwera virus ( BUNV ) , the prototype of the family , is a non-essential gene product for viral replication that has been shown to play a role in the inhibition of the host cell mRNA and protein synthesis . The NSs protein contributes to viral pathogenesis by blocking the production of interferon ( IFN ) through transcriptional inhibition and consequently inhibiting the innate responses of the host [19]–[21] . Studies on the molecular biology of RNA viruses has been transformed by the development of reverse genetics that allow the rescue of infectious virus from cloned cDNA copies of the viral genomes [22] . A reverse genetics for BUNV has proven invaluable in unraveling the underpinning mechanisms of viral replication and pathogenesis of this virus family [19] , [21] , [23] , [24] . In this study , we developed reverse genetics platforms for SBV and used them to characterize the biology and pathogenesis of this emerging pathogen . We obtained SBV mutants revealing key determinants of viral virulence and show that the viral non-structural protein NSs counteracts the innate immunity of the host . In addition , we show that SBV replicates in neurons of both experimentally infected mice and in naturally occurring SBV infected lambs and calves .
Our first goal was to gain insight into the in vitro growth kinetics of SBV in several cell lines derived from various animal species and humans . SBV grew efficiently in all the cell lines tested including sheep CPT-Tert , bovine BFAE , human 293T , dog MDCK and hamster BHK-21 and BSR cells ( Figure 1 ) . SBV reached titers of 106 PFU/ml at 48 h post-infection and induced cytopathic effect ( CPE ) in most cell lines with the exception of BFAE ( data not shown ) . We found SBV to replicate very efficiently in sheep CPT-Tert , where it induced well-defined plaques of approximately 3 mm in diameter at 72 h post-infection . Consequently , we used CPT-Tert for all the titrations of SBV performed in this study . Several members of the Bunyaviridae have been successfully rescued by reverse genetics [25]–[28] . The most successful and simple approach involves the transfection of plasmids encoding full-length antigenome viral RNAs under the control of the T7 polymerase promoter . We adopted a synthetic biology approach and obtained the 3 plasmids harboring the full full-length antigenome RNAs of each viral segment by in vitro synthesis , using the sequences of the first SBV isolate available in the International Nucleotide Sequence Database Collaboration in January 2011 ( accession numbers HE649912–HE649914 ) [3] . The complete sequences of the 5′ and 3′ untranslated regions ( UTRs ) of the SBV S , M and L segments were unavailable at that time and therefore we predicted the missing sequences based on the high similarity with the sequences of AKAV . The rescue plasmids were designed to contain the full-length antigenome RNAs flanked by the T7 promoter immediately upstream of the 5′ UTR and the hepatitis δ ribozyme followed by T7 terminator sequences immediately downstream of the 3′ UTR as previously described [29] within the pUC57 vector ( Figure 2A ) . We transfected the 3 antigenome-encoding plasmids in BSR-T7/5 cells , a BHK-derived cell line that constitutively expresses the T7 RNA polymerase [30] . Five days post transfection supernatants were collected and the presence of virus was assessed by standard plaque assays in CPT-Tert cells ( Figure 2B ) . Negative controls included cells transfected with only two of the rescue plasmids . We successfully rescued SBV following this method , although in some cases transfections did not result in viable rescued SBV . From the in vitro growth assays described above , it appeared that SBV infected 293T cells readily . Thus , we reasoned that it would be possible to rescue SBV more efficiently in this cell line known to be highly transfectable if we provided the T7 RNA polymerase in trans . To this end , we transfected 293T cells with the 3 SBV antigenome plasmids ( pUCSBVST7 , pUCSBVMT7 and pUCSBVLT7 ) along with a plasmid expressing the T7 RNA polymerase under the control of the CMV immediate early promoter ( pCMV-T7 ) ( Figure 2C ) . SBV was efficiently rescued following this method , which we found overall more reproducible . We were also able to rescue BUNV [31] in transiently transfected 293T cells ( data not shown ) demonstrating that this method can be easily applied to other viruses within this family . sSBV rescued in either 293T or BSR-T7/5 cells produced plaques of similar size and shape to SBV ( Figure 3A ) . In addition , we found that sSBV grew with the same kinetics and reached approximately the same titers of SBV in both sheep CPT-Tert and bovine BFAE cells ( Figure 3B ) . The presence of SBV in infected cells was confirmed by western blotting of cell lysates of infected cells ( Figure 3C ) . These results indicate that wild type SBV and sSBV display similar phenotypic characteristics in vitro . As mentioned above , we predicted the complete sequences of the 5′ and 3′ UTRs of the S , M and L segments of SBV on the basis of published AKAV sequences . We performed 5′ and 3′ RACE PCRs on RNA extracted from SBV-infected cells in order to establish that the sequence of sSBV faithfully represented the wild type field strain . The sequences of the 3′ and 5′ UTR of the M and L segments were 100% identical to the predicted sequences used in the rescue plasmids for sSBV ( Figure 4 ) . We did not find with our RACE PCR an extra 54 nucleotides that were reported in the 5′ UTR of the M segment ( accession number HE649913 ) ( not shown ) which represent most likely a sequencing artifact beyond the correct 3′ end of the published antigenome sequence . We found polymorphisms in SBV at positions 17 and 25 of the 3′ UTR of the S segment . Some of the clones analyzed contained thymine ( T ) at position 17 while others contained an adenine ( A ) at that position . Similarly , at position 25 , some clones harbored a guanine ( G ) while other clones contained an A . The 3′ UTR sequences of the synthesized plasmids used for reverse genetics contained a T at position 17 and a G at position 25 indicating that the original sequence prediction was correct ( Figure 4 ) . We also sequenced the remaining full genome of the SBV strain used in this study and we found it to be identical to the original sequences submitted in GenBank ( data not shown ) . We next investigated whether mice could be used as an experimental model of SBV infection and pathogenesis . Firstly , we inoculated 3 litters of 2-day old newborn NIH-Swiss mice ( n = 8–14 ) intracerebrally with 400 PFU of SBV , sSBV or cell culture media as a control ( study 1 ) . All mice inoculated with SBV and sSBV died within 8 days post-inoculation while all control mice survived until the end of the experiment ( Figure 5A , left panel ) . These data clearly indicate that sSBV is as virulent as SBV at least in this experimental model . In order to determine whether age susceptibility existed for SBV infection in this mouse model , we inoculated litters of 10 and 18-day old NIH-Swiss mice as described above with sSBV ( study 2 ) . We found that SBV infection was lethal for most of the infected mice of these age groups , displaying similar kinetics to the one displayed by SBV infection in newborn mice ( Figure 5A , right panel ) . Histopathology of brains collected at 72 h post-infection revealed bilateral symmetrical vacuolation and loosening of the neuropil of the superficial cerebral cortex and the mesencephalon ( Figure 5B–C ) . In particular , we found small areas of haemorrhage within large areas of malacia ( necrosis of brain tissue ) in the cerebral cortex ( Figure 5C , arrow ) . In brains collected at 120 h post-infection ( Figure 5D–E ) there was random multifocal vacuolation of the white matter of the cerebrum with small amount of nuclear debris ( Figure 5E ) . There was a minimal , multifocal perivascular infiltrate of lymphocytes in the adjacent grey matter . The presence of SBV was confirmed by immunohistochemistry using a polyclonal antibody against the N protein . We found no reactivity in mock-infected mice ( Figure 5F–G ) . We found patchy areas of positive staining in brain sections taken from mice euthanized 48 h post infection corresponding to the cytoplasm of neurons ( Figure 5H–I ) . Extensive immunoreactivity for SBV was noted within the cerebrum and mesencephalon of mice euthanized 72 h after inoculation corresponding to infection of neurons . SBV antigens were also detected in cells that morphologically resembled astrocytes ( Fig . 5 J–K ) . However , by immunofluorescence we were not able to detect SBV in cells expressing GFAP ( glial fibrillary acidic protein ) , a specific marker for astrocytes ( data not shown ) . Next , we analysed tissue sections of brain and spinal cord from a total of 8 naturally infected lambs and calves presenting malformations commonly shown by animals congenitally infected with SBV such as , arthrogryposis , brachygnatia inferior , torticollis and curvature of the spine ( Figure 6A ) [32] . All samples derived from abortion or stillborn cases that occurred in farms in an SBV-endemic area in Germany , although we cannot ascertain the time of infection nor the specific SBV strain involved in these cases . Histopathology of brain sections showed lesions commonly observed in SBV infected animals , including porencephaly associated with widespread tissue destruction ( Figure 6B ) . Clusters of myelin laden macrophages ( Fig . 6C , arrow ) were noted in areas of rarefaction . Glial nodules were randomly scattered throughout the cerebrum ( Fig . 6D , arrow ) and there was a mild , multifocal perivascular infiltrate of lymphocytes and macrophages/microglia . By immunohistochemistry , we detected SBV predominantly in the cell body and processes of neurons of the grey matter , similarly to what we have observed in mice ( Figure 6E–F ) . In addition , we found expression of abundant SBV antigen also in the grey matter of the spinal cord ( Figure 6I–J ) . Our controls included serial sections of brains collected from SBV-infected animals incubated with the pre-immune serum collected from the rabbit used for the production of the polyclonal antibody against SBV N protein ( Figure 6G–H ) . No positive reaction was observed in all the SBV positive or suspected cases that we tested in this study . In addition , we used also as negative controls tissue sections collected from the brains of lambs or calves that died as result of unrelated pathologies from Germany ( collected before 2001 ) or Scotland ( n = 7 ) . As expected , none of these negative controls showed immunoreactive cells ( Figure 6K–L ) . Moreover , our studies strongly indicate that SBV replicates in neurones of brain and spinal cord of animal naturally infected with SBV . The data obtained in calves and lambs suggested that newborn NIH-Swiss mice inoculated intracerebrally can be an useful experimental model of SBV infection . We attempted to further exploit this tool in order to identify determinants of SBV virulence using two different approaches . First , we attempted to attenuate SBV in tissue culture by passaging the virus serially in CPT-Tert cells , after which the resulting virus was plaque purified twice and a stock generated in the same cells reaching a total of 32 passages in tissue culture ( virus referred to as SBVp32 ) . In addition , given that the NSs proteins of several Bunyaviruses including BUNV [19] , Rift Valley fever virus ( RVFV ) [33] , [34] , AKAV [26] and La Crosse virus ( LCV ) [35] have been shown to play key roles in viral replication and pathogenesis , we attempted to rescue by reverse genetics a SBV mutant lacking this non-structural protein . Similarly to other orthobunyavirus , the predicted NSs of SBV is encoded by the S segment in an overlapping reading frame within the N gene . Because of the inherent capabilities of RNA viruses to mutate and revert to a wild type phenotype , we introduced 2 silent mutations in pUCSBVST7 within the N gene , abrogating the second and third initiation codons of the NSs gene . Moreover , we introduced 3 premature stop codons within NSs that did not affect the N reading frame ( Figure 7A ) . The resulting SBV NSs deletion mutant ( termed SBVΔNSs ) was then rescued in 293T cells as described above . We confirmed the identity of the deleted NSs mutant by sequencing . We hypothesized that sequence changes were likely to have occurred in SBVp32 following extensive passage and plaque purification in tissue culture . Consequently , we amplified the coding regions of all 3 SBV genome segments by RT-PCR and sequenced the PCR products directly without cloning . We identified a total of 17 nucleotide changes among the 3 genome segments , most of which were non-synonymous mutations ( Figure 7C ) . Most of the mutations were identified within the M and S segments . In particular , all the mutations found in the M segment were located within Gc and NSm proteins and none within Gn . We found that the S segment harbored the highest number of mutations in relation to its size . Three of the 4 changes found in the S segment of SBVp32 were non-synonymous mutations of the N protein . In addition , 3 of these mutations also affected the NSs protein leading to 1 synonymous and 2 non-synonymous changes . We next characterized the in vitro growth properties of the rescued SBVΔNSs and SBVp32 in sheep cells ( Figure 7B ) . We found that both SBVΔNSs and SBVp32 had comparable growth kinetics to sSBV and SBV respectively and both viruses reached similar titers in infected cells . Thereafter , we inoculated litters of 3 and 7-day old NIH-Swiss mice intracerebrally with either 100 or 400 PFU of sSBV , SBVΔNSs , SBVp32 or cell culture media as a control ( study 3 ) . All mice inoculated with sSBV died within 7 days post-inoculation , with the exception of the litter of 7-day old mice inoculated with 100 PFU where 100% mortality was reached at day 9 post-inoculation ( Figure 8A ) . All control mice were healthy until the end of the experiment . SBVΔNSs showed an attenuated phenotype . There was a clear delay in the time of death in the groups inoculated with SBVΔNSs and 40–60% of the inoculated mice survived infection . Unexpectedly , SVBp32 was more virulent than sSBV in this experimental model . Inoculation of SBVp32 resulted in 100% lethality by day 4 and 5 post-infection in 3 and 7-day old mice respectively ( Figure 8A ) . To gain insight into the nature of the enhanced pathogenicity of SBVp32 observed in our mouse model of infection , we inoculated litters of 7-day old NIH-Swiss mice intracerebrally with SBVp32 , sSBV ( and media as a control ) and brain samples were collected at different time points post-infection ( study 4 ) . Brain sections were analyzed by immunohistochemistry to detect the presence of SBV . We detected the presence of foci of virus-infected cells as early as 24 h post-infection in samples derived from mice infected with SBVp32 ( Figure 8B ) . In these mice , SBV antigens were widely spread by 48 h post-infection . In contrast , we first detected virus antigens at 72 h post-infection in brain sections derived from mice infected with sSBV . These results suggest that SBVp32 is able to spread faster than sSBV in the brain of experimentally infected mice . The attenuation of virulence displayed by SBVΔNSs suggested that the NSs protein plays an important role in viral pathogenesis . The NSs proteins of Orthobunyaviruses have been shown to indirectly inhibit synthesis of IFN-α and β by shutting down cellular mRNA synthesis [20] , [22] , [28] , [36] . Consequently , we performed a series of assays to determine whether SBV and SBVΔNSs induce the synthesis of IFN in infected cells . Firstly , we tested the human 2fTGH cells , which are known to be IFN competent . Cell lines were infected with sSBV or SBVΔNSs and at 24 h post-infection the supernatant were collected and virus UV inactivated . An IFN-protection assay was then performed in CPT-Tert cells , which respond to IFN but are defective for its production ( data not shown ) and thus are a good target cell line for these experiments . Virus-inactivated supernatant from 2fTGH cells was added to CPT-Tert cells and incubated for 24 h before infection with encephalomyocarditis virus ( EMCV ) , a virus susceptible to IFN [37] . In parallel , cells were also incubated with a known amount of universal IFN as a control . We found that supernatants from cells infected with sSBV did not contain IFN as they were not able to protect CPT-Tert cells from infection with EMCV ( Figure 9A ) . In contrast , supernatant from cells infected with SBVΔNSs was able to confer protection against infection of the target cells by EMCV , and contained an average of 40 IFN international units per ml ( IU/ml ) . Secondly , we investigated whether the lack of IFN production upon infection with sSBV was due to the inhibition of transcription of the type I IFN gene . To this end , we investigated the presence of IFN-β mRNA by RT-PCR in 2fTGH cells following infection with sSBV and SBVΔNSs , or transfection with poly I∶C as a positive control . We did not detect IFN-β mRNA in sSBV-infected or mock-infected cells , while it was clearly detectable in cells infected with SBVΔNSs or transfected with poly I∶C ( Figure 9B ) . These results suggest that the SBV NSs protein interferes with transcription of the IFN-β gene upon infection . Furthermore , in an attempt to evaluate the data obtained above in a more relevant cell culture system , we repeated the IFN protection assays in sheep primary endothelial cell cultures ( ovEC ) and ovine trophoblast cells ( oTr-1 ) . Similarly to the data obtained in human cells , we found that the supernatant from sSBV-infected cells did not protect CPT-Tert cells from infection with EMCV . On the other hand , supernatant from SBVΔNSs-infected ovEC and oTr-1 was found to contain abundant amounts of IFN ( estimated to be between 500 and 1000 IU/ml; Figure 9C ) . Finally , to evaluate the role of SBV NSs protein in counteracting the IFN response of the host in vivo , we inoculated litters of 7 day old IFN receptor null mice ( IFNAR ( −/− ) ) intracerebrally with sSBV ( n = 11 ) , SBVΔNSs ( n = 10 ) and culture media as a control ( n = 8 ) ( study 5 ) . All the control mice survived during the observation period ( Figure 9D ) . All mice inoculated with SBVΔNSs died by 4 post-infection mice while 80% of mice infected with sSBV died at the same time point and the remaining 20% died by day 6 post-infection . These data strongly suggest that animals lacking a competent IFN response are equally susceptible to sSBV and SBVΔNSs , confirming the role of the NSs protein as a modulator , at least indirectly , of the IFN response in vivo .
SBV is a new emerging orthobunyavirus that has been associated with abortions and malformations in sheep and cattle . The Bunyaviridae include many viruses that have emerged or re-emerged in the last decade such as OROPV , Henan fever virus , Crimean-Congo hemorrhagic fever , RVFV , LCV and others [38]–[42] . SBV was isolated for the first time in Germany in October 2011 [3] . However , the virus probably entered Northwestern Europe in the border region of The Netherlands , Germany and Belgium [8] and subsequently spread rapidly to neighboring countries , including Luxembourg , France , Italy , Spain , Denmark and the United Kingdom . In this study , we have established an experimental platform that comprises both in vitro and in vivo systems to study SBV biology and pathogenesis . Studies on SBV have thus far been concentrated in understanding the animal species susceptible to this virus , the spreading of the infection , and its impact on animal health and the farming industry . In this study , we undertook several steps towards understanding the molecular biology of SBV , its cellular tropism , pathogenesis and host-virus interaction . Using a synthetic biology approach , we developed two reverse genetics protocols for SBV , as powerful tools to characterize this emerging pathogen and to manipulate its genome . Using SBV sequences deposited in public databases , we synthesized the three SBV antigenome segments in vitro and rescued replication competent SBV in BSR-T7/5 transfected cells , as previously described for other bunyaviruses [29] . We also developed an alternative rescue system , using the plasmids above in transiently transfected 293T cells , where the T7 RNA polymerase was provided in trans . Using RACE RT-PCR performed on viral RNA extracted from early passages of SBV stocks we confirmed that the sequence identity of the viral UTRs in our synthetic plasmids indeed corresponded to SBV . This allowed us to provide for the first time the full-length sequence of circulating SBV since previous reports only provided partial sequences of the viral UTRs . The “synthetic” SBV ( sSBV ) that we obtained was able to replicate in vitro as efficiently as wild type SBV . In addition , both wild type and sSBV were lethal in suckling mice injected intracerebrally . This experimental mouse system proved to be very useful in order to define SBV tropism . The histological lesions within the brains of mice inoculated with SBV show a progression from per-acute hemorrhage at 48 h post-infection to malacia at 72 h that extends to more widespread vacuolation of the white matter at 96–120 h post-inoculation . The strong immunoreactivity for SBV within the cerebral neurons of mice euthanized as early as 48 h post-inoculation , confirms that the histological changes within the brain are related to SBV infection . Importantly , we also found SBV to replicate in neurons of in utero infected calves and lambs . In fetal sheep , SBV infection can result in cavitation of the white matter of the cerebrum , cerebellar hypoplasia , mild lymphohistiocytic perivascular encephalitis , and small glial nodules scattered throughout the brain [32] , [43] . Thus , the cavitary lesions observed in naturally SBV infected lambs/calves appear to be the natural progression of vacuolar changes within the white matter observed suckling mice acutely infected with SBV . The similarities in the pathologic changes between naturally infected fetal sheep and mice inoculated with SBV intracerebrally , suggest that the mouse can be a useful model to study at least some aspects of SBV virulence . In this study we have made substantial progress by generating an SBV antiserum that allowed us to define that neurons in the brain seem to be the major target for viral replication in the developing fetus . The limited time frame in which sheep fetuses seem to be susceptible to SBV infection ( 28–50 days of gestation ) [44] coincides with the development of the blood brain barrier ( BBB ) . In sheep the BBB starts to develop between days 50 and 60 of gestation and reaches full development by day 123 [45] . Thus , the virus could have easy access to the brain of the fetus during a short period of time: from day 28 of gestation when the placentomes ( functional units of exchange between mother and fetus in the ruminant placenta ) develop until day 50 when the BBB starts to develop . This would explain why disease in adult animals , that have an intact BBB , is mild with no apparent development of lesions in the CNS . Newborn mice have a fully mature BBB , however the developing cerebral vessels can be fragile and allow leakage of infectious agent [46] . SBV could potentially reach the mouse brain tissue from subcutaneous inoculation proving an opportunity to develop a challenge model of infection that would better mimic natural exposure . It has been reported that subcutaneous inoculation of IFNAR−/− mice with SBV resulted in weight loss and 10–20% mortality , therefore a different aspect of SBV infection could be explored also with this model [47] . The malformations and deformities observed in SBV-infected lambs and calves are accompanied by muscle hypoplasia and demyelination . Here , we found SBV to infect the neurons of the grey matter of the spinal cord , which would suggest that muscular hypoplasia and muscular defects observed in SBV infected lambs and calves are mostly secondary to damage of the central nervous system ( CNS ) . Using reverse genetics we engineered a SBV NSs deletion mutant as this protein has been found in other orthobunyaviruses to be a virulence factor , acting by inhibiting cellular transcription and therefore indirectly antagonizing the host IFN response . SBVΔNSs replicated at levels comparable to wild type SBV in established cell lines , indicating that the NSs protein is not essential for viral replication in vitro . However SBVΔNSs was strongly attenuated in newborn mice . It is unlikely that the attenuation observed for SBVΔNSs is due to a change in cell tropism . On the contrary , we attribute its attenuated phenotype to its inability to counteract the innate immune response of host . This is supported by the fact that we were able to detect induction of IFN in IFN-competent human cell lines and in sheep primary cells when they were infected with SBVΔNSs but not when they were infected with wild type SBV . The production of IFN in SBVΔNSs-infected cells was associated with the presence of IFN-β transcripts , which were not detected in cells infected with wild type SBV indicating that the inhibition of IFN production occurred at the level of transcription . Most importantly , we found no differences in the virulence of sSBV and SBVΔNSs in IFNAR ( −/− ) mice , further reinforcing the notion that the SBV NSs protein is able to modulate at least indirectly the host innate immune response . Thus , the NSs protein of SBV acts as a virulence factor . Similar roles have been identified for the NSs proteins of RVFV [36] and LCV [35] underlying the importance of overcoming the host innate immune response for efficient viral replication . In an attempt to identify other determinants of SBV virulence , we serially passaged SBV in CPT-Tert cells . Normally , serial passage of pathogenic viruses in cell culture results in decreased virulence especially when the cells used are defective in the production of factors mediating innate immune responses . However , using this approach , we unexpectedly obtained an SBV mutant ( SBVp32 ) with increased pathogenicity in suckling mice . SBVp32 appeared to spread more rapidly than SBV in the brain of infected animals . SBVp32 accumulated a variety of mutations in all 3 viral segments . Of these , the ones found in the M and S segment are potentially the most interesting ones given that: i ) we have already shown that the NSs is a virulence factor; and ii ) most of the mutations found in the M segment are present in the Gc protein that is exposed in the outer surface of the virion and thus is the principal target of neutralizing antibodies . The mutations generated in Gc in an immunological unconstrained environment could be associated for example with an increased cell receptor affinity . In conclusion , our synthetic biology approach demonstrates that , at least for the Bunyaviridae , it is possible very rapidly to investigate and characterize an emerging virus whilst only having knowledge of the complete sequence of the virus . Using synthetic plasmids we developed 2 reverse genetic protocols for the rescue of replication competent SBV . Most importantly , we developed a mouse model of infection that allowed us to identify a viral gene critical for virulence . Viral tropism for neurons of the CNS was shown to be similar in experimentally infected mice and naturally infected calves and lambs . Altogether , the molecular virology tools that we generated and the findings of this work open new avenues to study the biology and pathogenesis of a novel and rapidly spreading emerging virus .
All experimental procedures carried out in this study were approved by the ethical committee of the Istituto G . Caporale ( protocol number 5383/2012 ) and further approved by the Italian Ministry of Health ( Ministero della Salute ) in accordance with Council Directive 86/609/EEC of the European Union and the Italian D . Igs 116/92 . The 293T , 2fTGH , BHK-21 , BSR and MDCK cell lines were grown in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) . BSR-T7/5 ( provided by Karl Conzelmann ) cells stably expressing the T7 polymerase were grown in Glasgow modified Eagle's medium supplemented with 10% FBS , 10% of tryptose phosphate broth and G418 at a final concentration of 1 mg/ml . Sheep choroid plexus cells ( CPT-Tert ) were grown in Iscove's modified Dulbecco's medium supplemented with 10% FBS ( provided by David Griffiths ) [48] . Bovine fetal aorta endothelial cells ( BFAE , Health Protection Agency collection number 87022601 ) were cultured in Ham's F12 medium supplemented with 20% FBS . Ovine trophoblast cells , oTr-1 , ( provided by Thomas Spencer ) [49] were cultured in DMEM/F12 media supplemented with 15% FBS , 1 mM pyruvate , 700 nM of human recombinant insulin and 0 . 1 mM of nonessential amino acids . All cell lines were cultured at 37°C in a 5% CO2 and 95% humidified atmosphere and were supplemented with 10 , 000 U/ml of penicillin and 10 mg/ml of streptomycin . Ovine aortic endothelial ( ovEC ) cells were isolated by collagenase treatment using a method adapted from Gillespie et al . [50] . Aortas were harvested from recently euthanized animals and washed twice in sterile PBS . The aortas were then incubated at room temperature for 1 h in DMEM supplemented with 5% FBS , 25 µg/ml penicillin/streptomycin and 50 ng/ml amphotericin B . After incubation , the aortas were placed into collagenase in a Petri dish ( 2 mg/ml in DMEM ) for 1 h at 37°C . After incubation , the endothelial cells were removed by scraping and seeded in 6-well plates . Cells were maintained at 37°C and 5% CO2 in large vessel endothelial cell basal medium ( TCS cellworks ) supplemented with 20% FBS , human large vessel endothelial cell growth supplement ( TCS cellworks ) , 25 µg/ml penicillin/streptomycin and 50 ng/ml amphotericin B . Cells were confirmed as endothelial cells by assessing their morphology using light microscopy and by immunofluorescence using antibodies against endothelial and smooth muscle cell markers . Cells used for this study were only passaged once . Antisera used in this study included a rabbit polyclonal antiserum against the SBV N protein expressed in bacteria as Glutathione-S-transferase ( GST ) -tagged recombinant protein ( Proteintech ) . Antibodies against CD-31 ( marker for endothelial cells ) and actin ( smooth muscle marker ) were purchased from Source Bioscience and Abcam respectively . Wild type SBV was originally isolated at the Friedrich-Loeffler-Institut ( Germany ) . This virus was initially isolated from the blood of an infected cow and passaged once in KC cells and 6 times in BHK-21 cells . The virus was plaque purified and stocks were produced in BHK-21 cells . Virus titers were determined by standard plaque assays in CPT-Tert cells and expressed as plaque forming units per milliliter ( PFU/ml ) . Encephalomyocarditis virus was provided by Rick Randall [37] . pUCSBVST7 , pUCSBVMT7 and pUCSBVLT7 encode the full-length antigenomic S , M and L SBV segments and were used for the SBV reverse genetics protocols described in this study ( see below ) . In each plasmid , the SBV antigenome is placed downstream of the bacteriophage T7 promoter and upstream of the hepatitis δ ribozyme and the T7 terminator similarly to what described previously for other Bunyaviruses [29] . Sequences of pUCSBVST7 , pUCSBVMT7 and pUCSBVLT7 were synthesized commercially and were derived from the incomplete SBV sequences available in GenBank in January 2012 ( HE649912; HE649913; and HE649914 ) . Where missing , the 5′ and 3′ end sequences for each genome segment ( not available in GenBank at that time ) were predicted by alignment of AKAV 5′ and 3′ sequences . pUCSBVΔNSsT7 was obtained by inserting 5 point mutations in pUCSBVST7 as described below . Two mutations altered the second and third initiation codons in the NSs while the remaining 3 introduced premature stop codons . pCMV-T7 expresses the T7 polymerase under the control of the CMV immediate early promoter . 293T cells or BSR-T7/5 cells were plated in 6 well plates one day before transfection without in a final volume of 2 ml of tissue culture media without antibiotics . For rescue in BSR-T7/5 cells , 1 µg of each antigenome-encoding plasmid was transfected using Fugene HD at a 1∶3 ratio ( µg of plasmid: µl of Fugene ) . For rescue in 293T cells , a plasmid expressing the T7 polymerase under the control of the CMV promoter was transfected alongside the plasmids containing the SBV antigenomes ( or derived deletion mutant antigenomes ) . In both protocols , cells transfected with only two genomic segments were used as negative control . Supernatants were collected 5 days post transfection , clarified by low speed centrifugation and the presence of virus ( termed sSBV for “synthetic SBV” in this study ) was assessed by standard plaque assays in CPT-Tert cells as already described [51] . Virus stocks were generated from plaque purified virus in CPT-Tert cells . The in vitro growth kinetics of SBV was determined by infecting a variety of cell lines at a multiplicity of infection ( MOI ) of 0 . 05 . The presence of infectious virus was then assessed from clarified supernatants collected at different time points post infection by end point dilution analysis performed in CPT-Tert cells . Each experiment was performed in triplicate and repeated at least twice . The 5′ and 3′ ends of the 3 genome segments of SBV were sequenced using the 5′/3′ RACE kit according to the manufacturer's instructions ( Roche ) . 5′ RACE was carried out on the genome strand to obtain the sequence of the 5′ end of the virus segments . Briefly , 1 µg total RNA extracted from SBV infected BHK-21 cells was reverse transcribed using a gene specific cDNA synthesis primer . The synthesised cDNA was column purified and poly-A tailed at the 3′ end and then amplified using the oligodT anchor primer and a second nested gene specific primer . To obtain the 3′ end of the virus segments , 3′ RACE was performed on the genome strand . Briefly , 1 µg total RNA extracted from SBV infected BHK-21 cells was poly-A tailed at the 3′ end of the RNA using poly A polymerase ( NEB ) according to the manufacturer's protocol . The poly-A tailed RNA was reversed transcribed using the oligodT anchor primer . cDNA was then amplified using the anchor primer and a nested gene specific primer . Primer sequences are available upon request . 2fTGH cells were plated at a density of 1 . 7×105 cells/ml in 24 well plates . Cells were infected with the indicated viruses at MOI of 5 or transfected with poly I∶C ( 10 ng ) using Lipofectamine 2000 according to the manufacturer's protocol . Cells were incubated at 37°C for 24 h , then lysed in TRizol Reagent ( Invitrogen ) and RNA extracted according to the manufacturer's protocol . 1 µg of total RNA was reverse transcribed using Superscript III ( Invitrogen ) according to the manufacturer's instructions . This cDNA was used as a template for amplification of the SBV S segment or 45S RNA using GoTaq polymerase ( Promega ) following the manufacturer's protocol . The sequences of the primers used for SBV detection are available on request . Primers for the amplification of the IFN-β and 45S RNAs have been previously published [52] , [53] . Measurement of IFN levels was based on the methods described previously [37] , [54] . 2fTGH , OvEC and oTr-1 cells were first seeded in 12-well plates and incubated for 2 days . Cells were then infected with the appropriate virus at a MOI of 5 , 4 and 2 respectively and the medium was collected 24 h later . The medium was UV-treated for 5 minutes using a Spectrolinker XL-1500 UV crosslinker ( Spectronics Corporation ) in order to remove any infectious virus . CPT-Tert cells were then seeded in 96-well plates and 24 h later 2-fold serial dilutions ( in duplicate ) of the UV-treated media were added . Cells were then further incubated for 24 h . In parallel , cells were also incubated with 2-fold dilutions of a known amount of universal IFN . Cells were then infected with encephalomyocarditis virus ( EMCV ) and incubated for an additional 48 h . The levels of IFN were calculated by monitoring wells that were protected from cell death induced by EMCV and comparing them to the universal IFN control . SDS-PAGE and Western blots were performed from total cell lysates prepared 24 h post-infection as previously described [55] , [56] . SBV was detected using a rabbit polyclonal antibody against the SBV nucleocapsid protein . Animal experiments were carried out at the Istituto G . Caporale ( Teramo , Italy ) following local and national approved protocols for animal experimentation . Study 1 . Litters of 2-day old NIH-Swiss mice were inoculated intra-cerebrally with 400 PFU of SBV , sSBV or mock inoculated using cell culture medium . Animals were monitored daily for signs of disease for a period of 14 days . Study 2 . Litters of 10-day and 18-day old NIH-Swiss mice were inoculated intracerebrally with 400 PFU of sSBV and monitored for signs of disease as above . Study 3 . Litters of 3 and 7-day old mice were inoculated intracerebrally with 100 or 400 PFU with sSBV , SBVp32 , SBVΔNSs or cell culture media and monitored for signs of disease for a period of 14 days . Study 4 . Litters of 7-day old NIH-Swiss mice were inoculated intracerebrally with 400 PFU with sSBV , SBVp32 , or cell culture media and 2 animals per group were euthanized at 24 , 48 , 72 and 96 h post-infection . Study 5 . Litters of 4 day old mice deficient of the type I IFN receptor ( IFN Alpha Ro/o IFN ( −/− ) 129/Sv ) were inoculated intracerebrally with 400 PFU with sSBV , SBVΔNSs , or cell culture media as a control and monitored for signs of disease . Brains from infected and control mice were collected and fixed in 10% neutral buffered formalin and paraffin embedded following standard histological procedures . In addition , brain tissues were also collected from 6 lambs and 5 calves born in North Rhine-Westphalia , a SBV-endemic area of Germany . At post-mortem , animals presented a variety of malformations including arthrogryposis , brachygnatia , torticollis , cerebellar hypoplasia , hydrocephalus , muscular hypoplasia . Negative controls included brain sections derived from a sheep and 3 calves , which died as a result of diseases unrelated to SBV-infection in Germany between 1994 and 2001 . We also used as negative controls 3 brain tissues from Scotland , an area so far free of SBV infection . Tissue sections ( 4–6 µm ) from mice , lambs and calves were stained with hematoxylin and eosin for histopathological examination . Sections were also examined for the presence of the SBV N protein using a specific antiserum and the EnVision ( DAKO ) detection system as described before [51] , [57] . The sequences of the synthetic SBV genomic segments have been deposited in GenBank ( accession numbers JX853179 , JX853180 , JX853181 ) .
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Schmallenberg virus ( SBV ) was discovered in Germany ( near the town of Schmallenberg ) in November 2011 and since then has been found to be the cause of malformations and stillbirths in ruminants . SBV has spread very rapidly to many European countries including the Netherlands , Belgium , France and the United Kingdom . Very little is known about the biological properties of this virus and there is no vaccine available . In this study ( i ) we developed an approach ( called reverse genetics ) that allows the recovery of “synthetic” SBV under laboratory conditions; ( ii ) we developed a mouse model of infection for SBV; ( iii ) we showed that SBV replicates in neurons of experimentally infected mice similar to naturally infected lambs and calves; ( iv ) we developed viral mutants that are not as pathogenic as the original virus due to the inability to counteract the host cell defenses; and v ) we identified mutations that are associated with increased virulence . This work provides the experimental tools to understand how this newly emerged virus causes disease in ruminants . In addition , it will now be possible to manipulate the SBV genome in order to develop highly effective vaccines .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"rna",
"viruses",
"virology",
"viral",
"classification",
"biology",
"microbiology",
"veterinary",
"science"
] |
2013
|
Schmallenberg Virus Pathogenesis, Tropism and Interaction with the Innate Immune System of the Host
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In metazoans , the majority of mRNAs coding for secreted and membrane-bound proteins are translated on the surface of the endoplasmic reticulum ( ER ) . Although the targeting of these transcripts to the surface of the ER can be mediated by the translation of a signal sequence and their maintenance is mediated by interactions between the ribosome and the translocon , it is becoming increasingly clear that additional ER-localization pathways exist . Here we demonstrate that many of these mRNAs can be targeted to , and remain associated with , the ER independently of ribosomes and translation . Using a mass spectrometry analysis of proteins that associate with ER-bound polysomes , we identified putative mRNA receptors that may mediate this alternative mechanism , including p180 , an abundant , positively charged membrane-bound protein . We demonstrate that p180 over-expression can enhance the association of generic mRNAs with the ER . We then show that p180 contains a lysine-rich region that can directly interact with RNA in vitro . Finally , we demonstrate that p180 is required for the efficient ER-anchoring of bulk poly ( A ) and of certain transcripts , such as placental alkaline phosphatase and calreticulin , to the ER . In summary , we provide , to our knowledge , the first mechanistic details for an alternative pathway to target and maintain mRNA at the ER . It is likely that this alternative pathway not only enhances the fidelity of protein sorting , but also localizes mRNAs to various subdomains of the ER and thus contributes to cellular organization .
The localization of mRNAs to various subcellular sites , through the interaction of transcripts with mRNA localization proteins , is a widespread phenomenon important for the proper sorting of proteins to their final destination , and for the fine tuning of gene expression to the local requirements of a subcellular region . A systematic analysis of the distribution of mRNAs in the Drosophila melanogaster embryo indicates that transcripts from approximately 70% of the protein-coding genes localized to particular subcellular regions [1] . One major class of transcripts , those encoding membrane and secreted proteins , are targeted to and translated on the endoplasmic reticulum ( ER ) . While the ER is one continuous membrane system that is distributed throughout the cell , even transcripts translated on this organelle can be distributed asymmetrically . One prominent example is the localization of the wingless transcript to the apical cytoplasm of Drosophila ectodermal cells , which is crucial to embryonic development [2] . In a variety of other polarized systems , including Xenopus oocytes [3] , plant endosperm cells [4] , and budding yeast [5] , asymmetrically localized mRNAs have been reported to use the ER as a scaffold . How mRNAs can be localized to distinct ER locales , however , still remains largely unknown . Presumably , subsets of mRNAs that share a common subcellular distribution should bind to a common RNA receptor . This idea is supported by two large-scale analyses which demonstrated that each RNA-binding protein in Saccharomyces cerevisiae tends to associate with transcripts encoding functionally related proteins [6] , [7] . These associations may help to localize certain classes of mRNAs to different organelles . For example , 90% of the transcripts associated with the pumilio protein , Puf3p , code for mitochondrial proteins in budding yeast [6] . Puf3p localizes to mitochondria [8] and is required for the targeting of many of these mRNAs to this organelle [9] , [10] . Several other RNA-binding proteins have been shown to preferentially associate with mRNAs encoding secreted or membrane-bound proteins in yeast [7] , [11] , [12] . It remains unclear , however , whether these interactions function to localize mRNAs to the ER . The only conserved mechanism identified thus far for localizing mRNAs to the ER is through the canonical signal sequence directed pathway . This targeting process is initiated during the translation of mRNAs encoding secreted and membrane-bound proteins , when a nascent N-terminal signal sequence or transmembrane segment recruits the signal recognition particle ( SRP ) to the translating ribosome [13] . Subsequent interactions between SRP and an ER-bound SRP receptor promote the re-localization of the mRNA/ribosome/nascent polypeptide chain complex to the surface of the ER [14] . After targeting is complete , the signal sequence or transmembrane segment is transferred to the protein-conducting channel formed by the Sec61 translocon complex [15] and the mRNA is retained on the surface of the ER by direct interactions of the translating ribosome with this channel [16] . Despite all the intensive work performed on the secretory pathway , it remained unclear until very recently whether additional ribosomal-independent interactions exist between these mRNAs and putative RNA receptors on the ER . Classic cell fractionation studies have provided evidence both for [17]–[20] and against [21] , [22] ribosome-independent interactions . More recent studies have provided data that support the existence of an alternative mRNA targeting pathway . For example , certain mRNAs remain associated with ER-derived microsomes even after ribosomes are partially stripped off [23] , [24] . Moreover , mRNAs that encode cytoplasmic polypeptides have also been found to bind to microsomes [23]–[26] . Furthermore , mRNAs remained ER-associated in HeLa cells that are depleted of SRP54 , an essential component of the SRP [24] . Despite all these observations , it remains possible that alternative polypeptide-based targeting pathways exist that recognize other features in the newly synthesized protein besides the signal sequence . For example , in vertebrates , the Sec62/Sec63 complex and the ERj1 protein , which have both chaperone and ribosome binding domains facing the cytoplasm , might serve to anchor translating ribosomes to the surface of the ER independently of the signal sequence and the SRP system [27]–[29] . Here we provide conclusive evidence that mRNAs are targeted and retained on the surface of the ER independent of translation and ribosomes . We also provide , to our knowledge , the first mechanistic details on this alternative ER-localization pathway . In particular we demonstrate that p180 , an abundant membrane-bound protein that co-fractionates with ER-derived mRNA , promotes the general association of mRNA with the surface of the ER membrane . This activity is likely mediated in part by a lysine-rich region in this ER-resident protein that can directly interact with RNA in vitro . Finally , we show that p180 is required for the ER-anchoring of certain transcripts . We thus shed light on the workings of a basic biological process that up until now remained poorly characterized and underappreciated .
Although the ribosome-independent association of mRNA to the ER has been extensively examined using cell fractionation , these measurements require the interaction between ribosome-free transcripts and ER-derived microsomes to be stable over long time intervals outside of the cellular context . Ideally one could overcome these problems by investigating the ER-association of poly ( A ) transcripts within the cellular environment . To overcome these potential problems we investigated the ER-association of poly ( A ) transcripts within the cellular environment using microscopic analysis . In order to visualize ER-bound poly ( A ) mRNA , mammalian tissue culture cells were first treated with low levels of digitonin to selectively permeabilize the plasma membrane , thereby extracting the cytoplasm and all unbound transcripts while maintaining the integrity of the ER membrane [25] . In order to ensure that this technique effectively separates these two classes of mRNA while simultaneously preserving the ultrastructure of the ER , the distribution of various versions of the fushi tarazu ( ftz ) mRNA fragment were examined in COS-7 cells by fluorescent in situ hybridization ( FISH ) . First we monitored t-ftz mRNA , which encodes a secreted version of the ftz protein [30] . This mRNA , which localizes to the ER ( [30] and Figure S1A ) , remained associated with the cells after digitonin extraction ( Figure S1B–C ) . Next we monitored c-ftz-i mRNA , which encodes a soluble , cytoplasmic version of the ftz polypeptide . The majority of this transcript distributed diffusely across the cytoplasm in intact cells ( Figure S1D ) and was extracted when cells were treated with digitonin ( Figure S1E ) . Note that nuclear t-ftz and c-ftz-i transcripts were resistant to extraction since digitonin treatment does not permeabilize the nuclear envelope [31] . We also monitored the distribution of the soluble adenosine kinase ( AdK ) enzyme by indirect immunofluorescence . As previously reported [32] , this protein was present in both the nucleus and cytoplasm in intact COS-7 cells ( Figure S1F ) . However , after digitonin treatment only the nuclear fraction remained ( Figure S1G ) . Finally , we monitored the distributions of ribosomes and Trapα , a membrane-bound ER protein that associates with the Sec61 translocon [33] . The cellular distribution of Trapα was largely unaffected by extraction , indicating that digitonin extraction did not disrupt the integrity of the ER . In contrast , the large ribosomal protein RPLP0 localized in a diffuse cytoplasmic pattern in intact cells ( Figure S1H ) and in a reticular pattern that co-localized with Trapα in digitonin-extracted cells ( Figure S1I ) . In summary , digitonin extraction effectively removes cytoplasmic , but not nuclear or ER-associated factors , while simultaneously preserving ER-morphology . Having validated a procedure to visually isolate ER-bound molecules , the distribution of ER-associated poly ( A ) transcripts was then analyzed . We performed FISH on digitonin extracted COS-7 cells with fluorescently labeled poly ( dT ) oligonucleotides and found a substantial amount of fluorescence in the cytoplasm that co-localized with the ER marker Trapα ( Figure 1A ) . This co-localization was verified by analyzing line scans of the respective fluorescent intensities across the same region of the cell ( Figure 1B ) . Interestingly , although the overall distribution was similar ( Figure 1B , black arrows ) , different regions of the ER were enriched in either poly ( A ) or Trapα ( Figure 1B , note the relative levels of the two markers at each black arrow ) . To ensure that the FISH signal was caused by an association of our probes with mRNA , we treated the hybridized cells with RNase H , an enzyme that specifically degrades RNA that is hybridized to DNA . Indeed , this treatment dramatically reduced the FISH signal when compared to samples exposed to control buffer ( Figure 1C , compare “Cont” to “RNase H”; see Figure 1D for quantification ) . From these results , we conclude that the staining observed with fluorescent poly ( dT ) oligonucleotide in digitonin-treated cells represented ER-bound poly ( A ) transcripts . Next , the ribosome-independent retention of mRNAs on the ER was assessed . First , poly ( A ) transcripts were visualized in cells treated with homoharringtonine ( HHT ) , a compound that prevents the initiation of translation while allowing engaged ribosomes to complete translation and naturally fall off the transcript [34] . About half of the mRNA remained associated with the ER after cells were treated with HHT for 30 min ( Figure 1C–D ) , despite the fact that all detectable translation was inhibited after 15 min of treatment , as assayed by the incorporation of 35S-methionine into newly synthesized proteins ( Figure 1E ) . Again , the incubation of HHT-treated cells with RNase H eliminated the fluorescence signal ( Figure 1C–D ) . Next , mRNA was visualized in cells treated with the translation inhibitor puromycin . This compound ejects the nascent polypeptide chain from the ribosome , facilitating the dissociation of small and large ribosomal subunits . After dissociation , the large subunit will remain bound to the Sec61 channel , while the small subunit is released from the membrane [35] , [36] . To further disrupt ribosomes , EDTA was included in the digitonin extraction buffer to chelate magnesium , which is required for the subunits to remain bound to each other . In order to monitor the release of the small ribosomal subunit , we probed ER and cytoplasmic ( i . e . , non-ER ) fractions with antibodies directed against the small ribosomal protein S6 . In untreated COS-7 cells , about half of all small ribosomal subunits were associated with ER membranes ( Figure 1F , compare non-ER cytoplasm “C” with ER membranes “ER” in the control “Cont” cell fractions ) . However when cells were treated with puromycin for 30 min and then extracted in the presence of EDTA , small ribosomal subunits were efficiently removed from the ER ( Figure 1F , “Puro+EDTA” ) . Note the incomplete removal of small subunits when cells were treated with EDTA or puromycin alone . We then monitored the distribution of mRNA in these cells . In agreement with our previous results , we found that approximately half of all mRNA remained associated to the ER after ribosomes were disrupted by puromycin and EDTA ( Figure 1C–D ) . Again the FISH signal was reduced after RNase H treatment ( Figure 1C–D ) . We also observed that mRNA was retained on the ER in a human osteosarcoma cell line ( U2OS ) treated with either HHT or a combination of puromycin and EDTA , as analyzed by poly ( A ) staining ( unpublished data ) . To further confirm these results , we biochemically analyzed the poly ( A ) RNA content in subcellular fractions that were prepared from cells treated with either cyclohexamide , a translation inhibitor that stabilizes polysomes , or puromycin and EDTA . We then converted mRNA isolated from cytoplasmic and ER cell fractions into cDNA using poly ( dT ) primers and radiolabeled nucleotides and then quantified the radioactivity incorporated in each library . We found that in both cyclohexamide-treated COS-7 and U2OS cells , about 50% of the non-nuclear RNA was associated with the ER ( Figure 1G , “Cont” ) and that this fraction dropped to about 35% after puromycin/EDTA treatment ( Figure 1G , “Puro+EDTA” ) . From these results , we concluded that a substantial fraction of ER-anchored transcripts are maintained on the ER independently of ribosomes in various mammalian tissue culture cell lines . Next , the distribution of transcripts from individual genes was monitored by conventional FISH in COS-7 cells . The majority of these genes have a signal sequence coding region ( SSCR ) , which not only encodes ER-targeting polypeptides but also contains an RNA element that promotes nuclear export and the proper cytoplasmic localization of transcripts [30] , [37] . With this in mind , we first investigated the cellular distribution of the reporter transcript t-ftz , which contains the SSCR from a mouse Major Histocompatibility Complex ( MHC ) H2kb gene [30] . Besides the SSCR , this artificial transcript does not contain any sequence that is normally associated with the ER , as it was derived from a transcription factor gene from Drosophila [38] . Interestingly , t-ftz mRNA , which localizes to the ER in extracted cells ( Figure S1A ) , no longer associated with this organelle after ribosome disruption using either HHT or puromycin/EDTA ( Figure 2A , Figure S2A ) . Note that the amount of nuclear t-ftz transcript was unaltered by any of the treatments ( Figure 2A ) , indicating that the change in ER-associated fluorescence was not due to changes in expression levels or FISH efficiency . Thus , we conclude that although the MHC SSCR can promote nuclear export , it is not sufficient to allow mRNAs to be maintained on the surface of the ER after ribosome dissociation . To determine whether natural mRNAs are maintained on the ER independently of ribosomes , the distribution of transcripts generated from transfected plasmids containing the insulin-like 3 ( INSL3 ) , placental alkaline phosphatase ( ALPP ) , or calreticulin ( CALR ) genes was monitored in extracted cells . Previously it had been demonstrated that CALR mRNA co-fractionated with microsomes in cells with inactivated SRP and partly remained associated to microsomes after they were partially stripped of ribosomes [24] . Although the association between INSL3 mRNA and the ER was abolished by either HHT or puromycin/EDTA treatment ( Figure 2A–B ) , about half of the ALPP and CALR transcripts remained ER-associated under similar conditions ( Figure 2A , C , Figure S2B ) . As seen previously with the t-ftz transcript , the amount of nuclear INSL3 mRNA was unaffected by either HHT or puromycin/EDTA treatments ( see arrows in 2B , for quantification see Figure 2A ) . Interestingly the amount of nuclear CALR mRNA increased after the inhibition of translation , although this was quite variable ( Figure 2A ) . This increase is likely attributable to a slight block in nuclear export experienced by certain mRNAs after translation inhibition , as we previously reported [30] . The retention of ALPP mRNA on the ER after HHT-treatment was confirmed by the co-localization of these transcripts with Trapα in digitonin-extracted cells ( Figure S2C ) . To further validate our findings , we repeated these experiments with pactamycin , another inhibitor of translation initiation . Pactamycin also allows ribosomes to naturally fall off the transcript while preventing new ribosomes from associating with the mRNA [39] . Indeed , pactamycin effectively inhibits all protein production within 15 min ( Figure S3A ) and disrupts the ER-localization of t-ftz mRNA ( Figure S3B ) , as seen previously [30] . Moreover , in agreement with our other findings , pactamycin treatment disrupts the ER-localization of INSL3 but not ALPP mRNA ( Figure S3C–D ) . To eliminate the possibility , however remote , that the ER-association of ALPP mRNA is signal-sequence dependent , we generated a new construct where the signal sequence and transmembrane domain coding regions of this gene were eliminated . To ensure that the newly synthesized mRNA was efficiently exported from the nucleus , we inserted the frame shifted SSCR from MHC to the 5′end of the truncated ALPP ORF . Previously we demonstrated that frame shifted MHC SSCR , which encodes a soluble cytoplasmic ( i . e . , not ER-targeted ) polypeptide , promotes efficient nuclear mRNA export but not ER-targeting of either the mRNA or the encoded protein [30] . We expressed this new fusion construct ( cyto-ALPP ) in COS-7 cells and analyzed its association to the ER . Indeed this mRNA was efficiently retained on the ER after extraction ( Figure 2A , D ) despite the fact that the encoded protein lacked any features that would target it for secretion . Strikingly , the level of ER-association for cyto-ALPP mRNA was unaffected by HHT or puromycin/EDTA treatments ( Figure 2A , D ) . This result further supports the notion that the localization of this transcript to the ER was completely independent of translation . Moreover , the association of cyto-ALPP mRNA to the ER after HHT treatment was validated by the co-localization of these transcripts with Trapα in digitonin-extracted cells ( Figure 2E ) . Thus we concluded that the ER-association of ALPP mRNA to the ER was independent of features within the encoded polypeptide that are recognized by the SRP targeting pathway . To determine whether endogenous mRNAs also displayed this activity , we analyzed the level of 10 different transcripts in ER and cytoplasmic fractions ( see Figure 1G ) using quantitative reverse-transcription PCR . To ensure that our fractionation protocol separated ER-bound transcripts from the rest , we first analyzed the distribution of Sec61α and βactin mRNAs . The first mRNA encodes the central component of the translocon and was predominantly found in the ER fraction , even when the fractions were derived from cells treated with puromycin and EDTA ( Figure 3A ) . In contrast most of the βactin mRNA was in the cytoplasmic fraction . We then extended these studies to transcripts encoding ER-resident , Golgi , plasma membrane , and secreted proteins . The majority of these mRNAs remained in the ER fraction even after puromycin/EDTA treatments ( Figure 3B ) . As with over-expressed CALR , endogenous CALR mRNA was retained on the ER to a high degree after puromycin/EDTA treatment . Again this activity varied between different transcripts; for example , mRNAs encoding the Inositol-3-Phosphate Receptor ( IP3 Receptor ) and Fatty Acid Desaturase 3 proteins ( FADS3 ) exhibited a greater dependency on translation than other tested transcripts . From these results we concluded that mRNAs from the majority of genes that encode secreted or membrane-bound proteins are retained on the ER in a manner that does not require translation or ribosome-association . Our data indicated that once certain transcripts are targeted to the ER , they are retained on the surface of this organelle independently of ribosomes . It , however , remained unclear whether the initial targeting step could occur independently of translation . To address this question , cells were pretreated with HHT to inhibit translation and then microinjected with plasmid DNA . Two hours later , the distribution of the newly synthesized mRNA , which was never in contact with functional ribosomes , was assessed . Surprisingly , both ALPP and CALR mRNA targeted to the ER independently of translation ( Figure 4A–B ) . In contrast INSL3 mRNA only displayed weak translation-independent targeting activity , while t-ftz mRNA failed to target to the ER under these conditions ( Figure 4A–B ) . All of the tested transcripts targeted to the ER in the absence of translation inhibitors ( i . e . , DMSO treatment ) . To ensure that any changes in fluorescence were not due to changes in mRNA expression or variability in FISH staining , we monitored the nuclear mRNA levels of each construct , and these did not drastically change between experiments ( Figure 4B ) . The targeting of ALPP mRNA to the ER in HHT-treated cells was confirmed by co-localization of the digitonin-resistant transcripts with Trapα ( Figure 4C ) . From these results , we conclude that certain mRNA species , but not others , are efficiently targeted to the ER independently of translation or ribosome-association . This targeting is also likely to be independent of factors that recognize nascent polypeptides , such as SRP , or ER-resident membrane proteins that bind to ribosomes , such as the Sec61 complex , Sec62/Sec63 complex , and ERj1 . To identify proteins that may mediate the ribosome-independent interaction of mRNAs with the ER , cells were subfractionated to enrich for proteins that interact with ER-associated mRNAs . Since the annotation of the human proteome is more complete than that of the African green monkey ( from which COS-7 cells are derived ) , we performed this experiment in human U2OS cells . Trypsinized cyclohexamide-treated cells were washed , then treated with digitonin , and the resulting lysates were subjected to low speed centrifugation to separate the intact ER-nuclear fractions from the rest of the cytoplasm ( see the Coomassie stained gel in Figure 5 , lanes 2 and 3 ) . This ER-nuclear fraction was then solubilized with TritonX-100 and subjected to low speed centrifugation to remove nuclei ( pellet ) and generate an ER fraction ( supernatant; lane 4 ) . Note that this “ER fraction” is free of histone proteins , which serve as a marker of nuclei ( labeled “H , ” lane 3 ) . The ER fraction was then subjected to high speed centrifugation through a high percentage sucrose cushion to isolate polysomes ( pellet; lane 6 ) , which was then treated with RNase A to digest mRNA and release any associated RNA-binding proteins , including the putative mRNA receptor . The ribosomes , which are mostly resistant to RNase A treatments , and ribosome-interacting proteins , such as the Sec61 complex , were removed from this fraction by high speed sedimentation ( pellet; lane 7 ) . Note that this fraction contains all of the ribosomal proteins , which are generally <40 kD ( labeled “R” ) . The remaining supernatant , which consists of proteins that associate with polysomes only when intact mRNA is present ( ER mRNA-associated proteins; ERMAP , lane 9 ) , was analyzed by mass spectrometry . As a control we also performed mass spectrometry on proteins that were released after treating polysomes with a buffer that lacked RNase A ( lane 10 ) . The experiment was repeated three times , and a list of proteins that were significantly enriched in the ERMAP fraction ( p<0 . 05 ) was compiled ( Table 1 , for proteins where p>0 . 05 , see Table S1 ) . The final list contained 37 different proteins , of which six contained at least one transmembrane segment ( p180 , kinectin , CLIMP63 , transmembrane protein 214 , mannosyl-oligosaccharide glucosidase , and magnesium transporter protein 1 ) , 16 were known to bind to RNA , and five function as tRNA synthetases ( Table 1 ) . Analyzing our results further we realized that all 10 of the tRNA synthetases that are known to form the large Multisynthetase Complex ( MSC ) [40] were enriched in the ERMAP ( see Table 1 ) . This complex also contains three core components , one of which , AIMP1 , was also present in this fraction . Significantly , none of the other 10 tRNA synthetases were enriched in the ERMAP . The ERMAP fraction was also free of any translocon components , suggesting that our preparation was relatively depleted of proteins that directly contact ribosomes . It is also worth noting that the composition of the ERMAP was different from other preparations , such as the ribosome-associated membrane protein ( RAMP ) fraction [15] . The ER can be subdivided into morphologically distinct domains , such as the nuclear envelope , perinuclear sheets , and peripheral tubules [41] . Intriguingly , three of the membrane-bound proteins from the ERMAP fraction—p180 , kinectin , and CLIMP63—are abundant proteins that localize to the perinuclear sheet portion of the ER , which is also enriched in translocon components [42] and ribosomes [41] , [43] . Interestingly , these three proteins diffuse into the ER-tubules and nuclear envelope after puromycin or pactamycin treatment , indicating that their enrichment in sheets was dependent on the integrity of polysomes and suggesting that they may interact either with ribosomes or mRNA [42] . In particular , p180 seems to be a suitable mRNA receptor candidate . It has a very short luminal N-terminal followed by a single transmembrane domain and a large C-terminal cytoplasmic region that is comprised of two basic domains ( a lysine-rich region followed by 54 tandem repeats of a basic decapeptide sequence ) and ends in a long coil-coil domain . The highly charged domains are of particular interest as they could potentially bind to the negatively charged phosphate backbone of RNAs . While p180 was initially identified as a ribosome receptor [44] , more definitive experiments have shown that the Sec61 translocon complex [15] , [16] and not p180 [45] , [46] is responsible for the majority of ribosome binding activity present in ER-derived microsomes . If p180 acts as a non-specific mRNA receptor , one would expect that the over-expression of this protein would enhance the ribosome-independent ER-association of transcripts that normally do not have this activity . With this in mind we monitored the ER-association of t-ftz mRNA in COS-7 cells that over-expressed either green fluorescent protein ( GFP ) -tagged p180 ( see Figure 6A ) in the presence and absence of HHT . As a control , we monitored the distribution of t-ftz mRNA in cells expressing GFP-CLIMP63 and histone 1B-GFP ( H1B-GFP ) . The distribution of H1B-GFP , which binds to DNA in the nucleus , is not affected by extraction and allowed us to identify co-expressing cells after digitonin-treatment . We observed that GFP-p180 over-expression promoted the ER-association of t-ftz mRNA in both control and HHT-treated cells ( Figure 6B–C ) . In contrast , over-expression of either GFP-CLIMP63 or H1B-GFP had no effect ( Figure 6B , D ) . Since the expression of GFP-p180 did not significantly affect the cytoplasmic/nuclear distribution ( Figure S4A ) or the total level ( Figure S4B ) of t-ftz mRNA in intact cells , we could rule out the possibility that the elevated level of ER-bound t-ftz was caused by an upregulation of its nuclear export , production , or stability . Moreover , the level of nuclear t-ftz FISH signal did not significantly change , except for cells expressing H1B-GFP , and this was likely due to the fact that these cells had a lower overall expression of t-ftz mRNA ( Figure S4B ) . Next , we investigated whether kinectin could act as a general mRNA receptor . In the majority of cells , over-expression of GFP-kinectin did not promote a dramatic increase in the level of ER-bound t-ftz mRNA ( Figure S5A–B ) . We , however , did observe a drop in nuclear t-ftz mRNA compared to mock co-transfected cells . This was caused by a decrease in the total level of t-ftz mRNA ( Figure S5C ) and not changes in cytoplasmic/nuclear distribution ( Figure S5D ) . As the absolute level of ER-associated t-ftz mRNA after HHT-treatment did not change ( Figure S5B ) , despite the drop in its expression level ( Figure S5C ) , we re-evaluated our data . Upon closer inspection we found that in certain cells with high levels of GFP-kinectin , there was an increase in the ribosome-independent ER-association of t-ftz ( for example , see Figure S5E ) . Indeed , in HHT-treated cells the level of ER-associated t-ftz correlated with the amount of GFP-kinectin , but not H1B-GFP ( Figure S5F ) . We then examined whether over-expression of p180 affected the ER-association of bulk mRNA . Indeed in cells expressing GFP-p180 there was almost a doubling in the amount of ER-associated mRNA as compared to either H1B-GFP expressing , or untransfected , cells ( Figure S6 ) . This was true for both untreated and HHT-treated cells . Although it is likely that a substantial fraction of this enhanced ER-targeting was due to the recruitment of endogenous transcripts , part of the observed increase was probably due to ER-bound GFP-p180 mRNA , which is not present in the control transfected cells . Since the expression of p180 has been shown to be important in upregulating secretion in specialized secretory cells [47] , [48] , our results could have been ascribed to an increase in ribosome-anchoring proteins . However , cells over-expressing GFP-p180 and t-ftz did not have altered levels of translocon components , such as Sec61β or Trapα , as seen by immunoblot ( Figure 6E ) . Over-expression of kinectin also had no effect on the levels of Sec61β or Trapα ( Figure S5A ) . In order to determine whether the lysine-rich region and basic repeats were required for ER-anchoring of mRNA , we over-expressed a version of GFP-p180 that lacks both these domains ( GFP-p180ΔLysΔRepeat; Figure 6F ) and monitored t-ftz distribution . Cells that over-expressed this construct retained about half as much t-ftz on the ER after HHT treatment as compared to cells over-expressing p180 ( Figure 6F–G , see Figure 6A to compare the expression levels of the two constructs ) . Notably this level of residual ER-associated t-ftz was above control HHT-treated cells , indicating that GFP-p180-ΔLysΔRepeat still had some activity . Interestingly , in the absence of translation inhibitors , cells expressing this construct had elevated levels of ER-associated t-ftz mRNA ( Figure 6H ) . This increase was not due to changes in either the nuclear/cytoplasmic distribution or total levels of t-ftz mRNA in cells expressing GFP-p180-ΔLysΔRepeat ( Figure S4 ) . Thus , it is likely that p180 has the ability to enhance the translation-dependent association of t-ftz mRNA with the ER and that this activity does not require the lysine-rich region or the basic repeats . From these results we conclude that the over-expression of p180 promotes the ribosome- and translation-independent association of mRNAs with the ER . Moreover , our data suggest that this activity is mediated in part by the basic domains found in the cytoplasmic region of p180 . In addition , our results indicate that p180 stimulates the recruitment of mRNAs to the ER even in the presence of translating ribosomes , however the basic regions are dispensable for this second activity . In addition , it is likely that kinectin may have some weak ability to anchor mRNAs to the ER independently of ribosomes . Next we investigated whether the basic cytoplasmic domains of p180 could associate directly with RNA in vitro . In support of this idea we found that a bacterially expressed p180 lysine-rich region , fused to glutathione s-transferase ( GST-p180-Lys; Figure 7A ) , could form a complex with a 32P-labeled RNA derived from the human insulin SSCR ( Figure 7B ) . In contrast , no complex was formed between this RNA and a control protein , GST-Ran ( Figure 7A–B ) . By varying the amount of protein in our binding assay , we estimate that the GST-p180-Lys binds to RNA with an affinity of about 0 . 8 µM . Since this protein could form complexes equally well with other RNAs , such as a fragment of the human β-globin transcript ( unpublished data ) , it is unlikely that this domain has specificity for any particular sequence . We also tested a peptide containing three copies of the consensus p180 decapeptide repeats; however , we did not observe any complex between this reagent and any of the tested RNAs ( unpublished data ) . This result suggests that the repeats are not critical for mRNA interaction , although we could not rule out the possibility that the peptide , which is 30 amino acids in length and predicted to be disordered , failed to adopt some particular confirmation that is required for RNA-interaction . Next , we depleted p180 , kinectin , or CLIMP63 , by infecting U2OS cells with lentiviruses that deliver short hairpin RNAs ( shRNAs ) that are processed into small interfering RNAs directed against the human genes of interest . These treatments effectively depleted p180 and kinectin ( Figure 8A ) , but the level of CLIMP63 after shRNA knockdown was quite variable . In addition depletion of CLIMP63 occasionally resulted in a decrease in kinectin levels ( Figure 8A ) , however this was not consistent throughout all our experiments . Note that in these preliminary experiments p180 was depleted with shRNA clone B9 . Previously it was shown that the depletion of these three factors had no obvious effect on ER-morphology except that CLIMP63 depletion decreased the average width of the ER lumen [42] . Moreover , p180 depletion did not significantly affect the level of translocon components , such as Sec61β or Trapα ( Figure 8B ) . We did observe , however , that the average cell size increased after p180 depletion ( Figure S7A ) . As a consequence , the total area occupied by the ER and the nucleus also increased ( Figure S7B ) . Since we also observed an increase in bi-nucleate cells ( unpublished data ) , it is possible that p180 is required to complete cytokinesis , which would explain the increase in cell and nuclear sizes . We next determined whether p180 was required for the ER-association of bulk mRNA to the ER using poly ( dT ) FISH probes . To control for changes in cell size , we imaged and quantified poly ( A ) FISH staining in extracted cells and plotted the total fluorescence intensity in the ER versus the cell area for each cell . When cells of a similar size were compared , we observed a decrease in the steady-state levels of ER-associated mRNA after p180 depletion ( Figure 8C ) . In contrast , kinectin depletion had no effect on the level of ER-associated mRNA ( Figure 8D ) . When p180 knockdown cells , which already had a low level of ER-associated mRNA , were treated with HHT the amount of mRNA on the ER only decreased slightly ( Figure 8C ) . In contrast when control or kinectin-depleted cells were treated with HHT , the amount of ER-associated mRNA dropped but was still higher than what was seen in p180 knockdown cells with or without HHT treatment ( Figure 8C–D ) . In order to quantify the amount of ER-associated mRNA while controlling for changes in cell size and variation in FISH signals between experiments , we normalized the integrated fluorescence intensities of FISH signal in the ER to the nucleus for each cell . We found that p180-depleted cells had significantly less ER-associated mRNA in comparison to control cells ( Figure 8E ) . Using this analysis , we found that HHT treatment reduced the amount of ER-associated mRNA in both control and p180 knockdown cells , however even in the absence of p180 and translation , there was still ER-associated transcripts ( Figure 8E ) . From these experiments we conclude that p180 promotes the efficient anchoring of bulk mRNA to the ER , however as p180 depletion did not abolish the ribosome-independent ER-association of mRNA , it is likely that other mRNA receptors exist . We then tested the requirement of p180 for ER-association of specific transcripts by analyzing the level of mRNA in the cytoplasm and the nucleus by FISH . p180 depletion reduced the association of ALPP to the ER in both control and HHT-treated cells ( Figure 8F–G ) . In contrast to poly ( A ) staining , we did not observe an increase in nuclear ALPP in the knockdown cells . We believe that this is due to the fact that the total amount of ALPP mRNA produced per transfected cell did not change despite the increase in cell and nuclear size . Depletion of p180 with a second shRNA construct ( clone B10 , Figure 8B ) also reduced the association of ALPP to the ER in both control and HHT-treated cells ( Figure 8G ) . In contrast , depletion of kinectin had no effect on the level of ER-associated ALPP mRNA in either control or HHT-treated cells ( Figure 8F–G ) . p180 depletion by either shRNA clone also reduced the ER-association of CALR mRNA in both control and HHT-treated cells ( Figure 8H ) . From these experiments we concluded that the ribosome-independent anchoring of ALPP and CALR mRNA to the ER requires p180 .
The work presented in this article provides , to our knowledge , the first molecular insight into how a large fraction of ER-anchored transcripts are maintained on the surface of this organelle independently of ribosomes in mammalian cells . Importantly , we demonstrate that the degree of ribosome- and translation-independent targeting and maintenance at the ER varies greatly between different transcripts . We then provide evidence that p180 acts as a general mRNA receptor on the ER . Over-expression of a GFP-tagged version of this protein potentiates mRNA-ER interaction , while its depletion reduces the amount of ER-associated mRNA . Finally we demonstrate that p180 is required for the ribosome-independent anchoring of ALPP and CALR mRNAs . Although p180 appears to be a metazoan-specific gene , recent findings have suggested that mRNA may be anchored directly to membranes in prokaryotes [49] , suggesting that the ribosome-independent association of mRNAs to membrane-bound receptors is universally conserved [50] . Indeed our data suggest that other mRNA receptors for the ER exist in mammalian cells . One potential candidate that we have yet to rule out is kinectin . Although its depletion has little to no effect on the distribution of bulk poly ( A ) or ALPP mRNA ( Figure 8D , F , G ) , its over-expression promoted a small but detectable increase in the ribosome-independent association of t-ftz mRNA with the ER ( Figure S5 ) . Moreover , kinectin has a cytoplasmic lysine-rich domain that resembles the RNA-binding region of p180 . Experiments are currently underway to determine the exact contribution of kinectin to this process . Our results indicate that p180- and ribosome/translation-dependent targeting mechanisms act synergistically to enhance ER-anchoring of mRNAs ( Figures 6B , H and 8E , G–H ) . In agreement with our results , several groups have demonstrated that p180 expression promotes secretion [47] , [48] . Interestingly , the over-expression of p180 in budding yeast , which does not express any endogenous p180-like proteins , leads to the proliferation of ER , the enhancement of mRNA-ER association , and an increase in the half-life of ER-bound transcripts [51] , [52] . Furthermore , while ER-proliferation is stimulated by the over-expression of a version of p180 that lacks the basic domains , the enhanced mRNA-ER association requires these domains [52] . Although these results have been ascribed to the ability of p180 to directly recruit ribosomes , our data support an alternative model where the basic domains of p180 associate directly to mRNA , thus enhancing the partitioning polysomes to the ER . It is also likely that p180 may have other domains that mediate mRNA-ER association in mammalian cells . Indeed we found that the expression of p180 lacking any basic regions ( GFP-p180-ΔLysΔRepeat ) can promote ribosome-dependent ER-anchoring of mRNA ( Figure 6G–H ) . Taken together , our data suggest that the coil-coil domain may function primarily within the context of translation to enhance ER-association . This result is in agreement with a recent study performed in collagen secreting cells which demonstrated that p180 can promote the assembly of ER-bound polysomes , but that this activity did not require its basic domains [53] . Importantly we demonstrate that p180 has a lysine-rich region that can directly bind to RNA in vitro ( Figure 7 ) , likely through non-specific interactions with the mRNA backbone . In light of this we predict that p180 acts in concert with proteins that recognize specific RNA sequences to recruit particular mRNAs , such as ALPP and CALR , to the ER . Many candidate proteins that could fulfill this function are likely found in the ERMAP fraction ( Table 1 ) . Further studies will be required in order to determine whether these other ERMAP proteins play a role in mediating specific interactions between mRNAs and the ER . Intriguingly , our analysis also uncovered that the MSC , containing 10 tRNA synthetases , and eEF1A1 , which delivers charged tRNA to the ribosome , co-fractionate with ER-associated mRNAs ( Table 1 ) . Recently it has been shown that the MSC not only co-fractionates with polysomes in a sucrose gradient , but also is distributed in a reticular pattern that is resistant to cellular extraction with digitonin [54] , suggesting that this complex associates predominantly with ER-bound mRNA . It is possible that the MSC may mediate the direct delivery of charged tRNAs to the ribosome ( known as “tRNA channeling” [55] ) , and thus be responsible for the enhanced rate of protein synthesis experienced by ER-targeted transcripts [56] . Finally , it is likely that mRNA receptors may restrict various transcripts to particular subdomains of the ER . As mentioned previously , many asymmetrically localized mRNAs are anchored by mRNA receptors that are present in particular ER-subdomains . This is best illustrated in rice endosperm cells , where the transport and anchoring of specialized mRNAs to specific ER-domains is dependent on an RNA binding protein that is homologous to SND1/Tudor [4] , [57] , a protein we identified in the ERMAP fraction ( Table 1 ) . Interestingly , the differential distribution of ER-bound transcripts is also seen in mammalian cells . For example , t-ftz , but not ALPP , appears to be excluded from the nuclear envelope ( X . Cui and A . Palazzo , unpublished observations ) . Moreover unlike translocon-associated proteins , which are concentrated in ER-sheets [41] , [42] , poly ( A ) appears to be distributed more evenly across all of the ER ( for example , compare the distribution of Trapα and poly ( A ) in Figure 1A ) , suggesting that the association of certain mRNAs with ER-tubules is mediated by interactions with some additional unidentified RNA receptor ( s ) . Ultimately , the restricted localization of certain mRNAs may help to target newly synthesized proteins to distinct areas of the ER . This may be critical for the proper localization of proteins with polarized distributions [2]–[5] , especially for secretory proteins that are exported at specific ER exit sites and are processed in specialized Golgi outposts [58] , which are present at peripheral cellular sites , such as in neuronal dendrites . The restricted distribution of particular ER-bound transcripts may also be important to confine certain newly synthesized ER-resident proteins which function in certain subdomains of this organelle , such as the nuclear envelope [59] . Again further analysis of RNA-binding proteins ( particularly those found in the ERMAP fraction ) , and their interacting RNA elements , will be required for a clearer understanding of these processes .
Full-length human INSL3 ( GeneID: 3640 ) , CALR ( GeneID: 811 ) , and ALPP ( GeneID: 250 ) cDNAs ( i . e . , including both the open reading frames and complete untranslated regions ) , inserted into the pSPORT6 vector , were purchased from Open Biosystems . cyto-ALPP was constructed by amplifying nucleotides 123–1585 of the ALPP cDNA by PCR . The PCR product was then inserted between the frame-shifted MHC SSCR and the ftz ORF in the fs-ftz pCDNA3 construct [30] using restriction-free PCR subcloning [60] . GFP-p180 , GFP-CLIMP63 , and H1B-GFP were described previously [42] , [61] . The GFP-p180ΔLysΔRepeat construct , which lacks nucleotides 175–2028 of the p180 ORF , was constructed from GFP-p180 using restriction-free PCR-based deletion [60] . Cell culture , DNA transfection , and DNA/mRNA microinjection were performed as previously described [30] , [37] , [62] . All reagents were purchased from Sigma Aldrich unless specified . HHT was used at 5 µM , puromycin was used at 200 µM , and pactamycin was used at 200 nM for the indicated times . For extractions , cells were rapidly washed twice in 37°C CHO buffer ( 115 mM Potassium Acetate , 25 mM HEPES pH 7 . 4 , 2 . 5 mM MgCl2 , 2 mM EGTA , 150 mM Sucrose ) , then incubated in CHO+0 . 025% digitonin ( ±20 mM EDTA ) at 37–42°C for 10 s . Extraction was terminated by the addition of excess PBS+4% paraformaldehyde at 37°C . FISH and immunostaining were performed as previously described [30] , [37] , [62] . The deoxyoligonucleotides used to stain bulk mRNA ( polymer of 60 dT; poly ( dT ) ) , ftz ( GTCGAGCCTGCCTTTGTCATCGTCGTCCTTGTAGTCACAACAGCCGGGACAACACCCCAT ) , INSL3 ( GGGCCCCCGCACACGCGCACTAGCGCGCGTACGAAGTGGTGGCCGCA ) , CALR ( CAGATGTCGGGACCAAACATGATGTTGTATTCTGAGTCTCCGTGCATGTC ) , and ALPP ( CAGCTTCTTGGCAGCATCCAGGGCCTCGGCTGCCTTTCGGTTCCAGAAG ) were conjugated at the 5′ end with Alexa546 ( Integrated DNA Technologies ) . To ensure that poly ( dT ) signal was dependent on mRNA , coverslips with FISH-stained cells were incubated in 1× RNase H reaction buffer ( NEB ) with ( “+” ) or without ( “−” ) RNase H ( New England Biolabs ) at 10 units per coverslip for 1 h at 37°C . For immunofluorescence , fixed cells were probed with polyclonal rabbit antibody against Adenosine Kinase ( 1∶250 dilution , [32] ) and FISH-stained samples were probed with the human antibody against the RPLP0 ribosomal protein ( “P0 , ” 1∶50 dilution [63] ) or the rabbit polyclonal against Trapα ( 1∶500 dilution [33] ) and then stained with Alexa488- or Alexa647-conjugated secondary antibodies ( 1∶200 dilution , Invitrogen ) . Microscopy , imaging , and quantification were performed as previously described [30] , [37] , [62] . Plasmids encoding shRNA against p180 ( clone B9 - TRCN0000117407 , clone B10 - TRCN0000117408 , Sigma ) , CLIMP63 ( clone TRCN0000123296 ) , kinectin ( clone TRCN0000063520 ) , or empty vector ( pLKO . 1 ) were transfected into the HEK293T cells together with the accessory plasmids , VSVG and Δ8 . 9 , to generate Lentivirus carrying specific shRNA plasmids . Lentivirus was harvested from the medium 24 h and 48 h post-transfection by filtering through a 0 . 44 µm filter . For infection , Lentivirus was applied to U2OS cells with 8 µg/ml hexamethrine bromide . Puromycin was applied to the cell 24 h post-infection at 2 µg/ml to select for infected cells , and puromycin containing medium was changed every other day . Cell lysates were collected 5 d post-infection to assess the level of knockdown , and the cells were used for various experiments as described . To isolate fractions , cells were first pre-treated with puromycin ( 200 µM ) , cycloheximide ( 200 µM ) , or control media ( 0 . 1% Dimethyl sulfoxide; DMSO ) for 30 min; then trypsinized , pelleted at 800 g for 2 min , washed 3 times with ice cold PBS+Soy Bean Trypsin Inhibitor ( 0 . 1 mg/ml; Sigma ) , ±200 µM cyclohexamide , ±200 µM puromycin; washed once with ice cold Phy Buffer ( 150 mM Potassium Acetate , 5 mM Magnesium Acetate , 20 mM HEPES pH 7 . 4 , 5 mM DTT , and protease inhibitors , with either DMSO , 200 µM puromycin , 200 µM cyclohexamide , and/or 20 mM EDTA ) ; and then resuspended in cold 0 . 5 ml Phy Buffer again with indicated reagents . Cells were extracted by adding an equal volume ( 0 . 5 ml ) of cold Phy Buffer+0 . 2% digitonin . Lyastes were then centrifuged at 800 g for 2 min to produce a suspension ( cytoplasmic fraction ) and pellet ( ER+nuclear fraction ) . The pellet was then washed once with cold Phy Buffer , then resuspended in cold 0 . 5 ml Phy Buffer and extracted by adding an equal volume ( 0 . 5 ml ) of Phy Buffer+0 . 5% TritonX-100 . This sample was then centrifuged at 800 g for 2 min to produce a suspension ( ER fraction ) and pellet ( nuclear fraction ) . Both cytoplasmic and ER fractions were then centrifuged at 10 , 000 g for 10 min to remove contaminating organelles such as mitochondria and nuclei . These fractions were either immediately analyzed for protein ( Figure 1F ) or mRNA ( Figures 1G , 3 ) or further fractionated ( Figure 5 ) by layering the ER fraction over 500 µl of Phy Buffer supplemented with 80% sucrose and cyclohexamide and centrifugation at 100 , 000 g for 60 min to produce a suspension ( ER , non-polysomes ) and a pellet ( ER-derived polysomes ) . The polysome fraction was then resuspended in 50 µl of Phy Buffer ±0 . 5 µl RNAse A ( 1 mg/ml ) and incubated at room temperature for 15 min . The polysome samples were then centrifuged for 60 min at 100 , 000 g to produce a suspension ( ER mRNA-associated proteins ) and a pellet ( ribosomes and associated proteins ) . Cell fractions were mixed with Laemmli sample buffer , heated to 70°C for 5 min , and separated by SDS-PAGE on a 4%–20% gradient gel . For protein identification the gel was Coomassie stained and sequenced by microcapillary liquid chromatography tandem mass spectrometry ( Taplin Mass Spectrometry Facility , Harvard Medical School ) . Cell lysates from various culture cell lines were collected in RIPA buffer ( 50 mM Tris-HCL , 150 mM NaCl , 0 . 1%SDS , 0 . 5% Triton-X100 , 1 mM PMSF , and 1× protease inhibitor cocktail , Roche ) from various cell lines . Proteins were separated by SDS-PAGE and transferred onto the nitrocellulose membrane and probed with antibodies specific to p180 ( polyclonal , 1∶1 , 000 dilution , Sigma ) , CLIMP63 ( polyclonal , 1∶1 , 000 dilution , Sigma ) , kinectin ( polyclonal , 1∶1 , 000 dilution , Sigma ) , GFP ( polyclonal , 1∶1 , 000 dilution , Invitogen ) , S6 ( rabbit monoclonal 5G10 , 1∶250 dilution , Cell Signalling ) , Trapα ( rabbit polyclonal , 1∶5 , 000 dilution [33] ) , Sec61β ( rabbit polyclonal , 1∶5 , 000 dilution [64] ) , or αtubulin ( mouse monoclonal DM1A , 1∶20 , 000 dilution , Sigma ) . To isolate RNA , cell fractions were treated with five times the volume of TRIzol ( Invitrogen ) and then centrifuged at 10 , 000 g for 10 min . To the supernatant , one times the original volume of chloroform was added and then centrifuged at 10 , 000 g for 10 min . RNA was precipitated from the aqueous phase by adding one volume of isopropanol , one-twentieth volume of ammonium acetate , and 2 µl of 10 mg/ml glycerol as a non-specific carrier , and incubating the mixture at −80°C for 1 h . The sample was centrifuged at 20 , 000 g for 30 min , and the pellet was washed with 50 µl of 70% ethanol . The precipitate was dried and then resuspended in 50 µl of water . For quantification of total RNA ( Figure 1G ) , 1 µl of the RNA preparation was converted to cDNA using Superscript reverse transcriptase II ( Invitrogen ) in the presence of α[32P]-UTP ( Perkin Elmer ) and a 40-nucleotide-long dT primer at 42°C using the manufacturer's protocol . After 2 h the reaction was terminated by incubating the samples at 80°C for 15 min . cDNA products were separated from free nucleotides using a G25 column ( GE Healthcare ) . Counts per minute ( CPM ) were determined by a scintillation counter . Background CPM , as calculated from a cDNA sample prepared from an RNA-free reaction , was subtracted from each tube . When increasing amounts of poly-adenylated mRNAs were added to the reaction , a linearly proportional amount of radioactive cDNA product was recovered ( unpublished data ) . For quantitative RT-PCR ( Figure 3 ) ER and cytoplasmic fractions were converted to cDNA with Superscript reverse transcriptase II ( Invitrogen ) and random hexanucleotides at 37°C using the manufacturer's protocol . After 2 h the reaction was terminated by incubating the samples at 80°C for 15 min . Samples were treated with RNAse , and cDNA products were purified by phenol chloroform extraction followed by ethanol precipitation . The libraries were assayed by the Biopolymers Facility at Harvard Medical School using kits from Applied Biosystems for βactin ( Hs03023943_g1 , GeneID: 60 ) , BiP ( Hs99999174_m1 , Gene ID: 3309 ) , Calreticulin ( Hs00189032_m1 ) , Fatty Acid Desaturase 3 ( Hs00222230_m1 , GeneID: 3395 ) , Integrin-β1 ( Hs00236976_m1 , GeneID: 3688 ) , Interleukin 7 ( Hs00174202_m1 , GeneID: 3574 ) , Inositol-3-Phosphate Receptor 3 ( Hs01573555_m1 , GeneID: 3710 ) , Manosidase 2A1 ( Hs00159007_m1 , GeneID: 4124 ) , Sec61α ( Hs00273698_m1 , GeneID: 29927 ) , Transferrin Receptor ( Hs00174609_m1 , GeneID:7037 ) , Trapα ( Hs00162340_m1 , GeneID: 6745 ) , and 28S rRNA ( custom design 4331348 , GeneID: 100008589 ) . Note that 28S rRNA was used to normalize samples as the large ribosomal subunit remains bound to the ER after puromycin treatment [35] . The p180 lysine-rich domain ( amino acid residues 52–136 ) was amplified from pCDNA-GFP-p180 and inserted into pGEX2T vector ( Novagen ) downstream of an N-terminal GST-tag to create GST-p180-Lys . GST-protein was expressed in Escherichia coli BL21 by growing 500 ml of culture in LB until it reached an OD600 of 0 . 6 , then adding 500 ml ice-cold LB ( with 4% ethanol and 1 mM IPTG ) and incubating the culture at 18°C for 18 h . Cell pellets were resuspended in 20 ml of protein purification buffer ( 1% ( v/v ) TritonX-100 , 50 mM HEPES pH 8 . 0 , 5 mM MgCl2 , 100 mM KCl , and 500 mM NaCl , 0 . 1 mg/ml PMSF ) and lysed by French press . The recombinant proteins were purified on glutathione sepharose beads ( Sigma ) and eluted with 10 mM reduced glutathione dissolved in PBS buffer . For EMSA experiments , the SSCR sequence of insulin ( ACCATGGCCCTGTGGATGCGCCTCCTGCCCCTGCTGGCGCTGCTGGCCCTCTGGGGACCTGACCCAGCCGCAGCC ) , were cloned by restriction free cloning between the HinDIII and XhoI sites of pCDNA3 . These plasmids were digested with XhoI and transcribed using T7 RNA polymerase ( Ambion ) in the presence of 0 . 4 µCi/µl 32P-GTP . Synthesized RNA products were denatured in 50% formamide and resolved by polyacrylamide gel electrophoresis ( TBE , 10% acrylamide; acrylamid/bisacrylamide ration of 19∶1 ) and then gel isolated . The labeled RNA was incubated alone , or with an excess of either GST-Ran or GST-p180-Lys ( final concentration of the protein 12 µM ) in PBS with 10 µg/ml denatured yeast tRNA ( Sigma ) at room temperature for 20 min . The free and complexed RNAs were separated by native polyacrylamide gel electrophoresis ( TBE , 10% acrylamide; acrylamid/bisacrylamide ratio of 19∶1 ) , and labeled RNA was visualized using a Typhoon phosphorimager ( GE Healthcare ) .
|
Messenger RNAs ( mRNAs ) that encode secreted or membrane-bound proteins must be delivered to , and then maintained on , the surface of the endoplasmic reticulum ( ER ) . These mRNAs encode a short polypeptide that targets the mRNA/ribosome/nascent protein complexes to the ER surface during translation; however , recent studies support the existence of additional ER-localization signals that might be present within the mRNA molecules themselves . Here , we demonstrate that a fraction of these mRNAs , whose encoded proteins are destined for secretion , contain information that targets and anchors them to the ER independently of their encoded polypeptide or their association to ribosomes . We identify proteins on the ER that may serve as receptors for these mRNAs . We then show that one of these candidate membrane-bound receptors , p180 , is required for the maintenance of certain mRNAs on the surface of the ER even after their translation into protein is disrupted . We also demonstrate that p180 contains a region that binds directly to RNA and likely mediates the anchoring of mRNA to the ER . Our study thus provides the first mechanistic details of an alternative pathway used to ensure that secretory mRNAs , and their encoded proteins , reach their proper destination in the ER .
|
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"and",
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2012
|
p180 Promotes the Ribosome-Independent Localization of a Subset of mRNA to the Endoplasmic Reticulum
|
The contribution of basal and luminal cells to cancer progression and metastasis is poorly understood . We report generation of reporter systems driven by either keratin-14 ( K14 ) or keratin-8 ( K8 ) promoter that not only express a fluorescent protein but also an inducible suicide gene . Transgenic mice express the reporter genes in the right cell compartments of mammary gland epithelia and respond to treatment with toxins . In addition , we engineered the reporters into 4T1 metastatic mouse tumor cell line and demonstrate that K14+ cells , but not K14− or K8+ , are both highly invasive in three-dimensional ( 3D ) culture and metastatic in vivo . Treatment of cells in culture , or tumors in mice , with reporter-targeting toxin inhibited both invasive behavior and metastasis in vivo . RNA sequencing ( RNA-seq ) , secretome , and epigenome analysis of K14+ and K14− cells led to the identification of amphoterin-induced protein 2 ( Amigo2 ) as a new cell invasion driver whose expression correlated with decreased relapse-free survival in patients with TP53 wild-type ( WT ) breast cancer .
Carcinomas are defined as cancers of epithelial cell origin . All carcinomas contain cancer cells in multiple differentiation statuses such as luminal and basal . Keratins ( cytokeratins , abbreviated as K ) are intermediate filament proteins that are expressed in a differentiation status–specific manner in luminal ( K7 , K8 , K18 , K19 ) or basal ( K5 , K6 , K14 , K17 ) epithelial cells and are routinely used as diagnostic markers for cancer tissues [1 , 2] . It is thought that cancer epithelia are plastic to an extent and can interconvert between basal and luminal differentiation states during initiation , progression of cancer , and in response to treatment [3] . Therefore , there is a significant need for experimental model systems that facilitate the study of this plasticity and assess the importance of the epithelial differentiation state in modulating the biology of cancer cells . Despite advances in treatment , patients with breast cancer often relapse and develop metastatic disease , which accounts for over 90% of the 450 , 000 breast cancer–related deaths each year worldwide [4 , 5] . Tumor metastasis is a poorly understood , complex , multistep process by which tumor cells disseminate from primary tumors to form secondary tumors [6] . Understanding the key molecular determinants that regulate the metastatic process is fundamental to identifying ways to limit the spread of and to target metastatic breast cancer . Majority of breast carcinomas express luminal keratins , and a subset express basal keratins [7–9] . Basal keratin expression correlates with higher tumor grade , poor prognosis , and reduced relapse-free and overall survival [7 , 9 , 10] . Among the breast cancer subtypes , basal-like ( BL ) breast carcinomas express both basal and luminal keratins and are associated with aggressive clinical behavior and increased frequency of metastasis compared to luminal subtypes [11] . Recent observations show that collective invasion needs K14-positive leading cells for efficient dissemination and metastases [12] . Conversions of luminal to basal lineage have been observed ex vivo in mouse breast cancer models [12] , suggesting that lineage plasticity of cancer cells may be involved in the metastatic process . However , the relative contribution of basal versus luminal breast cancer epithelia for tumor progression and metastasis remains unknown . The goal of the present study is to develop and characterize reporter systems that are more versatile than the frequently used causes recombination ( CRE ) -based systems . Using the same K14 and K8 promoter used in the CRE expression systems [13] , we generated K14 and K8 reporters that coexpress fluorescent proteins and toxin receptors in culture and in vivo . The reporters not only mark K14 and K8 positive cells but also enable inducible elimination of specific populations using diphtheria toxin ( DT ) or ganciclovir ( GCV ) . We demonstrated that elimination of K14 reporter–positive cells decreases invasive phenotype in vitro and tumor growth and metastatic load in vivo , using 4T1 mammary carcinoma cells [14] , a syngeneic , orthotopic transplantation model of metastatic breast cancer . Molecular characterization of K14 reporter cell lines led to the identification of Amigo2 as a novel mediator of invasion , which demonstrates the utility of the reporter system for understanding biology of metastasis and as a discovery tool to identify novel molecular regulators of cancer metastasis . Our reporter system has significant benefits over the more frequently used CRE recombinase–based systems [13] . Whereas CRE-based systems are effective in tracing the lineage of marked cells , they do not have the capacity to report plasticity between K8 and K14 differentiation states . Using fluorescence proteins with a very short half-life , we report our ability to monitor changes in K8+ or K14+ status . In addition , we report coexpression of K8 and K14 reporters that can not only allow monitoring transitions between differentiation states but also permit elimination of one cell type while sparing the other . Since differentiation state plasticity plays a critical role in development and cancer , having the ability to eliminate cells in a specific differentiation state is of significant importance . Thus , our model provides the ability to track and control plasticity and hence will provide unprecedented opportunity for those interested is studying K8+ and K14+ epithelial lineages during development and disease .
Using a human K14 gene promoter developed by the Fuchs laboratory that has been previously shown to drive expression of transgenes in an analogous manner to the endogenous K14 promoter [15–18] , we generated a construct that expresses both a fluorescent reporter ( enhanced green fluorescent protein [EGFP] ) and a monkey diphtheria toxin receptor ( DTR ) separated by a self-cleaving P2A peptide ( Fig 1A ) . DTR receptor confers sensitivity to DT and has been successfully used for targeted cell ablation in transgenic mice [19–21] . Presence of P2A peptide facilitates translation-coupled cleavage of green fluorescent protein ( GFP ) and DTR to generate active GFP and DTR products in every cell with active K14 reporter . The 4T1 mouse mammary carcinoma cell line is comprised of both basal and luminal epithelial cells and forms aggressive BL tumors that metastasize to multiple organs when orthotopically injected into the mammary fat pad of syngeneic BALB/c mice [14] . The K14 . GFP reporter was cotransfected with a selection marker containing plasmid , and antibiotic-resistant cell populations were used to enrich for GFP-positive cells by fluorescence-activated cell sorting ( FACS ) . GFP expression was colocalized to endogenous K14 , as determined by immunofluorescence ( Fig 1B , S1A Fig ) . Over time , the FACS-sorted K14 . GFP+ cell population gave rise to a GFP-negative ( K14 . GFP− ) population , suggesting that the K14-positive lineage was not terminally differentiated . In contrast , the K14 . GFP− cell population did not give rise to a K14 . GFP+ population , even when cultured for extended periods ( >50 days ) , suggesting a hierarchical relationship between K14-positive and K14-negative states under normal cell culture conditions ( Fig 1C ) . The K14 . GFP− population , generated from the K14 . GFP+ population , retained the reporter plasmid in the genomic DNA ( S1B Fig ) , demonstrating that the K14 . GFP promoter was present but not active in K14 . GFP− cells . In monolayer cultures , K14 . GFP− cells formed the characteristic cobblestone epithelial morphology , with clearly identifiable cell–cell contacts , whereas the K14 . GFP+ cell population was pleomorphic ( Fig 1D ) . The presence of rounded cells in the K14 . GFP+ population prompted us to investigate whether K14 . GFP+ and K14 . GFP− cells differ in their proliferation rates . Surprisingly , cell proliferation measured using 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) assay or changes in total cell number or 5-ethynyl-2′-deoxyuridine ( EdU ) incorporation combined with DNA staining for cell cycle analysis did not show any difference between K14 . GFP+ and K14 . GFP− cell populations . ( Fig 1E and 1F , S1C and S1D Fig ) . K14-positive cancer cells are associated with aggressive behavior in culture and in vivo [12] . Consistent with this logic , about 50% of K14 . GFP+ and wild-type ( WT ) cells formed invasive structures , bearing multiple invasive projections when plated in a 3D culture on a bed of a 1:1 mixture of Matrigel/Collagen-I ( M/Col-I ) . In contrast , less than 10% of K14 . GFP− cells formed invasive structures ( Fig 1G and 1H ) . To determine if the expression of DTR would provide us the ability to eliminate K14 . GFP+ , we treated cells with DT . DT addition to the K14 . GFP+ population resulted in an almost complete loss of structures with invasive protrusions ( Fig 1G and 1H ) , whereas DT did not affect WT or K14 . GFP− cells , demonstrating the utility of DT treatment to eliminate K14+ cells with invasive properties . Previous studies have shown that K14+ cells are present at the leading edge of invasive protrusions [12] . To understand the relationship between our K14 . GFP reporter expression and cell invasion , we monitored GFP expression in 3D culture . We observed that GFP-expressing cells were present at both invasive protrusions and in noninvasive 3D structures , demonstrating that our K14 . GFP reporter marks cells irrespective of the invasion behavior ( Fig 1I ) . We also noted that not all GFP-expressing cells were lost upon DT treatment in 3D ( Fig 1I ) and in monolayer cultures ( S1E Fig ) . The results obtained with the 4T1 model in vitro prompted us to investigate the in vivo behavior of the K14 reporter cell lines . Control , K14 . GFP+ , or K14 . GFP− cells were injected orthotopically into the abdominal mammary fat pad of syngeneic BALB/c mice . While all mice receiving injections developed fast-growing tumors , primary tumors derived from K14 . GFP− cells grew slower than control or K14 . GFP+ cells ( Fig 2A ) . All mice were euthanized 4 weeks after injections when the largest tumor reached 1 . 5 cm in diameter . Tumors derived from control or K14 . GFP+ cells had a significantly larger mass compared to those generated by K14 . GFP− cells ( Fig 2B ) . Histological examination of tumors showed that both K14 . GFP+ or K14 . GFP− cells were similar with regard to the cellular morphology , tissue organization , and extent of necrosis ( S2A Fig ) . However , examination of lung sections revealed that metastatic burden was significantly increased in mice injected with K14 . GFP+ cells compared to those injected with K14 . GFP− cells , as quantified by percentage of total lung area occupied by tumors ( Fig 2C and 2D ) . Lung metastasis was also enhanced for K14 . GFP+ cells relative to that of control cells; however , the difference was not statistically significant ( Fig 2C ) . Although both tumor size and lung metastasis were greater for the K14 . GFP+ group , correlation coefficient analysis of matched pairs of primary tumors and lung metastases from all groups showed that lung metastasis did not correlate with primary tumor mass ( Fig 2E ) , demonstrating that the increase in lung metastasis is not simply due to the increase in mass of the primary tumor in mice injected with K14 . GFP+ cells . Thus , K14 . GFP+ cell populations form more aggressive tumors in vivo with increased metastatic potential compared to K14 . GFP− cells . Next , we investigated cell proliferation and cell death in primary tumors to understand why K14 . GFP+ cells form faster-growing primary tumors . Surprisingly , neither Ki67 ( a proliferation marker ) nor cleaved caspase-3 ( an apoptosis marker ) was significantly different between K14 . GFP+ and K14 . GFP− tumors ( Fig 2F and 2G and S2B Fig ) . Since increase in tumor size can be due to increased vascularization , we also evaluated the expression of the endothelial marker CD31 in the K14 . GFP+ and K14 . GFP− tumors , but no significant difference was found ( S2C Fig ) . These results are consistent with the lack of a difference in cell proliferation rates between K14 . GFP+ and K14 . GFP− cell lines in culture . It is likely that differences in the size of tumors derived from K14 . GFP+ or K14 . GFP− are due to cell intrinsic or extrinsic factors , which remain to be understood ( see Discussion ) . To assess the importance of the K14 . GFP+ population for primary tumor growth and metastasis formation , we investigated the effects of eliminating K14 . GFP+ cell population in vivo . Mice with approximately 3 . 0-mm tumors generated using K14 . GFP+ or K14 . GFP− cells were injected with DT every other day for 7 days . The mice were euthanized 4 weeks after cell injections and evaluated for primary tumor and lung metastases . DT administration significantly reduced both primary tumor mass and lung metastasis in mice injected with K14 . GFP+ cells but had no impact on the K14 . GFP− group ( Fig 2H and 2I ) . These results support the notion that K14+ cells are critical regulators of metastatic spread and demonstrate the utility of the reporter to investigate the role of K14+ populations in vivo . As expected , tumors generated from K14 . GFP+ cells had high frequency of K14-positive cells compared to tumors generated from K14 . GFP− cells , as determined by immunofluorescence . Coimmunostaining with anti-GFP antibodies showed colocalization of GFP with K14 immunostaining in tumors generated from K14 . GFP+ cells but not in tumors generated from K14 . GFP− cells or DT treated ( Fig 2J and S3A Fig ) . Furthermore , K14 and GFP colocalized in the metastatic lesions present in the lung of mice with K14 . GFP+ tumors but not in K14 . GFP− tumors or DT treated ( Fig 2K and S3B Fig ) . Together , these observations demonstrate that expression of the reporter was associated with endogenous K14 expression in both primary tumor and metastasis . Consistent with the observations in tumor and lung sections obtained from K14 . GFP− or DT-treated mice , anti-GFP immunoblot analysis did not show expression of GFP in those tumors ( Fig 2L ) , demonstrating that DT administration was effective in eliminating reporter-expressing cells . During DT-mediated depletion of K14 . GFP+ cells in monolayer cultures , 1% to 2% of GFP-positive cells remained , even after prolonged DT treatment ( S1E Fig ) . We reasoned that this may be because of the long half-life ( approximately 26 hours ) of the GFP protein [22] . To rule out the effect of protein half-life on the presence of GFP-positive cells , we generated a new set of reporters carrying a fast-maturating and short-half-life ( 2 . 0 hours ) fluorescent protein coupled with a suicide gene . In addition to K14 , we also generated a reporter to mark K8-positive cells . The fluorescent protein and toxin were chosen such that they can be coexpressed in the same cell to better monitor changes in the cell differentiation state . K14 promoter followed by a turbo red fluorescent protein ( tRFP ) and the suicide gene herpes simplex virus thymidine kinase [23] ( TK; K14 . tRPT ) and a K8 promoter followed by turbo green fluorescent protein ( tGFP ) and DTR ( K8 . tGPD ) were stably transfected into 4T1 ( Fig 3A and 3B ) . To directly compare K14+ and K8+ cells , we established populations of K14 . tRFP- and K8 . tGFP-positive cells by performing 2 sequential rounds of FACS sorting . Similar to the K14 . GFP+ , the K14 . tRPT+ ( referred to as K14+ ) population gave rise to K14 . tRPT− ( referred to as K14− ) cells over time , but the K14− cells did not generate K14+ cells . In contrast , K8+ cells did not show any significant decrease in the percentage of reporter-positive cells over multiple passages ( S4A Fig ) . Endogenous K8 and K14 in the K8+ , K14+ , and K14− monolayers colocalized with tGFP and K14 expression with tRFP expression ( S4B and S4C Fig , respectively ) . As expected , K14+ cell populations had a less organized monolayer as compared with K14− or K8+ populations ( Fig 3C ) . As observed for K14 . GFP+ cells , almost 50% of K14+ cells formed highly invasive structures of variable size in M/Col-I basement membrane matrix , whereas the K8+ and K14− population showed almost exclusively noninvasive structures ( Fig 3C and 3D ) . Consistently , K14+ were found to migrate faster than K14− in a scratch assay ( S4D Fig ) . In addition , the K14+ and K14− cells did not show any detectable difference in their proliferation rates , as determined by MTT and EdU assays ( S5A and S5B Fig ) . To demonstrate the utility of the inducible killing strategy , we exposed monolayers to GCV for K14 reporter–expressing TK and to DT for K8 reporter–expressing DTR . Toxin treatment led to complete ablation of reporter-positive cells ( Fig 3E and S5C Fig ) , confirming that the system would be a reliable method to deplete reporter-positive cells . To determine if the invasive behavior in culture and the metastatic properties in vivo are associated with mesenchymal characteristics in K14 positive cells , we compared K14+ and K14− cells for changes in expression of epithelial and mesenchymal markers . Among the mesenchymal markers analyzed , we observed a 3 . 0-fold increase in the levels of vimentin ( Fig 3F ) . Immunofluorescence analysis showed an increase in vimentin expression in the majority of the K14+ cells , demonstrating that increase in vimentin protein levels is not restricted to a subpopulation of K4+ cells ( Fig 3G ) . Interestingly , although β-catenin protein levels did not change , immunofluorescence analysis demonstrated a loss of cell–cell junction localization in K14+ cells and a gain in cytoplasmic and nuclear signal ( Fig 3G ) . Similar changes in vimentin expression and β-catenin localization were observed in K14 . GFP+ cell monolayers and tumors ( S6A and S6B Fig ) . Consistent with β-catenin mislocalization from cell–cell junctions , E-cadherin localization was altered in K14 . GFP+ cells in culture and in tumors in vivo ( S7A and S7B Fig ) . These observations demonstrate that K14+ have mesenchymal plasticity through which they coexpress both mesenchymal and epithelial markers . Because epithelial–mesenchymal transition ( EMT ) has been linked with increased cancer stem cell properties [24] , we reasoned that it could be the reason why K14 . GFP+ tumors are bigger than K14 . GFP− . To evaluate the percentage of cells that would potentially have cancer stem cell features , we analyzed the surface expression of CD44 and CD24 in both our K14-derived cell lines but did not find any differences between K14+ , K14− , K14 . GFP+ , and K14 . GFP− cells ( S7C and S7D Fig ) . To expand the utility of our reporters for in vivo studies , we generated 2 transgenic Friend leukemia virus B ( FVB ) mice expressing either the K8 . tGPD or the K14 . tRPT construct ( Fig 3A and 3B ) . Male mice carrying the K14 . tRPT reporter were nearly sterile , likely because of the toxicity associated with the TK gene [23] , but females showed normal fertility . To determine the ability of reporters to mark appropriate cell compartment in vivo , we analyzed the relationship between expression of reporter gene and epithelial differentiation status in the mammary gland . Primary mouse mammary epithelial cells were analyzed using well-characterized luminal and basal cell surface markers [25] in conjunction with expression of reporter genes . The K8 . tGFP reporter–expressing cells were restricted to the luminal compartment , and the K14 . tRFP reporter–expressing cells were restricted to the basal ( Fig 4A and S8A Fig ) compartment , demonstrating the ability of the reporters to mark appropriate cell populations in vivo . Percentage of reporter-positive cells showed modest variation among mice , but the fluorescent protein was always restricted to the right compartment of mammary gland epithelia ( S8B Fig ) . In addition , immunohistochemistry analysis of the lung shows tGFP expression specifically restricted to K8-positive cells ( Fig 4B ) , demonstrating the specificity of the reporters in organs other than the mammary gland . To assess if the suicide genes were effective in vivo , we injected reporter-positive and WT animals with either a high or a low dose of GCV or DT intraperitoneally ( i . p . ) . DT administration induced a lethal response within 48 hours for the K8 . tGPD line , ( Fig 4C ) likely due to elimination of differentiated epithelial cells in most of the internal organs . In K14 . tRPT mice , both high and low doses of GCV caused massive weight loss or a lethal response . Nontransgenic mice did not show any phenotype in response to DT or GCV administration . Hematoxylin and eosin ( H&E ) analysis of the liver of DT-treated K8 . tGPD mouse showed a decreased number of cells compared to DT-treated WT mouse ( S8C Fig ) . For K14 . tRPT-positive but not WT mice , GCV-treated mammary gland showed a loss of the mammary ductal organization and an increased lymphocyte infiltration , likely due to inflammation induced by GCV-induced cell death and a significant change in adipose tissue where the normal fat tissues were replaced by brown fat–like tissues , presumably due to the large consumption of energy to compensate for cell loss , which turns energy-storing mature fat cells into energy-burning brown fat cells . ( S8D Fig ) . These results demonstrate that the transgenic mice express the reporter in the right compartments and that the suicide genes that respond to the stimuli making cells from these mouse models can be used in orthotopic transplantation or in vitro differentiation settings as powerful tools for the investigation of epithelial lineage biology in culture and in vivo . To gain insight into the molecular basis for the phenotypic differences in invasive , tumorigenic , and metastatic behaviors observed between K14+ and K14− cells , we conducted secretome , RNA sequencing ( RNA-seq ) , and epigenome analysis . Secreted proteins can modify the extracellular matrix and are therefore potential key candidates to influence invasion and metastasis . Mass spectrometry analysis of conditioned media ( CM ) from WT , K14 . GFP+ , and K14 . GFP− cells identified approximately 1 , 900 proteins ( S1 Data ) . The Collagen VI subunit A ( Col6a1 ) was the most differentially secreted protein between K14 . GFP+ and K14 . GFP− cells ( Table 1 ) , with an approximately 10-fold increase in abundance for K14 . GFP+ compared to K14 . GFP− cells . Immunoblot analysis of CM confirmed that Col6a1 was highly secreted by K14 . GFP+ cells and relatively undetectable in K14 . GFP− cells’ CM ( Fig 5A ) . Two recent secretome studies reported that secreted Col6a1 levels are increased for several strongly metastatic cancer cell lines relative to their less-metastatic counterparts [26 , 27] . Serpine2 was also found to be more secreted by K14+ cells , and its expression and secretion have been correlated with metastatic disease [28] . Other proteins that showed at least 4-fold difference in levels are listed in Table 1 . These results provide a strong validation of our reporter system and its potential to uncover hypothesis-generating observations . To identify genes that are differentially expressed , we performed RNA-seq analysis for WT , K14 . GFP+ , and K14 . GFP− cells ( S2 Data ) . Interestingly , among the transcripts most differentially expressed between K14 . GFP+ and K14 . GFP− cells , we found both new and known metastasis-associated genes ( Table 2 ) . Genes including metallothionein-2 ( Mt2 ) [29 , 30] , transmembrane glycoprotein nonmetastatic B ( Gpnmb ) [31] , caveolin 1 ( Cav1 ) [32] , Col6A1 [26 , 27] , HLA-DR antigens-associated invariant chain ( Cd74 ) [33 , 34] , secretory leukocyte peptidase inhibitor ( SLPI ) [35] , carbonic anhydrase ( Car9 ) , amphiregulin ( Areg ) [36–38] , and peripheral myelin protein 22 ( PMP22 ) [39] have previously been reported in association with invasive and/or metastatic phenotypes or as markers for poor prognosis in human breast cancer . We pursued analysis of a transmembrane protein , amphoterin-induced protein 2 ( Amigo2 ) , thought to be involved in regulation of cell–cell interactions [40] but not known to be involved in regulation of cell invasion . We validated the RNA-seq data by demonstrating that mRNA and the protein levels of Amigo2 were significantly higher in K14+ 4T1 cells as compared to K14− ( Fig 5B and 5C ) . To determine if the difference in Amigo2 gene expression relates to underlying differences in the epigenetic states in the Amigo2 gene , we performed chromatin immunoprecipitation sequencing ( ChIPseq ) analysis for acetylation of histone 3 lysine 27 ( H3K27Ac ) , which is a marker for active chromatin [41] . In two independent analyses , a stronger signal at the Amigo2 locus was consistently observed in the K14+ cells as compared with the K14− counterpart ( Fig 5D and S9A Fig ) . To determine if differential expression of Amigo2 is observed in other models of breast cancer , we used HCC1143 , a human breast cancer cell line that is known to express both luminal and basal keratins [42] . To facilitate transduction of human cells , we regenerated K8 and K14 reporter in a self-inactivating ( SIN ) lentiviral-based expression vector ( Fig 5E ) that constitutively coexpresses a blue fluorescence protein ( tBFP ) . Cells were transduced and sorted first for tBFP and subsequently for expression of either tRFP ( K14 ) or tGFP ( K8 ) ( S9B Fig ) . Consistent with what we found for 4T1 cells , HCC1143 K14 . tRFP+ populations had significantly higher levels of Amigo2 mRNA compared with the K14− population ( Fig 5F ) , demonstrating the relationship between K14 status and Amigo2 expression in human breast cancer cells . To gain insight into the role of Amigo2 in human breast cancer , we analyzed the relationship between Amigo2 levels in different breast cancer subtypes . A high level of Amigo2 expression was not associated with prognosis in all breast cancers , suggesting that it is not an independent prognostic indicator ( S10A Fig ) . However , a high level of Amigo2 was associated with poor relapse-free survival , irrespective of estrogen receptor ( ER ) status of the tumors ( S10B Fig ) , but had no predictive value associated with human epidermal growth factor receptor 2 ( HER2 ) status ( S10C Fig ) . Interestingly , high Amigo2 was significantly associated with poor relapse-free survival in patients with TP53 WT and not in patients with TP53 mutant breast cancers ( Fig 5G ) , [43] identifying an intriguing relationship between TP53 status and Amigo2 biology in breast cancer . To determine if Amigo2 regulates cell invasion , we knocked down its expression in 4T1 cells . Amigo2 knock-down ( AM2 KD ) cells have a dramatic decrease in the protein levels of Amigo2 and a 90% decrease in mRNA expression as compared to the control vector ( VECT ) -transduced cells ( Fig 6A ) . AM2 KD cells gained cobblestone morphology in monolayer culture and a complete loss of ability to invade in M/Col-I matrix as compared to the VECT control cell line ( Fig 6B and 6C ) . To rule out any possible off-target effects of the short hairpin RNA ( shRNA ) , we rescued the Amigo2 expression by transducing the human Amigo2 cDNA ( or a VECT control ) into the 4T1 AM2 KD ( Amigo2 knock-down rescue [AM2 KD-RE] and Amigo2 knock-down control vector [AM2 KD-VECT] , respectively ) ( Fig 6D ) . Reexpression of Amigo2 restored both loss of cobblestone cell morphology and a restoration of 3D invasive properties ( Fig 6E and 6F ) , demonstrating that Amigo2 is required for the invasive behavior of K14+ cells . Interestingly , overexpression of Amigo2 in the K14− cells ( K14-AM2 ) did not affect 2D or 3D phenotype . Thus , while Amigo2 is necessary for the maintenance of invasive behavior of 4T1 K14+ cells , it is not sufficient to promote invasive behavior in K14− mammary epithelial cell populations .
In this study , we generated and characterized novel reporter systems that combine the ability to track cells using a fluorescence protein expression with the more unique ability to selectively eliminate them using a toxin treatment . This strategy allowed us to select for K14+ mammary tumor cells and to demonstrate that they are indeed more invasive in culture and more tumorigenic and metastatic in vivo . By depleting K14+ cells in vivo , we demonstrated that K14+ cells are required for metastasis . Consistently , K14− and K8+ showed reduced invasion ability and a more organized epithelial-like monolayer than the K14+ population . Our studies also demonstrate the limits of differentiation plasticity , as K14− cells were unable to generate K14+ cells , whereas K14+ cells were capable of generating K14− or K14+ cells . To broaden the utility of the reporter system , we generated transgenic models and demonstrated that our promoters are active in the appropriate cellular compartment of the mammary gland and that K8 . tGFP expression also matches endogenous K8 protein–expressing cells in lungs . Although the percentage of reporter-positive cells in our transgenic model is lower than the CRE-based systems previously reported , we would like to highlight that our platform is a “real-time” readout of the promoter activity , which creates opportunity to study plasticity of differentiation states . In the CRE-based lineage-tracing models , CRE is induced during the prolonged labeling periods ( several days to a week ) , and the cells and their progenies are permanently marked irrespective of changes in its differentiation states . Furthermore , the combination of reporter and toxin gene expression in vivo makes these models valuable for all fields of biology involving K14 and K8 lineages . Since differentiation state plasticity plays a critical role in development and cancer , having the ability to eliminate cells that are present in a given differentiation state at the given time window is of significant importance for almost all types of carcinoma in multiple organs . Orthotopic transplantation of K14− 4T1 cells resulted in formation of tumors that were smaller in size compared to K14+ or WT cells . Surprisingly , we did not observe any difference in the cell proliferation rates between K14− and K14+ cells in culture or in vivo ( see S5A and S5B Fig ) . Although it is not clear why K14− cells form smaller tumors in vivo , there may be two possible explanations . First , albeit being transplanted into syngeneic background mice , K14+ cells may be better at evading immune systems compared to K14− cells and hence grow better in vivo . Second , a more provocative possibility relates to an interesting hypothesis proposed by a recent study that suggests that tumor growth rate can be substantially altered by changes in dispersal rate of cancer cells even in the absence of any change in doubling time [44] . Given that K14+ cells express mesenchymal state markers and demonstrate increased ability to migrate , it is possible that K14+ cells are more efficient in forming tumors in vivo . Additional experiments are needed to understand why K14− cells form smaller tumors compared to K14+ cells . Previous studies demonstrate that K14− can become K14+ to lead the invasive front during metastasis of mouse mammary tumor virus polyomavirus middle T-antigen ( MMTV-PyMT ) luminal metastatic mouse model of breast cancer [12] . In our studies , we did not observe any significant impact on either primary tumor mass or metastatic lesions upon orthotopic injection of K14− treated with DT to deplete K14+ cells that could be generated from K14− cells . This observation suggests that plasticity of differentiation state may be a context-dependent phenomenon or that the K14− cells transition to a K14+ state without involving activation of the K14 reporter . We demonstrate the utility of the platform to gain new molecular insights by comparing K14+ cells to K14− by RNA-seq and secretome analysis . We identified a number of genes differentially expressed or secreted . Many of these genes were previously reported to be associated with either bad prognosis or with invasion and/or metastatic phenotypes in human breast cancer , validating the power of the platform . In addition , we report the identification of Amigo2 as a new mediator of invasion in breast cancer . Interestingly , Amigo2 knockdown in 4T1 suppresses invasive behavior , but its overexpression in K14− cells was not sufficient to induce invasion , demonstrating that Amigo2 is necessary but not sufficient for the invasiveness . Amigo2 is an adhesion molecule that was first identified in 2003 and has been found to play a role in axon development [40] . It has been shown to be differentially expressed in gastric adenocarcinoma [45] and has been putatively identified as a possible metastasis-associated gene [46] . More recently , Amigo2 has been found to play a role in endothelial and melanoma cells , as Amigo2 regulates apoptosis [47–49] . Amigo2 mediates adhesion of fibrosarcoma cells to the liver endothelium , resulting in colonization in the liver , which indicates another possible role for Amigo2 in extravagation and colonization [50] . Our findings , together with the above observations , identify Amigo2 as a potential therapeutic target for controlling metastasis .
All animal experiments were performed according to the protocol ( AUP3218 ) , which has been approved by the University Health Network Institutional Animal Care and Use Committee ( IACUC ) . Primary antibodies to E-cadherin ( clone 36 ) and β-catenin ( clone 14 ) secondary antibody PE-Cy7 streptavidin and conjugated antibody CD24-APC ( Clone M1/69 ) were purchased from BD Transduction Laboratories . Primary antibodies to fibronectin , β-actin ( clone AC-15 ) , Vimentin ( for western blot , Clone VIM13 . 2 ) , and α-tubulin ( clone B-5-1-2 ) were purchased from Sigma-Aldrich . Primary antibody to GFP ( NB100-1678 ) was from Novus Biologics , whereas to α-SMA ( ab5694 ) , CD31 ( ab28364 ) , GAPDH ( ab8245 ) , vimentin ( immunofluorescence , ab92547 ) , and Histone 3 Acetyl K27 ( ab4729 ) were purchased from Abcam . Primary antibody to cleaved caspase-3 ( CST 9661s ) was purchased from Cell Signaling Technology . Primary antibody to Ki67 ( RM-9106-S ) was purchased from Thermo Scientific . Primary antibody to Col6a1 ( H-200 ) , Amigo2 ( C-15 ) , and β-1 integrin ( M-106 ) were purchased from Santa Cruz Biotechnology . Primary antibodies to turboRFP ( AB234 ) and turboGFP ( AB513 ) were purchased from evrogen . K14 ( AF 64 ) was purchased from Covance ( PRB-155P ) . K8 was purchased from developmental studies hybridoma bank ( Troma-1 ) . Conjugated antibody CD49f-PerCP-Cy5 . 5 ( Clone GoH3 ) and CD-44-Brilliant Violet 421 ( Clone IM7 ) were purchased from Biolegend . Biotin-CD45 ( Clone 30-F11 ) , biotin-CD31 ( Clone390 ) , and biotin-Ter119 ( Clone Ter119 ) ( Lin- antibodies ) were purchased from ebioscience . Hoechst 33342 and 4’ , 6-diamidino-2-phenylindole ( DAPI ) were purchased from life technologies . Secondary antibodies conjugated to Alexa Fluor 488 or Alexa Fluor 568 and Alexa Fluor 647 were purchased from Life Technologies . Secondary antibodies conjugated to peroxidase were purchased from Jackson ImmunoResearch Laboratories . DT purified from Corynebacterium diphtheria was purchased from Enzo Life Sciences . GCV was purchased from Sigma . Growth factor-reduced Matrigel and bovine Collagen I ( BD Biosciences ) were used for 3D culture , and Matrigel was used for orthotopic injection experiments . The K14-Cre plasmid containing the human KRT14 ( K14 ) gene promoter followed by the rabbit β-globin intron [18] was kindly provided by Jos Jonkers ( Netherlands Cancer Institute , Netherlands ) . The pEGFP-N1 vector was obtained from Clontech . The K14 promoter–driven EGFP-P2A-DTR ( K14-EPD ) reporter construct was generated by multisite Gateway cloning ( Life Technologies ) according to the manufacturer’s instructions . The K14-β-globin entry clone was made by PCR amplification using sequence from K14-Cre as a template . The EPD entry clone was made by overlap extension PCR using EGFP and DTR cDNA templates and inserting P2A peptide coding sequence in between . The SV40pA entry clone was made by overlap extension PCR using oligonucleotides matching the late polyadenylation signal sequence ( pA ) of Simian virus 40 as a template . The three entry clones—K14-β-globin , EPD , and SV40pA—were recombined into a Gateway-compatible pBluescript KS+ destination vector to produce the final construct , pBS-K14β-EPD-SV40pA . The K14 promoter–driven TurboRFP ( K14-tRFP ) reporter construct was generated in a similar manner , except using cDNA template for fast-maturating destabilized red ( orange ) fluorescent protein TurboRFP ( tRFP , pTurboRFP-dest1; Evrogen ) instead of EGFP , and DTR was replaced by herpes simplex virus TK . The K8 construct was also generated in a similar manner . The 3 . 5-kb sequence upstream the ATG codon of the murine K8 gene was obtained from the BAC clone RP23-254K21 ( BACPAC Resources Center , Children’s Hospital Oakland Research Institute ) using the forward primer 5′-GGTGGATCACTTGCCCCCTCCGTTTG-3′ and the reverse primer 5′-GGGACAGCGCCCAGCGAAGGCCC-3′ as previously done [13] . K8 promoter was followed by the rabbit β-globin intron [18] , a fast-maturating and short-half-life turbo green fluorescent protein ( tGFP , pTurboGFP-dest1 vector; Evrogen ) , a P2A followed by a DTR , and sv40polyA signal as described above . The two turbo constructs were also modified by adding a neomycin ( for K14 ) and a hygromycin ( for K8 ) resistance to allow selection . SIN constructs were generated by PCR amplification from promoter to the end of the suicide gene . Primers were flanked with attB1 and attB2 sequence to allow gateway cloning into the destination SIN lentiviral vector pLBC2-B-RFCA . 4T1 mouse mammary tumor cells were maintained in DMEM ( 4 . 5 g/L glucose ) supplemented with 10% fetal bovine serum ( FBS; Life Technologies ) , 1X MEM nonessential amino acids ( NEAA; Life Technologies ) , 100 U/ml penicillin , and 100 μg/ml streptomycin . If not specified , 3D cultures of 4T1 cells were grown embedded in a matrix of Matrigel or M/Col-I . Briefly , 96-well plates ( BD Falcon ) were coated with 30 μL of Matrigel or M/Col-I . The plate was put for 30 minutes at 37°C . Cells were added on top of the matrix in 20 ul of Matrigel or Matrigel:Collagen . After 30 minutes at 37°C , growing media supplemented with 5% Matrigel were added to the well . From day 3 to day 5 , 3D structures were monitored for invasive protrusions . All cells were grown in a humidified atmosphere with 5% CO2 at 37°C . For DTR-mediated cell ablation in culture , cells were treated with 2 . 5 or 5 ng/ml DT ( in 10 mM Tris-HCl pH 7 . 5 , 1 mM Na2EDTA ) for 2 to 7 days . For TK-mediated ablation , cells were treated with 1 or 5 ug/ml GCV for 5 to 7 days . To generate stable cell lines , cells were cotransfected with the pBluescript-K14-EPD reporter construct and the pMSCV-zeo vector containing the Sh ble gene , using Lipofectamine 2000 ( Life Technologies ) according to the manufacturer’s instructions . After 48 hours , cells were replated in fresh growth medium containing 400 μg/ml Zeocin ( Life Technologies ) , which was replenished every 4 days for a total of 16 days . Zeocin-resistant clones were pooled , replated , and expanded in the presence of Zeocin for an additional 7 days ( pre-sort ) , and then EGFP-expressing cells were isolated by FACS ( sort-1 ) using a MoFlo XDP High-Speed Cell Sorter ( Beckman Coulter ) equipped with a 488-nm laser . After 7 passages , these cells were subjected to a second round of FACS ( sort-2 ) from which both the EGFP+ and EGFP− cells were collected . Cells were allowed to recover from sorting stress and then frozen down in batches . Except where indicated , all experiments using sorted cells were performed within 2 weeks after thawing . All cell lines were tested and found to be negative for mycoplasma and viral contamination by PCR . Turbo cell lines were generated in a similar manner , except selections were carried out with G418 ( life technology ) or Hygromicin ( Roche ) for K14 or K8 , respectively . Cells were sorted with MoFlo Astrios EQ High Speed Cell Sorter ( Beckman Coulter ) . AM2 KD ( mouse specific ) and overexpression were achieved by lentiviral transduction of pLKO . 1 shB7 ( GTGTTCTCAGACACACCCTTT; TRCN0000182478 ) and pLX304 expressing full-length human Amigo2 cDNA ( Gene ID 347902 , both kind gifts of Jason Moffat ) . Generation of the transgenic line was performed at the Centre for Phenogenomics . The K14 . tRPT and K8 . tGPD reporters were purified , and pronuclear microinjections into FVB/N zygotes produced from mating of superovulated females and stud males . Viable microinjected zygotes were transferred the same day into pseudopregnant CD-1 females for gestation and birth . The resultant pups were genotyped to identify founders . Every founder was mated with FVB/N mice and constituted 1 line . Every line has been screened for percentage of positive cells in the right compartment in regards to the mammary gland ( see below Mammary gland isolation and flow cytometry analysis ) and for the responsiveness to the depletion agents ( GCV and DT ) . Based on the outcome , the best-performing lines ( 1 per K8 . tGFP and 1 per K14 . tRFP ) were kept and propagated . DNA isolation and PCR genotyping were carried out with AccuStart II Mouse Genotyping Kit ( Quanta Biosciences ) . Primer sequence for K14 mice were Fw AGCTTCATGTACGGCAGC , Rv GTACTTGGCCACAGCCATC; for K8 mice Fw CACGTGATGGGCTACGGC , Rv GTACTCCACGATGCCCAG . Single-cell mammary gland suspensions were generated from freshly isolated mammary glands of 11–21-week-old female mice by enzymatic digestion , adapting a previously described protocol [25] . Mammary glands were dissected using razor blades and digested in mouse Epicult-B media and 750 U/ml collagenase and 250 U/ml hyaluronidase for 2 . 5 hours at 37°C . Organoids obtained were quickly vortexed ( 3 pulses of 3 seconds each , medium speed ) and resuspended in 0 . 8% ammonium chloride solution to lyse red blood cells . Organoids were further dissociated in 0 . 25% trypsin for 2 minutes , 5 mg/ml dispase 0 . 1mg/ml DNase I for 2 minutes and filtered through a 40-μm mesh to obtain single cells . All reagents were purchased from StemCell Technologies . Single cells were stained with Lin- antibody for exclusion and for CD24 and CD49f to identify luminal and basal population . DAPI was used for live/dead exclusion . Flow cytometry analyses were carried out with BD Biosciences Fortessa . All PCR assays with reverse transcription were performed by using qScript cDNA SuperMix , ( Quanta Biosciences ) . Real-time PCR was performed on a 7900HT system with TaqMan Universal Master Mix II ( Life Technology ) . Taqman assays: Amigo2 was purchased from IDT ( Mm . PT . 58 . 42252903 ) ; GAPDH was purchased from Applied Biosystems ( #4352932 ) . PCR was performed using genomic DNA isolated from cells lysed in ice-cold cell lysis buffer ( 10 mM Tris-HCl pH 8 . 0 , 1 mM EDTA , 0 . 1% SDS ) . PCR reactions were carried out using 100 ng of DNA in a final volume of 25 μl . The primer sequences used are as follows: monkey DTR forward: 5′-AGCTCCTTCTGGCTGCAGTTCTTT-3′; monkey DTR reverse: 5′-TTTCCGAAGACATGGGTCCCTCTT-3′; mouse GAPDH forward: 5′-AACTTTGGCATTGTGGAAGGGCTC-3′; mouse GAPDH reverse: 5′-TGGAAGAGTGGGAGTTGCTGTTGA-3′ . Growth curves were generated from cells seeded at 3 . 5 × 105 cells/dish in 60-mm dishes . At each time point , cells were trypsinized and counted using an automatic cell counter ( TC10 Automated Cell Counter , Biorad ) . MTT assays were performed on cells seeded in 96-well plates at 2 , 500 cells/well , in triplicate in 100 ul growing media . At each time point , cells were incubated with 0 . 5 mg/ml MTT ( Thiazolyl Blue Tetrazolium Bromide; Sigma ) for 4 hours at 37°C . Solubilization solution ( 10% SDS in 0 . 01 N HCL ) was added ( 100 μl/well ) , and absorbance was measured at 560 nm using a spectrophotometer ( BMG Labtech , FLUOstar Omega ) . Results were normalized at day 1 . Click-iT Plus EdU Alexa Fluor 647 Flow Cytometry Assay Kit was purchased from Thermo Scientific and used according to the standard protocol . The 4T1-generated cell lines were incubated with 10 μM EdU for 1 hour . DNA was stained with FxCycle Violet ( Thermo Scientific ) . Results were acquired with 5 Laser LSR II ( BD Biosciences ) and analyzed by flowjo . Confluent monolayer was scratched with a p10 pipet tip , media were changed , and pictures were taken for time 0 . After testing a series of time windows , we determined 6 hours to be the best time to assess differences . Therefore , 6 hours postscratch , pictures were taken . Pictures at time 0 and 6 hours were analyzed with ImageJ to calculate the area cells migrated ( in pixels ) . Immunoblotting was performed using lysates from cells lysed in ice-cold RIPA buffer ( 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 5 mM EDTA , 1% Triton X-100 , 0 . 5% NaDOC , 0 . 1% SDS , and protease inhibitors ) . Protein concentrations of clarified cell lysates were determined using a bicinchoninic acid protein assay kit ( Pierce , Thermo Scientific ) . Proteins were separated by SDS-PAGE and transferred to polyvinylidene fluoride membranes . Membranes were blocked in 5% milk , incubated with primary antibodies for 1 hour or overnight , and incubated with peroxidase-conjugated secondary antibodies for 1 hour minimum . Bound antibodies were detected using enhanced chemiluminescence ( Pierce , Thermo Scientific ) . The protocol was adapted from [51] . Cells grown on glass coverslips were washed twice with PBS at room temperature , fixed with 4% formaldehyde in PBS for 12 minutes , permeabilized with 0 . 5% Triton X-100 in PBS for 10 minutes , and then blocked with 3% BSA in PBS for 30 minutes . Fixed cells were incubated with primary antibodies for 1 hour and incubated with fluorophore-conjugated secondary antibodies for 1 hour . Coverslips and slides were mounted with ProLong Gold antifade reagent ( Life Technologies ) . Cells were imaged using a 60X Plan-Apochromat/1 . 40 oil or a 40X Apochromat LWD/1 . 15 water immersion objective on an inverted laser-scanning confocal microscope ( C1si Confocal Microscope; Nikon ) , and images were captured using EZ-C1 Software ( Nikon ) or with Zeiss Axioimager M1 microscope with Plan-Apochromat 20×/0 . 8 air . The protocol was adapted from [51] . Female syngeneic BALB/c mice ( Jackson Laboratory ) were used for orthotopic injections at 8 weeks of age . The 4T1 cells ( 2 . 5 × 105 ) in 20% Matrigel in a volume of 25 μl were injected into the abdominal mammary fat pad ( #9 ) of anesthetized mice . Once primary tumors were palpable , they were measured 1–2 times per week using vernier calipers . For DTR-mediated cell ablation in vivo , mice were injected i . p . with 25 μg DT/kg body weight every other day for a total of 4 doses , beginning at day 7 after orthotopic cell injections . All mice were euthanized 4 weeks after cell injections . Tumors and lungs were collected and fixed in 10% neutral buffered formalin , dehydrated , and embedded in paraffin for tissue sectioning . Tumor measurements and analysis of lung metastasis were derived from multiple experiments using a total of 4–8 mice per group . H&E staining of lung sections was performed by the Campbell Family Institute of Breast Cancer Research Histology Core Unit . Metastases were identified by histopathological examination of images of H&E-stained sections acquired on a NanoZoomer 2 . 0-HT digital slide scanner ( Hamamatsu ) . Lung metastasis was quantified by measuring the average percentage of area occupied by the tumor ( % area = [area tumor / area total tissue] × 100% ) in 5 sections ( 5-μm thickness ) cut at 200-μm intervals using ImageJ software ( National Institutes of Health ) . For immunohistochemistry , 5-μm paraffin sections were deparaffinized in xylene and rehydrated in a graded ethanol series . Sections were boiled in citrate buffer ( 10 mM sodium citrate pH 6 . 0 ) or Trilogy ( Cell Marque ) for 5 minutes in a microwave oven and allowed to cool at room temperature for 1 hour . Sections were blocked 5% BSA 0 . 5% Tween-20 in PBS for 1 hour , then incubated with primary antibodies overnight at 4°C . Sections were incubated with fluorophore-conjugated secondary antibodies for 1 hour and with Hoechst or DAPI to stain nuclei . Slides were mounted with Vectashield Mounting Medium ( Vector Laboratories ) . Sections were imaged using a 40X Super Apochromat/0 . 95 air objective on an inverted laser-scanning confocal microscope ( FluoView FV1000; Olympus ) , and images were captured using FV10 Software ( Olympus ) or with Zeiss Axioimager M1 microscope with Plan-Apochromat 20x/0 . 8 air and Plan-Apochromat 40x/1 . 3 Oil DIC objective lenses . Only for Fig 1I , the images were acquired with Zeiss LSM 880 Inverted Laser Scanning Microscope with Plan-Apochromat 10x/0 . 45 and Plan-Apochromat 20x/0 . 8 air objective lenses . Postprocessing was carried out with ImageJ and photoshop . Acquisition and postprocessing parameter were kept constant across the figures that are directly compared . Only for Fig 1I , the bright field contrast was modified for 1 of the pictures because of different shades in different fields due to the irregularity of the Mat/Col-I matrix . The single immunohistochemistry antigen labeling of the paraffin-embedded murine samples was performed with Ki-67 and cleaved caspase 3 antibodies . The paraffin sections were deparaffinized and rehydrated , followed by antigen retrieval using sodium citrate buffer ( pH 6 ) . The sections were then incubated with 3% hydrogen peroxide to block endogenous peroxidase activity for 10 minutes at room temperature . After 3 washes with TBS , the sections were incubated with 2% bovine serum albumin ( Jackson ImmunoResearch Lab Inc , West Grove , PA ) for 1 hour at room temperature . Slides were then incubated separately with rabbit anti-Ki67 ( RM-9106-S ) and rabbit anti-cleaved caspase 3 ( CST 9661s ) overnight at 4°C . The slides were washed 3 times and incubated with goat anti-rabbit HRP polymer ( 1:1 , Abcam ab214880 lot: GR3196509-1 ) for 2 hours at room temperature . Samples were washed thrice and then developed using the ImmPACT DAB Peroxidase ( HRP ) Substrate kit ( Vector Laboratories , SK-4105 ) . Samples were counterstained with hematoxylin and then dehydrated before mounting the slides with permount . Ki-67 and CC3 immunohistochemistry expression were quantified using an automated imaging analysis software program , Definiens Tissue Studio version 4 . 4 . 2 ( Munich , Germany ) , which produced a 0%–100% continuous estimate of expression . Nuclear reactivity was quantified for Ki-67 , while cytoplasmic reactivity was quantified for CC3 . The percentage of positive reactivity was calculated by dividing the number of positive nuclei/cells by the total number of nuclei/cells detected on each whole slide image . CM from cells cultured for at least 24 hours in phenol red- and FBS-free media were collected , cell debris was removed by centrifugation , and the clarified CM were concentrated using centrifugal filter units ( Amicon Ultracel MWCO 3 kDa ) . Secreted proteins were acetone-precipitated and resuspended in 8 M urea and 50 mM ammonium bicarbonate and reduced and alkylated with 10 mM dithiotreitol and 50 mM iodoacetamide , respectively . Samples were diluted with ammonium bicarbonate pH 8 . 5 to 1 . 5 M urea and digested with proteomics-grade trypsin ( Promega ) . Multidimensional protein identification technology ( MudPIT ) analysis was performed as previously described [52] . Briefly , a 4-cycle 2D chromatography sequence was set up , and peptides were separated based on charge by strong cation exchange resin and on hydrophobicity by C18 reverse-phased resin . Samples were run in triplicate on a hybrid ion trap-Orbitrap mass spectrometer ( LTQ Orbitrap XL; Thermo Scientific ) . Three biological replicates per condition were analyzed . Spectral counting ( SpC ) was used as a measure of protein abundance . The SpCs for peptides corresponding to a protein were normalized against the total number of spectra for a given MudPIT sequence , averaged over the triplicates . An arbitrary value of 0 . 1 was added to every SpC to avoid division by 0 . The relative abundance of each protein was calculated as the ratio of averaged normalized SpCs in the CM from K14+ versus K14− cells . Total RNA was purified and DNAse-treated using the RNeasy Mini Kit ( Qiagen ) . RNA quality ( RNA integrity number > 9 ) and quantity were measured on a Bioanalyzer ( RNA Nano kit; Agilent ) . The NuGEN Ovation RNA-Seq V2 protocol was carried out on 100 ng of total RNA . In brief , RNA was reverse transcribed using oligo-d ( T ) primers and random hexamers to generate cDNA , which was followed by SPIA ( NuGEN ) linear amplification . The cDNA was fragmented by sonication ( Covaris E-series ultrasonicator ) according to the manufacturer’s instructions to yield a target fragment size of 200 bp . The fragmented cDNA was subsequently processed according to the Illumina mRNA sequencing sample preparation guide ( end repair , A-tailing , and ligation of sequencing adapters ) . The ligation products were run on a precast 4% NuSieve Agarose gel ( Lonza ) , stained with SYBR Gold ( Life Technologies ) , and selected for the 200–300-bp range . The size-selected cDNA was gel purified , PCR-amplified , and quantified using a Bioanalyzer ( DNA 1000 kit; Agilent ) . Each sample was sequenced using an Illumina GAII sequencer on a single lane of a flow cell generating 50 nt single-end ( SE ) reads . The sequencing reads were aligned to the mouse genome ( mm9 ) using TopHat software ( v2 . 0 . 4 ) , restricting only uniquely mapped reads to the genome . Cuffdiff software ( v2 . 0 . 2 ) was employed to find significant changes in transcript expression between two conditions . Gene-level expression measurements are reported in fragments per kilobase per million reads ( FPKM ) . Genes were designated as differentially expressed between two conditions if the false discovery rate ( FDR ) -corrected p < 0 . 05 for differential expression , fold change > log2 ( 2 ) , and FPKM > 5 in at least one condition . One million sorted cells per condition ( 2 biological replicates ) were fixed by immersion in 1 ml of 1% formaldehyde/PBS for 10 minutes at room temperature with rotation . Following fixation , cells were spun down at 3 , 000 rpm for 3 minutes , resuspended in 500 μL ice-cold PBS/BSA , spun down again , and resuspended in 500 μL ice-cold PBS . Cells were then spun a final time and resuspended in 350 μL of cell lysis buffer ( 1% SDS , 10 mM EDTA , 50 mM Tris-HCl pH 8 . 1 ) . Cells were then sonicated on high setting for 30 cycles of 30 seconds on/30 seconds off using a Diagenode Bioruptor 300 . Insoluble cell debris was removed by centrifugation at 4°C for 15 minutes at 15 , 000 rpm . Then , 20 uL of each sample was removed and set aside as an input sample , while 320 uL was added to 1 . 6 mL of cold dilution buffer ( 1% Triton X-100 , 2 mM EDTA , 150 mM NaCl , 20 mM Tris-HCl pH8 . 1 ) . Per ChIP , 10 uL each of protein A and protein G Dynabeads ( Thermo Fisher Scientific , cat#10002D and 10004D , respectively ) were washed 3 times in cold PBS/BSA ( 5 mg/mL ) and resuspended in 300 uL cold PBS/BSA , and 3 ug of H3K27ac antibody was added . The antibody/bead mixture was then incubated at 4°C for 6 hours with rotation , washed twice with cold PBS/BSA , and resuspended in 100 uL cold dilution buffer . The antibody/bead solution was then added to the processed chromatin sample and incubated overnight at 4°C with rotation . The following day , immunoprecipitated chromatin was washed 3 times with cold washing RIPA buffer ( 1% NP-40 , 0 . 7% sodium deoxycholate , 50 mM HEPES , 1 mM EDTA ) , twice with TE buffer , and resuspended in 100 uL decrosslinking buffer ( 1% SDS , 0 . 1 M NaHCO3 ) . Samples were then incubated for ≥6 hours at 65°C , and DNA was purified using a Qiagen MinElute kit . The protocol was adapted from [53] . Sequencing libraries were prepared using 0 . 5–10 ng of ChIP or input DNA with the Rubicon Thruplex FD kit , using the manufacturer’s recommended protocol , and were size selected in the range of 240–360 bp using a Caliper LabChIP XT DNA 750 kit ( Perkin-Elmer ) . Size-selected libraries were then sequenced on an Illumina HiSeq 2000 with SE 50-bp reads . Casava-processed reads were aligned to the mouse genome ( mm9 ) using Bowtie 2 . 0 software ( v2 . 0 . 5 ) with default parameters . Duplicate reads were removed using Samtools software ( v0 . 1 . 18 ) . Peaks were called using MACS1 . 4 using default settings . The protocol was adapted from [52] . The specific statistical tests used are indicated in the figure legends . Briefly , statistical significance for tumor diameter was assessed by one-way ANOVA followed by Newman-Keuls multiple comparisons posttest . For final tumor masses ( no DT experiment ) and lung metastatic area , one-way ANOVA was followed by Tukey’s multiple comparisons posttest to assess differences between the means . Linear regression analysis was used to assess no significant correlation between tumor mass and lung metastatic load . Kaplan-Meier plot significance was calculated by log-rank test . All the other p-values in this study were calculated by t test .
|
Most , if not all , cancer-related deaths result from metastasis . The differentiation states of the cancer epithelial cells are thought to be a critical determinant of metastasis . Epithelial cancer cells with a basal cell type are more aggressive in forming metastasis than cancer cells with luminal cell type . Very little is known about how the differentiation states impact metastasis or the molecular mechanisms involved . In this study , we develop and characterize new reporters that fluorescently mark cells in luminal or basal status . These reporters are also coupled to “suicide genes , ” which can be used to inducibly and selectively eliminate cells expressing the reporters . We find that elimination of the basal cell type dramatically decreases metastasis and identify amphoterin-induced protein 2 ( Amigo2 ) as a new regulator of cell invasion in basal cells .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"respiratory",
"infections",
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"basic",
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] |
2018
|
Reporters to mark and eliminate basal or luminal epithelial cells in culture and in vivo
|
Eggs of the helminth Schistosoma mansoni accumulate in the colon following infection and generate Th2-biassed inflammatory granulomas which become down- modulated in size as the infection proceeds to chronicity . However , although CD4+CD25+FoxP3+regulatory T cells ( Tregs ) are known to suppress Th1-mediated colitis , it is not clear whether they control Th2 –associated pathologies of the large intestine which characterise several helminth infections . Here we used a novel 3D-multiphoton confocal microscopy approach to visualise and quantify changes in the size and composition of colonic granulomas at the acute and chronic phases of S . mansoni infection . We observed decreased granuloma size , as well as reductions in the abundance of DsRed+ T cells and collagen deposition at 14 weeks ( chronic ) compared to 8 weeks ( acute ) post-infection . Th2 cytokine production ( i . e . IL-4 , IL-5 ) in the colonic tissue and draining mesenteric lymph node ( mLN ) decreased during the chronic phase of infection , whilst levels of TGF-β1 increased , co-incident with reduced mLN proliferative responses , granuloma size and fibrosis . The proportion of CD4+CD25+FoxP3+Tregs: CD4+ cells in the mLN increased during chronic disease , while within colonic granulomas there was an approximate 4-fold increase . The proportion of CD4+CD25+FoxP3+Tregs in the mLN that were CD103+ and CCR5+ also increased indicating an enhanced potential to home to intestinal sites . CD4+CD25+ cells suppressed antigen-specific Th2 mLN cell proliferation in vitro , while their removal during chronic disease resulted in significantly larger granulomas , partial reversal of Th2 hypo-responsiveness and an increase in the number of eosinophils in colonic granulomas . Finally , transfer of schistosome infection-expanded CD4+CD25+Tregs down-modulated the development of colonic granulomas , including collagen deposition . Therefore , CD4+CD25+FoxP3+Tregs appear to control Th2 colonic granulomas during chronic infection , and are likely to play a role in containing pathology during intestinal schistosomiasis .
Schistosomiasis is an important parasitic helminth disease afflicting more than 200 million people , causing approximately 280 thousand deaths annually , with a further estimated 700 million at risk of infection [1] , [2] . In the case of Schistosoma mansoni , infections are typically chronic ( >10 years ) and the majority ( >90% ) give rise to an intestinal form of disease [3] caused by the deposition of parasite eggs in the intestinal mesenteries ( mainly of the colon and terminal ileum ) and the subsequent development of Th2-associated granulomatous infiltrates rich in macrophages and eosinophils [4] . Such infections lead to diarrhoea , pseudopolyposis , microulceration , bleeding and fibrosis [5] . Recent re-appraisal of Disability-Associated Life Years ( DALYs ) attributable to schistosomiasis , where more subtle disease manifestations such as intestinal schistosomiasis have been included , raises the disease burden caused by this infection as much as 40-fold , putting schistosomiasis on a par with malaria as a global public health problem [6] . Variation in granuloma size in the colon between patients is positively associated with peripheral blood mononuclear cell ( PBMC ) reactivity to soluble egg antigens ( SEA ) [7] . Thus , changes in lymphocyte responsiveness appear to be related to the size of granulomas in the intestine and by implication , the severity of pathologies in patients with intestinal disease . In order to investigate the phenomenon of Th2-associated colonic inflammation and possible mechanisms underlying its regulation , we utilized a murine model of infection with S . mansoni which provides a well accepted permissive experimental host . In the murine model , myeloid antigen presenting cells , including dendritic cells [8] , [9] , and basophils [10] , are primed to induce potent anti-egg Th2 CD4+ lymphocyte responses . Th2 activation appears necessary to protect the host from lethal hepatic and intestinal damage during acute infection [11] and to keep Th1 inflammatory immunopathology in check [12] . However , survival to the chronic stage of infection , representative of human disease , is dependent on modulation of the Th2 granulomatous response in order to subvert IL-4/IL-13-driven morbidity [13] . ‘Naturally occurring’ ( n ) Tregs bearing the IL-2 receptor α chain molecule ( CD25 ) and expressing the transcription factor forkhead box P3 ( FoxP3 ) have been demonstrated to play a role in the regulation of Th2 anti-egg hepatic inflammation in an IL-10-independent manner [14] , [15] , although their role in regulating intestinal inflammation induced by egg deposition has not been determined . Our data presented herein support a role for CD4+CD25+FoxP3+Tregs in regulating colonic inflammation by modulating both anti-egg Th2 responses within the mesenteric lymph nodes ( mLN ) and granulomatous , pro-fibrotic Th2 responses within the colon . Thus , our study implicates CD4+CD25+FoxP3+Tregs as a source of regulatory pressure during chronic intestinal schistosomiasis and in the wider context , as suppressors of Th2-driven pathology in the colon .
All experiments were carried out in accordance with UK Animal's Scientific Procedures Act 1986 and with the approval of The University of York Ethics Committee . C57BL/6 ( B . 6 ) and hCD2-VaDsRed-B . 6 mice were maintained within the University of York under specific pathogen-free conditions . hCD2-DsRed-B6 mice , were a gift of D . Kioussis and A . Patel ( National Institute for Medical Research , London ) and express fluorescent DSRed T cells ( >90% CD3+ ) to facilitate in situ detection of T cells by multiphoton microscopy ( see below ) . Eight to ten-week female mice were infected percutaneously via the abdomen with 25 S . mansoni cercariae , and infections allowed to mature for either 8 or 14 weeks representing the acute and chronic phases of infection respectively . Adoptive transfer recipients were infected with 100 cercariae . Egg burdens in the 5 cm of colon proximal to the cecum were enumerated following digestion in 4% KOH . Eggs in faecal material were enumerated following dispersion in PBS , filtration through 100 µm pore mesh , and concentration . Colonic granulomas were isolated as previously described [16] . Volumes were calculated by measuring the longest and widest points and extrapolating volume using standard formulae for sphere or cylinder , depending on individual granuloma shape . Colonic tissue were fixed in 4% formaldehyde and embedded in wax . Transverse cross-sections ( 5 µm ) were stained with H&E , or haemotoxylin and Van Geison ( Department of Veterinary Pathology , University of Liverpool ) . Digital photomicrographs were analysed using AxioVision software ( Zeiss ) . For multiphoton imaging , proximal colon segments were mounted within 10 mm depression slides , and granulomas imaged from the serosal surface to egg mid-point using a 510 NLO laser-scanning microscope ( LSM , Zeiss ) with multi-photon laser ( Coherent ) tuned to 872 nm . 3D projections of ‘half-granulomas’ were rendered from z stacks using Volocity 4 software ( Improvision ) . Quantification of Ds-Red+ lymphocytes , granuloma and collagen volumes were performed using “ROI” and “RGB” measurement tools within Volocity . For immunofluorescent staining , frozen tissues were cryosectioned at 8 µm intervals , fixed with 10% methanol , permeabilised with 0 . 5% saponin ( Sigma ) , and blocked with 5% rabbit serum / 1% FCS . Sections were labelled with anti-CD4 AF488 and anti-FoxP3 AF647 ( both eBioscience ) and fluorescence captured using the 510 NLO LSM . Settings for acute and chronic fluorescence images are matched both with respect to laser scanning settings at the time of image capture and post-image digital enhancement . Baseline laser scanning settings were undertaken on isotype controls and resultant negative control images contain undetectable fluorescent signal . Three doses of anti-CD25 mAb ( 50 µg; clone PC61 , a gift from F . Powrie , University of Oxford ) , or purified rat IgG2a , were delivered intraperitoneally to infected mice at 9 , 11 , and 13 weeks . Tregs from the mLN were purified by depletion of non-CD4+ cells followed by isolation of CD25+ cells using antibodies conjugated to magnetic beads ( Miltenyi Biotec ) . For adoptive transfer , 2 . 5×106 CD4+CD25+Tregs ( >90% purity ) were injected via the lateral tail vein . Total mLN cells ( 2×106/ml ) , sorted CD4+CD25− effector cells ( 1×106/ml ) , and CD4+CD25+Tregs ( 0 . 5×106/ml ) from infected mice cultured in complete RPMI-1640 medium ( containing 10% FCS , 50 µg/ml penicillin/streptomycin ) , in combination with naïve mLN CD4−CD25− cells ( 0 . 1×106/ml ) as a source of APC . Cells were stimulated with plate-bound anti-CD3 mAb ( 1 µg; Becton Dickinson ) , or SEA ( 50 µg/ml ) [16] . Cells were cultured for 72 h and supernatants retained for cytokine analysis . Proliferation was measured from 72 to 96 h by 3H-thymidine incorporation and scintillation counting . ELISAs were used to quantify IL-4 , IL-5 and IFNγ [17] , while IL-10 and IL-13 were measured by Cytoset ( Invitrogen ) or DuoSet ( R&D Systems ) kits respectively . A TGFβ-sensitive , mink lung epithelial cell bio-assay ( MLEC transfected with firefly luciferase; gift from Daniel Rifkin , NY Medical Center ) was used to determine levels of bio-active TGFβ1 [18] . As the bio-assay was not compatible with tissue extracts , a TGFβ1 ELISA ( R&D Systems ) was employed . In order to determine cytokine levels in the colon , frozen tissues were first homogenised in proprietary tissue extraction buffer containing detergent and protease inhibitors ( Thermo Scientific ) and then incubated/rotated overnight at 4°C and the soluble fractions isolated by centrifugation prior analysis by ELISA . Salt-soluble collagen was quantified using colorimetric assay ( Sircol , Biocolor ) . Total colonic mRNA was used to generate cDNA using Superscript III DNA polymerase ( Invitrogen ) and foxp3 transcript analysed by qRT-PCR ( ABI PRISM 7000; Applied Biosystems ) using Taqman probes ( Sigma-Aldrich ) . The relative expression of foxp3 was normalised to values obtained for cd3 . Primer pairs and probes were; foxp3 5′-GCAGTGTGGACCGTAGATGA , 5′-CACAGCCTCAGTCTCATGGT , Probe 5′-ACAAGTGCTCCAATCCCTGCCCTT and cd3 5′-GAGCACCCTGCTACTCCTTG , 5′- ATGTCCCAGCACTGGCTACT , Probe 5′- TGCTCTTCAGCCTCCTGGTGAACAC . Cells were blocked with anti-CD16/CD32 ( eBioscience ) at 0 . 5 µg / 1×106 cells , then labelled with anti-CD4-Pacific Blue , anti-CD25-APC ( PC-61 ) , anti-CD103-PE ( all eBioscience ) , anti-CD25-FITC ( 7D4 ) , anti-CTLA-4-FITC , or anti-CCR5-biotin ( BD Bioscience ) for 30 minutes . Biotinylated antibodies were sequentially detected with streptavidin-PE-Cy7 ( eBioscience ) . For intracellular staining of FoxP3 , cells were fixed in 1% formalin , re-suspended in permeablisation buffer ( Becton Dickinson ) prior to labelling with anti-FoxP3-PE or -AF647 ( eBioscience ) . Cells were analysed using a Cyan flow cytometer with Summit software ( Beckman Coulter ) . Significant differences between two experimental groups were determined by unpaired Student's T test , and between three or more groups by 1-way ANOVA with Tukey post-hoc tests using Prism software ( GraphPad ) . Because colonic egg counts were skewed , analysis was undertaken after Log10 transformation . All data are representative of a minimum of two independent experiments . Significance is indicated ***P<0 . 001 , **P<0 . 01 , *P<0 . 05 . Significance values are shown on the figures with line connectors between the appropriate groups . Where statistical significance was not achieved ( P>0 . 05 ) , figures are intentionally left blank .
Egg deposition and anti-egg granulomatous responses in proximal colons were examined over a time-course of infection in B . 6 mice . The numbers of eggs increased during infection from 292±121 . 8 ( day 42 ) to 2339±863 . 5 ( day 98 ) ( Fig . 1A ) . Mean areas of isolated colonic granulomas declined from the acute to chronic time point visualised by H&E staining ( Fig . 1B & C ) , supporting previous observations [16] , [19] . Estimates of volumes of granulomas isolated from enzymatically digested colons ( Fig . 1D ) corroborated histological observation and showed a significant decrease in size between the acute and chronic stage of infection . In addition , the decrease in granuloma size at the chronic stage was accompanied by a decrease in collagen , indicative of fibrosis , as shown by Van-Geison stained sections ( Fig . 1E ) . Multiphoton imaging of proximal colon derived from infected hCD2-VaDsRed-B . 6 mice revealed further quantitative information on temporal granuloma modulation in situ ( Fig . 1F , Videos S1 & S2 ) and analysis of 3D images showed granuloma volumes were significantly decreased at the chronic stage ( Fig . 1G ) . Furthermore , numbers of granuloma-associated DsRed+ lymphocytes ( Fig 1H; >90% CD3+T lymphocytes , data not shown ) , and granuloma-associated type-1 collagen deposition , revealed as second harmonic imaging ( blue ) , was significantly also reduced ( Fig . 1I ) . Although a significant increase in the recently synthesised ( salt-soluble ) collagen pool within the colon was apparent by the chronic phase , modulation of the egg-driven fibrotic response was demonstrable when adjusted for the increased numbers of eggs ( as a surrogate for numbers of granulomas ) in chronic infected colons ( Fig . 1J ) . At the acute stage , anti-CD3 mAb and SEA-specific proliferation of mLN cells were significantly elevated and biased towards secreting Th2-type cytokines ( Fig . 2A ) . However , by the chronic stage , SEA-induced cell proliferation and production of IL-4 , IL-5 and IL-13 were significantly reduced ( Fig . 2B ) . While the secretion of IL-10 in response to SEA was significantly lower during chronic compared to acute infection the production of bio-active TGFβ1 to SEA at the chronic stage was significantly elevated compared to naïve state ( Fig . 2B ) . It was not possible to obtain sufficient numbers of viable lymphocytes via enzymatic digestion of granulomatous colons due to the fibrotic nature of these intestinal granulomas but levels of IL-4 and IL-5 in whole colonic extracts were elevated at the acute phase ( Fig . 2C ) , suggesting that tissue inflammatory responses in infected colons mirrored the Th2 response in the mLN . Surprisingly , levels of IL-10 significantly decreased in infected colonic tissue ( Fig . 2C ) but levels of bio-active colonic TGFβ1 were elevated during chronic disease . When adjusted for numbers of deposited eggs , production of colonic IL-4 and IL-5 was significantly diminished at the chronic phase ( Fig . 2D ) . Thus , whilst local cytokine responses to egg deposition has both shared and distinct facets to those of the mLN , measurements indicate that colonic Th2 responses establish during the onset of egg deposition and subsequently diminish as chronicity proceeds . The proportion of CD4+ FoxP3+Tregs in the mLN as a proportion of total CD4+ cells , as determined by flow cytometry of cell suspensions , increased from 13 . 1±0 . 2% in naïve mice to 16 . 0±0 . 4% during acute infection ( P<0 . 001 ) , and increased further to 20 . 6±0 . 2% during chronic infection ( P<0 . 001; Fig . 3A ) . Absolute numbers of both mLN CD4+ effector and CD4+FoxP3+Treg cells increased during acute disease from naïve levels , and remained significantly elevated during chronic infection ( Fig . 3A ) . The increase was confirmed by enumeration of FoxP3 Tregs in stained sections of mLN from naïve mice and those with acute and chronic infection ( Fig . 3B & C ) . In contrast , absolute numbers or proportions of CD4+FoxP3+Tregs in the spleens did not expand ( 12 . 8±0 . 6% cf . 13 . 9±0 . 8% cf . 14 . 8±1 . 3% , Fig . 3A ) . A pronounced increase in the proportion of CD4+FoxP3+Tregs within colonic granulomas at the chronic phase of infection , from 2 . 9±0 . 6% to 18 . 8±0 . 7% was revealed by enumeration of double positive versus single positive cells in anti-CD4 / anti-FoxP3 immunostained cryosections of colonic tissue ( Fig . 3D & E ) . This profound ( >10 fold ) proportional elevation in FoxP3+ cells compared with total number of T lymphocytes in gut tissue was corroborated by qRT-PCR of FoxP3 transcript normalised to CD3 transcript ( Fig . 3F ) . Thus , during enteric S . mansoni infection , relative and absolute expansion in the numbers of CD4+FoxP3+ cells occurs preferentially within gut-associated lymphoid tissue ( GALT ) . Furthermore , relative increases of CD4+FoxP3+ cells within colonic granulomas are apparent during chronic disease . CD103 , the αE molecule of the αEβ7 mucosal integrin involved in homing of T cells to intestinal sites [20] , increased on mLN CD4+FoxP3+Tregs at the chronic stage compared to naïve mice ( 31 . 0±2 . 3% cf . 62 . 4±3 . 1% , P<0 . 001 , Fig . 3G ) . CCR5 is also involved in T cell homing to intestinal inflammatory sites [21] , and significantly , the proportion of CCR5+CD4+FoxP3+Tregs in the mLN increased markedly ( 9 . 6±2 . 3% cf 19 . 3±2 . 6%; Fig . 3G ) . Thus , increases in the number of mLN CD4+FoxP3+Tregs expressing CD103 and CCR5 suggests these cells have enhanced potential to be recruited and retained within the colonic infection site during chronic infection . More than 75% of CD4+CD25+ mLN cells co-expressed FoxP3 , regardless of the stage of infection confirming that the majority of CD4+CD25+ cells can be classified as CD4+FoxP3+Tregs ( Fig . 4A ) . In addition , while CD4+CD25−effector cells from mice with an acute infection exhibited a strong proliferative response in vitro to SEA , CD4+CD25+Tregs taken at the chronic stage of infection did not proliferate ( Fig . 4B ) . Moreover , co-culture of these two cell populations in a 2∶1 ratio prevented optimum antigen-specific proliferation of CD4+CD25− effector T cells ( Fig . 4B ) . Thus , CD4+CD25+ ( FoxP3+ ) cells within the mLN during chronic schistosome infection displayed a regulatory phenotype in vitro . CD4+CD25+ Tregs isolated from the mLN of naïve mice , compared to mice with an acute or chronic infection , exerted similar degrees of suppression on the acute-stage anti-SEA CD4+ T cell proliferative response ( Fig . 4C ) . Moreover , following depletion of CD4+CD25+ Tregs , or after their re-addition in a 1∶2 Treg / effector T cell ratio , we observed that while the Tregs conferred a significant degree of suppression on the anti-SEA response , chronic CD4+ CD25− effector T cells remained hypo-responsive , or anergic , compared with their acute-stage counterparts ( Fig . 4D ) . Taken together , these in vitro assays provide evidence that schistosome-expanded CD4+CD25+Tregs suppress the pre-dominant Th2 anti-SEA response . However , compared with Tregs from naïve mice , they are not enhanced in their ability to suppress antigen-specific CD4+ proliferation . Depletion of CD25+ cells is a common technique to experimentally induce Treg deficiency [14] , [22] , [23] , [24] , [25] , [26] . Whilst not all CD25+ cells are FoxP3+ Tregs ( 75%–80% in our infection model ) and FoxP3+Treg populations can be rapidly induced following CD25+ depletion during infection [27] , a single antibody treatment with anti-CD25 clone PC61 can significantly reduce FoxP3+ cells by 70% [28] , with reduced FoxP3+ cells persisting in the face of ensuing inflammation for two weeks [27] . We therefore treated 9 week schistosome infected mice with a regimen of PC61 once per two weeks for six weeks and assessed CD25+ lymphocyte depletion and effects on intestinal granuloma parameters one week ( +14 weeks infected ) following the last PC61 antibody treatment . Anti-CD25 mAb treatment of infected mice effectively depleted CD25+ cells in the mLN , and reduced the proportion of CTLA-4+ lymphocytes ( Fig . 5A ) . Anti-CD25-treated mice also retained significantly larger granulomas in chronic colonic tissues compared to their isotype control cohorts ( P<0 . 01; Fig . 5B ) . Eosinophils adjacent to schistosome eggs were significantly more numerous in anti-CD25 treated mice , while the numbers of large mononuclear cells remained stable ( Fig . 5C & D ) . This suggests that reductions in CD25+ cells , the majority of which are FoxP3+Tregs , lifted suppression of eosinophil recruitment , or their retention within colonic granulomas . It was also co-incident with increased antigen-specific mLN cell proliferation and IL-4 production at the chronic phase of infection ( Fig . 5E ) . However , in CD25+-depleted mice , colonic granulomas remained , on average , significantly smaller in area compared to those at the acute stage of infection ( 43 , 397±5477 µm2 compared with 107 , 128±12062 µm2 , P<0 . 01 ) . Thus , in vivo depletion of CD25+ lymphocytes partially , yet significantly , reverses the down-modulation of Th2 granulomatous pathology in the colon during chronic S . mansoni infection . Purified CD4+CD25+ mLN cells ( 2 . 5×106 , >90% purity; Fig . 6A ) from mice at the chronic stage of infection were administered to hCD2-VaDsRed-B . 6 mice co-incident with the onset of egg deposition . Four weeks later ( +9 weeks post-infection ) , the number of eggs in tissues or excreted from CD25+ cell recipients were not significantly different compared with controls ( Table S1 ) . This showed that immune cell transfers did not significantly affect adult worm development , fecundity , or egg transmission . Recipients of CD4+CD25+ cells had significantly smaller granuloma area and collagen deposits but Ds-Red+ T cell numbers within the colonic granulomas were not significantly altered ( Fig . 6B &C; Videos S3 & S4 ) . Recipients also had decreased levels of recently synthesised collagen and IL-4 in colonic extracts ( Fig . 6D ) although simultaneous down-regulation of Th2 mLN responses were not observable in recipient mice ( data not shown ) . Lower numbers of transferred cells ( 1×106 ) did not significantly alter acute-stage enteric granuloma formation ( data not shown ) . Thus , CD4+CD25+Tregs cells , which expand within the mLN of chronically-infected mice , exert a suppressive effect on the development of acute-phase Th2 inflammation .
Our data provides both in vitro and in vivo evidence that intestinal-associated CD4+CD25+FoxP3+Tregs expand during chronic , schistosome-induced colitic inflammation . They mediate significant levels of antigen-specific Th2 suppression in vivo including reduced IL-4 production , eosinophil recruitment , collagen production , and an overall reduction in the size of egg-induced granulomas in the large intestine . Thus , our data demonstrates experimentally , that CD4+CD25+ Tregs are capable of modulating Th2 inflammation and fibrosis associated with intestinal disorders . Whilst an expansion of gut-associated CD4+FoxP3+Tregs was observed following schistosome infection , this could be a product of proliferating , naturally occurring ( n ) Tregs in response to auto-antigens ( e . g . arising from disrupted intestinal barrier ) , or comprise an induced population of FoxP3+Tregs recognising schistosome antigens . However , in a syngenic adoptive transfer model , numbers of nTregs did not expand relative to CD4+ effector cells within the mLN [24] , suggesting that nTreg expansion does not fully account for the heightened ratio of FoxP3+CD4+ : CD4+ cells during chronic infection . The development of schistosome infection-induced Tregs is likely to be favoured by the constitutive production of intestinal signals such as TGFβ and retinoic acid [29] , [30] . Intriguingly , SEA induces TGFβ1 secretion [31] and is critical for the development of auto-immune suppressing FoxP3+ Tregs following in vivo SEA injection [32] . Since we observed heightened bioactive TGFβ1 from mLN cells and within the colon at the chronic phase of infection , we speculate that TGFβ1 might have a role in the induction of gut-homing CD4+FoxP3+Tregs from naïve precursors . Both proportions and absolute numbers of CD4+FoxP3+Tregs significantly increased within the mLN during acute infection , when anti-egg Th2 responses are at their peak . Although inhibition of Treg induction by Th2 differentiation programs has been reported [33] , our data would suggest that Th2 differentiation is insufficient to block outgrowth of a regulatory T cell phenotype during acute intestinal schistosomiasis . CD4+FoxP3+Tregs within the mLN of mice with a chronic infection expressed elevated levels of CD103 and CCR5 , both of which are associated with homing to mucosal tissues during inflammation [20] , [21] . However , infection status did not alter the in vitro suppressive ability on a per-cell basis of CD4+CD25+Tregs from the mLN , indicating that infection-expanded CD4+FoxP3+Tregs and those from naïve mice share a common mechanism of Th2-effector cell suppression . Therefore , it is likely that the increased regulatory activity of CD4+FoxP3+Tregs in vivo during chronic infection is a product of either increased numbers trafficking to the site of egg deposition , or greater survival/ retention in colonic granulomas . As recipients of schistosome-expanded CD4+CD25+ cells displayed selective suppression of Th2 activity at the enteric infection site versus the mLN , this may reflect a biased homing of Tregs in the colon that provides increased regulatory pressure on the local granulomatous response ( i . e . collagen synthesis and eosinophil recruitment ) . Taken with the observation that ablation of CD4+CD25+ cells in vivo significantly restores the anti-egg IL-4 and proliferative mLN cell response , these data suggest that intestinal-associated , schistosome infection-expanded FoxP3+ Tregs exert layers of Th2 suppression both within gut-draining lymph nodes and within the colonic infection site . Our data are consistent with recent findings that CD4+FoxP3+Tregs constitute a partial component of the modulation of granuloma development in the liver [14] , [24] , [34] . We show that depletion of CD4+CD25+ cells in vivo does not fully reverse the Th2 hypo-responsiveness , nor do chronic enteric granulomas in treated mice fully recover the florid cellularity of the acute phase . The use of transgenic mice deficient for foxp3 [35] could help further investigation of this phenomenon . The development of other regulatory cells such as alternatively-activated macrophages [36] , IL-10-secreting non-FoxP3 cells [14] , and regulatory B cells [37] , may explain the partial role of CD4+FoxP3+Tregs in our system . T cell intrinsic anergy , for instance mediated by GRAIL signalling [38] , could also account for the remaining hypo-responsiveness after depletion of CD4+ CD25+ cells . However , because the micro-environment of the intestines is favourable towards the expansion of CD4+FoxP3+Tregs , it is possible that CD4+FoxP3+Treg-mediated suppression of enteric granulomas is more apparent compared with their hepatic counterparts . This could explain why colonic granulomas modulate more rapidly versus hepatic granulomas after the acute stage , and why hepatic granulomas retain a greater size and cellularity than enteric granulomas during chronic disease [19] . During experimental helminth infections , CD4+CD25+Treg-mediated suppression of Th2 effector responses confer a permissive state by stifling effective Th2-mediated worm attrition [23] , [25] , [26] . In the case of gut-helminths , this potentially operates by regulating Th2 cytokine signalling on smooth muscle contractility and epithelial cell turnover [39] , [40] . Paradoxically , during S . mansoni infections , while Th2 granulomatous responses protect and aid survival of the host [11] , [41] , intact Th2 responses are also essential to propagate the parasite's life cycle , as egg transmission from the gut is impaired in the absence of T cells [42] , [43] , [44] , IL-4 [41] , or IL-4Rα signalling [45] . Nevertheless , transfer of CD4+CD25+ cells to acute stage recipients does not impinge on egg transmission in spite of a modulatory effect on IL-4 in the colon . In fact , during the chronic stage of infection , where Th2 inflammation in the colon becomes modulated , egg excretion rates are unaffected ( our unpublished observations ) . How intestinal schistosome parasites mediate sustained , chronic egg transmission in the face of marked Th2 hypo-responsiveness remains to be identified . Enteric helminth infections , or products derived from helminths , are gaining prominence as potential therapies to reverse the effects of inflammatory bowel disease [46] . Some ameliorating capacities may be attributed to antagonism of Th1 processes by induction of IL-4-secreting cells within intestinal tissues rather than by induction of Tregs . Indeed , most experimental studies demonstrating the modulatory action of helminth infection used trinitrobenzene sulphate as a haptenizing agent to drive Th1 colitis reminiscent of Crohn's Disease [46] . Initiation of Th2/NKT colitis by oxazolone in conjunction with Th2-promoting helminth infection exacerbated pathology [47] , further supporting that the mechanism of helminth suppression of colitis is based on Th2/Th1 antagonism . Thus , from a clinical perspective , helminth-based therapies might be considered inappropriate for ulcerative colitis or other intestinal disorders with Th2 aetiology . However , our data demonstrates that helminth infection-expanded FoxP3+ Tregs clearly regulate coincident pro-fibrotic Th2 processes in the colon . Indeed , distinct molecules released by schistosome eggs deliver triggers that polarize naïve CD4+ T cells towards Th2 , or a regulatory phenotype [8] , [9] , [10] , [48] . Potentially , the release of somatic molecules with regulatory potential from degrading eggs that fail to breach colonic tissues could favour Treg expansion during chronic disease in the context of a TGFβ-enriched microenvironment . Exploitation of such regulatory molecules may be of benefit in the treatment of intestinal schistosomiasis or other Th2-based intestinal disorders via the expansion of Tregs with bystander potential .
|
Schistosomiasis is an important parasitic helminth disease afflicting more than 200 million people worldwide . Infections are typically chronic and in the case of Schistosoma mansoni and S . japonicum the majority give rise to an intestinal form of disease caused by the deposition of parasite eggs in the colon and terminal ileum . The eggs cause Th2-associated inflammatory immune granulomas to form , which as the disease develops , are down-regulated by cells of the immune system . However , the mechanisms which underpin the down-regulation of granulomas in the large intestine are not known . In order to investigate the phenomenon of Th2-associated colonic inflammation , we utilized a murine model of infection with S . mansoni and compared immune responses at the acute and chronic phases of infection . We show that a type of regulatory T helper lymphocyte ( CD4+CD25+FoxP3+Treg ) contributes to regulation of colonic inflammation . These cells modulate anti-egg Th2 responses within the mesenteric lymph nodes and granulomatous pro-fibrotic Th2 responses within the colon . Our study highlights the importance of CD4+CD25+FoxP3+Tregs as a source of regulatory pressure on granuloma formation in the colon and by implication humans with chronic intestinal schistosomiasis .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"colon",
"medicine",
"infectious",
"diseases",
"schistosomiasis",
"gastroenterology",
"and",
"hepatology",
"neglected",
"tropical",
"diseases",
"parasitic",
"diseases"
] |
2011
|
CD4+CD25+ Regulatory Cells Contribute to the Regulation of Colonic Th2 Granulomatous Pathology Caused by Schistosome Infection
|
Rift Valley fever virus is an arthropod-borne human and animal pathogen responsible for large outbreaks of acute and febrile illness throughout Africa and the Arabian Peninsula . Reverse genetics technology has been used to develop deletion mutants of the virus that lack the NSs and/or NSm virulence genes and have been shown to be stable , immunogenic and protective against Rift Valley fever virus infection in animals . We assessed the potential for these deletion mutant viruses to infect and be transmitted by Aedes mosquitoes , which are the principal vectors for maintenance of the virus in nature and emergence of virus initiating disease outbreaks , and by Culex mosquitoes which are important amplification vectors . Aedes aegypti and Culex quinquefasciatus mosquitoes were fed bloodmeals containing the deletion mutant viruses . Two weeks post-exposure mosquitoes were assayed for infection , dissemination , and transmission . In Ae . aegypti , infection and transmission rates of the NSs deletion virus were similar to wild type virus while dissemination rates were significantly reduced . Infection and dissemination rates for the NSm deletion virus were lower compared to wild type . Virus lacking both NSs and NSm failed to infect Ae . aegypti . In Cx . quinquefasciatus , infection rates for viruses lacking NSm or both NSs and NSm were lower than for wild type virus . In both species , deletion of NSm or both NSs and NSm reduced the infection and transmission potential of the virus . Deletion of both NSs and NSm resulted in the highest level of attenuation of virus replication . Deletion of NSm alone was sufficient to nearly abolish infection in Aedes aegypti mosquitoes , indicating an important role for this protein . The double deleted viruses represent an ideal vaccine profile in terms of environmental containment due to lack of ability to efficiently infect and be transmitted by mosquitoes .
Rift Valley fever virus ( RVFV ) , a human and animal pathogen that is endemic in much of Africa , has in recent decades spread to Saudi Arabia , Madagascar and Yemen and has the potential to spread to other parts of the world via transport of infected livestock , humans or mosquitoes or by an act of bioterrorism [1]–[6] . An arthropod-borne member of the Phlebovirus genus of the family Bunyaviridae , RVFV causes significant outbreaks of severe disease in livestock , including mortality in young animals , fetal deformities and abortion . RVFV infection in humans can result in a self-limiting febrile illness or more severe disease such as retinitis , hepatic necrosis , encephalitis , neurologic deficits or fatal hemorrhagic fever [7]–[9] . The primary maintenance host and source of RVFV initiating disease outbreaks is considered to be mosquitoes in the Aedes genus . Mosquitoes in the Culex genus are thought to be important in amplification of virus activity during outbreaks . The virus has also been detected in phlebotomine sand flies , Culicoides midges , and Amblyomma tick species although these infections are not thought to play an important role in the life cycle of the virus or in disease outbreak settings [5] , [10]–[13] . In laboratory studies , several North American Aedes and Culex mosquito species have been shown to be competent vectors of the virus , indicating the potential for establishment of RVFV transmission cycles in North America [14]–[17] . Infection , replication and transmission of an arthropod-borne virus involve complex interactions between the virus and various cells/tissues/organs of the vector . Successful transmission requires that after being ingested in a viremic bloodmeal the virus must enter the epithelial cells of the midgut , replicate and escape from the midgut cells into the hemolymph . This is followed by infection of secondary organs , including the salivary glands , where the virus enters the saliva and can then be transmitted to a new host . Potential barriers in this process have been identified that can block infection , replication and/or transmission of a virus by the mosquito [18] , [19] . These include the midgut infection and escape barriers and the salivary gland infection and escape barriers . The presence or absence of these barriers and the degree to which they are effective appears to be influenced by the genetics of both the virus and the vector [18] . The RVFV genome is comprised of three segments of single-stranded , negative sense RNA . The small ( S ) segment codes for the structural nucleoprotein ( NP ) and the nonstructural NSs protein , the medium ( M ) segment encodes the two structural glycoproteins , Gn and Gc , as well as two nonstructural proteins ( NSm and NSm-Gn ) and the large ( L ) segment codes for the viral RNA-dependent RNA polymerase . The nonstructural NSs and NSm proteins have been shown to function as virulence factors . The NSs protein has multiple functions that suppress the mammalian host cell antiviral response by inhibiting IFN-β gene transcription , promoting degradation of protein kinase ( PKR ) and suppressing host transcription [20]–[24] . The RVFV NSm protein plays a role in viral pathogenesis by suppression of virus-induced apoptosis in infected cells although it has been shown to be dispensable for efficient virus growth in cell culture [25]–[27] . To date , little is known regarding the role of the NSs and NSm proteins in the RVFV replication cycle and dissemination and transmission in arthropod vectors . Historically , a number of different methods have been employed in development of RVFV vaccines , however due to drawbacks associated with currently available vaccines including the necessity for multiple inoculations , abortions/teratologic effects in some vaccinated animals or risk of reversion to virulent phenotype , none of the existing vaccines is approved for veterinary use in North America or Europe [3] , [28] . More recently , a reverse genetics methodology has been used to develop recombinant ( rRVF ) vaccine candidate viruses which contain complete deletions of one or both of the RVFV virulence genes NSs and NSm [29] . These rRVF viruses have been shown to be highly immunogenic and effective at preventing RVFV-associated morbidity and mortality [29] . Additionally , these gene deletions provide the basis for assays to differentiate between vaccinated and naturally infected animals [30] . The purpose of this study was to determine the effects of NSs and NSm gene deletions on infection , dissemination and transmission of these rRVF vaccine candidate viruses in mosquitoes . Results are presented for each of the three deletion mutant rRVF viruses and rRVF-wild type evaluated side-by-side in two mosquito species representing two different genera: Aedes ( Stegomyia ) aegypti L . and Culex ( Culex ) quinquefasciatus ( Say ) .
Construction of the rRVF viruses has been previously described [25] , [29] , [31] . Reverse genetics-generated rRVF-wild type ( rRVF-wt ) and three deletion mutant viruses were used in this study ( Table 1 ) . Rescue of rRVF viruses was as previously described [29] . All rescued viruses were fully sequenced as previously described [32] . Virus nomenclature and titers of the Vero E6-2 passage of the viruses are listed in Table 1 . Growth curves for each rRVF virus were conducted in Vero E6 cells to determine the optimal virus growth time for bloodmeal preparation . Cell monolayers were infected with virus in Dulbecco's Minimal Essential Medium/2% fetal bovine serum ( FBS ) with 100 U/mL penicillin and 100 µg/mL streptomycin ( DMEM ) at a multiplicity of infection ( m . o . i . ) of 0 . 1 plaque forming unit ( PFU ) per cell . Following adsorption for 1 hr at 37°C cells were washed three times with DMEM and then maintained in DMEM at 37°C . Samples were removed daily for 5 days and titers were determined by double overlay plaque titration assay on Vero cells as previously described [33] . Second overlays were added at 3 days post infection ( p . i . ) and plaques were counted on days 4–7 p . i . Two mosquito species were used in this study . The Aedes aegypti Rexville D mosquito strain used was an isofemale line derived from a population of Ae . aegypti collected as larvae in San Juan , Puerto Rico ( Rexville ) in 1991 [34] . The Culex quinquefasciatus Sebring mosquito strain used was originally colonized in Florida in 1998 and has been in colony at the CDC in Fort Collins since 2004 [35] . The species identity of the Cx . quinquefasciatus colony was verified by examination of genetalia and by HotAce PCR [36]–[38] . These species were selected because they are found in Africa , where the RVFV candidate vaccines being tested will primarily be used , because of their availability as colonized populations and because their vector competence for RVFV has been previously characterized [13] , [15] , [16] , [39] . Multiple blood-feeding experiments were undertaken . Each experiment utilized freshly prepared virus due to an observed reduction in infection rates when frozen virus was used in bloodmeals: in a separate experiment we observed only a 10% ( n = 19 ) rate of infection in Ae . aegypti mosquitoes fed a bloodmeal containing frozen rRVF-wt virus compared to 63% ( n = 32 ) with freshly harvested virus . For each experiment , adult 7- to 10-day old mosquitoes were placed in pint cartons ( approx . 50–100 mosquitoes per carton ) and starved for 24 hours prior to feeding . Artificial virus-laden bloodmeals were prepared using fresh virus grown in Vero E6 cells as described above . Virus was harvested 2–3 days after infection depending on growth curve results for each virus ( data not shown ) . Infected cell culture supernatant was clarified by centrifugation at 10 K rpm , 4°C for 10 min . Defibrinated chicken blood ( Colorado Serum Co . , Denver , CO ) was washed 3 times with 1 volume of ice-cold phosphate buffered saline and centrifuged at 3 K rpm , 4°C for 3 min after each wash . Two parts clarified virus were mixed with 2 parts washed blood and 1 part FBS/10% sucrose . The bloodmeal was heated to 37°C and offered to mosquitoes using cotton balls that were soaked in the bloodmeal and applied to the mesh top of the mosquito cartons . Mosquitoes were allowed to feed for 30 min at 28°C/95% humidity after which the bloodmeal was removed . Fully engorged mosquitoes were collected , double-caged and held for 14 days at 28°C/95% humidity with 5% sugar water . Three engorged mosquitoes were immediately removed for each virus and titrated to determine the amount of virus ingested ( input virus titer ) . Twenty-five to fifty mosquitoes from each virus group were tested for virus transmission at 14 days post exposure by collection of saliva as previously described [40] . Briefly , specimens were anesthetized by chilling at −20°C for 1 min , then , inside a glove box , wings were removed and the proboscis of each specimen was inserted into a capillary tube containing 5 µL immersion oil and saliva collected for 20 min . The tip of each capillary tube was clipped off into a microfuge tube containing 250 µL DMEM/10% FBS , tubes were centrifuged 5 min at 5000 rpm at 4°C to draw the oil out of the capillary tube and titers were determined as described above [33] . Observation of one or more viral plaques was considered a positive result . Following saliva collection , individual mosquito bodies were stored at −80°C . Additional day 14 mosquitoes were stored at −80°C and were tested only for dissemination and/or infection status . Mosquitoes were subsequently tested for virus dissemination by head squash and immunofluorescence assay ( IFA ) as previously described , using mouse-anti RVFV strain ZH501 hyperimmune ascitic fluid diluted 1∶2500 as the primary antibody and goat-anti-mouse IgG-Alexa488 ( Invitrogen , Baltimore , MD ) diluted 1∶2000 as the secondary antibody conjugate [40] . Observation of specific fluorescence as compared to uninfected controls was considered a positive result . The infection status of mosquitoes was determined by trituration of bodies in 2 mL conical microcentrifuge tubes with 1 mL BA-1 medium ( 1× medium 199 with Earle's salts , 1% bovine albumin , 100 U/mL penicillin , 100 µg/mL streptomycin , and 1 µL/mL amphotericin B ) and one copper BB per tube using a Qiagen Tissuelyser ( Qiagen , Valencia , CA ) . Triturated mosquito preparations were clarified by centrifugation at 9 K rpm/4°C for 10 min followed by plaque titration of the clarified supernatant on Vero cells as above . Virus was isolated and sequenced from selected mosquitoes at 14 days post-exposure as follows . Viral RNA was extracted either from triturated mosquito supernatant or from a Vero cell amplification of mosquito supernatant ( 25 µL mosquito supernatant grown 3 days in a T25 flask of Vero cells ) using the QIAamp viral RNA kit ( Qiagen ) . RT-PCR was performed using the Titan One-Step RT-PCR kit ( Roche , Indianapolis , IN ) . Products were agarose gel purified and sequenced using the BigDye Terminator v3 . 1 Ready Reaction Cycle Sequencing mix ( Applied Biosystems , Foster City , CA ) . Reactions were purified using the BigDye Xterminator Purification kit ( Applied Biosystems ) and analyzed on an ABI 3130 Genetic Analyzer ( Applied Biosystems ) . Linear regression methods were used to compare ( log10-transformed ) titers among the virus constructs , while logistic regression was used to compare their infection , dissemination , and transmission rates . Wald 95% confidence intervals were computed for parameters of interest , and likelihood ratio tests were used to compare models . Because the data have cases for which all individuals in a virus test group were either negative or positive , we use Firth's penalized likelihood adjustment to the estimating score equations , as detailed in Heinze and Schemper ( 2002 ) and Heinze and Puhr ( 2010 ) and implemented in R ( www . r-project . org ) in the package logistf [41] , [42] . All analyses were conducted in R version 2 . 11 . 1 ( www . r-project . org ) . Confidence intervals for the differences of virus effects were adjusted for multiple comparisons in both normal and logistic models using the methods described in Hothorn et al . ( 2008 ) [43] . Due to the necessity of using freshly grown , and therefore untitrated , virus in the oral mosquito feeds , the standard regression methods for the body titers and infection rates were augmented to adjust for the unknown amount of virus taken up during the feedings by using the information collected from the mosquitoes fed concurrently and stored just after feeding ( input virus titer ) . This was necessary because the titers for the different viruses varied between each virus stock preparation . Although significant results were found , we cannot rule out that this variation in the virus titers may have affected the results in a manner that cannot be accounted for by the statistical analysis . To summarize the approach , we treated the unknown amount of virus taken up by the test mosquitoes as missing data , represented in the linear models as a simple , continuous random effect and in the logistic models as a continuous , random offset . We then used the estimated , predictive normal distributions of the concurrently fed individuals' input virus log10-titer measurements from the corresponding virus to impute values for the unknown virus uptake of the test individuals . For each individual we generated 100 such imputations , fit regression models to each of these “completed” datasets , and averaged the parameters from the resulting model fits . Statistical comparisons and tests , confidence intervals and p-values incorporated both the modeling uncertainty and the imputation uncertainty; see Little and Ruben ( 2002 ) for details related to analysis of missing data and incorporation of imputation error [44] . For the dissemination and transmission rates , Fisher's exact tests were used to test for an overall difference , and pairwise comparisons among viruses were made using score confidence intervals for the differences . The Bonferroni adjustment was used to account for the multiple comparisons .
Replication of the rRVF-wt and deletion mutant viruses in Ae . aegypti mosquitoes is summarized in Table 2 with statistical analysis results data available in Table S1 . The infection rate observed for the rRVF-ΔNSs virus ( 32/36 , 88 . 9% ) did not differ significantly from that observed for the rRVF-wt virus ( 20/32 , 62 . 5% ) , although the average body titer at 14 days post-exposure for individuals infected with rRVF-ΔNSs ( 3 . 7 log10 PFU/mL ) was significantly lower than for rRVF-wt ( 5 . 4 log10 PFU/mL ) ( P<0 . 01 , data not shown ) . Dissemination rates for the rRVF-ΔNSs virus ( De , from two experiments combined = 33/86 , 38 . 4% , and Di = 16/32 , 50% ) were significantly lower than for rRVF-wt ( De combined = 44/63 , 69 . 8% and Di = 18/20 , 90% ) , while transmission rates for rRVF-ΔNSs ( Te = 12/36 , 33 . 3% and Td = 12/16 , 75% ) and rRVF-wt ( Te = 15/32 , 46 . 9% and Td = 15/18 , 83 . 3% ) did not differ significantly . RVFV antigen was found to be similarly distributed throughout head tissues by IFA testing of individuals with disseminated rRVF-wt and rRVF-ΔNSs infections ( data not shown ) . The Ae . aegypti infection rate for the rRVF-ΔNSm virus ( 5/129 , 3 . 9% , combined ) was significantly less than for rRVF-wt ( 20/32 , 62 . 5% ) . The rRVF-ΔNSm infection rate in experiment 1 ( 5/45 , 11 . 1% ) was higher than that of experiment 2 ( 0/84 , 0% ) , most likely due to the higher experiment 1 bloodmeal titer , although when calculated individually both rates were significantly less than that of rRVF-wt . The average body titer of rRVF-ΔNSm-infected mosquitoes ( 1 . 9 log10 PFU/mL ) at 14 days post-exposure was significantly less than that of mosquitoes infected with rRVF-wt ( 5 . 4 log10 PFU/mL ) ( P<0 . 01 , data not shown ) . The dissemination rates for rRVF-ΔNSm ( De combined = 1/129 , 0 . 8% and Di = 1/5 , 20% ) were significantly less than rRVF-wt ( De combined = 44/63 , 69 . 8% and Di = 18/20 , 90% ) . The transmission rate for rRVF-ΔNSm ( Te combined = 1/95 , 1 . 1% , ) was significantly less than that of rRVF-wt ( Te = 15/32 , 46 . 9% ) when calculated as Te ( number positive/number exposed ) ; when calculated as Ti ( number positive/number disseminated ) the transmission rate did not differ significantly from rRVF-wt . Out of 129 Ae . aegypti mosquitoes that fed on a bloodmeal containing the rRVF-ΔNSm virus , five became infected and one developed a disseminated infection; this individual was also found to be transmission-positive . The distribution of RVFV antigen in head tissues of this individual did not appear to differ from that of rRVF-wt ( data not shown ) . Full length sequencing of virus isolated from this individual revealed no genetic differences compared to the virus in the blood meal . None of the 75 Ae . aegypti mosquitoes exposed to rRVF-ΔNSs-ΔNSm were found to be infection- , dissemination- or transmission-positive . Replication of the rRVF-wt and deletion mutant viruses in Cx . quinquefasciatus mosquitoes is summarized in Table 3 with statistical analysis data presented in Table S2 . Similarly high infection rates were observed for rRVF-wt ( 57/60 , 95% , combined ) and rRVF-ΔNSs ( 33/35 , 94 . 3% ) , while significantly lower rates were observed for the constructs containing the NSm deletion [rRVF-ΔNSm ( 4/35 , 11 . 4% ) and rRVF-ΔNSs-ΔNSm ( 33/50 , 66% ) ] . There were no significant differences in the dissemination or transmission rates of the rRVF-ΔNSs or rRVF-ΔNSs-ΔNSm viruses compared to the rRVF-wt virus . When calculated as De ( number positive/number exposed ) , the dissemination rate for the rRVF-ΔNSm virus ( De = 1/119 , 0 . 8% ) was significantly less than that of rRVF-wt ( De combined = 17/156 , 10 . 9% ) , however , the dissemination rate calculated as Di ( number positive/number infected ) was not significantly different and there were no significant differences in transmission rates between these viruses . IFA testing demonstrated the presence of RVFV antigen distributed throughout head tissues from Cx . quinquefasciatus individuals with a disseminated infection; no qualitative differences were observed between tissues infected with the deletion mutant viruses and rRVF-wt ( data not shown ) . Average body titers of individuals with disseminated infections ranged from 4 . 4–6 . 1 log10 PFU/mL , while titers in individuals with undisseminated infections ranged from 1 . 0–4 . 3 log10PFU/mL . These values are similar to those reported for Cx . pipiens mosquitoes by Turell et al . [14] .
We report the in vivo infection , dissemination and transmission characteristics of several recombinant RVF viruses lacking the entire coding regions of the NSs and/or the NSm genes and demonstrate the critical role of the NSm gene for infection and transmission in two mosquito species that exhibit different capacities for transmitting RVFV . Ae . aegypti has been shown to be a moderately competent vector of RVFV , although it has not been shown to be a vector in nature [13] , [39] . Cx . quinquefasciatus is a potential vector of RVFV in nature , although laboratory studies have shown it to be a less efficient vector than Ae . aegypti . [5] , [13] , [39] , [45] . These species were selected based on this difference in vector competence , because both are found in Africa where the candidate vaccine viruses tested here will primarily be used and because both have been colonized for use in laboratory investigations . Observed rates of dissemination of rRVF-wt in Ae . aegypti were much greater than those in Cx . quinquefasciatus mosquitoes in our study . At 14 days post-exposure , 90% of rRVF-wt-infected Ae . aegypti individuals had titers greater than the average input virus titer . This is in marked contrast to the Cx . quinquefasciatus mosquitoes , where at 14 days post-exposure only 10 . 5% had a rise in body titer that was greater than the input virus titer and 35% of individuals with detectable virus at 14 days post-exposure had body titers ≤2 . 0 log10 PFU/mL . Additionally , dissemination rates for rRVF-wt in Cx . quinquefasciatus were low ( ≤16 . 7% ) ( Table 3 ) . These observations support the hypothesis that a midgut infection and/or midgut escape barrier is responsible for the lower vector competence of this species compared to Aedes species for RVFV [13] , [15] , [16] . The recombinant viruses tested comprised three groups: those with a deletion of the NSs gene from the S segment of the virus , those with a deletion of the NSm gene from the M segment , and those with both the NSs and NSm genes deleted . Recombinant RVFV lacking the NSs gene has been shown to maintain the virulence and growth characteristics of the wild type virus in mammalian cell culture , and in vivo testing demonstrated it to be highly attenuated , immunogenic and protective against challenge with wild type virus making it a potential vaccine candidate [29] . RVFV Clone 13 , an attenuated clone containing a deletion of 70% of the NSs gene , has been shown to exhibit a lower infection rate in Cx . quinquefasciatus mosquitoes compared to wild type RVFV ZH548 , while no difference was observed in Ae . vexans mosquito infection rates [46] . In our study , deletion of the NSs gene alone did not significantly affect rates of infection or transmission compared to rRVF-wt in either Ae . aegypti or Cx . quinquefasciatus mosquitoes although the dissemination rate for rRVF-ΔNSs was significantly lower than rRVF-wt in Ae . aegypti . Recombinant RVFV lacking the NSm gene exhibits efficient replication in cell culture and although in vivo studies have demonstrated this mutant virus to be highly immunogenic it is only partially attenuated relative to the wild type virus , retaining the ability to cause lethal hepatic or neurologic disease in a minority of infected animals [29] , [31] . In the current study , deletion of the NSm gene significantly reduced infection rates in both mosquito species tested . In Ae . aegypti mosquitoes , dissemination and transmission rates were also significantly reduced suggesting an important role for the NSm proteins in this species . Given the characteristics of RVFV mutant viruses individually lacking the NSs or NSm genes , Bird , et al . , have hypothesized that combining these deletions in a single mutant virus would generate a stable , attenuated , immunogenic vaccine virus [29] . Results of in vitro and in vivo studies characterizing the double NSs and NSm deleted recombinant virus suggest that this hypothesis is correct [29] . The double deletion virus grows efficiently in cell culture and in animals this virus is highly attenuated , immunogenic and confers protective immunity from wild type virus challenge [29] . We observed that deletion of the NSs and NSm genes in combination affected RVFV growth differently in the two mosquito species tested . Deletion of both the NSs and NSm genes had a pronounced effect in Ae . aegypti mosquitoes; none of the Ae . aegypti that ingested a bloodmeal containing the rRVF-ΔNSs-ΔNSm virus became infected ( n = 75 ) . In contrast , infection with rRVF-ΔNSs-ΔNSm of the less competent vector , Cx . quinquefasciatus , was significantly reduced compared to rRVF-wt , although to a lesser degree than in Ae . aegypti , and no significant differences in dissemination or transmission rates were observed . It was apparent , however , that in Cx . quinquefasciatus the additional deletion of the NSm gene reduced the infection rate of the double deletion virus , rRVF-ΔNSs-ΔNSm , ( 33/50 , 66 . 0% ) compared to the rRVF-ΔNSs single deletion virus ( 33/35 , 94 . 3% ) ( Table 3 ) . The Cx . quinquefasciatus rRVF-ΔNSs-ΔNSm infection rate was higher than the rRVF-ΔNSm rate , however this is most likely due to the higher titer of the rRVF-ΔNSs-ΔNSm bloodmeal . In vivo studies by Bird , et al . , showed no detectable post-vaccination viremia in n = 20 rats [29] and more recently in n = 42 sheep inoculated with the double deletion virus [47] . These results , coupled with our observation that in the more competent Ae . aegypti vector a bloodmeal titer of 7 . 0 log10 PFU/mL did not result in infection of mosquitoes with this virus , suggest an extremely low likelihood that mosquitoes could acquire an infectious dose of virus from feeding on a vaccinated animal . This is important since , due to the segmented nature of the RVFV genome , the potential exists for reassortment and therefore reversion of mutations in a mosquito that has acquired both vaccine and wild type strains of the virus from feeding on multiple hosts [48] . The deletion of genes from multiple segments of the virus genome in the rRVF-ΔNSs-ΔNSm vaccine candidate virus adds an additional safeguard against this mechanism of reassortment-driven reversion to wild type . The results of this study demonstrate that deletion of the RVFV NSm gene alone or in combination with the NSs gene significantly affected the replication kinetics of the virus in the mosquito species tested , particularly in Ae . aegypti . The combined deletion of both gene regions resulted in the greatest attenuation of RVFV replication in these mosquitoes , suggesting that the rRVF-ΔNSs-ΔNsm virus is an acceptable vaccine candidate with little possibility of environmental contamination due to the lack of efficient infection and transmission in mosquitoes . The RVFV NSm has been demonstrated to function as a suppressor of virus-induced apoptosis in mammalian cells in culture although a similar role has not been demonstrated in arthropod cells [26] . However , NSm has also been shown to be non-essential for replication in cultured mammalian cells suggesting it may have a more significant function in the infection of insect vectors involved in amplification and transmission of RVFV in nature [25] , [27] . In our study , the reduced infection rates observed in both Ae . aegypti and Cx . quinquefasciatus species and the diminished whole body virus titers of infected Ae . aegypti mosquitoes suggest a possible role for NSm in modulation of a mosquito midgut infection barrier . Additionally , the reduction in rates of dissemination and transmission in Ae . aegypti indicate that NSm may also function as a suppressor of a midgut escape barrier . Although the mechanisms of these barriers are not understood , it is apparent that the genetic traits of the virus as well as those of the mosquito host species influence the infection , dissemination and transmission of arboviruses [18] . The rRVF-ΔNSm deletion mutant will be a valuable tool in future studies to elucidate the mechanisms of RVFV infection and transmission in mosquito vectors .
|
Rift Valley fever virus is transmitted mainly by mosquitoes and causes disease in humans and animals throughout Africa and the Arabian Peninsula . The impact of disease is large in terms of human illness and mortality , and economic impact on the livestock industry . For these reasons , and because there is a risk of this virus spreading to Europe and North America , it is important to develop a vaccine that is stable , safe and effective in preventing infection . Potential vaccine viruses have been developed through deletion of two genes ( NSs and NSm ) affecting virus virulence . Because this virus is normally transmitted by mosquitoes we must determine the effects of the deletions in these vaccine viruses on their ability to infect and be transmitted by mosquitoes . An optimal vaccine virus would not infect or be transmitted . The viruses were tested in two mosquito species: Aedes aegypti and Culex quinquefasciatus . Deletion of the NSm gene reduced infection of Ae . aegypti mosquitoes indicating a role for the NSm protein in mosquito infection . The virus with deletion of both NSs and NSm genes was the best vaccine candidate since it did not infect Ae . aegypti and showed reduced infection and transmission rates in Cx . quinquefasciatus .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"viral",
"vaccines",
"viral",
"classification",
"immunology",
"microbiology",
"vaccines",
"rna",
"viruses",
"vaccination",
"infectious",
"diseases",
"biology",
"vectors",
"and",
"hosts",
"mosquitoes",
"clinical",
"immunology",
"immunity",
"vector",
"biology",
"virology",
"vaccine",
"development"
] |
2012
|
Infection and Transmission of Rift Valley Fever Viruses Lacking the NSs and/or NSm Genes in Mosquitoes: Potential Role for NSm in Mosquito Infection
|
Understanding the pathogenesis of infection by neurotropic viruses represents a major challenge and may improve our knowledge of many human neurological diseases for which viruses are thought to play a role . Borna disease virus ( BDV ) represents an attractive model system to analyze the molecular mechanisms whereby a virus can persist in the central nervous system ( CNS ) and lead to altered brain function , in the absence of overt cytolysis or inflammation . Recently , we showed that BDV selectively impairs neuronal plasticity through interfering with protein kinase C ( PKC ) –dependent signaling in neurons . Here , we tested the hypothesis that BDV phosphoprotein ( P ) may serve as a PKC decoy substrate when expressed in neurons , resulting in an interference with PKC-dependent signaling and impaired neuronal activity . By using a recombinant BDV with mutated PKC phosphorylation site on P , we demonstrate the central role of this protein in BDV pathogenesis . We first showed that the kinetics of dissemination of this recombinant virus was strongly delayed , suggesting that phosphorylation of P by PKC is required for optimal viral spread in neurons . Moreover , neurons infected with this mutant virus exhibited a normal pattern of phosphorylation of the PKC endogenous substrates MARCKS and SNAP-25 . Finally , activity-dependent modulation of synaptic activity was restored , as assessed by measuring calcium dynamics in response to depolarization and the electrical properties of neuronal networks grown on microelectrode arrays . Therefore , preventing P phosphorylation by PKC abolishes viral interference with neuronal activity in response to stimulation . Our findings illustrate a novel example of viral interference with a differentiated neuronal function , mainly through competition with the PKC signaling pathway . In addition , we provide the first evidence that a viral protein can specifically interfere with stimulus-induced synaptic plasticity in neurons .
The finding that persistent viruses could selectively affect differentiated functions of their target cell without causing cell lysis or widespread inflammation was first demonstrated more than 25 years ago [1] . This type of viral persistence , characterized by minimal cell damage , seems particularly well suited for the central nervous system ( CNS ) given the limited capacity of renewal of CNS resident cells , in particular of neurons . Viral interference with selected signaling pathways will nevertheless disrupt cellular homeostasis and cause disease [2] . As viral impairment of neurons may lead to behavioral or cognitive impairment , it was therefore hypothesized that persistent viruses could play a role in human mental disorders of unclear etiology [3] , [4] . To date , the mechanisms whereby viruses can interfere with brain function are not well understood and are strongly dependent on the strategy that a given virus has developed to persist in the CNS [5] , [6] . For viruses actively replicating in neuronal cells , one hypothesis is that the expression and/or accumulation of viral products in the cell may affect neuronal activity and cause disease . To date , it is clear that much is needed for a better understanding of the pathogenesis of persistent viral infections of the CNS and for the identification of the viral determinants responsible for the associated diseases . Borna disease virus ( BDV ) is a highly neurotropic , non-cytolytic virus that provides an ideal paradigm for studying the behavioral correlates of CNS viral infections . BDV is an enveloped virus with a non-segmented , negative strand RNA genome [7] , [8] . In contrast to other Mononegavirales , BDV replicates in the nucleus of infected cells [9] and uses the host cell splicing machinery for maturation of viral transcripts [10] , [11] . The BDV compact genome encodes for six proteins , namely , the nucleoprotein ( N ) , phosphoprotein ( P ) , protein X , matrix protein ( M ) , glycoprotein ( G ) , and polymerase ( L ) . Whereas M and G are involved in particle formation , P , N , L , and X are components of the polymerase complex . BDV infects a wide variety of mammals [12] , [13] and is associated with a large spectrum of neurological disorders , ranging from immune-mediated diseases to behavioral alterations without inflammation [12] , [14] , [15] . These disorders are reminiscent of symptoms observed in certain human neuropsychiatric diseases [16] . Evidence suggest that BDV infections may also occur in humans , although a link between BDV infection and any human neurological disease has not been firmly established yet [17]–[19] . The neurobehavioral manifestations associated with BDV infections in animals are partly due to the selective tropism of BDV in the CNS for neurons of the cortex and hippocampus [15] , [20] , [21] , which govern many cognitive and behavioral functions [22] . In an effort to better characterize the impact of BDV persistence on neuronal function , we recently analyzed the neuronal activity of primary cultures of neurons infected with BDV , using both functional imaging and electrophysiological approaches [23] , [24] . These studies clearly showed that BDV interferes with activity-dependent plasticity , while leaving the basal properties of neuronal activity unaffected . Moreover , the selective impairment of neuronal plasticity due to BDV infection was correlated to a reduced phosphorylation of the neuronal targets of Protein Kinase C ( PKC ) , a kinase that plays important roles in the regulation of neuronal activity [25] . Amongst the different viral proteins , BDV P appeared as the most plausible candidate for mediating this interference . Similar to the phosphoproteins of other Mononegavirales , BDV P is a component of the viral polymerase complex , which serves several functions in the viral life cycle . These functions are thought to be regulated , at least in part , by its phosphorylation by cellular kinases [26] . BDV P is preferentially phosphorylated at serine residues 26 and 28 by PKC and , to a lesser extent , at serine residues 70 and 86 by casein kinase II ( CKII ) [27] . Taken together , these observations led us to postulate that BDV P may serve as a PKC kinase decoy substrate when expressed in neurons , resulting in the decreased phosphorylation of other PKC neuronal targets . Although transfection experiments using a BDV P expressing plasmid provided the first evidence that this could indeed be the case [24] , a formal demonstration of the role of BDV P in PKC-dependent signaling and on neuronal activity was needed . The newly established reverse genetics technique allowing the generation of recombinant BDV ( rBDV ) entirely from cDNA has provided means to test this hypothesis directly [28] . Recently , we characterized rBDV expressing P mutants lacking either the PKC or the CKII phosphorylation sites , upon replacement of the corresponding serine residues with alanines to abrogate phosphorylation [29] . We showed that phosphorylation of BDV P acts as a negative regulator of the viral polymerase complex activity , in contrast to what has been shown for other Mononegavirales . Here , we infected primary cultures of rat hippocampal and cortical neurons with these different recombinant viruses and analyzed their phenotype and responses to stimulation . Using a rBDV where P can no longer be phosphorylated by PKC , we demonstrate a complete suppression of viral interference with neuronal activity . This was shown not only at the molecular level in terms of PKC-dependent signaling , but also at the functional level by studying calcium responsiveness to depolarization and the electrophysiological network properties of cultured neurons . Thus , our findings illustrate a novel example of viral interference with a differentiated neuronal function , mainly through competition with the PKC signaling pathway . In addition , we provide the first evidence that a viral protein can specifically interfere with stimulus-induced synaptic plasticity in neurons .
To study the impact of BDV P phosphorylation on viral neuropathogenesis , we used a recombinant BDV in which the two serine ( S ) residues in position 26 and 28 of P have been replaced by alanine ( A ) residues ( rBDV-AASS ) . We have previously shown that these mutations , when introduced using the recently established technique for generating BDV from cDNA [28] , completely abolishes BDV P phosphorylation by PKC , while still supporting full polymerase activity and viral growth [27] , [29] . As a control , we also used a recombinant wild-type BDV ( rBDV-wt ) , composed of the canonical sequence of wild-type BDV strain He/80 [30] . We first analyzed the impact of these mutations on the kinetics of viral spread in primary hippocampal rat neuronal cultures . To this end , we infected neuron-rich cultures ( >80% neurons ) one day after their plating with 300 focus forming units ( FFU ) per well of either rBDV-wt or rBDV-AASS cell-released virus ( prepared from persistently infected Vero cells ) . We studied viral dissemination occurring after primary infection by assessing expression of the BDV nucleoprotein at different times post-infection , using immunofluorescence microscopy to quantify viral spread . Consistent with our previous reports using wild-type BDV [31] , infection with rBDV-wt was apparent by 5 to 7 days post-infection , with a low percentage ( <10% ) of all neurons being positive for BDV nucleoprotein . The virus spread rapidly increased thereafter and by day 20 , more than 95% of neurons were infected ( Figure 1A ) . In contrast , spread of the rBDV-AASS virus was significantly delayed , in particular between days 10 and 17 post-infection , a time when viral dissemination speed is usually maximal due to the increased density of the neuronal network [31] . Thus , the phosphorylation of BDV P seems to be required for optimal transneuronal virus spread in primary neurons . Importantly , despite the delayed kinetics for rBDV-AASS , infection ultimately disseminated to the whole neuronal cultures . By 20–21 days post-infection , the large majority of neurons was positive for BDV antigens , with no significant differences between cultures infected with either rBDV-wt or rBDV-AASS , as assessed by immunofluorescence analysis ( Figure 1B ) . At this stage , quantitative Western blot analysis revealed that comparable amounts of N , P and X viral proteins were present in neurons infected with both rBDV-wt and rBDV-AASS viruses . Consistent with our previous characterization of rBDV-AASS , we observed a delayed migration for the X protein [29] , presumably resulting from the two amino acids substitutions introduced in X when generating the rBDV-AASS mutant . In conclusion , both recombinant viruses were able to infect the totality of our neuronal cultures , albeit with a delayed kinetics for rBDV-AASS . For our subsequent signaling and functional studies , we therefore used neurons that had been infected for at least 21 days and verified for each experiment that infection was indeed complete prior to any subsequent analysis . Recently , we have shown that BDV-induced impairment of potentiation of synaptic activity was due to an interference with the PKC-dependent phosphorylation of synaptic proteins that modulate neuronal activity [24] . To determine the impact of the destruction of the PKC phosphorylation sites on BDV P , we directly stimulated the PKC pathway of neurons using the phorbol ester PMA and analyzed the phosphorylation status of two major PKC targets in neurons , myristoylated alanine-rich C kinase substrate ( MARCKS ) and synaptosomal-associated protein of 25 kDa ( SNAP25 ) [32] , [33] . Consistent with our previous reports , we showed by quantitative Western blot analysis that the phosphorylation of these two neuronal PKC targets was significantly impaired in neurons infected with rBDV-wt ( Figure 2A and 2B ) . In contrast , there was a complete restoration of phospho-MARCKS and phospho-SNAP25 levels in neurons infected with rBDV-AASS , with levels being comparable to those observed in non-infected neurons following PKC stimulation . SNAP25 is a synaptic protein that plays an essential role in neurotransmitter release through regulation of synaptic vesicle exocytosis [34] . In addition , SNAP-25 also modulates calcium dynamics in response to depolarization by acting on Voltage-Gated Calcium Channels ( VGCC ) . It has recently been shown that activity-dependent phosphorylation of SNAP-25 is mediated by PKC and is required for negative regulation of VGCCs [35] . Indeed , PKC phosphorylation of SNAP-25 , by promoting inhibition of VGCCs decreases calcium signaling and controls neuronal excitability . This led us to investigate whether the differences observed between neurons infected with rBDV-wt and rBDV-AASS in the phosphorylation of SNAP25 would have an impact on their calcium responsiveness upon depolarization . We therefore analyzed the kinetics of calcium changes in response to depolarization after exposure of infected and control neurons to 50 mM KCl . Infection of neurons with rBDV-wt significantly enhanced their response to depolarization , with a peak calcium response being about fifty percent stronger than control non-infected neurons . In sharp contrast , the calcium response of neurons infected with rBDV-AASS was similar to that of non-infected neurons ( Figure 3A and 3B ) . Together , these findings suggest that BDV mediated interference with PKC-dependent phosphorylation , by reducing phospho-SNAP-25 levels , leads to calcium hyper-responsiveness and that mutation of the BDV P phosphorylation sites restores normal calcium responses . Since P is expressed from a bicistronic mRNA encoding P and X , the introduced mutation into the open reading frame of P in the rBDV-AASS mutant also resulted in two amino acid substitutions in the X protein ( Figure 4A ) . We previously showed that these mutations had no impact on X binding efficiency to P or its ability to interfere with the polymerase activity [29] . However , we wanted to exclude formally the possible contribution of the X mutations in the phenotype of the rBDV-AASS mutant . To explore the importance of BDV-P phosphorylation in the presence of wild-type X , we generated a new recombinant BDV , in which S26 and S28 of BDV P were substituted with leucine residues ( rBDV-LLSS , Figure 4A ) . Similar to rBDV-AASS , viral spread of rBDV-LLSS was delayed upon infection of hippocampal neurons ( Figure 4B ) . Likewise , we also demonstrated the restoration of phospho-MARCKS and phospho-SNAP25 levels in neurons infected with rBDV-LLSS , with levels being comparable to those observed in non-infected neurons following PKC stimulation ( Figure 4C ) . Finally , the calcium hyper-responsiveness observed in rBDV-wt infected neurons was also corrected in neurons infected with rBDV-LLSS ( Figure 4D ) . Thus , the phenotype observed with rBDV-LLSS is very similar to rBDV-AASS , providing additional evidence that our findings are indeed due to phosphorylation of BDV-P by PKC and not to the mutations present in X . The S26/S28A mutation of BDV P corrects the defects in electrical activity observed in rBDV-wt infected neurons . Given the dramatic consequences of the S26/28A mutation on neuronal signaling and calcium responsiveness , we next studied its impact on the electrophysiological properties of infected neurons . Recently , we described a cell culture system using 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 ) [36] , [37] . Using this system , we showed that BDV selectively blocks activity-dependent enhancement of neuronal network activity , one form of synaptic plasticity thought to be important for learning and memory [23] , [38] . Given the central role of PKC in synaptic plasticity , we hypothesized that this defect could be linked to BDV interference with this signaling pathway . To test this hypothesis , we compared the electrophysiological properties of cultures of cortical neurons infected with rBDV-wt or rBDV-AASS viruses , using the MEA culture system . All experiments were again performed at day 21 , to allow spreading of both viruses to the totality of the MEA cultures . At this time point , neurons have developed a rich network of processes and form numerous functional synaptic contacts [31] , [39] . In agreement with our previous results [23] , we did not observe any significant difference in the spontaneous network firing activity between neurons infected with either rBDV-wt or rBDV-AASS viruses ( Figure 5B ) . This firing pattern was also indistinguishable from that of control non-infected neurons . Next , we induced increased synaptic efficacy by exposing neuronal cultures for 15 min to 50 µM of bicuculline , a GABAA receptor antagonist . Treatment with this antagonist leads to the removal of the tonic inhibition imposed by GABAergic interneurons on the network [40] . As a result , we observed a significant increase of the mean burst frequency , which shifted from 0 . 175 Hz to 0 . 285–0 . 31 Hz ( Figure 5B ) . Here again , the behavior of all neuronal cultures was remarkably similar , regardless of their infection status . Interestingly , the increase in the strength of the synaptic connections triggered by bicuculline lasts for several hours upon removal of the drug [40] , [41] and is thought to represent the cellular basis of learning and memory [38] . In non-infected neurons , we indeed observed this maintenance of a high level of network activity , lasting more than two hours after washout of the drug . In contrast , neurons infected with rBDV-wt had returned to basal levels of network activity already one hour after bicuculline washout , confirming our previous results using wild-type BDV [23] . Very strikingly , the network properties of neurons infected with rBDV-AASS were completely different , as we observed the maintenance of high levels of synaptic activity persisting up to two hours after bicuculline washout , similarly to non-infected neurons . Therefore , a recombinant BDV which can no longer be phosphorylated by PKC on its mutated P protein has lost its capacity to block activity-induced synaptic potentiation .
The goal of our study was to provide further information about the mechanisms whereby BDV infection of neurons selectively interferes with synaptic plasticity and to identify the viral determinant responsible for this interference . Using a recombinant virus in which the PKC phosphorylation sites of BDV P protein have been destroyed , we demonstrate that primary cultures of neurons infected with this recombinant virus exhibit a behavior that becomes indistinguishable in many aspects to that of control , non-infected neurons . Therefore , our results clearly establish that BDV interference with PKC signaling , but also with calcium responses to depolarization and with network electrical properties , all result from the competition mediated by P with the phosphorylation of endogenous PKC substrates in neurons . We therefore propose a pathogenesis mechanism by which BDV would use PKC-dependent phosphorylation of P for its optimal spread in neurons , at the expense of an impaired response to potentiation stimuli of the infected neurons ( Figure 6 ) . Our findings provide strong evidence for a novel mechanism whereby a viral protein selectively blocks neuronal plasticity , representing a fascinating aspect of viral interference with neuronal functioning . Interestingly , both mutant viruses rBDV-AASS and rBDV-LLSS were strongly delayed in its capacity to spread within the neuronal cultures . These findings suggest that PKC-dependent phosphorylation of P is an important parameter for efficient transmission of BDV from neuron to neuron . This may also explain the preferential BDV infection of CNS regions where PKC activity is high , such as hippocampus [42] . The underlying mechanism for this delayed spread is unclear and it is presently not possible to discriminate whether it is due to reduced cell-to-cell transmission or to a reduced capacity of the virus to be transported along neuronal processes . It does not seem to result from a decreased efficacy of viral replication , since analysis of the steady state levels of viral transcripts in rBDV-AASS- as well as in rBDV-LLSS-infected cells appeared to be normal [29] . It could be due to a less efficient transport of BDV ribonucleoparticles along the neuronal processes , resulting from impaired interaction of non-phosphorylated P with yet unidentified neuronal motor proteins involved in BDV trans-neuronal spread . Alternatively , the delayed spread may be a consequence of impaired virus assembly or release , similar to human respiratory syncytial virus , where phosphorylation of P regulates the viral budding process by blocking the interaction of P with the viral matrix protein [43] . Finally , mutations present in the X protein should also be taken into account , as we cannot exclude the possibility that these mutations may affect unknown functions of X , leading to impaired viral spread . However , this latter possibility seems unlikely as the rBDV-LLSS mutant , which has no mutation in X , also exhibits delayed spread kinetics in neurons . Infection with the rBDV-AASS virus led to a complete restoration of the PKC-dependent phosphorylation of two neuronal targets that play crucial roles in modulating neuronal plasticity . Indeed , phosphorylation of MARCKS by PKC is implicated in actin-dependent cytoskeletal plasticity [44] and in the maintenance of long-term potentiation in vivo [45] . SNAP-25 is not only central for neuronal exocytosis but also for the regulation of calcium responsiveness . Modulation of neuronal excitability by SNAP-25 , which is dependent on PKC , is thought to have crucial consequence for brain functioning [34] . The calcium hyper-excitability in response to depolarization that we observed after infection with rBDV-wt is consistent with a recent study showing that non-phosphorylable ( S187A ) SNAP-25 mutants also display increased calcium responsiveness [35] . Moreover , SNAP-25 S187A mutant mice exhibit behavioral abnormalities and hyperlocomotor activity , similar to what has been described following BDV infection [46] . Finally , genetic studies have demonstrated an association of polymorphisms in the human SNAP-25 gene with attention deficit hyperactivity disorder or cognitive performance [47] , [48] . Nevertheless , as BDV is likely to affect all PKC neuronal substrates in the infected neurons , the relative contribution of the decreased phosphorylation of each of these substrates , including SNAP-25 , is difficult to appreciate . Very striking was the impact on the neuronal electrical activity measured using the MEA system . Since neurons infected with the rBDV-AASS mutant displayed a response to bicuculline stimulation that became indistinguishable from that of non-infected neurons , it is likely that interference with PKC signaling is indeed a main determinant for BDV impairment of neuronal functioning . The question remains open of whether the impairment of neuronal activity due to BDV infection is solely dependent on PKC inhibition . Using a global proteomic approach , we recently demonstrated that other neuronal pathways were altered by BDV infection , even prior to any stimulation [49] . At present , the link between some of these pathways , such as chromatin dynamics or transcriptional regulation and PKC-dependent signaling is not clear . Using infection with rBDV-AASS , in which PKC interference is suppressed , will be instrumental to test whether these other pathways are still affected , allowing to gain further insight on the relationship between the different BDV targets in neurons . Phosphoproteins of non-segmented negative-strand RNA viruses are subunits of the viral polymerase complex and are all phosphorylated by host kinases [26] . Although many studies have addressed the role of P phosphorylation on the virus life cycle , very few have tested its consequences on the physiology of the target cell . In theory , other viral phosphoproteins could also block endogenous phosphorylation , including that of PKC . For example , it has been shown that rabies P can be phosphorylated by PKC [50] . However , BDV represents a unique example where neurons can be persistently infected with strong antigenic load and no widespread cytolysis . Thus , a possible interference with PKC-dependent phosphorylation in neurons becomes apparent following BDV infection due to its outstanding non-cytolytic replication strategy , whereas it would not be detected with other viral systems that kill their target cell within a few days . As for the other Mononegavirales , BDV P is a multifunctional protein , which plays many roles in the virus life cycle . Besides its interference with PKC signaling , P has been reported to influence cellular functions at different levels . In particular , recent studies revealed interactions of P with a neurite outgrowth factor , amphoterin/HMG-1 [51] , with the Traf family member-associated NF-κB ( TANK ) -binding kinase-1 ( TBK-1 ) [52] and the gamma-aminobutyric acid receptor-associated protein [53] . Since these studies were based on transient transfection using non-neuronal cells , the relevance of these findings for BDV pathogenesis remains elusive and awaits further confirmation . It has also been shown that the expression of P in glial cells of transgenic mice leads to behavioral abnormalities [54] , although the underlying mechanism was not identified . Given the important role of astrocytes in the regulation of neuronal activity [55] , one hypothesis could be that P could also interfere with PKC-signaling in astrocytes and thereby disrupt glia-neuron communication . Interestingly , MARCKS , the main PKC substrate , is also expressed in astrocytes [56] . Finally , the two phospho-serine residues of P in themselves could have other unknown effects that could contribute to the phenotype observed , besides acting as PKC decoy substrates . Although none of the other known functions of P were affected , there may still be other uncharacterized functions of P that may be involved in the regulation of neuronal activity . To date , the analysis of the mechanisms underlying viral interference with neuron-specific differentiated functions , particularly those that support synaptic activity has been hampered by the lack of suitable model systems and easily testable hypotheses . BDV infection has provided considerable new insight on these issues , and the recent availability of a reverse genetics system has offered a powerful tool to assess directly the role of individual viral proteins in virus-host interplay and pathogenicity . Our data unambiguously demonstrate the role of P as a decoy substrate interfering with PKC signaling pathway , a kinase which plays important roles in learning and behavior [25] . Moreover , they reveal an original strategy for a neurotropic persistent virus and provide clues to better understand the basis of neuronal impairment caused by BDV . It will be important in the future to test the impact of BDV P in vivo , either using animal models for BDV infection or expressing the wild-type and mutant forms of P in selected brain areas .
Hippocampal neurons were prepared from newborn Sprague-Dawley rats and maintained in Neurobasal medium ( Invitrogen , Cergy-Pontoise , France ) supplemented with 0 . 5 mM glutamine , 1% fetal calf serum , 1% Penicillin/Streptomycin and 2% B-27 supplement ( Invitrogen ) , as described [31] , [57] . Neuronal cultures contained more than 80% neurons , as assessed by staining with the neuron-specific markers MAP-2 or ß-III Tubulin ( data not shown ) . Neurons were infected one day after plating with cell-free BDV . Cell-released virus stocks were prepared as described [31] , [57] , using Vero cells persistently infected with the different recombinant viruses . BDV infection of neurons was verified by immunofluorescence for each experiment . We used: mouse monoclonal antibodies to SNAP25 ( Synaptic Systems , Goettingen , Germany ) , ß-tubulin ( Sigma-Aldrich , Lyon , France ) , rabbit polyclonals to phospho-MARCKS ( Ser152/156 , a site specifically phosphorylated by PKC; Cell Signaling Technology , Danvers , Massachusetts , USA ) , MARCKS ( Chemicon-Millipore , Saint-Quentin-en-Yvelines , France ) . Phospho-SNAP25 ( Ser187 ) antibody , a site specific for PKC phosphorylation [33] was kindly provided by Pr . M . Takahashi ( Kitasato-University School of Medicine , Kitasato , Japan ) . All other antibodies have been described elsewhere [57] . Pharmacological agents were used at the following final concentrations: 1 µM PMA , 1 mM tetrodotoxin ( TTX; Sigma-Aldrich ) , 100 mM of NMDA receptor blocker D- ( - ) -2-amino-5-phosphopentanoic acid ( APV ) and 40 mM of AMPA/kainate receptor blocker 6-cyano-7-nitroquinoxaline-2 , 3-dione disodium ( CNQX; Tocris Bioscience , Bristol , United Kingdom ) . Bicuculline ( Bicuculline methiodide , Tocris Biosciences ) was used at a final concentration of 50 µM . To introduce point mutations into the P gene of the full-length BDV genome ( pBDV-LLSS ) , assembly PCR using the plasmid pBRPolII-HrBDVc as a template was carried out as described [29] . Standard immunofluorescence was performed as described previously [49] . Briefly , cells 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 by using Vectashield containing DAPI to stain nuclei ( Vector Laboratories , Burlingame , CA ) . Neurons were incubated for 60 min at 37°C in culture medium containing the blockers of neuronal activity TTX , APV and CNQX . Following this resting period , the medium was replaced by medium containing 1 µM PMA and neurons were stimulated for 10 min . Neurons were then rapidly washed in ice-cold PBS and harvested in lysis buffer containing phosphatase inhibitors [57] . The rest of the procedure was performed as described [49] . Briefly , equivalent amounts of cell lysates were separated by electrophoresis using 10% Bis-Tris Nu-PAGE gels ( Invitrogen ) and then transferred onto nitrocellulose membranes ( Hybond-C extra , Amersham Biosciences , Orsay , France ) . After blocking ( Li-Cor blocking buffer , ScienceTec , Les Ulis , France , or Tris buffer saline containing 5% non-fat dry milk ) , membranes were incubated with primary antibodies . Secondary fluorescent antibodies used were the following: IRDye 800CW goat anti-Mouse IgG ( Li-Cor ) or Alexa Fluor 680 goat anti-rabbit IgG ( Invitrogen ) . Laser scanning and quantitative analyses of the blots were performed using the Odyssey Infrared Imaging System ( Li-Cor ) . Quantification of protein phosphorylation was carried out by measuring the intensity of fluorescence of the band corresponding to the phosphorylated protein normalized by ß-tubulin expression , due to inefficient stripping of phospho-MARCKS and phospho-SNAP-25 antibodies binding . In parallel , total levels for each protein was verified on a separate blot . Results are expressed as percentage of increase over the mean of unstimulated controls , which was set to 100% . Neurons grown on flat-bottom 96-well plates were incubated for 30 min at 37°C in HBSS ( Invitrogen ) –BSA ( Sigma ) solution containing 2 . 5 mM probenecid ( Sigma ) , pH 7 . 45 supplemented with fluo-3 acetoxymethyl ( AM ) 1 mM ( Invitrogen ) and 20% pluronic F-17 ( Sigma ) . After this incubation period , neurons were washed twice with HBSS-BSA-probenecid solution and placed into a 37°C incubator in the dark for 30 min . Fluorescence was measured at 460–490 nm excitation and 515 nm emission in each well , using a Novostar plate reader ( BMG Labtech , Champigny s/Marne , France ) . For each well , a series of 250 recordings ( one per second ) was performed; neurons were first exposed to 50 mM KCl ( Sigma ) from 10 to 90 s , then to HBSS-BSA-probenecid solution containing 50 µM thapsigargin ( Calbiochem , Fontenay-sous-Bois , France ) , 5 µM ionomycin ( Calbiochem ) and 10 µM EGTA ( Sigma ) from 90 to 177 s and finally to HBSS-BSA-probenecid solution containing 120 mM CaCl2 from 177 to 250s . [Ca2+]i was calculated using the equation [Ca2+]i = Kd ( F−Fmin ) / ( Fmax−F ) , where Kd is the dissociation constant of the Ca2+-fluo-3 complex ( 390 nM ) , and F represents the fluorescence intensity of the cells expressed as the ratio between the highest fluorescence measurement between 10 and 90 s and the baseline . Fmin corresponds to the minimum fluorescence between 90 and 177 s . Fmax represents the maximum fluorescence between 177 and 250 s ( see Figure 3A ) . Neuronal cortical cultures were prepared from embryonic Sprague Dawley rats at gestational day 18 , according to a previously described protocol [37] . Neurons were seeded at a density of 105 cells per MEA and half of the MEA dishes were infected with BDV on day 1 . All experiments were made on day 21 , to allow spreading of the different recombinant viruses to the totality of the MEA dishes . 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 ) for 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 [58] . Spikes and bursts were detected by a dedicated analysis software developed at INSERM U862 ( Bordeaux , France ) [37] , 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 [59] . Bursts were defined as a series of ≥3 spikes occurring in less than 100 ms . Measures were performed under spontaneous conditions , during a 15 min stimulation period using 50 µM Bicuculline , and after washout of bicuculline upon perfusing the MEA dish with 2 . 5 ml medium . After each manipulation , neurons were allowed to rest for 2 min before recordings were taken , to avoid vehicle effects . For each condition , recordings were performed over a 3 min period , and the mean burst frequency was calculated by averaging the results obtained for all electrodes . Data are presented as mean±standard error of the mean ( s . e . m ) . Statistical significance was determined using Student's unpaired t-test . The SwissProt ( http://www . expasy . org/sprot/ ) accession numbers for proteins mentioned in the text are BDV Nucleoprotein ( Q01552 ) , BDV Phosphoprotein ( P26668 ) , BDV X protein ( Q912Z9 ) , BDV matrix protein ( P52637 ) , BDV polymerase ( P52639 ) , MARCKS ( P30009 ) , SNAP-25 ( P60881 ) , PKC epsilon ( P09216 ) , CKII ( P19139 ) , TBK-1 ( Q9WUN2 ) . The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession number for the strain discussed in this paper is BDV strain He/80/FR ( AJ311522 ) .
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Neurotropic viruses have evolved diverse strategies to persist in their host , with variable consequences for brain function . The investigation of these mechanisms of persistence and associated disease represent a major issue in viral pathogenesis , as it may also improve our understanding of human neurological diseases of unclear etiology for which viruses are thought to play a role . In this study , we have examined the mechanisms whereby the neurotropic Borna disease virus ( BDV ) can selectively interfere with synaptic plasticity upon infection of neurons . Using genetically engineered recombinant viruses , we show that the phosphorylation of BDV phosphoprotein ( P ) by the cellular protein kinase C ( PKC ) is the main determinant for this interference , mainly by competing with the phosphorylation of the natural PKC substrates in neurons . A mutant virus in which the PKC phosphorylation site of P has been destroyed no longer interferes with this signaling pathway . As a result , the calcium dynamics and electrical activity in response to stimulation of neurons infected with this mutant virus are completely corrected and become similar to that of non-infected neurons . Thus , our findings uncover a previously undescribed mechanism whereby a viral protein interferes with neuronal response to stimulation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"infectious",
"diseases/infectious",
"diseases",
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"nervous",
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2009
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Mutation of the Protein Kinase C Site in Borna Disease Virus Phosphoprotein Abrogates Viral Interference with Neuronal Signaling and Restores Normal Synaptic Activity
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The generation and resolution of joint molecule recombination intermediates is required to ensure bipolar chromosome segregation during meiosis . During wild type meiosis in Caenorhabditis elegans , SPO-11-generated double stranded breaks are resolved to generate a single crossover per bivalent and the remaining recombination intermediates are resolved as noncrossovers . We discovered that early recombination intermediates are limited by the C . elegans BLM ortholog , HIM-6 , and in the absence of HIM-6 by the structure specific endonuclease MUS-81 . In the absence of both MUS-81 and HIM-6 , recombination intermediates persist , leading to chromosome breakage at diakinesis and inviable embryos . MUS-81 has an additional role in resolving late recombination intermediates in C . elegans . mus-81 mutants exhibited reduced crossover recombination frequencies suggesting that MUS-81 is required to generate a subset of meiotic crossovers . Similarly , the Mus81-related endonuclease XPF-1 is also required for a subset of meiotic crossovers . Although C . elegans gen-1 mutants have no detectable meiotic defect either alone or in combination with him-6 , mus-81 or xpf-1 mutations , mus-81;xpf-1 double mutants are synthetic lethal . While mus-81;xpf-1 double mutants are proficient for the processing of early recombination intermediates , they exhibit defects in the post-pachytene chromosome reorganization and the asymmetric disassembly of the synaptonemal complex , presumably triggered by crossovers or crossover precursors . Consistent with a defect in resolving late recombination intermediates , mus-81; xpf-1 diakinetic bivalents are aberrant with fine DNA bridges visible between two distinct DAPI staining bodies . We were able to suppress the aberrant bivalent phenotype by microinjection of activated human GEN1 protein , which can cleave Holliday junctions , suggesting that the DNA bridges in mus-81; xpf-1 diakinetic oocytes are unresolved Holliday junctions . We propose that the MUS-81 and XPF-1 endonucleases act redundantly to process late recombination intermediates to form crossovers during C . elegans meiosis .
Meiotic recombination generates chiasmata that join homologous chromosomes together to ensure proper meiotic chromosome segregation . The efficient generation and resolution of joint molecules ( JM ) is essential for meiosis; therefore , JM formation and resolution is carefully regulated . In most organisms , meiotic recombination is initiated by the generation of Spo11-induced double strand breaks ( DSBs ) . DSBs are resected to produce a 3′ single-stranded stretch of DNA onto which Rad51 is loaded , forming a nucleoprotein filament . Rad51 catalyzes invasion of the homologous chromosome and JM intermediates physically linking homologous chromosomes are formed ( reviewed in [1] ) . JMs must be resolved before homologs segregate at meiosis I . JMs can be resolved to form crossover ( CO ) products in which flanking markers are exchanged , or they can be resolved to form non-crossover ( NCO ) products . The overall progression of JM resolution appears to be similar in diverse organisms , however the proteins and their relative involvement in JM resolution vary from species to species . Thus , the same initiating lesion ( Spo11-induced DSB ) is repaired through diverse mechanisms . Study of meiotic DSB repair in a range of organisms illuminates the modularity of repair and how different organisms have evolved to favor distinct endonucleases to repair Spo11 generated DSBs . There are two main pathways that process meiotic JMs to COs , which are utilized to varying extents in different organisms . Much of what we know about meiotic crossover resolution comes from studies in budding yeast and fission yeast . The predominant pathway in budding yeast involves the synaptonemal complex-associated ZMM ( Zip1/2/3/4 , Msh4/5 , Mer3 ) proteins . It has been proposed that the ZMM proteins protect recombination intermediates ( RIs ) from NCO resolution , ensuring CO resolution of ZMM-associated RIs [2] , [3] . ZMM-dependent COs are subject to crossover interference , which occurs when the presence of one CO decreases the probability that another CO will occur nearby . A second ZMM-independent pathway is characterized by the structure specific endonuclease Mus81/Mms4 that resolves RIs to either CO or NCO outcomes [4] , [5] . These ZMM-independent COs are not subject to crossover interference . Fission yeast , which lack both a synaptonemal complex and ZMM proteins , represent an extreme case in which all COs are ZMM-independent and are resolved by Mus81/Eme1 . Loss of Mus81 in S . pombe results in spore inviability due to meiotic chromosome segregation defects and a profound decrease in the frequency of COs [6] , [7] . Consistent with Mus81 being the major meiotic Holliday junction ( HJ ) resolvase in fission yeast , the meiotic chromosome segregation defects of Mus81 mutants can be rescued by the expression of the bacterial HJ resolvase RusA [7] or by the expression of the human Holliday junction resolvase GEN1 [8] . Loss of Mus81 in budding yeast , which has a synaptonemal complex and ZMM-proteins , results in only a minor reduction in spore viability and a modest decrease in the frequency of COs [9] . These data are consistent with a model in which Mus81 is only responsible for ZMM-independent COs and that ZMM-dependent COs predominate in budding yeast [5] . This bias towards the ZMM-dependent CO pathway in budding yeast is mediated by the BLM-helicase homolog , Sgs1 [10]–[13] . Sgs1 is thought to prevent the accumulation of JMs by channeling most double strand breaks towards NCO resolution and ensuring that remaining DSBs are associated with ZMM-proteins and resolve as COs [10] , [13] . When Sgs1 is absent , JMs accumulate and require Mus81 for resolution . Cells lacking both Sgs1 and Mus81 during meiosis accumulate JMs and cannot segregate at meiosis I [11] , [12] . Although , loss of Mus81 does not greatly affect meiotic segregation in budding yeast , it appears that the Yen1 and Slx1/Slx4 endonucleases act redundantly with Mus81 to resolve persistent JMs . Loss of Yen1 alone does not result in meiotic phenotypes whereas mus81 yen1 double mutants exhibit a profound decrease in spore viability due to a failure of JM resolution and chromosome segregation at meiosis I [14] . Similarly , the Slx4/Slx1 endonuclease processes some RIs in the absence of Mus81 [10] , [13] . The structure-specific endonucleases are also critical for crossing over in more complex organisms . In Drosophila , MUS81 does not appear to play a role in the formation of COs [15] . Most COs are catalyzed by the Mus81-related structure specific endonuclease MEI-9 ( fly nucleotide excision repair endonuclease XPF ortholog ) , and MUS312 ( fly SLX4 ortholog ) . Loss of function of either MEI-9 or MUS312 results in a severe decrease in crossing-over [16] suggesting that MUS312 and MEI-9 resolve meiotic HJ intermediates . The MUS81 and XPF endonucleases also play a role in mouse meiosis . Although Mus81 knockout mice are viable and fertile [17] , [18] , MUS81 appears to be required for the repair of at least some meiotic DSBs during murine meiosis . Mus81-/- male mice exhibit significant meiotic defects in the germ line: mature sperm are depleted , MLH1 foci , which mark ZMM-dependent crossovers , are increased , and a subset of meiotic DSBs is not repaired [19] . Similar to Mus81 mutant mice , mice lacking ERCC1 ( the binding partner of XPF ) or BTBD12 ( Slx4 ortholog ) exhibit sperm defects , persistent unrepaired DSBs in the germ line , and increased MLH1 foci [20] , [21] . C . elegans is a powerful model for study of metazoan meiotic DSB repair . The germ line is temporally and spatially polarized with respect to meiotic progression , allowing detection of subtle but significant alterations in the kinetics of repair events . Synaptonemal complex formation and DSB induction are independent during C . elegans meiosis , allowing separation of homolog pairing and DSB repair . Finally , crossover interference is incredibly robust in C . elegans with only a single CO occurring per pair of homologous chromosomes . In C . elegans , it has been proposed that all COs are ZMM-dependent [22] and as such would not require MUS-81 or related structure-specific endonucleases for resolution . However , the study of rtel-1 anti-recombinase mutants revealed that there are two classes of COs in C . elegans: ZMM-dependent COs; and ZMM-independent COs , which require MUS-81 for resolution [23] . Other structure-specific endonucleases also play a role in CO formation in C . elegans; loss of XPF-1 ( XPF/MEI-9 ortholog ) results in a decrease in the number of COs compared to wild type animals . Furthermore , loss of both MUS-81 and HIM-18 , the C . elegans Slx4 ortholog , results in an increase in mitotic and meiotic RIs [24] . Together , these data suggest that MUS-81 and HIM-18 play overlapping , non-redundant roles in processing RIs . Unlike MUS-81 , XPF-1 , or HIM-18 , and in contrast to yeast , the related endonuclease GEN-1 ( Yen1 ortholog ) does not appear to have a role during C . elegans meiosis [25] . What remains unclear is the relative contribution of each of the structure-specific endonucleases to CO formation and where in the CO pathway each endonuclease functions . Are the endonucleases acting early to process RIs to NCO outcomes or are they acting late to resolve RIs as COs ? Here , we investigated the relative roles of structure-specific endonucleases in processing RIs during meiosis in C . elegans with the goal of identifying the enzyme ( s ) responsible for processing JMs to produce COs . Unexpectedly , we found that MUS-81 performs both early and late roles in processing RIs during meiosis I in C . elegans and that its loss results in an overall decrease in the total number of COs . We show that MUS-81 and HIM-6 ( Sgs1 homolog ) act early during pachytene to limit the accumulation and persistence of RAD-51-associated RIs during meiosis , a defect that also manifests as chromosome fragmentation at diakinesis . Surprisingly , we found that MUS-81 and XPF-1 endonucleases , but not GEN-1 or EXO-1 , act redundantly to process late stage JMs to form COs . Loss of both MUS-81 and XPF-1 resulted in defective CO maturation and persistent JMs at diakinesis , which could be rescued by germline injection of the human Holliday junction resolvase GEN1 . As human GEN1 is able to cleave HJs in vitro , these results strongly suggest that the persistent JMs in mus-81; xpf-1 double mutants are HJs . Together , these data support a redundant role for MUS-81 and XPF-1 in processing HJ intermediates to produce interhomolog crossovers in C . elegans .
To examine the function of MUS-81 during meiosis in C . elegans , we characterized the meiotic phenotype of a mus-81 null mutant [26] . C . elegans mus-81 ( tm1937 ) mutants exhibited no obvious phenotypes attributed to meiotic defects such as a high frequency of embryonic inviability or an increased frequency of XO males . mus-81 mutants had reduced brood sizes ( 142+/−19 , approximately 50% of wild type , Student's t-test p<0 . 005 ) ( Table 1 ) . mus-81 brood sizes were variable , ranging from rare animals that were completely sterile ( 1/20 broods scored ) to animals that produced more than 200 progeny ( 5/20 broods scored ) . The brood size in C . elegans is dictated by the number of viable sperm , so defects in the germ line that lead to reduction in the number of viable sperm could result in a smaller brood size . A checkpoint in the C . elegans female germ line senses DNA damage or persistent recombination intermediates , triggering apoptosis of the damaged nuclei [27] , [28] . To determine if this apoptotic checkpoint protects the mus-81 germ line by removing defective nuclei before they develop into oocytes , we constructed mus-81 ( tm1937 ) ; ced-4 ( n1162 ) double mutants . ced-4 is essential for the initiation of apoptosis in C . elegans [29] . mus-81; ced-4 double mutants had a significantly reduced brood when compared to either ced-4 or mus-81 single mutants ( Table 1 , Student's t-Test p<0 . 01 ) and 4/10 lines were completely sterile suggesting that the apoptotic checkpoint removes nuclei with DNA damage or aberrant meiotic recombination intermediates in the mus-81 mutant . This observation together with the reduced brood suggested that there are defects in the mus-81 germ line . We next assayed the distribution of early RIs in the C . elegans germ line using an antibody that recognizes the recombination protein RAD-51 . In wild type animals , SPO-11-dependent RAD-51 foci are visible in early pachytene and are resolved by late pachytene ( Figure 1; Figure S1; Figure S2 ) . In mus-81 animals the average number of RAD-51 foci was slightly but significantly increased in all zones except diplotene when compared to wild type animals ( Student's t-test p<0 . 05 ) . Apart from a small but significant increase of RAD-51 foci in the mitotic zone ( average 0 . 54 mus-81 , 0 . 05 WT ) and the persistence of foci in late pachytene , the same general pattern of RAD-51 staining was observed in mus-81 ( tm1937 ) and wild type backgrounds ( Figure 1A , B ) . Consistent with this observation , mus-81 mutants did not exhibit high levels of chromosome non-disjunction or fragmentation in diakinetic oocytes ( Figure 1C , D ) . In yeast , loss of Mus81 and the helicase Sgs1 confers synthetic lethality as a result of the accumulation of unresolved RIs [7] , [9] , [30] , [31] . Meiosis-specific mutant alleles of mus81 and sgs1 indicate that Sgs1 limits JMs and that , in the absence of Sgs1 , Mus81 resolves these structures to prevent the accumulation or persistence of JMs during meiosis [11] , [12] . To test whether a similar relationship exists in C . elegans , we characterized the formation of RIs in animals lacking MUS-81 and the C . elegans Sgs1 helicase ortholog HIM-6 [32] . Similar to yeast mus81 sgs1 double mutants , C . elegans mus-81; him-6 double mutants exhibited a severely reduced brood size and ∼100% embryonic lethality ( Table 1 ) . DAPI staining revealed that germline nuclei progression in mus-81; him-6 double mutants was grossly normal with nuclei progressing from the mitotic zone to pachytene and finally diakinesis ( Figure S1 , Figure S2 ) . To further examine this phenotype we stained germ lines with an anti-RAD-51 antibody to monitor the distribution of early meiotic RIs . Both mus-81 and him-6 single mutants exhibited increased RAD-51-associated RIs in mid-pachytene compared to wild type animals ( 5 . 94 and 7 . 92 respectively; WT 2 . 96 , Student's t-Test p<0 . 01 ) . However , the mus-81; him-6 double mutant accumulated significantly more RIs in mid-pachytene ( 21 . 1 per nucleus , Student's t-test p<0 . 01 compared to either single mutant ) than would be predicted for the additive effect of the two mutations ( Figure 1A , B ) . Consistent with the generation of large numbers of persistent RIs , mus-81; him-6 double mutant animals exhibited evidence of chromosome breakage with significantly more DAPI staining bodies in diakinetic oocytes than would be predicted for the additive effect of the two mutants ( Figure 1C , D ) . Approximately 20% of oocytes contained more than 12 DAPI staining bodies and many of the DAPI-stained bodies were very small , suggesting that at least some of these DAPI staining bodies represented chromosome fragments rather than loss of chiasmata between homologs , which would produce 12 univalents . The increased RAD-51 foci in the mus-81; him-6 double mutant coincided with the meiotic zones in which SPO-11 is active and Agostinho et al . [33] demonstrated that the chromosome fragmentation phenotype of mus-81; him-6 is SPO-11-dependent , Therefore , these RAD-51 foci likely result from SPO-11 generated DSBs , though it is formally possible that these foci resulted from DNA damage arising in the transition zone and early pachytene . Collectively , these data support roles for HIM-6 and MUS-81 in limiting early RIs during meiosis . The increased persistent RIs in mus-81; him-6 double mutants raised the possibility that MUS-81 resolves RIs in the C . elegans germ line . Previously , we demonstrated that MUS-81 is required in rtel-1 mutants to resolve supernumerary meiotic RIs to produce COs [23] . In the absence of MUS-81 in rtel-1 mutants , large numbers of RAD-51 foci persist into late pachytene [23] , which is similar to what we observed in the mus-81; him-6 double mutant . To determine if MUS-81 played a role in the formation of endogenous COs , we used visible genetic markers to measure recombination frequency in two genetic intervals , unc-45 dpy-17 and dpy-17 unc-64 , that span 48 . 8 map units of chromosome III ( approximately 98% of the genetic length ) . Unexpectedly , recombinant progeny were reduced in both intervals in mus-81 mutants compared to mus-81/+ heterozygotes ( Figure 2A ) , demonstrating that MUS-81 promoted meiotic CO generation . The apoptotic checkpoint removes nuclei with DNA damage or meiotic defects and is only active in the female germ line [27] , [28] , allowing us to test whether this checkpoint was ameliorating the mus-81 mutant phenotypes . We therefore measured recombination frequencies between dpy-17 and unc-64 in mus-81 males . mus-81 mutant males exhibited a much greater effect on recombination distances than that observed for hermaphrodites ( WT males 29 . 2 [95% CI 24 . 2–34 . 5] , mus-81 males 14 . 1 [95% CI 11 . 6–17 . 3] ) . The activity of the apoptotic checkpoint in the female germ line could explain the differences in CO frequency observed in our data compared to those reported by Saito et al . [34] and Agostinho et al . [33] , who used a method that only measured COs in oocytes . The recombination frequency phenotype of mus-81 mutants was surprising as COs in C . elegans require MSH-4/MSH-5 ( ZMM-dependent ) and are subject to strong crossover interference ( Class I crossovers ) and therefore would not be expected to require MUS-81 for resolution . Moreover , mus-81 mutants exhibited a near wild type number of ZHP-3 foci , which mark emerging COs [35] and suggested that MUS-81 acts after ZHP-3 foci formation ( Figure 2B , Figure S3 ) . Several recent studies have described redundancy between Mus81 and other related structure specific endonucleases in the resolution of meiotic and mitotic JMs in yeast [14] , [36]–[38] . To test whether MUS-81 acted redundantly with other structure-specific nucleases in C . elegans , we constructed double mutants with mus-81 and the C . elegans orthologs of the endonucleases , gen-1 and xpf-1 , and the endo/exonuclease exo-1 . Animals lacking GEN-1 or EXO-1 did not show statistically significant differences in brood size , arrested embryos , or frequency of males compared to wild type ( Figure 3 , Table 1 ) . In contrast to the gen-1 and exo-1 single mutants , xpf-1 mutants produced 9% arrested embryos and 2% males ( Student's t-test p<0 . 005 ) , most likely as a result of general chromosome non-disjunction [39] . Loss of gen-1 or exo-1 enhanced the phenotype of the mus-81 mutant , increasing the frequency of arrested embryos from ∼8% in mus-81 to 18% in mus-81; gen-1 and 39% in mus-81; exo-1 ( Table 1 ) . In contrast , loss of xpf-1 in the mus-81 mutant resulted in a dramatic increase in the frequency of inviable embryos to levels far greater than would be expected for the additive effects of the two mutations ( 74% vs 16% expected for additive effects of the mus-81 and xpf-1 ) . Furthermore , the mus-81; xpf-1 double mutant could not be maintained as a homozygous strain . The effect on viability was increasingly more pronounced in the mus-81; xpf-1 F2 and F3 generations with the brood size decreasing significantly and ∼95% ( F2 ) and ∼99% ( F3 ) of the embryos arresting ( Figure 3 ) . In addition , the frequency of sterile animals or animals producing 100% inviable embryos increased from 2 of 23 lines in the F1 generation to 5 of 10 lines in the F2 and 9 of 10 lines in the F3 . These data suggested that MUS-81 and XPF-1 have redundant roles in maintaining a functional germ line whereas GEN-1 and EXO-1 did not , either alone or in combination with loss of MUS-81 . The inviability of mus-81; xpf-1 strains precluded measuring CO frequencies with visible markers in the double mutant so we opted to measure COs in animals homozygous for xpf-1 and heterozygous for mus-81 . Strikingly , mus-81/+; xpf-1 animals exhibited a significant decrease in COs compared to mus-81/+ animals suggesting that MUS-81 and XPF-1 function redundantly to promote COs ( Figure 2 ) . This result is consistent with the decrease in COs observed in mus-81; xpf-1 double mutants by Saito et al . [34] and Agostinho et al . [33] . Although the number of COs was reduced in mus-81/+; xpf-1 animals , mus-81; xpf-1 mutants did not show a significant difference in the number of ZHP-3 foci in meiotic nuclei ( Figure 2B; Figure S3 ) suggesting that MUS-81 and XPF-1 act downstream of ZHP-3 in meiotic progression . To further investigate the phenotypic consequence of losing both MUS-81 and XPF-1 , we stained double mutant germ lines with an anti-RAD-51 antibody to observe the progression of early meiotic RIs . We observed that the appearance and subsequent disappearance of RIs was not as profoundly affected in mus-81; xpf-1 mutant animals compared to mus-81; him-6 double mutants ( Figure 4A , B ) . RAD-51 foci were only slightly but significantly increased in the mid and late pachytene zones of the mus-81; xpf-1 double mutant compared to either single mutant ( Student's t-test p<0 . 05 ) . Consistent with these observations , there was no measurable increase in DAPI-stained bodies in the mus-81; xpf-1 double mutant beyond what would be expected from additive effects of the two mutants ( Figure 4C , D ) . Although mus-81; him-6 and mus-81; xpf-1 double mutants produced severely reduced broods and increased embryonic arrest , the mechanisms underlying their respective phenotypes appear to be distinct . The lack of obvious early RI defects to account for the inviability of the mus-81; xpf-1 animals and the decrease in COs observed in mus-81/+; xpf-1 mutants raised the possibility that MUS-81 and XPF-1 act redundantly to resolve late JMs to produce meiotic COs . To test this hypothesis , we first examined whether loss of MUS-81 and XPF-1 affected the kinetics of CO resolution by monitoring the assembly/disassembly of the synaptonemal complex ( SC ) component SYP-1 . Previous studies in C . elegans have shown that either COs or CO precursors trigger the asymmetric dissolution of the SC [40] . SYP-1 is first disassembled in the region between the CO ( or CO precursor ) and the most distant telomere of bivalent chromosomes in diplotene nuclei . This creates an asymmetry with a long arm of the bivalent that lacks SYP-1 and a short arm that contains SYP-1 . Later in diakinesis , the remaining SYP-1 dissociates from the short arms and the Aurora-like kinase AIR-2 becomes concentrated on the short arms . Thus , the maturation of COs during diakinesis can be followed by observing the asymmetric diassembly of the SC and the appearance of AIR-2 on diakinesis oocytes . SYP-1 staining was normal in pachytene and diplotene of all single and double mutants consistent with proper chromosome synapsis and formation of early RIs ( Figure 5A ) . In contrast , mus-81 and mus-81; xpf-1 animals exhibited defects later in meiotic progression with the timely disassembly of SYP-1 in diakinetic oocytes . In wild type animals the SC was disassembled in diakinetic oocytes with no visible SYP-1 remaining in the most proximal oocyte ( diakinesis −1 ) . In mus-81 animals , SYP-1 staining persisted in late diakinesis with 20% of −2 oocytes and ∼5% of −1 oocytes containing visible SYP-1 staining . This phenotype was exacerbated in the mus-81; xpf-1 double mutant with ∼100% of −2 oocytes and 25% of −1 oocytes containing SYP-1 staining ( Figure 5B ) . In addition to the defects in SYP-1 disassembly , bivalents in mus-81; xpf-1 double mutants also exhibited a highly unusual morphology with DNA bridges present between the two DAPI-staining bodies in each bivalent ( Figure 6B ) . We observed 12 univalents in mus-81; xpf-1; spo-11 animals , indicating that the DNA bridges in mus-81; xpf-1 animals were dependent on meiotic DSBs ( Figure S4 ) . In wild type animals , the axial element protein HTP-3 forms a cruciform pattern between sister chromatids along both the short and long arms of the diakinesis bivalent [41] ( Figure 6D ) . In mus-81; xpf-1 mutants HTP-3 localization on most bivalents was highly disorganized suggesting that their structure was disrupted ( Figure 6D ) . Further evidence for the disruption of bivalents in mus-81; xpf-1 mutants came from AIR-2 staining . In wild type animals , AIR-2 was localized between the short arms of the sister chromatids in the bivalent , appearing as a single distinct line , whereas in mus-81; xpf-1 , AIR-2 appeared in two distinct spots on either side of the DNA bridge spanning the two DAPI bodies ( Figure 6C ) . Three lines of evidence support the hypothesis that unresolved late RIs , possibly HJs , were responsible for disrupting bivalent maturation in the mus-81; xpf-1 double mutant: i ) the retarded SYP-1 disassembly in late stage oocytes; ii ) the disorganized structure of the axial element and AIR-2 staining in the bivalent; and iii ) the presence of a fine DNA bridge between bivalents . To determine if these phenotypes were the result of persistent unresolved JMs , we examined the impact of germline injection of human GEN1 on the presence of DNA bridges between DNA bodies , presumably homologs , in late diakinesis bivalents . Human GEN1 has been previously shown to promote HJ resolution in vitro and in mus81 mutant S . pombe strains [8] , [42] . Strikingly , germline injection of human GEN1 , but not buffer control , significantly reduced the number of nuclei containing bivalents with DNA bridges from 100% to 19% ( Figure 7 ) . Taken together , these results suggested that the defects observed in mus-81; xpf-1 diakinesis oocytes were due to a failure to resolve HJs to produce COs .
In budding yeast , Sgs1 regulates the processing of meiotic RIs . Loss of Sgs1 function in meiosis results in an accumulation of JMs that require Mus81 and other enzymes for resolution; when both Sgs1 and Mus81 are lost , unresolved JMs persist into anaphase and cause meiotic catastrophe and death [11] , [12] . We found that in the absence of HIM-6 , MUS-81 was required to prevent the accumulation and persistence of RIs during pachytene . Based on the large number of RAD-51 foci in mus-81; him-6 double mutants it appears that most RIs are processed during pachytene by either HIM-6 or MUS-81 . It is estimated that there are between 30–65 recombination intermediates formed per nucleus during pachytene [44]–[46] . This assertion is based on RAD-51 foci in rad-54 mutants , which are compromised for the later steps of homologous recombination downstream of RAD-51 loading onto the processed DSBs . Therefore , 80–90% of all meiotic recombination intermediates are processed to form NCOs by HIM-6 , and in the absence of HIM-6 , by MUS-81 . In the absence of both HIM-6 and MUS-81 , early RIs are not resolved and persist resulting in chromosome breakage and inviability . In some cases , fine DNA bridges could be seen between late diakinesis bivalents ( data not shown ) . However , given the large number of unresolved early RIs in mus-81; him-6 mutants , we could not ascertain whether these bridges resulted from early unresolved RIs that persisted to diakinesis such as multichromatid JMs or if they were interhomolog JMs , as seen in the mus-81; xpf-1 mutant . In contrast to MUS-81 , XPF-1 does not appear to have a role in processing these early RAD-51-associated RIs . The meiotic phenotype of xpf-1; him-6 mutant animals was no worse than that expected for an additive effect of the two mutations ( Figure 4A ) . MUS-81 and XPF-1 acted redundantly to resolve late RIs , but not the early RIs that arise in the absence of HIM-6 . Moreover , mutation in mus-81 , but not in xpf-1 , was synthetic lethal when combined with mutations in the anti-recombinase rtel-1 . We presume this synthetic lethality reflects a failure to resolve aberrant meiotic RI that form in the rtel-1 mutant [23] . Consistent with this hypothesis , mus-81 rtel-1 animals exhibited elevated numbers of RAD-51 foci [47] similar to mus-81; him-6 . This data suggested that HIM-6 and RTEL-1 act to limit or remove recombination intermediates and in their absence MUS-81 is needed to process these RIs . It is likely that RTEL-1 and HIM-6 have different roles in the processing of early RIs . Loss of RTEL-1 results in an increase in COs , presumably because D-loops are not disassembled leading to an increase in CO-forming RIs , whereas loss of HIM-6 results in a decrease in the frequency of COs [48] . How HIM-6 promotes CO formation in C . elegans is not yet clear . In budding yeast , the HIM-6 homolog , Sgs1 , is proposed to be the central regulator of JM resolution , directing ∼50% of RIs to NCO outcomes before they form stable JM intermediates and the remaining RIs to CO outcomes , perhaps by preventing intersister and multichromatid JMs thereby ensuring that the remaining RIs result in productive JMs that can be resolved as COs [10] , [13] . It is possible that HIM-6 functions similarly in C . elegans , since loss of HIM-6 results in an increase in RAD-51-associated RIs in pachytene and as in budding yeast these RIs require MUS-81 for processing . Furthermore , loss of HIM-6 results in a decrease in the frequency of COs . Similar to MUS-81 , HIM-18 ( the Slx4 ortholog ) is required for wild type levels of crossovers in C . elegans . Like mus-81 mutants , him-18 mutants exhibit synthetic lethality when combined with mutations in him-6 , with evidence of increased meiotic recombination intermediates [24] . However , him-18; him-6 double mutants do not exhibit an increase in DAPI-stained bodies at diakinesis , unlike mus-81; him-6 mutants . In fact , loss of HIM-18 suppressed the increase in DAPI-stained bodies at diakinesis in him-6 . This suppression of additional DAPI-stained bodies suggested that the chromosome disjunction phenotype associated with him-6 mutant animals may be the result of inappropriate processing of RIs by HIM-18-associated endonucleases , leading to premature chromosome disjunction . The multiple binding partners of HIM-18 could account for this difference . Saito et al . [34] report a physical interaction between HIM-18 and MUS-81 , SLX-1 , and XPF-1 . Loss of HIM-18 could limit the activity of all three endonucleases resulting in persistent recombination intermediates and a reduction in the number of DAPI-stained bodies . Whereas loss of either MUS-81 , SLX-1 , or XPF-1 in him-6 would still allow for endonuclease activity from one of the other endonucleases resulting in inappropriate cleavage and chromosome fragmentation . Overall , our data suggests that HIM-6 and MUS-81 have roles similar to those of Sgs1 and Mus81 budding yeast in the processing of early RIs to prevent the formation of aberrant JMs . Mus81 has been implicated in ZMM-independent COs in a number of organisms and as such is required for most COs in fission yeast , which lack ZMM proteins and crossover interference . Mus81 is also required to resolve a subset of COs in budding yeast and for a subset of COs in the murine male germline [5]–[7] , [19] . In C . elegans , COs are tightly regulated with one crossover occurring on each bivalent [49] . Consistent with strong crossover interference , most COs in wild type animals are thought to be ZMM-dependent [22] . In support of this assertion , six ZHP-3 foci , which mark emerging crossovers , are observed in wild type animals [35] . Previously , we found that ZMM-independent COs could occur in certain circumstances , such as in the rtel-1 mutant or after the generation of excess COs by ionizing radiation-induced breaks , and that these COs were dependent on MUS-81 [23] . Surprisingly , we have found that MUS-81 and XPF-1 are also required for wild type levels of COs . Our data suggest that there are either significant numbers of ZMM-independent COs in C . elegans or that MUS-81 , XPF-1 , and HIM-18 can resolve ZMM-dependent crossovers . Multiple endonucleases are capable of resolving Holliday junctions , complicating the search for eukaryotic resolvases in vivo . It is apparent that in many organisms the resolution of Holliday junctions is buffered by redundant resolvases . For example , in budding yeast there are at least three different endonucleases that can contribute to the resolution of meiotic RIs: Mus81 , Yen1 , and Slx1/Slx4 [10] , [13] , [14] . In mice there are at least two pathways for CO resolution: one dependent on Mlh1 and Mlh3 and another dependent on Mus81 [19] . We have found that in C . elegans , meiotic JM resolution depends on the redundant activities of MUS-81 and XPF-1 . Both single mutants showed relatively minor reductions in COs and in the resolution of early RAD-51-associated meiotic RIs . However , mus-81;xpf-1 double mutants exhibited severe late meiotic phenotypes in diakinesis oocytes consistent with loss of Holliday junction resolution . Unlike mus-81; him-6 double mutants , the number of RIs in the mus-81;xpf-1 mutant germline was not elevated significantly compared to either single mutant , and defects in meiotic progression was not observed until late in meiotic prophase at the onset of diplotene . The asymmetric disassembly of SYP-1 , which is triggered by COs or CO precursors , was significantly delayed in mus-81; xpf-1 double mutants compared to wild type animals ( 100% vs . 10% of −2 oocytes exhibiting SYP-1 staining , respectively ) . HTP-3 staining , which marks the axial element , was normal in early meiotic mus-81; xpf-1 nuclei but was highly disorganized in diakinesis oocytes . These data indicate that bivalent maturation , which occurs in response to CO maturation , is compromised . AIR-2 , which is concentrated on the short arm of the bivalent at diakinesis , was also disrupted in the mus-81; xpf-1 mutant , further supporting the hypothesis that the CO or CO precursor is abnormal . The most striking observation was that the mus-81; xpf-1 double mutant exhibited fine DNA bridges between the two DAPI-staining bodies of a single bivalent . These bridges occurred between AIR-2 staining regions , supporting the hypothesis that these bridges represent a crossover intermediate; AIR-2 concentration in diakinesis is dictated by CO or CO precursors that act as symmetry breaking events in the C . elegans meiotic bivalent [40] . Finally , the most compelling evidence that these DNA bridges are unresolved JMs came from germline injection of the human Holliday junction resolvase GEN1 into mus-81; xpf-1 double mutants . GEN1 injections rescued the persistent SYP-1 staining on late diakinesis chromosomes ( data not shown ) and also eliminated the DNA bridges evident in the mus-81; xpf-1 double mutant . These results are consistent with the ability of human GEN1 to both resolve Holliday junctions in vitro [42] and to substitute for Mus81 in promoting crossover formation in fission yeast mus81 mutants [8] . It is interesting to note that endogenous GEN-1 cannot resolve these meiotic JMs in C . elegans . It remains to be determined if this is due to GEN-1 not being active at the appropriate time in meiosis or whether C . elegans GEN-1 lacks the ability to resolve these meiotic JMs . In summary , our data support the hypothesis that MUS-81 and HIM-6 act early in meiosis to limit the formation or accumulation of JMs and that the related structure-specific endonucleases MUS-81 and XPF-1 act redundantly to resolve late JMs , which are likely Holliday junctions , to produce crossovers . Further study into the specific contributions of HIM-6 , MUS-81 , XPF-1 , SLX-1 and HIM-18 will shed light on the nature of joint molecule and recombination intermediate processing , control of crossovers , and evolution of HJ resolution .
Strains were cultured as described previously [50] . The strains used in this work include: Wild type Bristol N2 , VC193 him-6 ( ok412 ) , FX1937 mus-81 ( tm1937 ) , CB1487 xpf-1 ( e1487 ) , FX2842 xpf-1 ( tm2842 ) , FX2940 T12A2 . 8 ( tm2940 ) , FX1842 F45G2 . 3 ( tm1842 ) , DW395 mus-81 ( tm1937 ) ;T12A2 . 8 ( tm2940 ) , DW402 mus-81 ( tm1937 ) ;F45G2 . 3 ( tm1842 ) , DW116 mus-81 ( tm1937 ) ;xpf-1 ( e1487 ) /mIn II , DW485 mus-81 ( tm1937 ) ; him-6 ( ok412 ) /nT1[gfp] , KR4825 unc-45 ( e286 ) dpy-17 ( e164 ) , KR4821 dpy-17 ( e164 ) unc-64 ( e246 ) , mus-81 ( tm1937 ) ; unc-45 ( e286 ) dpy-17 ( e164 ) , mus81 ( tm1937 ) ; dpy-17 ( e164 ) unc-64 ( e246 ) , xpf-1 ( e1487 ) ; dpy-17 ( e164 ) unc-64 ( e246 ) . Individual animals heterozygous for visible markers and of the genotypes of interest were plated and transferred daily for four days . In each of the broods , wild type , Dpy , Unc and Dpy Unc phenotypes were scored . Recombination frequencies were calculated as in [48] . Individual male animals heterozygous for visible markers and of the genotypes of interest were mated to tester hermaphrodites homozygous for both visible markers and transferred daily for four days . In each of the broods that contained ∼50% male outcross progeny , wild type , Dpy , Unc , and Dpy Unc phenotypes were scored . Recombination frequencies were calculated as recombinants/total brood . RAD-51 immunofluorescence was performed as in [47] . RAD-51 foci in the germlines were assessed when the animals were adults . Foci were counted in 100 RAD-51 positive early to mid-pachytene nuclei . This was done in order to avoid counting earlier nuclei that may not have yet formed meiotic DSBs and later nuclei that may be undergoing apoptosis . Primary antibodies ( guinea pig anti-SYP-1 , chicken anti-HTP-3 , rabbit anti-AIR-2 , and guinea pig anti-ZHP-3 ( pre-adsorbed against zhp-3 worms ) ) were all used under standard conditions ( as in [47] ) at 1∶250 . All secondary antibodies were used at 1∶2500 ( anti-rabbit Cy3 , anti-guinea pig and anti-chicken FITC ) . DNA was stained with DAPI ( 0 . 5 mg/ml ) at 1/500 . All images were captured using Deltavision microscopy and images were deconvolved using SoftWorx software ( Applied Precision ) . Active C-terminally truncated human GEN1 protein ( kindly provided by Steve West ) , was microinjected into the germline syncytium of adult N2 wild type and mus-81; xpf-1 double mutant animals at 1 ng/µl . Twenty-four hours after injection , germlines were extracted from surviving worms , fixed , and immunostained as detailed above . 20 germlines were scored for each condition . The Mann Whitney test was used to analyze the different conditions . Gaussian approximation was used for calculation of the indicated P-value .
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Meiotic recombination generates joint molecules that ensure chromosomes segregate correctly . Failure to generate or resolve joint molecules can have profound effects on fertility and on the viability of resulting progeny . The generation and resolution of joint molecules is carefully regulated . Generation of joint molecules is highly similar across a broad range of organisms , from yeast to mammals . Yet , the resolution of the resultant joint molecules varies across organisms , with helicases and endonucleases contributing to varying extents in different organisms . We used the genetically tractable model organism , Caenorhabditis elegans ( C . elegans ) to uncover redundancies between joint molecule processing proteins . Specifically , we investigated the contribution of the C . elegans BLM helicase ortholog , HIM-6 , and the endonucleases MUS-81 , XPF-1 , GEN-1 and EXO-1 to the resolution of meiotic joint molecules . We found that MUS-81 and HIM-6 act redundantly to resolve joint molecules early in meiosis , presumably to form noncrossovers . Late in meiosis , MUS-81 and XPF-1 act redundantly to resolve joint molecules to form crossovers . When both MUS-81 and XPF-1 are absent , joint molecules are not resolved , resulting in disorganized chromosomes in the oocyte and embryonic death . Joint molecules in mus-81;xpf-1 animals are rescued by microinjection of the human GEN1 protein , indicating these intermediates are Holliday junctions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology"
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2013
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Joint Molecule Resolution Requires the Redundant Activities of MUS-81 and XPF-1 during Caenorhabditis elegans Meiosis
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Sheep are a key source of meat , milk and fibre for the global livestock sector , and an important biomedical model . Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes . We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile , neonatal and prenatal developmental time points . The Ovis aries reference genome ( Oar v3 . 1 ) includes 27 , 504 genes ( 20 , 921 protein coding ) , of which 25 , 350 ( 19 , 921 protein coding ) had detectable expression in at least one tissue in the sheep gene expression atlas dataset . Network-based cluster analysis of this dataset grouped genes according to their expression pattern . The principle of ‘guilt by association’ was used to infer the function of uncharacterised genes from their co-expression with genes of known function . We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures , where possible , to specific cell populations or pathways . The findings are related to innate immunity by focusing on clusters with an immune signature , and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals . This high-resolution gene expression atlas for sheep is , to our knowledge , the largest transcriptomic dataset from any livestock species to date . It provides a resource to improve the annotation of the current reference genome for sheep , presenting a model transcriptome for ruminants and insight into gene , cell and tissue function at multiple developmental stages .
Sheep ( Ovis aries ) represent an important livestock species globally and are a key source of animal products including meat , milk and fibre . They remain an essential part of the rural economy in many developed countries and are central to sustainable agriculture in developing countries . They are also an important source of greenhouse gases [1] . Although genetic improvement is often considered to have been less effective in sheep than in the dairy cattle , pig and poultry sectors , advanced genomics-enabled breeding schemes are being implemented in New Zealand and elsewhere [2–4] . A better understanding of functional sequences , including transcribed sequences and the transcriptional control of complex traits such as disease resilience , reproductive capacity and feed conversion efficiency will enable further improvements in productivity with concomitant reductions in environmental impact . RNA-Sequencing ( RNA-Seq ) has transformed the analysis of gene expression from the single-gene to the genome-wide scale allowing visualisation of the transcriptome and re-defining how we view the transcriptional control of complex traits ( reviewed in [5] ) . Large-scale gene expression atlas projects have defined the mammalian transcriptome in multiple species , initially using microarrays [6–9] and more recently by the sequencing of full length transcripts or of 5’ ends , for example in the horse [10] , and in human and mouse by the FANTOM 5 Consortium [11–13] , ENCODE project [14] and Genotype-Tissue Expression ( GTEx ) Consortium [15] . These efforts have focused mainly on mice and humans , for which there are high quality richly annotated reference genome sequences available as a frame of reference for the identification and analysis of transcribed sequences . Draft reference genome sequences have been established for the major livestock species ( chicken , pig , sheep , goat and cattle ) over the past decade , yet it is only with the recent deployment of long read sequencing technology that the contiguity of the reference genome sequences for these species has improved . This is exemplified by the recent goat genome assembly [16 , 17] . In these species there are still many predicted protein-coding and non-coding genes for which the gene model is incorrect or incomplete , or where there is no informative functional annotation . For example , in the current sheep reference genome , Oar v3 . 1 ( Ensembl release 87 ) ( http://www . ensembl . org/Ovis_aries/Info/Index ) , 30% of protein-coding genes are identified with an Ensembl placeholder ID [18] . Given the high proportion of such unannotated genes many are likely to be involved in important functions . Large-scale RNA-Seq gene expression datasets can be utilised to annotate and assign function to such unannotated genes [19] . With sufficiently large datasets , genes form co-expression clusters , which can either be generic , associated with a given pathway or be cell-/tissue- specific . This information can then be used to associate a function with genes co-expressed in the same cluster , a logic known as the ‘guilt by association principle’ [20] . Detailed knowledge of the expression pattern can provide a valuable window on likely gene function , as demonstrated in pig [6] , sheep [18 , 21] , human and mouse [8 , 9 , 22 , 23] . A high quality well-annotated reference genome is an exceptionally valuable resource for any livestock species , providing a comparative sequence dataset and a representative set of gene models . The International Sheep Genomics Consortium ( ISGC ) released a high quality draft sheep genome sequence ( Oar v3 . 1 ) in 2014 [18] . Included in the sheep genome paper were 83 RNA-Seq libraries from a gestating adult female Texel , 16 day embryo , 8 month old lamb and an adult ram . This Texel RNA-Seq transcriptome significantly improved the annotation of Oar v3 . 1 and identified numerous genes exhibiting changes in copy number and tissue specific expression [18] . To build on this resource and further improve the functional annotation of Oar v3 . 1 we have generated a much larger high-resolution transcriptional atlas from a comprehensive set of tissues and cell types from multiple individuals of an outbred cross of two economically important sheep breeds . To maximize heterozygosity we deliberately chose a cross of disparate breeds: the Texel , which is used as a terminal sire as it exhibits enhanced muscling relative to other sheep breeds [24] , and the Scottish Blackface , a breed selected for robustness on marginal upland grazing [25] . The sheep gene expression atlas dataset presented here is the largest of its kind from any livestock species to date and includes RNA-Seq libraries from tissues and cells representing all the major organ systems from adult sheep and from several juvenile , neonatal and prenatal developmental time points . Because the tissues were obtained from multiple healthy young adult animals , the atlas may also aid understanding of the function of orthologous human genes . Our aim was to provide a model transcriptome for ruminants and give insight into gene , cell and tissue function and the molecular basis of complex traits . To illustrate the value of the resource , we provide detailed examination of genes implicated in innate immunity and the advantages of cross breeding and provide putative gene names for hundreds of the unannotated genes in Oar v3 . 1 . The entire data set is available in a number of formats to support the research community and will contribute to the Functional Annotation of Animal Genomes ( FAANG ) project [26 , 27] .
This sheep gene expression atlas dataset expands on the RNA-Seq datasets already available for sheep , merging a new set of 441 RNA-Seq libraries from the Texel x Scottish Blackface ( TxBF ) with 83 existing libraries from Texel [18] . Details of the new TxBF libraries generated for the sheep gene expression atlas , including the developmental stages sampled , tissue/cell types and sex of the animals are summarised in Table 1 . These samples can be grouped into 4 subsets ( “Core Atlas” , “GI Tract Time Series” , “Early Development” and “Maternal Reproductive Time Series” ) . The animals used to generate the four subsets of samples are detailed in S1 Table . The “Core Atlas” subset was generated using six adult virgin sheep , approximately 2 years of age . Tissue samples were collected from all major organ systems from 3 males and 3 females to ensure , wherever possible , there were biological replicates from each sex to support an analysis of sex-specific gene expression . In addition , five cell types were sampled , including peripheral blood mononuclear cells ( PBMCs ) and blood leukocytes . Since macrophages are known to be a highly complex source of novel mRNAs [28] , and were not sampled previously , three types of macrophage ( +/- stimulation with lipopolysaccharide ( LPS ) ) were included . For the “GI Tract Time Series” subset of samples we focused on 12 regions of the gastro-intestinal ( GI ) tract , immediately at birth prior to first feed , at one week and at 8 weeks of age . These time points aimed to capture the transition from milk-feeding to rumination . Embryonic time points were chosen , at days 23 , 35 and 100 , to detect transcription in the liver , ovary and brain in “Early Development” . Parallel time points were included for placenta and ovary samples from gestating TxBF ewes , comprising the “Maternal Reproductive Time Series” subset . Finally , 3 pools of eight day 7 blastocysts were included to measure transcription pre-implantation and these were also included in the “Early Development” subset . A detailed list of all tissues and cell types included in each subset of samples can be found in S2 Table . Tissues and cell types were chosen to give as comprehensive a set of organ systems as possible and include those tissues relevant for phenotypic traits such as muscle growth and innate immunity . Approximately 37x109 sequenced reads were generated from the TxBF libraries , generating approximately 26x109 alignments in total . The raw number of reads and percentage of alignable reads per sample are included in S3 Table . For each tissue a set of expression estimates , as transcripts per million ( TPM ) , were obtained using the high speed transcript quantification tool Kallisto [29] . Kallisto is a new transcriptome-based quantification tool that avoids the considerable bias introduced by the genome alignment step [30] . Gene level expression atlases are available as S1 Dataset and , with expression estimates averaged per tissue per developmental stage , S2 Dataset . We have also made the files containing the expression estimates ( S1 Dataset , S2 Dataset and S3 Dataset ) available for download through the University of Edinburgh DataShare portal ( http://dx . doi . org/10 . 7488/ds/2112 ) . The data were corrected for library type ( as we described in [31] and summarised in S1 Methods ) . We used Principal Component Analysis ( PCA ) pre- and post-correction ( S1 Fig ) for library type to ensure the correction was satisfactory . Hierarchical clustering of the samples is included in ( Fig 1 ) and illustrates both the large diversity and logical clustering of samples included in the dataset . The O . aries reference genome ( Oar v3 . 1 ) includes 27 , 504 loci that are transcribed ( 20 , 921 protein coding ) , of which 25 , 350 ( 19 , 921 protein coding ) ( 95% ) were detectable with expression of TPM >1 , in at least one tissue from at least one individual , in the sheep gene expression atlas dataset , demonstrating the depth and scope of this dataset . The proportion of genes with detectable expression , after each ‘pass’ with Kallisto ( see Methods ) , is presented in Table 2 . After the 'second-pass' , only 3% ( 561 ) of transcripts from Oar v3 . 1 ( S4 Table ) did not meet the minimum detection threshold of TPM > 1 in at least one tissue and therefore were not detected in the sheep atlas dataset . In a minority of cases , genes were missing because they were highly specific to a tissue or cell which was not sampled , such as odontoblasts ( which uniquely produce tooth dentin , mediated by DSPP ( dentin sialophosphoprotein ) [32] ) . We also did not include any samples taken from the eye which expresses multiple unique proteins e . g . , the lens-specific enzyme LGSN ( lengsin ) [33] . The majority ( 77% ) of the genes not detected in the sheep atlas were unannotated , with no assigned gene name . A small number of these genes ( 36 ) lack sequence conservation and coding potential and so are potentially spurious models ( S4 Table ) . In the Oar v3 . 1 annotation , 6217 ( ~30% ) of the protein coding genes lack an informative gene name . Whilst the Ensembl annotation will often identify homologues of a sheep gene model , the automated annotation pipeline used is conservative in its assignment of gene names and symbols . Using an annotation pipeline ( described in S1 Methods and illustrated in S5 Table ) we were able to utilise the sheep gene expression atlas dataset to annotate >1000 of the previously unannotated protein coding genes in Oar v3 . 1 ( S6 Table ) . These genes were annotated by reference to the NCBI non-redundant ( nr ) peptide database v77 [34] and assigned a quality category based on reciprocal percentage identity , if any , to one of 9 known ruminant proteomes ( S7 Table ) . A short-list containing a conservative set of gene annotations , to HGNC ( HUGO Gene Nomenclature Committee ) gene symbols , is included in S8 Table . Many of these genes are found in syntenic regions , and are also supported by the up- and downstream conservation of genes in a related genome , cattle ( Bos taurus annotation UMD 3 . 1 ) . S9 Table contains the full list of genes annotated using this pipeline . Many unannotated genes can be associated with a gene description , but not necessarily an HGNC symbol; these are also listed in S10 Table . We manually validated the assigned gene names on this longer list using network cluster analysis and the “guilt by association” principle . Network cluster analysis of the sheep gene expression atlas was performed using Miru ( Kajeka Ltd , Edinburgh UK ) , a tool for the visualisation and analysis of network graphs from big data [35–37] . The atlas of unaveraged TPM estimates , available as S1 Dataset , were used for the network cluster analysis . The three blastocyst samples were removed from the network cluster analysis as they were generated using a library preparation method which was not corrected for and created a significant effect of library type . With a Pearson correlation co-efficient threshold of r = 0 . 75 and MCL ( Markov Cluster Algorithm [38] ) inflation value of 2 . 2 , the gene-to-gene network comprised 15 , 129 nodes ( transcripts ) and 811 , 213 edges ( correlations above the threshold value ) . This clustering excludes >30% of detected transcripts , most of which had idiosyncratic expression profiles . One of the major sources of unique expression patterns is the use of distinct promoters in different cell types . The transcription factor MITF ( Melanogenesis Associated Transcription Factor ) , for example , does not cluster with any other transcripts in sheep and is known in humans to have at least 7 distinct tissue-specific promoters in different cell types , including macrophages , melanocytes , kidney , heart and retinal pigment epithelium [13] . The resultant correlation network ( Fig 2 ) was very large and highly structured comprising 309 clusters varying in size . Genes found in each cluster are listed in S11 Table and clusters 1 to 50 ( numbered in order of size; cluster 1 being made up of 1199 genes ) were annotated by hand and assigned a broad functional ‘class’ and ‘sub-class’ ( Table 3 ) . Functional classes were assigned based on GO term enrichment [39] for molecular function and biological process ( S12 Table ) and gene expression pattern , as well as comparison with functional groupings observed in the pig expression atlas [6] . Fig 3 shows a network graph with the nodes collapsed , and the largest clusters numbered 1 to 30 , to illustrate the relative number of genes in each cluster and their functional class . The majority of co-expression clusters included genes exhibiting a specific cell/tissue expression pattern ( Fig 4A ) . There were a few exceptions , including the largest cluster ( cluster 1 ) , which contained ubiquitously expressed ‘house-keeping’ genes , encoding proteins that are functional in all cell types . The high proportion of unannotated genes ( 24% of the 1199 genes ) in cluster 1 may reflect the focus of functional genomics on genes exhibiting tissue specific expression , and inferred function in differentiation , leaving those with a house-keeping function uncharacterised [40] . With a few exceptions , the remaining co-expression clusters were composed of genes exhibiting either expression only in a distinct tissue or cell type e . g . macrophages ( cluster 5 ) ( Fig 4B ( i ) ) and fetal ovary ( cluster 7 ) ( Fig 4B ( ii ) ) , or a broader expression pattern associated with a cellular process e . g . oxidative phosphorylation ( cluster 15 ) ( Fig 4B ( iii ) ) . Some co-expression is shared between two or more organ systems , associated with known shared functions . For example , cluster 15 , exhibiting high expression in liver and kidney cortex , is enriched for expression of genes relating to the oxidation-reduction process , transmembrane transport , monocarboxylic acid catabolic process and fatty acid oxidation ( S13 Table ) . It includes numerous genes encoding enzymes involved in amino acid catabolism ( e . g . ACY1 , AGXT , AGXT2 , ASPDH , DPYD , DAO , DDO , EHHADH , HAO1 , and HPD ) and the rate-limiting enzymes of gluconeogenesis ( ALDOB , G6PC , PC , and PCK1 ) . The contributions of kidney and liver to amino acid turnover and gluconeogenesis are well known in humans [41] and rodents [42] . These observations suggest that the shared catabolic pathways of liver and kidney cortex are largely conserved in sheep , but detailed curation of the genes in this cluster could provide further specific insights . Alanine aminotransferase ( ALT1; synonym GPT1 ) , which generates alanine from the breakdown of amino acids in muscle and is transported to the liver for gluconeogenesis , is highly expressed in muscle as expected . The glutaminase genes , required for the turnover of glutamine , are absent from tissue or cell type specific clusters; the liver-specific enzyme GLS2 is also expressed in neuronal tissues , as it is in humans [43 , 44] . The tissue-specific expression patterns observed across clusters showed a high degree of similarity to those observed for pig [6] , human and mouse [8 , 9] . In some cases we were able to add functional detail to clusters of genes previously observed in pig and human . For example , genes within cluster 6 showed high expression in the fallopian tube and to a lesser extent the testes . Significantly enriched GO terms for cluster 6 included cilium ( p = 4 . 1x10-8 ) , microtubule motor activity ( p = 2 . 9x10-11 ) and motile cilium ( p = 2 . 8x10-19 ) suggesting the genes expressed in cluster 6 are likely to have a function related to motile cilia in sperm cells and the fallopian tube . Cluster 6 in the sheep atlas dataset corresponds to cluster 9 in the pig gene expression atlas [6] . Similarly , significantly enriched GO terms for genes in cluster 28 included primary cilium ( p = 7 . 5x10-21 ) and ciliary basal body ( p = 1 . 9x10-12 ) , indicating the genes in this cluster were associated with the function of primary cilia . Genes within this cluster showed a relatively wide expression pattern , with brain and reproductive tissues exhibiting the highest expression . For both clusters associated with cilial function , significantly enriched GO terms also included cell-cycle related cellular processes and cell-cycle associated genes , supporting the link between cilia and the cell-cycle [45–47] . The genes within some clusters , rather than being linked to the function of a particular tissue or cell type , showed varying levels of expression across multiple tissues , suggesting their involvement in a universal cellular process ( pathway ) . Significantly enriched GO terms for genes in cluster 8 , for example , included ‘cell cycle checkpoint’ ( p = 1x10-17 ) and ‘mitotic cell cycle’ ( p = <1x10-30 ) . The variation in expression of these genes across tissues likely reflects variation in the proportion of mitotically active cells . In the same way , it is possible to extract a similar cluster from large cancer gene expression data sets , correlating with their proliferative index [48] . Expression of genes in cluster 15 ( n = 182 ) was detectable in most ovine tissues and cells but with strongly-enriched expression in skeletal and cardiac muscle . The pig gene expression atlas [6] highlighted an oxidative phosphorylation cluster and a mitochondrial/tricarboxylic acid ( TCA ) cluster . This functional grouping is merged in cluster 15 . The majority of the transcripts in cluster 15 are present within the inventory of mitochondrial genes in humans and mice [49] , but the reciprocal is not true since many other genes encoding proteins that locate to mitochondria were not found in cluster 15 . Many mitochondrial proteins are unrelated to oxidative phosphorylation per se , and are enriched in other tissues including liver and kidney ( including mitochondrial enzymes of amino acid and fatty acid catabolism; see above ) and intestine . Cluster 15 also contains several genes associated with myosin and the sarcoplasmic reticulum which may indicate some level of coordination of their function with the oxidative metabolism of glucose . The majority of genes in the corresponding cluster in pig [6] were also present in this cluster with a few notable additions including dihydrolipoamide dehydrogenase ( DLD ) , which encodes a member of the class-I pyridine nucleotide-disulfide oxidoreductase family , and carnitine acetyltransferase ( CRAT ) , a key enzyme in the metabolic pathway in mitochondria [50] . We were able to assign gene names to the following genes associated with oxidative phosphorylation complex I in pig: NDUFA9 ( ENSOARG00000009435 ) , NDUFB1 ( ENSOARG00000020197 ) , NUDFB8 ( assigned to ENSOARG00000015378 ) , and NDUFC2 ( ENSOARG00000006694 ) . The gene name PTGES2 ( prostaglandin E2 synthase 2 ) was assigned to ENSOARG00000010878 , a gene that was associated with fatty acid ( long-chain ) beta oxidation in pig [6] but previously unannotated in sheep . Similarly , we assigned the gene name PDHB ( pyruvate dehydrogenase E1 component subunit beta ) , a member of the pyruvate dehydrogenase complex also previously unannotated in sheep , to ENSOARG00000012222 . The inclusion of the PDH complex , as well as the mitochondrial pyruvate carriers MPC1 and MPC2 , in the muscle-enriched cluster 15 reflects the fact that glucose , giving rise to pyruvate , is the preferred fuel for oxidative metabolism in muscle [51] . By using comparative clustering information in the pig and the “guilt by association” principle we were able to assign with confidence gene names and putative function to the majority of unannotated genes in clusters 8 ( cell-cycle ) and 15 ( oxidative phosphorylation ) ( S13 Table ) . Expression of cell cycle and metabolic genes has recently been shown to be positively correlated with dry matter intake , ruminal short chain fatty acid concentrations and methane production in sheep [52] . In the same study a weak correlation between lipid/oxo-acid metabolism genes and methane yield was also identified suggesting that the unannotated genes in these clusters are likely to be relevant in addressing methane production in ruminants [52] . Stringent coexpression clustering requires that each transcript is quantified in a sufficiently large number of different states to establish a strong correlation with all other transcripts with which it shares coordinated transcription and , by implication , a shared function or pathway . The impact of this approach was evident from the pig gene expression atlas [6] which was effective at dissecting region-specific gene expression in the GI tract . We have generated a comparable dataset for sheep . In ruminants , the rumen , reticulum and omasum are covered exclusively with stratified squamous epithelium similar to that observed in the tonsil [18 , 21] . Each of these organs has a very distinctive mucosal structure , which varies according to region sampled [53] . A network cluster analysis of regions of the GI tract from sheep has been published [21] using the Texel RNA-Seq dataset [18] . These co-expression clusters are better refined in this larger atlas , because many of the genes that are region-specific in the GI tract are also expressed elsewhere . We have , in addition , expanded the available dataset for the GI tract to include samples from neonatal and juvenile lambs . The postnatal development of the sheep GI tract is of particular interest because of the pre-ruminant to ruminant transition , which occurs over 8 weeks from birth . Genes in cluster 33 showed low levels of expression in neonatal lambs and a gradual increase into adulthood . Enriched GO terms for this cluster include regulation of interleukin 6 ( IL6 ) production ( p = 0 . 0016 ) and keratinocyte differentiation ( p = 1 . 7x10-8 ) ( S12 Table ) . The cluster includes genes such as HMGCS2 , HMGCL and BDH1 , required for ketogenesis , an essential function of rumen metabolism , as well as CA1 ( carbonic anhydrase 1 ) , implicated in the rumen-specific uptake of short chain fatty acids . The cluster does not contain any of the solute carriers implicated in nutrient uptake in the rumen , suggesting that these are more widely-expressed and/or regulated from multiple promoters [21] . The only carrier that is rumen-enriched is SLC9A3 ( also known as NHE3 ) , the key Na-H antiporter previously implicated in rumen sodium transport in both sheep and cattle [54] . Other genes in cluster 33 , for example , IL36A and IL36B , are thought to influence skin inflammatory response by acting directly on keratinocytes and macrophages and indirectly on T-lymphocytes to drive tissue infiltration , cell maturation and cell proliferation [55] . Many of these genes might also be part of the acute phase immune response , by regulating production of key cytokines such as IL-6 and thus mediating activation of the NF-κB signaling pathways . Nuclear factor ( NF ) -κB and inhibitor of NF-κB kinase ( IKK ) proteins regulate innate- and adaptive-immune responses and inflammation ( reviewed in [56] ) . Expression of many of these genes is likely to change as the immune system develops which we will describe in detail in a dedicated network cluster analysis of the GI tract developmental time series dataset . The genes in this cluster therefore appear to be involved both in the onset of rumination and in innate immunity ( which could be associated with the population of the rumen microbiome ) . Several clusters exhibited a strong immune signature . Clusters 11 , 12 , 18 , 31 and 32 , for example , contained genes with a strong T-lymphocyte signature [57] with high levels of expression in immune cell types and lymphoid tissues . Significantly enriched GO terms for cluster 12 , for example , included T-cell differentiation ( p = 2 . 10x10-11 ) , immune response ( p = 0 . 00182 ) and regulation of T-cell activation ( p = 0 . 00019 ) ( S12 Table ) . Manual gene annotation using Ensembl IDs within this cluster revealed the majority were T-cell receptors and T-cell surface glycoproteins ( S14 Table ) . Interestingly , ENSOARG00000008993 represents a gene with no orthologues to other species within the Ensembl database , but partial blast hits to T-lymphocyte surface antigen Ly-9 in mouflon , goat , bison and buffalo in the NCBI database . The ‘true’ gene LY9 , a member of the signalling lymphocyte activation molecule ( SLAM ) family [58] , is also unannotated in sheep and is assigned to ENSOARG00000008981 , having multiple orthologues in other placental mammals . We have assigned ENSOARG00000008993 the gene name ‘LY9-like’ and the symbol LY9L , and suggest this gene plays a role in T-lymphocyte pathogen recognition . Other immune clusters exhibited a macrophage-specific signature , with subsets highly expressed in alveolar macrophages ( AMs ) , monocyte derived macrophages ( MDMs ) and bone marrow derived macrophages ( BMDMs ) ( cluster 5 ) and two defined clusters of genes induced in BMDMs stimulated with LPS ( cluster 45 and 52 ) . Known macrophage-specific surface markers , receptors and proinflammatory cytokines predominated in these clusters , in addition to numerous unannotated genes , with as yet undefined but probable immune function ( S15 Table ) . For example , the CD63 antigen , which mediates signal transduction events , was assigned to ENSOARG00000011313 and BST2 ( bone marrow stromal cell antigen 2 ) , which is part of the interferon ( IFN ) alpha/beta signaling pathway , to ENSOARG00000016787 . A third cluster of LPS-inducible genes in macrophages , cluster 64 , contained a subset of the IFN-inducible antiviral effector genes , including DDX58 , IFIT1 , IFIT2 , MX1 , MX2 , RSAD2 and XAF1 , which are induced in mouse and humans through the MyD88-independent TLR4 signaling pathway via autocrine IFNB1 signaling ( reviewed in [59] ) . Many other components of this pathway identified in LPS-stimulated human macrophages [60] were either not annotated , or not clustered , and will be the target of detailed annotation efforts in the macrophage dataset . Significantly enriched GO terms for the macrophage-specific cluster 5 included ‘response to lipopolysaccharide’ ( p = 7 . 2x10-7 ) , and ‘toll-like receptor signaling pathway’ ( p = 3 . 2x10-5 ) . Many of the genes in this cluster are known components of the innate immune response in mammals . Interleukin 27 ( IL-27 ) , is a heterodimeric cytokine which has pro- and anti-inflammatory properties and a diverse effect on immune cells including the regulation of T-helper cell development , stimulation of cytotoxic T-cell activity and suppression of T-cell proliferation [61] . ADGRE1 encodes the protein EGF-like module-containing mucin-like hormone receptor-like 1 ( EMR1; also known as F4/80 ) , a classic macrophage marker in mice [62] . Several genes in cluster 5 encode proteins exclusively expressed in macrophages and monocytes . One such gene , CD163 , encodes a member of the scavenger receptor cysteine-rich ( SRCR ) superfamily , which protects against oxidative damage by the clearance and endocytosis of hemoglobin/haptoglobin complexes by macrophages , and may also function as an innate immune sensor of bacteria [63] . One of the largest macrophage populations in the body occupies the lamina propria of the small and large intestine [64] . They are so numerous that the expression of macrophage-related genes can be detected within the total mRNA from intestine samples . As noted previously in the pig , one can infer from the expression profiles that certain genes that are highly-expressed in AMs are repressed in the intestinal wall [6] . We proposed that such genes , which included many c-type lectins and other receptors involved in bacterial recognition , were necessary for the elimination of inhaled pathogens , where such responses would be undesirable in the gut [6] . In the sheep , there was no large cohort of receptors that showed elevated expression in AMs relative to MDMs or BMDMs , and that were absent in the gut wall . Only a small cluster ( 115 ) of 13 genes showed that profile , including the phagocytic receptor VSIG4 ( CRiG ) , which is a known strong negative regulator of T-cell proliferation and IL2 production [65] and SCIMP , recently identified as a novel adaptor of Toll-like receptor signaling that amplifies inflammatory cytokine production [66] . Six previously unannotated genes within this small cluster included the E3 ubiquitin ligase , MARCH1 , and likely members of the paired immunoglobulin type receptor and SIGLEC families , which cannot be definitively assigned as orthologues . Interestingly , macrophage colony-stimulating factor receptor ( CSF1R ) , which controls the survival , proliferation and differentiation of macrophage lineage cells [67 , 68] , was not within the macrophage-specific cluster 5 . Instead , CSF1R was in a small cluster ( cluster 102 ) along with several other macrophage-specific genes including the C1Q complex . As in humans and mice [9 , 12 , 69] , CSF1R was also expressed in sheep placenta . In humans and mice , placental ( trophoblast ) expression is directed from a separate promoter [11] . The small number of genes co-expressed with CSF1R are likely either co-expressed by trophoblasts as well as macrophages ( as is C1Q in humans; see BioGPS ( http://biogps . org/dataset/GSE1133/geneatlas-u133a-gcrma/ ) [9 , 70] ) , or highly-expressed in placenta-associated macrophages . The sheep gene expression atlas dataset includes multiple libraries from early developmental time points . Three of the larger clusters of co-expressed genes showed high levels of expression in the fetal ovary ( cluster 7 ) , fetal brain ( cluster 9 ) and fetal liver ( cluster 25 ) . ‘Testis-specific’ genes , particularly those involved in meiosis and gametogenesis , might also be expressed in the fetal ovary undergoing oogenesis [71 , 72] . Our dataset from sheep appears to validate this hypothesis , since genes within cluster 7 exhibited higher levels of expression in the fetal ovary and to a lesser extent the testes . Several genes were expressed both in the testes and the fetal ovary , including testis and ovary specific PAZ domain containing 1 ( TOPAZ1 ) , which has been shown in sheep to be expressed in adult male testes and in females during fetal development with a peak during prophase I of meiosis [73] , and fetal and adult testis expressed 1 ( FATE1 ) , which is strongly expressed in spermatogonia , primary spermatocytes , and Sertoli cells in seminiferous tubules in mouse and humans [74] . Significantly enriched GO terms for genes within cluster 7 included ‘female gonad development’ ( p = 4 . 9x10-6 ) , ‘spermatogenesis’ ( p = 4 . 6x10-8 ) and ‘growth factor activity’ ( p = 5x10-5 ) ( S12 Table ) . Several important genes for embryonic development were also co-expressed in cluster 7 . The germ-cell specific gene SRY-box 30 ( SOX30 ) encodes a member of the SOX ( SRY-related HMG-box ) family of transcription factors involved in the regulation of embryonic development and in the determination of cell fate [75] . Growth differentiation factor 3 ( GDF3 ) encodes a protein required for normal ocular and skeletal development . Although it is a major stem cell marker gene [76] , it has not previously been linked to germ cell expression . Similarly , POU class 5 homeobox 1 ( POU5F1 ) encodes a transcription factor containing a POU homeodomain that controls embryonic development and stem cell pluripotency [76] but is also required for primordial germ cell survival [77] . The expression of these genes in tissues containing germ cells in sheep suggests they contribute to meiosis and cellular differentiation . These observations illustrate the utility of the sheep as a non-human model for the study of gametogenesis . Cluster 7 also includes two related oocyte-derived members of the transforming growth factor-β ( TGFB1 ) superfamily , growth differentiation factor 9 ( GDF9 ) and bone morphogenetic protein 15 ( BMP15 ) , which are essential for ovarian follicular growth and have been shown to regulate ovulation rate and influence fecundity in sheep [78 , 79] . Lambing rate is an important production trait in sheep and can vary between breeds based on single nucleotide polymorphism ( SNP ) mutations in key genes influencing ovulation rate ( reviewed in [79 , 80] ) . A number of the known fertility genes in sheep ( reviewed in [81 , 82] ) , such as the estrogen receptor ( ESR ) and the Lacaune gene ( B4GALNT2 ) were not present in cluster 7 , which may be because they are not expressed in the ovary at the time points chosen for this study . Detailed analysis of the expression of key genes during early development in the fetal ovary in comparison with the ovary from the adult and gestating ewes may provide additional insights . Sex-specific differences in gene expression have been reported in humans [83 , 84] mice [85 , 86] , cattle [87 , 88] and pigs [89 , 90] . We examined male and female biased gene expression in the sheep atlas dataset by calculating the average TPM per sex for each gene and the female:male expression ratio ( S3 Dataset ) . Twenty genes exhibited strongly sex biased expression ( S16 Table ) ; 13 were female-enriched and 7 were male-enriched . Among the male enriched genes was thyroid stimulating hormone beta ( TSHB ) , which is expressed in thyrotroph cells in the pituitary gland and part of a neuro-endocrine signaling cascade in sheep [91] . Expression of TSHB in the pituitary gland of male TxBF was 3 . 6-fold higher than in female TxBF sheep . A similar sex bias has been observed in rats in which males exhibit significantly higher TSHB expression in the pituitary gland than females [92] . Other genes exhibiting similarly large sex specific fold-changes included keratin 36 ( KRT36 ) which was expressed 6 . 6-fold higher in the reticulum of male relative to female sheep and VSIG1 ( V-Set and immunoglobulin domain containing 1 ) , which is required for the differentiation of glandular gastric epithelia [93] . VSIG1 showed 4-fold greater expression in the female pylorus relative to the male . The unannotated gene ENSOARG00000020792 exhibited large fold change in male biased expression in immune tissues including popliteal and prescapular lymph node , tonsil and Peyer’s patch . This gene has a detectable blast hit to “immunoglobulin kappa-4 light chain variable region” and is a 1:1 orthologue with an unannotated gene in cattle , ENSBTAG00000045514 , with ≥70% reciprocal percentage identity and conservation of synteny . The dN/dS for ENSOARG00000020792 suggests it is evolving rapidly ( dN/dS > 2 ) . Male-biased genes are known to evolve quickly , as are immune genes [94] . GO term enrichment for the set of genes with five-fold sex-biased expression in at least one TxBF tissue ( S17 Table ) revealed that the genes enriched in females were predominately involved with the immune response while genes enriched in the male were broadly associated with muscle and connective tissue . This is likely to reflect inherent differences between the two sexes in allocation of resources towards growth or immunity . Genes exhibiting sex specific expression might therefore be relevant in sexual diamorphism in disease susceptibility , for example . Additionally , pregnancy is characterized by significant and complex changes in immune parameters which is likely to impact on sex specific differences in gene expression . Further investigation is warranted into the complexity of sex specific differences in gene expression throughout development . The majority of commercially-produced livestock are a cross of two or more different production breeds with distinct desired traits [95] . For example , in the UK , the crossing of lighter upland sheep breeds with heavier lowland meat breeds optimises carcass quality , lambing rate , growth rate and survivability [95] . In developing countries , sustainable crossing of indigenous small ruminants with elite western breeds is one approach to improve productivity [96 , 97] . An RNA-Seq dataset of this size from an outbred cross of two disparate sheep breeds provides an opportunity to investigate differential gene expression in a purebred parental line and crossbred animals . We compared gene expression across tissues in the F1 crossbred ( TxBF ) animals ( generated by crossing Texel rams with Scottish Blackface ewes; Fig 5A ) with the purebred Texel animals included in the previous sheep gene expression atlas dataset [18] . A gene was considered differentially expressed ( DE ) between the purebred Texel and hybrid TxBF if ( a ) it was expressed at ≥1 TPM in both Texel and TxBF ( considering TPM to be the mean of all replicates per tissue ) , ( b ) the fold change ( ratio of TxBF TPM to Texel TPM ) was ≥2 in ≥25% of the tissues in which expression was detected ( stipulating no minimum number of tissues , but noting that 23 tissues are common to Texel and TxBF ) , and ( c ) the fold change was ≥5 in at least 1 tissue . Fold changes of all genes expressed at ≥1 TPM in both breeds are given in S18 Table . The GO terms enriched in the set of DE genes ( n = 772 ) with higher expression in the TxBF than the Texel were predominantly related either to muscle or brain function ( S19 Table ) . The top 20 genes showing the largest up-regulation ( shown as absolute fold-change ) in the TxBF relative to the purebred Texel sheep are illustrated in Fig 5B . Enriched molecular function GO terms for the set of genes differentially expressed between TxBF and Texel sheep include ‘iron ion binding’ ( p = 3 . 6x10-4 ) , and ‘cytoskeletal protein binding’ ( p = 7 . 8x10-6 ) , biological process terms include ‘cellular iron ion homeostasis’ ( p = 6 . 3x10-4 ) and cellular component terms include ‘sarcomere’ ( p = 6 . 6x10-7 ) and ‘collagen trimer’ ( p = 5 . 5x10-7 ) ( S19 Table ) . Numerous genes with structural , motor and regulatory functions were highly expressed in TxBF compared to Texel bicep muscle , with approximately 5- to 18-fold expression increases for various members of the collagen ( COL1A1 , COL1A2 , COL3A1 ) and myosin families ( MYH2 , MYL4 ) , along with CSRP3 ( a mechanosensor ) [98] , FMOD ( fibromodulin , a regulator of fibrillogenesis ) [99] , keratocan ( KERA , a proteoglycan involved in myoblast differentiation ) [100] , matrix metalloproteinase 2 ( MMP2 , a proteolytic enzyme associated with muscle regeneration ) [101] , and calsequestrin 2 ( CASQ2 , one of the most abundant Ca2+-binding proteins in the sarcoplasmic reticulum , essential for muscle contraction ) [102] . Genes enriched in muscle are of particular biological and commercial interest because Texel sheep exhibit enhanced muscling and less fat [103] , due to a single nucleotide polymorphism ( SNP ) in the 3’ untranslated region of the myostatin gene MSTN ( synonym GDF-8 ) which generates an illegitimate miRNA binding site resulting in translational inhibition of myostatin synthesis and contributing to muscular hypertrophy [24 , 104] . Because heterozygotes have an intermediate phenotype , cross breeding of homozygous mutant Texel sheep with animals homozygous for the normal allele transmits something of the Texel muscle phenotype to the offspring . An effect on muscle synthesis in the TxBF animals could be related to the myostatin genotype; genes with higher expression in the Texel than in the cross may be targets for myostatin inhibition [105] , while those with lower expression in the Texel than in the cross may be directly or indirectly activated by myostatin and hence involved in the cessation of muscle differentiation . In cattle the myostatin mutation is associated with the downregulation of collagen genes including COL1A1 and COL1A2 [105] . This is consistent with the observation that these genes have higher expression in the heterozygous TxBF animals than the Texel animals . Since myostatin also regulates muscle fibre type [106] by suppressing the formation of fast-twitch fibres , individuals homozygous for inactivating myostatin mutations are likely to exhibit increased fast-twitch fibres [107] . Many of the genes up-regulated in the TxBF relative to the Texel animals ( e . g . CSRP3 and CASQ2 ) are known to be specifically expressed in slow-twitch muscle [106 , 108] , and several down-regulated genes are associated with fast-twitch muscle ( e . g . TNNC2 , TNNI2 and SERCA1 ) [109 , 110] . Consequently , the difference between the cross-breed and pure Texel is in part attributable to an increased contribution of slow-twitch fibres , which in turn has been associated with desirable meat quality traits [111] highlighting the potential advantages of cross-breeding . Enriched GO terms related to brain function include the ‘myelin sheath’ ( p = 6 . 1x10-8 ) and the ‘internode region of the axon’ ( p = 5 . 2x10-5 ) ( S19 Table ) . Candidate genes of particular interest were expressed in the cerebellum ( S18 Table ) . For instance , in the TxBF relative to the Texel animals , there were approximately 8-fold expression increases in cochlin ( COCH , which regulates intraocular pressure ) [112] and brevican ( BCAN , which functions throughout brain development in both cell-cell and cell-matrix interactions ) [113 , 114] and a 10-fold expression increase for myelin-associated oligodendrocyte basic protein , MOBP , which was previously unannotated in sheep ( ENSOARG00000002491 ) and has a function in late-stage myelin sheath compaction [115] . A 15-fold expression increase was observed for oligodendrocytic paranodal loop protein ( OPALIN , a transmembrane protein unique to the myelin sheath [116] ) and a 10-fold increase for another unannotated gene myelin basic protein ( MBP ) , which we have assigned to ENSOARG00000004374 [117] . Although these examples of a neuroendocrine-specific effect of cross-breeding are speculative , they are of interest as Scottish Blackface sheep exhibit both improved neonatal behavioural development [118] and more extensive foraging behaviour than lowland breeds such as the Texel , travelling further distances , covering greater areas and exploring higher altitudes [119] . We have provided the TxBF sheep gene expression atlas as a searchable database in the gene annotation portal BioGPS ( http://biogps . org/dataset/BDS_00015/sheep-atlas/ ) . By searching the dataset via the following link ( http://biogps . org/sheepatlas/ ) the expression profile of any given gene can be viewed across tissues . An example profile of the myostatin ( MSTN ) gene from sheep is included in Fig 6 . BioGPS allows comparison of expression profiles across species and links to gene information for each gene [70 , 120 , 121] . The Sheep Atlas BioGPS expression profiles are based on TPM estimates from the alignment-free Kallisto output for the TxBF libraries , averaged across samples from each developmental stage for ease of visualization ( S2 Dataset ) . It is important to note that there may be a degree of variation in the expression pattern of specific genes between individuals which is masked when the average profiles are displayed . In addition , to allow comparison between species BioGPS requires each gene have an Entrez ID , which is not the case for all genes in Oar v3 . 1 and as a consequence these genes do not have visualisable expression profiles in BioGPS . The expression profiles of the genes without Entrez IDs can be found in S1 Dataset and S2 Dataset . In parallel to the alignment-free Kallisto method , we also used an alignment-based approach to RNA-Seq processing , with the HISAT aligner [122] and StringTie assembler [123] ( detailed in S1 Methods ) . These alignments will be published as tracks in the Ensembl genome browser in the short term and integrated into the next Ensembl genome release for sheep . This work describes the transcriptional landscape of the sheep across all major organs and multiple developmental stages , providing the largest gene expression dataset from a livestock species to date . The diversity of samples included in the sheep transcriptional atlas is the greatest from any mammalian species , including humans . Livestock provide an attractive alternative to rodents as models for human conditions in that they are more human-like in their size , physiology and transcriptional regulation , as well as being economically important in their own right . Non-human models are required to study the fundamental biology of healthy adult mammals and as such this dataset represents a considerable new resource for understanding the biology of mammalian tissues and cells . In this sheep transcriptional atlas gene expression was quantified at the gene level across a comprehensive set of tissues and cell-types , providing a starting point for assigning function based on cellular expression patterns . We have provided functional annotation for hundreds of genes that previously had no meaningful gene name using co-expression patterns across tissues and cells . Future analysis of this dataset will use the co-expression clusters to link gene expression to observable phenotypes by highlighting the expression patterns of candidate genes associated with specific traits from classical genetic linkage studies or genome-wide association studies ( GWAS ) . Gene expression datasets have been used in this way to characterise cell populations in mouse [22 , 23] and in the biological discovery of candidate genes for key traits in sheep [124–126] and pigs [127 , 128] . We have already utilised the dataset to examine the expression patterns of a set of candidate genes linked to mastitis resistance [129] in sheep , including comparative analysis with a recently available RNA-Seq dataset from sheep lactating mammary gland and milk samples [130] . The research community will now be able to use the sheep gene expression atlas dataset to examine the expression patterns of their genes or systems of interest , to answer many of the outstanding questions in ruminant biology , health , welfare and production . Improving the functional annotation of livestock genomes is critical for biological discovery and in linking genotype to phenotype . The Functional Annotation of Animal Genomes Consortium ( FAANG ) aims to establish a data-sharing and research infrastructure capable of efficiently analysing genome wide functional data for animal species [26 , 27] . This analysis is undertaken on a large scale , including partner institutions from across the globe , to further our understanding of how variation in gene sequences and functional components shapes phenotypic diversity . Analysis of these data will improve our understanding of the link between genotype and phenotype , contribute to biological discovery of genes underlying complex traits and allow the development and exploitation of improved models for predicting complex phenotypes from sequence information . The sheep expression atlas is a major asset to genome assembly and functional annotation and provides the framework for interpretation of the relationship between genotype and phenotype in ruminants .
Approval was obtained from The Roslin Institute’s and the University of Edinburgh’s Protocols and Ethics Committees . All animal work was carried out under the regulations of the Animals ( Scientific Procedures ) Act 1986 . Three male and three female Texel x Scottish Blackface sheep of approximately two years of age were acquired locally and housed indoors for a 7–10 day “settling-in period” prior to being euthanased ( electrocution and exsanguination ) . It was not recorded whether the females were in estrus . Nine Texel x Scottish Blackface lambs were born at Dryden Farm Large Animal Unit , produced by mating Texel rams ( 4 different sires were used in total ) with Scottish Blackface ewes . Three neonatal lambs were observed at parturition and euthanised immediately prior to their first feed , three lambs were euthanised at one week of age prior to rumination ( no grass was present in their GI tract ) and three at 8 weeks of age once rumination was established . The lambs were euthanised by schedule one cranial bolt . To obtain developmental tissues six Scottish Blackface ewes were each mated to a Texel ram ( one of the four different sires used to produce the lambs ) and scanned to ensure successful pregnancy at 21 days . Two were euthanised at 23 days , two at 35 days and two at 100 days gestation ( electrocution followed by exsanguination ) . Corresponding time points ( day 23 , 35 and 100 ) from gestating TxBF ewes mated with a Texel ram ( one of four different sires as above ) , euthanised with the same method as the Blackface ewes , were also included . All the animals were fed ad libitum on a diet of hay and 16% sheep concentrate nut , with the exception of the lambs pre-weaning who suckled milk from their mothers . Details of the animals sampled are included in S1 Table . Tissues ( 95 tissues/female and 93 tissues/male ) and 5 cell types were collected from three male and three female adult Texel x Scottish Blackface ( TxBF ) sheep at two years of age . The same tissues were collected from nine lambs , 3 at birth , 3 at one week and 3 at 8 weeks of age . Three embryonic time points were also included: three day-23 TxBF whole embryos , three TxBF day 35 embryos from which tissue was collected from each region of the basic body plan and three day 100 TxBF embryos from which 80 tissues were collected . Reproductive tissue from the corresponding time points from 6 TxBF ewes , 2 at each gestational time point , was also collected . In addition , 3 pools of 8 day 7 blastocysts from abattoir derived oocytes ( of unknown breed ) fertilized with Texel semen were created using IVF . The majority of tissue samples were collected into RNAlater ( AM7021; Thermo Fisher Scientific , Waltham , USA ) and a subset were snap frozen , including lipid rich tissues such as adipose and brain . To maintain RNA integrity all tissue samples were harvested within an hour from the time of death . A detailed list of the tissues collected and sequenced can be found in S2 Table . Within the scope of the project we could not generate sequence data from all the samples collected and have archived the remainder for future analysis . Sample metadata , conforming to the FAANG Consortium metadata standards , for all the samples collected for the sheep gene expression atlas project has been deposited in the BioSamples database under project identifier GSB-718 ( https://www . ebi . ac . uk/biosamples/groups/SAMEG317052 ) . All cell types were isolated on the day of euthanasia . Bone marrow cells were isolated from 10 posterior ribs as detailed for pig [131] . BMDMs were obtained by culturing bone marrow cells for 7 days in complete medium: RPMI 1640 , Glutamax supplement ( 35050–61; Invitrogen , Paisley , UK ) , 20% sheep serum ( S2263; Sigma Aldrich , Dorset , UK ) , penicillin/streptomycin ( 15140; Invitrogen ) and in the presence of recombinant human CSF-1 ( rhCSF-1: 104 U/ml; a gift of Chiron , Emeryville , CA ) on 100-mm2 sterile petri dishes , essentially as described previously for pig [131] . For LPS stimulation the resulting macrophages were detached by vigorous washing with medium using a syringe and 18-g needle , washed , counted , and seeded in tissue culture plates at 106 cells/ml in CSF-1–containing medium . The cells were treated with LPS from Salmonella enterica serotype minnesota Re 595 ( L9764; Sigma-Aldrich ) at a final concentration of 100 ng/ml as previously described in pig [131] and harvested into TRIzol® ( 15596018; Thermo Fisher Scientific ) at 0 , 2 , 4 , 7 and 24 h post LPS treatment before storing at -80°C for downstream RNA extraction . PBMCs were isolated as described for pig [132] . MDMs were obtained by culturing PBMCs for 7 days in CSF-1–containing medium , as described above for BMDMs , and harvesting into TRIzol® ( 15596018; Thermo Fisher Scientific ) . Alveolar macrophages were obtained by broncho-alveolar lavage of the excised lungs with 500ml sterile PBS ( Mg2+ Ca2+ free ) ( P5493; Sigma Aldrich ) . The cells were kept on ice until processing . To remove surfactant and debris cells were filtered through 100uM cell strainers and centrifuged at 400 × g for 10 min . The supernatant was removed and 5ml red blood cell lysis buffer ( 420301; BioLegend , San Diego , USA ) added to the pellet for 5 min; then the cells were washed in PBS ( Mg2+ Ca2+ free ) ( P5493; Sigma Aldrich ) and centrifuged at 400 × g for 10 min . The pellet was collected , resuspended in sterile PBS ( Mg2+ Ca2+ free ) ( P5493; Sigma Aldrich ) , and counted . Alveolar macrophages were seeded in 6-well tissue culture plates in 2ml complete medium: RPMI 1640 , Glutamax supplement ( 35050–61; Invitrogen ) , 20% sheep serum ( S2263; Sigma Aldrich ) , penicillin/streptomycin ( 15140; Invitrogen ) in the presence of rhCSF1 ( 104 U/ml ) overnight . Blood leukocytes were isolated as described in [133] . Whole blood was spun at 500 x g for 10 min ( no brake ) to separate the buffy coats . These were then lysed in ammonium chloride lysis buffer ( 150mM NH4Cl , 10mM NaHCO3 , 0 . 1mM EDTA ) for 10 min on a shaking platform , then centrifuged at 4°C for 5 min at 500 x g . The resultant blood leukocyte pellets were stored in 1ml of RNAlater ( AM7021; Thermo Fisher Scientific ) at -80°C . To isolate embryonic fibroblasts we harvested a day 35 embryo whole and transferred to outgrowth media: DMEM , high glucose , glutamine , pyruvate ( 11995065; Thermo Fisher Scientific ) , FBS ( Fetal Bovine Serum ) ( 10500056; Thermo Fisher Scientifc ) , MEM NEAA ( 11140068; Thermo Fisher Scientific ) , penicillin/streptomycin ( 15140; Invitrogen ) , Fungizone ( 15290018; Amphotericin B; Thermo Fisher Scientific ) , Gentamicin ( 15750037; Thermo Fisher Scientific ) . In a sterile flow hood the head was removed and the body cavity eviscerated . The remaining tissue was washed 3 times in PBS ( Mg2+ Ca2+ free ) ( P5493; Sigma Aldrich ) with penicillin/streptomycin ( 15140; Invitrogen ) . 5ml of Trypsin-EDTA solution ( T4049; Sigma Aldrich ) was added and the sample incubated at 37°C for 5 min then vortexed and incubated for an additional 5min at 37°C . 3ml of solution was removed and filtered through a 100uM cell strainer , 5ml of outgrowth media was then passed through the strainer and combined with the sample . The sample was centrifuged at 200 x g for 3 min and the pellet resuspended in 9ml of outgrowth media before splitting the sample between 3x T75 flasks . The process was then repeated for the remaining 2ml of sample left after the digestion with Trypsin ( T4049; Sigma Aldrich ) . Embryonic fibroblasts were incubated for 5–7 days ( until 80–90% confluent ) then harvested into TRIzol® ( 15596018; Thermo Fisher Scientific ) . RNA was extracted using the same method as the Roslin RNA-Seq samples included in the sheep genome project detailed in [18] . For each RNA extraction <100mg of tissue was processed . Care was taken to ensure snap frozen samples remained frozen prior to homogenisation , and any cutting down to the appropriate size was carried out over dry ice . Tissue samples were first homogenised in 1ml of TRIzol ( 15596018; Thermo Fisher Scientific ) with either CK14 ( 432–3751; VWR , Radnor , USA ) or CKMIX ( 431–0170; VWR ) tissue homogenising ceramic beads on a Precellys Tissue Homogeniser ( Bertin Instruments; Montigny-le-Bretonneux , France ) . Homogenisation conditions were optimised for tissue type but most frequently 5000 rpm for 20 sec . Cell samples which had previously been collected in TRIzol ( 15596018; Thermo Fisher Scientific ) were mixed by pipetting to homogenise . Homogenised ( cell/tissue ) samples were then incubated at room temperature for 5 min to allow complete dissociation of the nucleoprotein complex , 200μl BCP ( 1-bromo-3-chloropropane ) ( B9673; Sigma Aldrich ) was added , then the sample was shaken vigorously for 15 sec and incubated at room temperature for 3 min . The sample was centrifuged for 15 min at 12 , 000 x g , at 4°C to separate the homogentate into a clear upper aqueous layer ( containing RNA ) , an interphase and red lower organic layers ( containing the DNA and proteins ) , for three min . DNA and trace phenol was removed using the RNeasy Mini Kit ( 74106; Qiagen Hilden , Germany ) column purification , following the manufacturer’s instructions ( RNeasy Mini Kit Protocol: Purification of Total RNA from Animal Tissues , from step 5 onwards ) . RNA quantity was measured using a Qubit RNA BR Assay kit ( Q10210; Thermo Fisher Scientific ) and RNA integrity estimated on an Agilent 2200 Tapestation System ( Agilent Genomics , Santa Clara , USA ) using the RNA Screentape ( 5067–5576; Agilent Genomics ) to ensure RNA quality was of RINe > 7 . RNA-Seq libraries were prepared by Edinburgh Genomics ( Edinburgh Genomics , Edinburgh , UK ) and run on the Illumina HiSeq 2500 sequencing platform ( Illumina , San Diego , USA ) . Details of the libraries generated can be found in S2 Table . Libraries were sequenced at a depth of either >100 million , >60 million or >25 million paired end reads per sample depending upon to which subset of samples they belonged . In each case depth refers to the number of paired end reads , therefore a depth of >100 million reads represents ~100M forward + 100M reverse . A subset of 10 tissue samples and BMDMs at 0 h and 7h ( +/-LPS ) ( Table 1 ) , from each individual , were sequenced at a depth of >100 million strand-specific 125bp paired-end reads per sample using the standard Illumina TruSeq total RNA library preparation protocol ( Ilumina; Part: 15031048 , Revision E ) . These samples were chosen to include the majority of transcriptional output , as in [134] . An additional 40 samples from the tissues and cell types collected per individual ( 44/female and 42/male ) , were selected and sequenced at a depth of >25 million strand-specific paired-end reads per sample using the standard Illumina TruSeq mRNA library preparation protocol ( poly-A selected ) ( Ilumina; Part: 15031047 Revision E ) . In addition to the samples from the 6 adults , tissue was also collected from other developmental time points . The GI tract tissues collected from the 9 TxBF lambs , 3 at birth , 3 at one week of age and 3 at 8 weeks of age were sequenced at a depth of >25 million paired-end reads per sample using the Illumina mRNA TruSeq library preparation protocol ( poly-A selected ) as above . Of the early developmental time points , the three 23 day old embryos from TxBF sheep were sequenced at >100 million paired-end reads using the Illumina total RNA TruSeq library preparation protocol ( as above ) , while the other embryonic samples and the ovary and placenta from the gestating ewes were sequenced at a depth of >25 million paired-end reads per sample using the Illumina mRNA TruSeq library preparation protocol ( as above ) . In addition , three libraries were generated using the NuGen Ovation Single Cell RNA-Seq System ( 0342-32-NUG; NuGen , San Carlos , USA ) from pooled samples of 8 blastocysts ( as in [135] ) , and sequenced at a depth of >60 million paired-end reads per sample . A detailed list of prepared libraries , including library type can be found in S2 Table . To identify spurious samples we used sample-to-sample correlation , of the transposed data from S1 Dataset , in Miru ( Kajeka Ltd , Edinburgh , UK ) [37] . The sample-to-sample graph is presented in S2 Fig . The expression profiles of any samples clustering unexpectedly ( i . e . those not found within clusters of samples of the same type/biological replicate ) were examined in detail . Generally the correlation between samples was high , although 4 spurious samples , 4 sets of swapped samples , and 3 samples where a sample collection issue ( collection of muscle rather than oesophageal tissue ) had occurred were identified . These samples were either relabeled or removed as appropriate and are listed in S20 Table . The raw data , in the form of . fastq files , for the 438 TxBF libraries is deposited in the European Nucleotide Archive under study accession number PRJEB19199 ( http://www . ebi . ac . uk/ena/data/view/PRJEB19199 ) ) . A description of the abbreviations used for each sequencing library in submission PRJEB19199 is included in S21 Table . The data submission to the ENA includes experimental metadata prepared according to the FAANG Consortium metadata and data sharing standards . The RNA-Seq data processing methodology and pipelines are described in detail in S1 Methods . For each tissue a set of expression estimates , as transcripts per million ( TPM ) , were obtained using the high speed transcript quantification tool Kallisto [29] . In total , the expression atlas utilised approximately 26 billion ( pseudo ) alignments ( S3 Table ) , capturing a large proportion of protein-coding genes per tissue ( S22 Table ) . The accuracy of Kallisto is dependent on a high quality index ( reference transcriptome ) [29] , so in order to ensure an accurate set of gene expression estimates we employed a ‘two-pass’ approach . We first ran Kallisto on all samples using as its index the Oar v3 . 1 reference transcriptome . We then parsed the resulting data to revise this index . This was in order to include , in the second index , those transcripts that should have been there but were not ( i . e . where the reference annotation is incomplete ) , and to remove those transcripts that should not be there but were ( i . e . where the reference annotation is poor quality and a spurious model has been introduced ) . For the first criterion , we obtained the subset of reads that Kallisto could not align , assembled those de novo into putative transcripts ( S1 Methods ) , then retained each transcript only if it could be robustly annotated ( by , for instance , encoding a protein similar to one of known function ) and showed coding potential ( S23 Table ) . For the second criterion , we identified those members of the reference transcriptome for which no evidence of expression could be found in any of the hundreds of samples comprising the atlas . These were then discarded from the index . Finally , this revised index was used for a second iteration of Kallisto , generating higher-confidence expression level estimates . This improved the capture rate of protein-coding genes ( S24 Table ) . A detailed description of this process can be found in S1 Methods . We complemented this alignment-free method with a conventional alignment-based approach to RNA-Seq processing , using the HISAT aligner [122] and StringTie assembler [123] . A detailed description of this pipeline is included in S1 Methods . This assembly is highly accurate with respect to the existing ( Oar v3 . 1 ) annotation , precisely reconstructing almost all exon ( 96% ) , transcript ( 98% ) and gene ( 99% ) models ( S25 Table ) . Although this validates the set of transcripts used to generate the Kallisto index , we did not use HISAT/StringTie to quantify expression . This is because a standardised RNA space is necessary to compare data from mRNA-Seq and total RNA-Seq libraries [31] , which cannot be applied if expression is quantified via genomic alignment . Unlike alignment-free methods , however , HISAT/StringTie can be used to identify novel transcript models ( S26 Table ) , particularly for ncRNAs , which will be described in detail in a dedicated analysis . We will publish the alignments from HISAT and StringTie as tracks in the Ensembl genome browser in the short term and integrate the alignments into Ensembl and Biomart in the next Ensembl release for sheep . Additional RNA-Seq data was obtained from a previous characterisation of the transcriptome of 3 Texel sheep included in the release of the current sheep genome Oar v3 . 1 [18] . The dataset included tissues from an adult Texel ram ( n = 29 ) , an adult Texel ewe ( n = 25 ) and their female ( 8–9 month old ) lamb ( n = 28 ) , plus a whole embryo ( day 15 gestation ) from the same ram-ewe pairing . The raw read data from the 83 Texel samples incorporated into this dataset and previously published in [18] is located in the European Nucleotide Archive ( ENA ) study accession PRJEB6169 ( http://www . ebi . ac . uk/ena/data/view/PRJEB6169 ) ) . The metadata for these individuals is included in the BioSamples database under Project Identifier GSB-1451 ( https://www . ebi . ac . uk/biosamples/groups/SAMEG317052 ) ) . A small proportion of the tissues included in the Texel RNA-Seq dataset were not sampled in the TxBF gene expression atlas . Those unique to the Texel are largely drawn from the female reproductive , integument and nervous systems: cervix , corpus luteum , ovarian follicles , hypothalamus , brain stem , omentum and skin ( side and back ) . Details of the Texel RNA-Seq libraries including tissue and cell type are included in S27 Table . The Texel samples were all prepared using the Illumina TruSeq stranded total RNA protocol with the Ribo-Zero Gold option for both cytoplasmic and mitochondrial rRNA removal , and sequenced using the Illumina HiSeq 2500 ( 151bp paired-end reads ) [18] . As above , Kallisto was used to estimate expression level for all samples , using the revised reference transcriptome ( from the ‘second pass’ ) as its index . To correct for the confounding effect of multiple library types we applied a batch effect correction . We have previously validated this method using a subset of the sheep expression atlas samples from BMDMs ( +/- LPS ) sequenced both as mRNA and total RNA libraries [31] . As described above , for the Kallisto second pass , we constrained the Kallisto index to contain only the transcripts of protein-coding genes , pseudogenes and processed pseudogenes , the majority of which are poly ( A ) + and so are present in both mRNA-Seq and total RNA-Seq samples . We then calculated , per gene , the ratio of mean TPM across all mRNA-Seq libraries to mean TPM across all total RNA-Seq libraries . Given the scope of the tissues sampled for both library types ( all major organ systems from both sexes and from different developmental states ) , neither mean is likely to be skewed by any tissue-specificity of expression . As such , any deviations of this ratio from 1 will reflect variance introduced by library type/depth . Thus , to correct each gene’s set of expression estimates for this effect of library type , we multiplied all total RNA-Seq TPMs by this ratio . To validate this approach we used principal component bi-plot analysis , described and shown in S1 Methods and S1 Fig . Network cluster analysis of the sheep gene expression atlas was performed using Miru ( Kajeka Ltd , Edinburgh , UK ) [35–37] . In brief , similarities between individual gene expression profiles were determined by calculating a Pearson correlation matrix for both gene-to-gene and sample-to-sample comparisons , and filtering to remove relationships where r < 0 . 75 . A network graph was constructed by connecting the remaining nodes ( genes ) with edges ( where the correlation exceeded the threshold value ) . This graph was interpreted by applying the Markov Cluster algorithm ( MCL ) [38] at an inflation value ( which determines cluster granularity ) of 2 . 2 . The local structure of the graph was then examined visually . Genes with robust co-expression patterns , implying related functions , clustered together , forming cliques of highly interconnected nodes . A principle of ‘guilt by association’ was then applied , i . e . the function of an unannotated gene could be inferred from the genes it clustered with [20 , 136] . Expression profiles for each cluster were examined in detail to understand the significance of each cluster in the context of the biology of sheep tissues and cells . Clusters 1 to 50 ( Table 2 ) were assigned a functional ‘class’ and ‘sub-class’ manually by first determining if multiple genes within a cluster shared a similar biological function based on both gene ontology [39] , determined using the Bioconductor package ‘topGO’ [137] ( GO term enrichment for clusters 1 to 50 is shown in S12 Table ) . We then compared the clusters with tissue- and cell-specific clusters in other large-scale network-based gene expression analyses including the pig gene expression atlas [6] , the human protein atlas [69 , 72 , 138] and the mouse atlas [9 , 139 , 140] . More specific annotation of the GI tract clusters in sheep was based on network and pathway analysis from the sheep genome paper and a subsequent satellite publication [18 , 21] . The gene component of all clusters can be found in S11 Table . We assigned gene names to unnannotated genes in Oar v3 . 1 based on their co-expression pattern , tissue specificity , and reciprocal percent identity to a set of nine known ruminant proteomes ( S7 Table ) . The annotation pipeline is described in detail in S1 Methods and included a set of quality categories summarised in S5 Table . We were able to assign gene names to >1000 previously unannotated genes in Oar v3 . 1 . Candidate gene names are given as both a shortlist ( S8 Table ) and a longlist ( S9 Table ) , the latter intended for manual review as informative annotations may still be made without every one of the above criteria being met .
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Sheep are ruminant mammals kept as livestock for the production of meat , milk and wool in agricultural industries across the globe . Genetic and genomic information can be used to improve production traits such as growth rate and health and fitness traits including disease resilience . The sheep genome is , however , missing important information relating to gene function and many genes , which may be important for productivity , have no informative gene name . This can be remedied using RNA-Sequencing to generate a global expression profile of all protein-coding genes , across multiple organ systems and developmental stages . Clustering genes based on their expression profile across tissues and cells allows us to assign function to those genes . If , for example , a gene with no informative gene name is expressed in macrophages and is found within a cluster of known macrophage related genes it is likely to be involved in macrophage function and play a role in innate immunity . This information improves the quality of the reference genome and provides insight into biological processes underlying the complex traits that influence the productivity of sheep and other livestock species .
|
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2017
|
A high resolution atlas of gene expression in the domestic sheep (Ovis aries)
|
MicroRNAs ( miRNA ) have emerged as key regulators of cell lineage differentiation and cancer . We used precursor miRNA profiling by a novel real-time QPCR method ( i ) to define progressive stages of endothelial cell transformation cumulating in Kaposi sarcoma ( KS ) and ( ii ) to identify specific miRNAs that serve as biomarkers for tumor progression . We were able to compare primary patient biopsies to well-established culture and mouse tumor models . Loss of mir-221 and gain of mir-15 expression demarked the transition from merely immortalized to fully tumorigenic endothelial cells . Mir-140 and Kaposi sarcoma–associated herpesvirus viral miRNAs increased linearly with the degree of transformation . Mir-24 emerged as a biomarker specific for KS .
Kaposi sarcoma ( KS ) is one of the few human cancers of endothelial origin . KS remains the most frequent AIDS-associated malignancy even in populations with ready access to highly active anti-retroviral therapy ( HAART ) [1] , [2] . Today , approximately one third of AIDS-KS tumors develop in patients on successful long-term HAART , i . e . with near normal T lymphocyte counts and undetectable HIV viral loads [3] . In sub-Saharan Africa , KS ranks among the most common cancers overall since HIV turned the endemic form of this disease into an epidemic . By comparison to epithelial cancers , endothelial-lineage cancers are less common still , and most study endothelial cells because of their ancillary role in tumor angiogenesis rather than their role as the driving force of tumor formation . In KS , endothelial lineage cells drive tumor growth . Recent data suggest that tumor-associated stromal cells , including endothelial cells can acquire epigenetic or perhaps even genetic features of transformation , which in turn support tumor growth [4] , [5] , [6] . KS offers the opportunity to study endothelial cell transformation and tumorigenesis in detail , and miRNAs provide one possible means of large-scale , stable epigenetic reprogramming . To test the hypothesis that miRNA signatures delineate progressive stages of endothelial cell transformation resulting in metastatic KS , we used high throughput , quantitative real-time PCR-based pre-miRNA profiling . KS is tightly associated with Kaposi sarcoma associated herpesvirus ( KSHV ) [7] , [8] , [9] . Every tumor cell carries the virus and expresses at least the viral latent proteins [10] , [11] . Here , we show for the first time using primary patient biopsies that every KS tumor transcribes the viral miRNAs ( miRNA ) as well . KSHV is also the etiological agent of the B cell lineage tumor primary effusion lymphoma ( PEL ) , as well as the B cell lineage hyperplasia , plasmablastic variant of multicentric Castleman disease ( MCD ) [12] , [13] . This dichotomous phenotype ( lymphoid/endothelial ) allows testing of the hypotheses that the miRNA profile for these two cancers reflect ( a ) their tissue of origin , ( b ) progressive cancer signatures , ( c ) a signature induced by latent viral infection or ( d ) a combination of all . The miRNAs have emerged as master regulators of cell lineage differentiation and key modulators of cancer ( reviewed in [14] ) . They are small , 22 nucleotide non-coding RNA molecules that , upon incorporation into the cytoplasmic RNA-induced silencing complex ( RISC ) , can inhibit translation of target messenger RNAs and ultimately target them for degradation . At present , the Sanger database has recorded 678 human miRNAs [15] each capable of targeting up to several hundred different mRNAs . KSHV encodes multiple viral miRNAs [16] , [17] , [18] , [19] , including a viral ortholog to miR-155 [20] , [21] . Even though some targets for these viral miRNAs have been identified [22] , exactly how they function in KS tumorigenesis is unresolved . MiRNA profiling has provided invaluable insights into tissue development and cancer . Many tumor-specific and cell lineage-specific signatures have been compiled ( e . g . [23] , [24] , [25] and many others ) . We previously established the miRNA signature for PEL [26] using real-time quantitative PCR . Pre-miRNA profiling has also been used successfully to stratify human tumors . It often correlates well with mature miRNA levels [27] , [28] , [29] , but we also found that pre-miRNA profiling provides non-redundant information with utility for tumor classification . Pre-miRNAs are an intermediate product for mature miRNAs , analogous—in the widest sense—to mRNAs being an intermediate product for proteins . They are generated by Drosher and DGCR8 from the nascent pri-miRNAs and eventually exported by Exportin-5 to the cytoplasm . For the purpose of profiling they offer advantages because they are longer ( ∼70 nt ) , each nucleotide contributing additional specificity in diagnostic assay , as opposed to mature miRNAs , which because of their limited target region of only ∼22 nt impose limitations due to cross hybridization and variable primer/probe annealing efficiency for different miRNAs . Here , we use real-time QPCR-based profiling to discern pre-miRNAs that identify KS , KSHV infection and distinct , progressive stages of endothelial cell transformation . KSHV transforms primary human endothelial cells in culture [30] , though this is a rare event . KSHV infection consistently only leads to morphological alterations ( “spindling” ) and reduced growth factor dependence [31] , [32] , [33] , [34] . KSHV infection of immortalized human endothelial cells leads to extended survival , and growth factor independence [35] , [36] , [37] but not complete transformation , as defined by the ability to form tumors in nude mice . Importantly , during latent viral infection , lymphatic endothelial cell differentiation markers remain expressed , and if not already of lymphatic endothelial origin , KSHV is capable of inducing this phenotype in human endothelial cell preparations derived from other tissues such as the vasculature [35] , [38] , [39] . KSHV infection per se does not induce dedifferentiation of lineage-committed lymphatic endothelial cells . A study by An et al . succeeded in deriving two fully tumorigenic clones of lymphatic endothelial cells ( TIVE E1 and TIVE L1 ) that maintain KSHV in the absence of selection [40] . Introduction of KSHV into murine endothelial progenitor cell preparations also resulted in the clonal outgrowth of at least one fully transformed cell line [41] . By contrast , attempts to culture tumor cells directly from KS lesions largely failed . Today , we have only a single KS tumor derived cell line , SLK , which is fully transformed , but has lost the KSHV genome [42] . Together with primary KS biopsies , these culture systems exemplify multiple stages of endothelial cell cancer progression ( Table 1 ) . In this study , we used high throughput profiling to identify cell lineage and cancer progression stage-specific pre-miRNAs for this representative set of KSHV-infected and uninfected immortalized endothelial cells , KS biopsies and PEL lymphoma cell lines .
A total of 47 samples were selected for profiling at the DNA and pre-miRNA level ( Table 1 and Table S1 ) . These include the largest number of PEL cell lines to date ( n = 14 ) . Four KSHV-negative Burkitt lymphoma cell lines were included as controls as they are expected to transcribe miRNAs that are common to B lineage lymphomas . The pre-miRNA difference between these samples and PEL defines part of the PEL signature [26] . For this study , we added 9 tonsil tissues as a normal tissue control . These serve to determine which pre-miRNAs are highly abundant in B cells , as tonsils consist of over 50% B cells , including germinal center ( GC ) B cells , which many assume to be the normal precursor of PEL [43] , [44] , [45] . Two T-cell lymphoma cell lines were included to differentiate T cell pre-miRNAs . For the first time , KS primary biopsies were also assessed for pre-miRNA transcription . We collected 9 AIDS-KS skin biopsies from the Americas . The biopsies were collected by individuals with experience in KS clinical trials . They are considered representative lesions for the purpose of tumor and response staging . The majority of cells in each biopsy are KSHV-infected endothelial cells [11] , [46] , [47] . All biopsies were from male subjects with a median age of 44 years ( range 30–57 ) . Patients had biopsy-confirmed KS and were on HAART as well as concurrent chemotherapy . Their median CD4 count was 78 cells/microliter ( range 7–402 ) . CD4 counts were not available for one subject . All patients had extensive cutaneous KS and with the exception of one are alive at present . Tumor samples were obtained within the last three years . Hence , these patients represent the current post-HAART AIDS epidemic . Two immortalized virus-negative endothelial cell lines were included in the arrays , as well as isogenic controls carrying latent KSHV [36] . A similar model exists in the E1 TIVE and L1 TIVE cell lines [40] . These currently represent the best human cell culture tumor model for KS , as these two cell lines induce KS-like tumors in nude mice with 100% efficiency . Also included is the only known KS-derived cell line , SLK [42] , which has lost the KSHV genome , but is tumorigenic in mice . As positive control we used DNA as the input and real-time QPCR and primers directed against the pre-miRNA as described [26] . We were able to independently verify KSHV-infection status for each sample . Likewise , the EBV miRNA genes were detectable only in the EBV-positive PEL and BL cell lines , but not KS or any other samples . EBV miRNA genes were not detectable in normal tonsil tissue . The relative copy number for the KSHV miRNA genes was significantly lower in KSHV carrying endothelial cell lines compared to PEL ( see Figure S4 ) . This is consistent with earlier reports that PEL carry more viral plasmids ( 50∼100 copies/cell ) than KS ( ∼10 copies/cell ) and KSHV-infected endothelial cell cultures [46] , [48] , [49] , [50] . Among the KSHV infected endothelial cell models , the HMVEC carried the highest KSHV genome number , suggesting that they are most capable of maintaining high levels of the KSHV plasmid . This is consistent with earlier studies showing that not all endothelial cells are equally permissive for KSHV infection , which drives reprogramming towards lymphatic endothelial cells [35] , [38] , [39] . Our pre-miRNA data set , which included 160 primer pairs , representing 145 cellular miRNAs , 9 viral miRNAs , 2 viral mRNAs and 4 cellular RNAs ( U6 ) , and 47 samples consisted of >20 , 000 individual data points . QPCR measures target abundance on a 2log scale with higher CT numbers reflecting lower abundance . For this analysis , the average of the triplicate CT values was taken . These were normalized to U6 levels , to give dCT . Note that dCT values represent the underlying pre-miRNA levels on a 2log scale thus facilitating robust clustering [51] , [52] . Following normalization , each sample set was Z-standardized to remove variation between samples [53] , [54] . Figure 1 shows the heatmap representation after hierarchical clustering for the full panel of samples , with red indicating a higher level of expression and blue indicating a lower level of expression compared to the median of all data ( white ) . 6 distinct groups were identified . These represent the minimal number of non-overlapping clusters based on principal component analysis ( PCA ) ( data not shown ) . The first two groups represent the pre-miRNAs that are unchanged across all samples , those with low levels of expression ( I in blue ) and those with high levels of expression across all samples ( II in red ) . The KSHV pre-miRNAs all cluster in group III . Group IV represents the pre-miRNAs that are downregulated in KSHV-positive cells . 20 miRNAs are contained in this group . Group V represents 11 cellular miRNAs that are highly expressed in immortalized HUVEC and HMVEC cells , both uninfected and KSHV-infected , but not any of the tumor cell lines and biopsies . They do not appear to be significantly enriched in any of the other endothelial cell types ( KS or TIVE ) . Finally , group VI contains cellular miRNAs that are downregulated in all B-cell lymphomas , including PEL , vis-à-vis tonsil and KS . To remove the impact of lineage-specific determinants [B cell ( PEL and Tonsil ) vs . endothelial cell] from the analysis , we analyzed the two KSHV-associated cell types separately . Our analysis of PEL specific miRNAs was previously published [26] and analysis of the extended data set confirmed this observation ( data not shown ) . When the endothelial-derived subset of samples was analyzed alone , a clearer picture emerged that highlights similarities and disparities between different stages of endothelial cell transformation ( Figure 2A ) . The groups represent the minimal number of non-overlapping clusters based on PCA ( data not shown ) . The first two groups ( I and II ) represent miRNAs with minimal discernable patterns across all samples—at least at the power of our analysis . Blue indicates low levels while red indicates comparable high levels of miRNAs , vis-à-vis the median of all data in this set . This is not to say that pre-miRNAs within these two clusters did not exhibit any change between samples classes , only that these changes were smaller compared to others and therefore less interesting from a biomarker perspective . For example , mir-222 clusters in group II because it was more highly transcribed in all samples relative to 50% of all other pre-miRNAs . Nevertheless mir-222 was downregulated in KSHV-infected , tumorigenic samples , compared to EC . The pattern of mir-222 parallels that of mir-221 , which is expected because of their known co-regulation [55] , [56] . However , the range of change was much larger for mir-221 as seen in group IV . Group III shows the pre-miRNAs that are upregulated upon KSHV infection of EC and increased even more in KS and in the tumorigenic L1/E1 clones . This group includes the KSHV pre-miRNAs along with the cellular pre-miRNAs let-7a-1 , let-7a-2 , let-7a-3 , mir-7-1 , mir-27a , mir-125b-2 , mir-140 , mir-152 , mir-181c , mir-194-2 , and mir-220 . The detailed transcription pattern for this group is shown as a bar graph for pre-miRNA let-7a-3 ( Figures 2D , all bar graphs show Z-standardized values of median dCTU6 ) . To demark the degree of viral latent transcription , LANA mRNA levels are shown ( Figure 2B ) . LANA is transcribed in all KSHV-positive samples but not the KSHV-negative SLK , HUVEC or HMVEC cell lines . KSHV latent RNA levels correlated positively with increasing tumor-forming capability of the infected cells ( p≤10−13 by ANOVA of linear model ) . They were undetectable in uninfected cells , lowest in KSHV-infected HUVEC and E1/L1 cells in culture , higher in E1 mouse tumors and KS lesions and highest in PEL ( data not shown ) . This was mirrored by KSHV pre-mir-K12-2 ( Figure 2C ) . KSHV pre-miRNA transcription levels correlated KSHV plasmid copy number ( DNA ) as measured by real-time QPCR using the same primer sets with DNA as input ( data not shown ) . The positive correlation between the level of viral miRNA and the relative tumorigenicity of the sample class supports a causal role for miRNA in KS tumorigenesis . It suggests that KSHV miRNAs are required to maintain the KS tumor phenotype . Group IV contains a set of 8 cellular miRNAs that are highest expressed in KS tumors only , compared to cell lines . These include mir-24-2 , mir-30c-2 , mir-125a , mir-130a , mir-196 , mir-215 , mir-218-2 , and mir-367 . The bar graph of mir-24-2 levels in Figure 2E serves as an example for the pre-miRNA expression pattern of this group , for which miRNA levels were highest in KS tumors and significantly lower in other samples whether KSHV-infected or not . As expected for all primary tumor samples , we observed more heterogeneity in the KS biopsies compared to clonal cell lines . This necessitated the use of 9 independent biopsies , which is a larger number then used in prior KS mRNA array analyses . With this number of biopsies , PCA analysis validated the significance of cluster membership for all pre-miRNA , including those that group in cluster IV . Group V compromises a group of 13 cellular pre-miRNAs with highest levels in the E1 and L1 TIVE cell lines . These pre-miRNAs were present at higher levels in E1/L1 cells even compared even to KS biopsies . These are mir-17 , mir-22 , mir-28 , mir-32 , mir-128b , mir-135b , mir-143 , mir-151 , mir-181b-2 , mir-205 , mir-213 , mir-216 and mir-372 . The bar graph of mir-32 expression in Figure 2F is an example of the pre-miRNA expression pattern for this group . Group VI consists of 13 pre-miRNAs with highest levels in the non-tumorigenic endothelial HUVEC and HMVEC cell lines , whether KSHV-infected or not . These are mir-26b , mir-29a , mir-34b , mir-92-1 , mir-93 , mir-133a-1 , mir-133a-2 , mir-193 , mir-221 , mir-223 , mir-301 , mir-323 and mir-346 . 11 of these miRNAs were also contained in the HUVEC/HMVEC upregulated cluster from the larger data set ( Figure 1 ) . Additionally , mir-34b and mir-92-1 fell into this group upon clustering of only the endothelial cell data . The histogram of mir-29a expression in Figure 2G is an example of the pre-miRNA transcription pattern for this group , with highest levels in both infected and uninfected HUVEC/HMVEC cells and significantly lower levels in all other samples . Group VII is the inverse of group VI and consists of miRNAs with undetectable levels in the endothelial HUVEC and HMVEC , whether KSHV-infected or not . This group compromises 11 cellular pre-miRNAs: mir-7-2 , mir-9-2 , mir-30b , mir-107 , mir-135a-2 , mir-153-1 , mir-153-2 , mir-181b-2 , mir-197 , mir-325 and mir-370 . The bar graph of mir-370 expression in Figure 2H is an example of the pre-miRNA transcription pattern of this group . In sum , unsupervised clustering as a discovery tool identified ( i ) distinct stages of endothelial cell transformation and ( ii ) specific pre-miRNAs that serve as biomarkers for each of them . One of the concerns in profiling cell lines in culture is that the transcription signature may be reflective of a particular proliferation state rather than a general characteristic of the tumor subtype . Proliferation dependence is well documented for mRNA levels in fibroblasts [57] . For several miRNAs , too , proliferation and miRNA transcription rates are linked [58] , [59] , [60] , [61] , [62] , [63] , [64] . To guard against this fallacy , we only used RNA derived from log-phase cells for our profiling analysis . Nevertheless , to test the hypothesis that some miRNA levels were proliferation state dependent , we conducted a time course experiment for the E1 and L1 TIVE cell lines ( see Figures S1 and S2 ) . This revealed a very limited number of pre-miRNAs that were enriched in log-phase cells compared to stationary phase cells and vice versa . They were at the lower limit of detection and additional experiments are needed to validate the biological significance of this observation . Unsupervised comparisons represent the first level of large scale profiling studies . Here , they revealed ( i ) the existence of multiple distinct steps of endothelial cell transformation and ( ii ) pre-miRNAs that were selectively transcribed in one or more stages and that therefore serve as biomarkers . The latter were further validated by supervised class prediction methods . Based upon pre-miRNA clustering ( Figure 1 and 2 ) and published phenotype ( Table 1 ) , we defined the following classes: Endothelial cells ( E ) , KSHV-infected endothelial cells ( EK ) , endothelial cells that have the ability to form tumors in nude mice ( ET ) , which includes the KSHV-positive TIVE- E1 , L1 cell lines as well as KSHV-negative SLK cells ) , xenograft tumors of TIVE- E1 cells consisting of 5 independent samples ( ETM ) , KS patient biopsies ( KS ) , PEL ( P ) , and as negative controls tonsil ( TN ) and non-KSHV associated lymphomas ( TM ) . First , we conducted pair-wise comparisons between classes using the median dCTU6 for each class ( Figure S2 ) . The two TERT-immortalized EC cell lines HUVEC and HMVEC exhibited a nearly identical pre-miRNA transcription pattern ( r2 = 0 . 7238 ) . Infection with KSHV of these immortalized cell lines did result in changes ( r2 = 0 . 6798 ) . Of note , this comparison is between median levels for the two EC cell lines ( HUVEC and HMVEC ) and three independent clones of tightly latently infected TERT-HUVEC cells . Thus , it exhibited more variability than a pair-wise comparison of just two cell lines . The most drastic change in overall pre-miRNA transcription emerged when comparing KSHV-infected , non-tumorigenic EC cell lines to the two KSHV-infected , highly tumorigenic E1/L1 cell lines . Here , we failed to detect any linear correlation . The two TIVE cell lines E1 and L1 , of course , exhibited a strikingly similar pattern of pre-miRNA transcription as shown in detail in Figure S4 and Figure S1 . The pair-wise comparison between E1/L1 cells in culture to E1 xenograft tumors showed a reasonable linear correlation , but less than between different culture models ( r2 = 0 . 5684 ) . Analysis of residuals identified all KSHV pre-miRNAs as well as mir-223 to be significantly upregulated in the tumorgraft ( data not shown ) . Since there are no human infiltrating lymphocytes in the SCID mouse model , and since the tumor vasculature is made of murine endothelial cells , any changes in pre-miRNA composition reflect the grafted human tumor cells . Importantly , the comparison between E1 xenograft tumor biopsies and patient KS biopsies yielded a better correlation ( r2 = 0 . 5846 ) than between E1/L1 cells in culture and E1 tumor grafts . This reinforces the results of the phenotypic characterization of E1/L1 cells [40] and demonstrates that the E1/L1 xenograft model adequately mimics primary KS patient biopsies . Next , we identified and validated a set of diagnostic pre-miRNA biomarkers that signify the different steps of endothelial cell transformation . To do so we used the miRNAs identified by hierarchical clustering ( Figure 2 ) , extended the dataset to include mouse xenograft tumor samples and used visual inspection followed by ANOVA and appropriate pair-wise t-test to identify pre-miRNAs with distinct distributions among the different steps of endothelial cell transformation . To give a better impression of within class variability , Figure 3 A–C plots individual dCTU6 for cellular pre-miRNAs including technical replicates for each class . The mir-221 pre-miRNA emerged as a biomarker for the transition from immortalized to tumorigenic endothelial cells independent of KSHV infection status ( Figure 4A ) . Mir-222 was co-regulated with mir-221 , but did not change as dramatically ( data not shown ) . Since mir-221/222 exhibit tumor suppressor activity in endothelial and other cancer models [55] , [56] , [65] , [66] , this suggests that the down-regulation of the mir-221 biomarker is of biological significance . The mir-15 pre-miRNA is an example for miRNAs that exhibit the opposite pattern of transcription as mir-221 . Therefore it did contribute additional information that would have improved tumor classification . It was high in tumorigenic KSHV-infected endothelial calls , KS and PEL ( data not shown ) . There was one significant difference between mir-15 and mir-221 expression: the KSHV-negative SLK cells transcribed significantly lower levels of mir-15 . In a separate analysis of only the endothelial/KS sample and excluding SLK cells ( data not shown ) , mir-15 levels correlated closely with KSHV latent mRNA and miRNA transcription and can thus be considered KSHV –regulated . The mir-140 pre-miRNA levels correlated linearly with tumor status . It was present at appreciable levels only in the xenograft tumors and KS biopsies , but not KSHV-infected cells grown in culture ( Figure 3B , class ETM , KS ) . Pre-mir-140 levels did not distinguish tonsil and PEL , since 50% of PEL lines as well as all KSHV-negative lymphoma lines had only very low levels of mir-140 . Hence , the utility of mir-140 as a biomarker is limited to the endothelial lineage , but not lymphatic lineage cancers . The mir-24-2 pre-miRNA levels were strikingly elevated only in KS biopsies , not E1 xenograft tumors or PEL ( Figure 3C ) . It therefore serves as a KS-specific biomarker and not as a marker for KSHV-associated transformation . This may have utility for clinical diagnosis , but more importantly it represents at least one molecular difference between clinical KS lesions and all available tissue culture models . In other words , any of the KS-specific mir-24-2 dependent reprogramming of target mRNA and protein levels is not captured in our current , laboratory-based understanding of KS and KSHV biology . To establish the utility of these four biomarkers for endothelial cell tumorigenesis , we calculated cumulative density distributions ( cdf ) ( Figure 3E–G ) and a decision tree ( Figure 3D ) . Pre-mir-221 and pre-mir-24-2 showed steep cdfs , which allowed for binary classification into positive and negative classes . Pre-mir-140 ( Figure 3F ) showed an almost linear cdf consistent with gradual changes among multiple sample classes . This is reflected in the minimal decision tree ( Figure 3D ) that computes cut-off values for each miRNA to yield the most parsimonious and accurate classification schema . Similar decision trees could be derived using other representative miRNAs from each of the clusters identified in Figure 2 . We also built decision trees based on just viral pre-miRNA levels ( data not shown ) . These were comparable to ANOVA for individual pre-miRNAs , since KSHV genome copy number ( Figure S3 ) , latent RNA levels and latent pre-miRNA levels were all correlated ( they clustered together by unsupervised clustering ( Figure 1 , 2 ) and increased progressively with increasing tumorigencity . In sum , supervised classification established ( i ) the presence of molecularly distinct , progressive steps of endothelial cell transformation and ( ii ) a set of biomarkers that distinguishes between these steps .
Mature miRNA profiling has previously been used to stratify lineage types and disease progression stages . Pre-miRNA profiling has also been used successfully to stratify human tumors [27] , [28] , [29] . We previously profiled pre- and mature miRNAs for PEL [26] in order to establish a PEL cancer signature , and found that pre-miRNA profiling offered technical advantages as well as provided additional , non-redundant information to mature miRNA-based PEL classification . Here , using 9 primary patient biopsies and validated pre-clinical cell culture models , we have ascertained the first pre-miRNA profile of KS . At the genomic level , we found a variety of changes between different cell lines and tissue types , but no deletions or amplifications common to all KS biopsies or all KSHV-positive samples . At the pre-miRNA level , we identified groups of cellular miRNAs that define distinctive tissue types . QPCR has been shown to be an effective form of miRNA profiling . Northern blotting has limitations including low throughput and poor sensitivity . Alternative high throughput profiling methods , like microarrays , require high concentrations of target input , show poor sensitivity for rare targets , a limited linear range and the need for post-array validation by real-time QPCR . Therefore , QPCR appears to be a better method for a limited set of targets such as the ∼650 human miRNAs , and it can be applied easily on a pre-miRNA level as well . The miRNA genes are named according to the 60–80 bp sequence of the pre-miRNA segment [67] . Each miRNA gene locus produces one pre-miRNA , which in turn can produce one or two mature miRNAs depending on whether both strands of the mature product are inserted into the functional RISC complex . While all miRNA genes , and therefore all pre-miRNAs , are made of unique sequence , different pre-miRNAs can be processed to yield an identical mature 22-nucleotide miRNA . For instance , there are 3 different let-7a genes: let7-a-1 , let-7a-2 and let-7a-3 , each located on a different chromosome ( 9 , 13 , and 22 , respectively ) and subject to different regulatory controls . Pre-miRNA profiling but not mature miRNA profiling distinguishes between these transcripts . How well do pre-miRNA levels correlate with mature miRNA levels ? This seemingly simple question has a non-trivial answer . ( i ) We and others have shown that pre-miRNA levels generally correlate with mature miRNA levels [26] , [28] , [29] , [68] , [69] , [70] , but we also found that some pre-miRNAs were present in a slightly different pattern of expression from the mature miRNAs . The obvious example , are the aforementioned pre-miRNA paralogs , which encode the same mature miRNA , but reside on different genomic locations . Furthermore , there are well-documented instances , where SNPs affect Dicer processing [71] , [72] . These exceptions are informative in their own right and only simultaneous quantification of pre- and mature miRNA levels can identify these . In the present case mature mir-221 levels were also downregulated in KS and PEL compared to non-tumorigenic controls , but for the two others ( mir-140 , mir-24-2 ) we could not establish a statistically significant pattern based on mature miRNA levels ( O'Hara et al . , in press ) . ( ii ) The two assays ( mature miRNA and pre-miRNA ) measure two different events and thus provide non-redundant information . The pre-miRNA pool represents an intermediate step and thus responds without delay to changes in cellular transcription . Pre-miRNAs are co-transcriptionally processed [73] , [74] . They have a short half-life , much like mRNAs , and thus provide a sensitive read-out for the purpose of tumor profiling . By contrast , mature miRNAs are part of the relatively stable RISC complex and thus provide a time-delayed read-out of the state of the cell . ( iii ) The two assays ( mature miRNA and pre-miRNA ) have different performance characteristics . Unfortunately , these are different for each miRNA ( data not shown ) . Even if relative levels of pre- and mature miRNAs correlate , the different assay formats for pre- and mature miRNAs have different sensitivities , different response characteristics and a different lower limit of detection ( much of which is dependent on the miRNA-specific primer sequences ) and thus they have a varying ability to distinguish between the presence and absence of a miRNA sequence . In the case of KS and its related cell culture and animal models , each class in our collection ( E , EK , ET , ETM , KS , PEL ) exhibited a distinctive cellular miRNA profile ( Figure 4 ) . Even though we found some differences in the transcription pattern between individual KSHV miRNAs ( unpublished ) , the KSHV miRNA levels as a group correlated with an increasing tumor-forming capability of infected cells ( p≤10−10 by ANOVA of linear model ) . They were present in KSHV-infected HUVEC clones , high in E1/L1 cells in culture , higher in E1 mouse tumors and KS lesions and highest in PEL . Of note , the non-tumorigenic EC clones were made with JSC-1 derived KSHV [75] , whereas TIVE E1/L1 clones were made from BCBL-1 derived KSHV [40] , which may yield to a difference in miRNA regulation . KSHV gene copy number also increased with increasing tumorforming ability in this set of samples . At present we cannot discern whether high KSHV pre-miRNA levels are a driver for or a consequence of increased gene copy number . There are also sequence differences between other KSHV isolates that may contribute to variability among the individual samples [76] . Sequence variation is more pronounced for pre-miRNAs because of length ( 70 vs 22 nt ) and less selective pressure for non-essential positions . Our data support a stepwise progression towards KS based on cellular pre-miRNA patterns alone ( Figure 3D ) or after integration of the KSHV miRNA data ( Figure 2 ) . This model is exemplified in Figure 4 . Initially , normal endothelial cells ( E ) are infected with KSHV to yield stage EK . Both the uninfected and infected endothelial cells share a common endothelial lineage pre-miRNA signature ( Figure 4 , group I ) . In addition , all KSHV-infected cells express low levels of KSHV miRNAs as well as a distinct group of cellular miRNAs ( Figure 4 , group IV ) . These are able to grow in reduced serum , indicating that KSHV is a transforming virus . However , these cell lines do not form tumors in mice and are therefore not oncogenic . The viral life cycle in these cells remains tightly latent . The E1 and L1 TIVE cells are infected cells that have undergone a second transformation event . As a result cells progress to the ET stage . These cells are capable of forming tumors in mice and express intermediate levels of KSHV miRNAs and a distinctive group of pre-miRNAs ( Figure 4 , group II ) . While these cells are highly transformed , similar to KS , the life cycle of the virus is still tightly latent . We were able , for the first time , to also profile primary KS biopsies . KS lesions exhibited the highest levels of KSHV miRNAs , as compared to other infected endothelial cell samples . PEL exhibited still higher levels due to a higher genome copy number . In addition , unsupervised clustering identified a group of pre-miRNAs that are highly upregulated only in primary KS lesions ( Figure 4 , group III ) . Unlike cell culture models , which are tightly latent , KS lesions are known to undergo spontaneous lytic reactivation to varying degrees [77] . In sum , each sample class profiled had a unique set of highly transcribed cellular pre-miRNAs , independent of the presence of virus , and a second set of pre-miRNAs that were dependent on the presence of KSHV . There exists an important distinction between tumorigenicity with is a phenotype of cell culture models and tumor take , which is a phenotype of primary tumor explants . In experimental transformation models such as NIH3T3 cells tumorgenicity in immune deficient mice is conferred by the adding one or two single oncogenes . In tumor explant models tumor take is defined as how many mice will form transplantable tumors after injection of a given dose of primary tumor cells . Tumor take is highly variable among cancer types and even individuals . It does necessarily correlate with clinical aggressiveness and does not easily correlate with a single gene . KS and EBV+ nasopharyngeal carcinoma are examples of highly aggressive , angiogenic tumors , which almost never yield stable cell lines in culture or transplantable xenografts in nude mice . Mir-221 is a tumor suppressor for endothelial cell lineage cancers independent of KSHV infection . We found the highest levels of pre-mir-221 in uninfected and KSHV latently infected tert-HUVEC and tert-HMVEC cell lines . This corroborates prior reports of high mir-221 levels in endothelial cell lines [66] , [78] , [79] . High levels of mir-221 exert anti-angiogenic effects in HUVEC cells , resulting in inhibited tube formation , migration and wound healing [66] . This anti-angiogenic effect correlated with downregulated expression of the mir-221 target protein c-kit [66] . In Dicer siRNA-transfected cells , mir-221 expression has also been shown to indirectly downregulate expression of endothelial nitric oxide synthase ( eNOS ) [78] . Nitric oxide is a key regulator of endothelial cell growth , migration , vascular remodeling and angiogenesis . The picture is more complicated , though , since depletion of mir-221 in HUVEC cells causes secondary changes in other miRNAs [66] , [80]; these included many that are predicted to also target c-kit . C-kit expression was also reduced by mir-221 in hematopoietic progenitor cells . In this system , mir-221 also inhibited proliferation [81] . Additional targets for mir-221 include CDKN1B/p27 and CDKN1C/p57 , which are cell cycle regulators [56] , [59] , [65] , [82] . Disregulation of mir-221 has been found in melanomas due to silencing of the promyelocytic leukemia zinc finger ( PLZF ) transcription factor [83] . In summary , mir-221 seems to possess endothelial cell lineage-specific differentiation functions as well as general tumor/proliferation suppressor functions . Pre-mir-34a and c were found at detectable levels in endothelial cells , PEL and KS , as these tumors retain wild-type p53 ( Figure 1 ) . The miR-34 promoter is p53-responsive [61] , [64] , [84] , [85] , [86] , [87] . Of all three p53-responsive miRNAs , mir-34a appears to be the most responsive in terms of fold change [61] . High levels of miR-34 are consistent with the biology of PEL and KS , which are unusual among human cancers because they almost universally retain fully functional , wild type p53 [88] , [89] . Three different miR-34 genes are present in the human genome . Mir-34a is located within the second exon of a non-coding gene , which contains a predicted p53-binding site . Genes mir-34b and mir-34c are located within a single non-coding precursor with a transcriptional start site adjacent to a predicted p53-binding site [86] . All three genes produce mature miRNAs with an identical seed sequence . It will be interesting to determine whether mir-34a , similar to p53-responsive mRNAs [88] , can be even further induced upon chemotherapy in PEL and KS . Pre-mir-140 levels were tightly correlated with KSHV latent mRNA ( LANA ) and latent miRNA levels in KS and KS tumor models . Currently , little is known regarding mir-140 expression profiles or possible mRNA targets [90] . TargetScan miRNA target software indicates a number of possible targets for mir-140 , including E2F3 , a member of the E2F family of transcription factors essential for cell cycle regulation . This prediction , however , awaits experimental verification . Pre-mir-24 emerged as a highly specific KS biomarker compared to all other pre-miRNAs in our array . Mir-24 has been shown to be important in cell-cycle regulation , cell growth and differentiation in a variety of cell types [91] , [92] . However , these as well as functional studies on mir-24 and its targets are still in the early stages . Tantalizing data predict p16 and dehydrofolate reductase ( DHFR ) as mir-24 targets among others [93] , [94] , [95] , [96] , [97] . In summary , the first pre-miRNA profiling of primary KS tumor biopsies and the subsequent comparison to well-studied culture and mouse xenograft models of KS yielded a progression model for endothelial lineage cancer and KS ( Figure 4 ) akin to the now classical model for colorectal cancer progression [98] . We hope that this will benefit basic and translational studies of KS , which remains the most frequent cancer in people living with HIV/AIDS today . We also identified specific KSHV and KS-associated pre-miRNAs , foremost among them mir-221 , mir-140 , mir-15a and mir-24 . Based upon their strength of association with specific stages of endothelial cell tumor progression , we speculate that these are also functionally involved in KS tumorigenesis .
Cells were grown in continuous culture on a 3T3-like schedule , i . e . passaged at subconfluency , and RNA collected in log phase , typically 24–48 hrs after reseeding . All B and T cells were cultured in RPMI containing 25mM HEPES , 10% fetal bovine serum ( AP2 , AP3 , AP5 in 20% ) , 0 . 05 mM 2-mercaptoethanol , 1 mM sodium pyruvate , 2 mM L-glutamine , 0 . 05 ug penicillin/mL , and 20 U streptomycin/mL at 37° and in 5% CO2 . TIVE cells were cultured in DMEM containing 10% fetal bovine serum , 2 mM L-glutamine , 0 . 05 ug penicillin/mL , and 20 U streptomycin/mL at 37° and in 5% CO2 . HUVEC-hTERT cells were cultured in EGM-2 containing 10% fetal bovine serum , hydrocortisone , hRGF , VEGF , R3-IGF , Ascorbic acid , hEGF , GA-1000 and heparin at 37° and in 5% CO2 . HUVEC+KSHV cells were cultured in the same media also containing 0 . 5pM/ul puromycin to maintain selection . De-identified frozen tonsil and melanoma tissue biopsies were obtained from the cooperative human tissue network ( CHTN ) . KS frozen tissue biopsies were obtained after informed consent at University of Miami , Prof . Edgard Santos University Hospital , Salvador , Brazil and Beth Israel Deaconess Medical Center . All cell lines and references are described in Table S1 . DNA was isolated from cell lines and samples using the Wizard SV Genomic kit ( Promega , Madison , WI ) . Total RNA was isolated using Triazol ( Sigma-Aldrich , St Louis , MO ) as previously described [78] . Total RNA was quantitated on a Nanodrop and equal amounts of RNA were subjected to DNase I treatment ( Ambion , Austin , TX ) . RNA was reversed transcribed using the cDNA Archive Kit ( Applied Biosystems , Foster City , CA ) , with the addition of RNase Inhibitor and , in pre-miRNA screening , the additions of T4 gene protein 32 . RNA integrity was evaluated using a 2100 Bioanalyzer Series C ( Agilent , Santa Clara , CA ) . Total RNA was measured using the RNA 6000 Series II Nano kit and small RNA was measured using the Small RNA kit , according to the manufacturers recommendations . All Chips were analyzed using 2100 Expert software version B . 02 . 04 . The average RNA integrity value for total RNA among all samples profiled was 8 . 00±2 . 60 . For DNA and pre-miRNA expression profiling , two 96-well plates containing 372 different primers were used . These primers represent 168 cellular and 12 viral pre-miRNA targets , as well as 6 cellular and viral control miRNA targets . All primers conform to universal real-time PCR conditions with a predicted Tm of 60 and 100-bp or smaller amplicon length . Real-Time QPCR was conducted under universal cycling conditions of 40 cycles with SYBR Green as the method of detection following our previously validated methods . A 36ul reaction mix was made using a CAS-1200 robot that uses filtered carbon-graphite pipette tips ( Tecan Inc . , Durham , NC ) for liquid level sensing , allowing for a pipetting accuracy of 0 . 1ul . The reaction mix was then distributed in triplicate into a 384-well plate using a Matrix repeat pipettor ( Thermo Inc . ) . The final primer concentration was 250nM in total of the 9ul reaction volume . Because pre-miRNA-specific primers also detect the corresponding gene , these primers were used for DNA gene profiling as well . For DNA QPCR , each reaction contained 1 . 67ng DNA/ul . For pre-miRNA QPCR , 40ul of the 100ul RT reaction was used for each 384 well plate , yielding a final amount of 0 . 1ul cDNA per each 9ul reaction . Real-time QPCR primers against 93 mature miRNAs and 1 cellular mRNA were used according to the manufacturers protocol ( Applied Biosystems Inc . ) . The combined pipetting and instrument error for all of the QPCR reactions was less than 6% ( data not shown ) . All reactions were done in technical triplicates . QPCR was performed on a 384 well LC480 ( Roche Inc . ) platform . Data were collected in triplicate for each RT reaction . Since averaging these replicates would mask individual reaction failures , we clustered all replicates individually after masking outliers . Each array of 160 primers contained four separate reactions for U6 yielding six CTU6 . We calculated the mean and median of these four reactions to yield ˆCTU6 . The maximal difference between mean and median was 0 . 30 CT units . All other CT data were normalized to ˆCTU6 . This yielded dCT for each primer/sample combination . The dCT were normalized to median for each array and subjected to unsupervised clustering using a Correlation metric [51] and the program Arrayminer™ . Our exploratory cluster analysis included all primers and all samples . However , for statistical analysis we excluded primers , which yielded CT <38 for the non-template and reverse transcriptase negative controls . We also excluded primers , which did not yield a signal ( CT<38 ) in at least one of the samples as uninformative . We used QQ plots for each sample and Kolgomoroff-Smirnoff statistics to test for normal distribution across arrays ( data not shown ) . For miRNA gene copy number analysis , data were collected in triplicate ( or duplicate ) for each sample . Since averaging these replicates would mask individual reaction failures , we clustered all replicates individually after masking outliers . Each set of 160 primer pairs contained four separate reactions for U6 yielding four CTU6 . We calculated the mean of these four reactions to yield ˆCTU6 . In only two samples did the median deviate significantly from the mean based on an analysis of residuals ( data not shown ) . This suggested individual reaction or pipetting failures . We imputed modified ˆCTU6 for those singular cases based on the following rule: if <50% of replicates differed by >1 CT from the mean of the remainder , they were replaced by the mean of the remaining data points . After imputation , the mean ˆCTU6 across all samples and all technical replicates ( n = 348 ) was 20 . 94±1 . 97 with a median of 21 . 26 . For individual quadruplicate CTU6 measurements , the SDs ranged from 0 . 03 to 1 . 00 CT . 71 . 26% of SDs for technical replicates were ≤0 . 32 CTs . Hence , this array identified 2-fold changes in copy number . All other CT data were normalized to ˆCTU6 . This yielded dCT for each primer/sample combination . The dCT were subjected to unsupervised clustering using an Euklidian metric and visualized on a log2 scale [51] . For supervised comparisons between two classes , we used the Welch-modified t-test as implemented in the R statistics program [99] . This yielded unadjusted , univariate p values for each individual miRNA gene . This particular variant of the t-test allows for unequal variances between the two classes . An analysis of variances showed that most miRNA genes had identical variances between the KSHV-infected ( n = 53 ) and normal tissue ( n = 18 ) data sets . We used q-value computation [100] , to assess the false discovery rate . The statistical methods for supervised comparisons between two classes of pre-miRNAs were as described above for miRNA gene loci . We only report the minimal set of miRNA genes for which we do not expect any false positives . The Bonferroni-adjusted p-value was ≤0 . 05 for each of the hits . Decision trees were computed as implemented in R [99] using 10 fold cross-validation . To monitor RNA integrity and yield , we used the Agilent bioanalyzer ( Figure 5A–D ) . The Agilent bioanalyzer provides two chips with different size resolution . The small RNA chip resolves RNA species from 4–150 nt ( Figure 5C , D ) , the RNA nano chip RNA species from 25–6000 nt ( Figure 5A , B ) . It allows size determination as well as quantitation . We compared two RNA preparations: total RNA isolated with Triazol and total RNA isolated with Triazol followed by high molecular weight depletion ( HMWD ) . The total RNA isolation with Triazol™ retained the highest concentration of miRNAs ( 20 . 5 pg/µl ) while preserving overall RNA integrity as measured by RIN value ( 9 . 7 ) , which is a proprietary estimate based on ratio of 28S to 18S peak ( Figure 5A ) . Subsequent HMWD as required for other mature miRNA profiling approaches ( e . g . [101] ) depleted mRNA and rRNA , as expected . It did not change the relative abundance of miRNA ( ∼22nt ) to pre-miRNA ∼70 nt ) , but decreased overall small RNA yield by half ( 10 . 5 pg/µl ) . Hence , we used total RNA for all further studies . We used real-time QPCR to determine individual pre-miRNA levels as per our published procedures [26] . Individual miRNA CT readings were normalized to CT of U6 rRNA ( dCTU6 ) to account for variation in sample input RNA or DNA . However , we generally obtained more consistent results if we used very similar amounts of RNA ( as determined by nanodrop™ based quantitation ) for the reverse transcriptase ( RT ) reaction . The reason for this pre-RT normalization step is that the RT reaction , too , has a linear range just as the real-time QPCR reaction , otherwise the RT reaction may be saturated or , in case of diluted samples , of lower than expected RT efficiency . Figure 5E shows average raw CT values for U6 for each DNA sample , aggregated by sample class . Figure 3F shows average CT values for U6 for each post RT cDNA sample , aggregated by sample class . Again , variation is minimal except for two outliers ( HMVEC and KS 101 ) , for which we had only small amounts of RNA available . Though even those two samples had U6 CT values of ≤27 cycles . Since we ran a 40 cycle QPCR reaction , this gave us an assay range of 2 ( 27-40 = 13 ) = 0 to 8192 fold above the level of detection . All experimental samples were run in triplicate for RT-positive and DNA reactions . Pooled samples were run in triplicate for RT-negative control reactions . Non-template control ( NTC ) reactions were run to assure that the primers were free of contamination and did not yield non-specific products or primer dimers at a significant rate . A reaction was considered positive if the corresponding CT was greater than 38 . 00 cycles . To remove primers with substantial capacity for primer dimer formation , any primer pair with mean CT <38 . 00 in the RT- or NTC reactions was omitted from further analysis . This removed 8 cellular pre-miRNA and 1 viral miRNA primer pairs ( KSHV-mir-k12-10a ) from our original set [26] . Conversely , any primer pair that failed to efficiently amplify the corresponding DNA target ( mean CTDNA >38 . 00 ) was also omitted from further analysis . This filtering removed 15 cellular pre-miRNA primer pairs , 1 viral miRNA primer ( KSHV-mir-k12-3 ) . It also removed all no primer controls from the data set . In all , the final array included 160 primer pairs , representing 145 cellular miRNAs , 6 KSHV miRNAs , 3 EBV miRNAs , 2 KSHV mRNAs and 4 cellular rRNAs ( U6 ) . These were run in parallel for each sample . The distribution of all data used in the analysis was as follows: for the NTC ( Figure 5J ) , 203 of 5120 reactions ( 3 . 96% ) of the reactions had a CT<38 . These were randomly distributed across all samples and all primers . For the RT- reactions ( Figure 5G ) , 286 of 5920 ( 4 . 83% ) of the reactions had a CT<38 . Pair-wise comparison of the RT- and NTC results indicated perfect overlap between the positive samples indicating these were incidences of shared positivity and should therefore only be counted once ( data not shown ) . Therefore the false positive rate was 4 . 83% . In the DNA samples ( Figure 5I ) , 12 , 567 of 13 , 920 ( 90 . 28% ) of the reactions had CT<38 . Given that less than 5% of the total number of reactions represent viral targets , the presence of which is variable from sample to sample , the remaining negative values most likely indicate deletions of miRNAs in certain samples . The distribution was unimodal and followed a normal distribution ( data not shown ) . Finally , the RT-positive ( Figure 5H ) set contained 8 , 299 of 20 , 000 ( 41 . 50% ) positive reactions ( CT <38 ) in total . These are less than half the positive reactions recorded for the corresponding DNA results . This means that although we could detect 86% of all miRNA genes ( DNA ) in all samples , only 45 . 54% were actively transcribed in our set of samples . This means that half of all miRNAs in our array were transcribed in the endothelial cell lineage . This is consistent with the known tissue specificity of miRNAs and underscores their value as differentially expressed biomarkers . This is likely due to a combination of factors , including tissue type and developmental stage . For instance , we would not expect liver or brain-specific miRNAs to be present in any of our samples .
|
MicroRNAs are key regulators of cancer and development . We can use their pattern of expression to classify different cancers or in our case different stages in cancer development . We used a novel method to define progressive stages for Kaposi sarcoma ( KS ) , which is a cancer of the endothelial cells . We identified specific precursor-miRNAs that accurately identify stages of KS tumor progression . For the first time , we were able to profile KS patient material . This is difficult to come by , but it is more closely related to the human cancer than even the best cell culture models . Our work statistically defined clusters of pre-miRNAs , each signifying one step in cancer progression . This is the first time that precursor miRNAs profiling was used to define cancer stages . It is also the first time that we have defined makers of any kind that allow us to distinguish between different types of the endothelial cancer KS . Loss of mir-221 precursor miRNA and gain of mir-15 precursor miRNA expression demarked the transition from merely immortal to fully tumorigenic cells . Mir-140 and Kaposi sarcoma–associated herpesvirus viral microRNAs increased progressively with the degree of transformation , i . e . more aggressive stages expressed higher levels of these biomarkers . High levels of the precursor microRNA mir-24 emerged as a biomarker only in patient derived KS samples , not in any of the culture models .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"oncology/sarcomas",
"oncology/skin",
"cancers",
"virology/viruses",
"and",
"cancer",
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"oncology/hematological",
"malignancies"
] |
2009
|
Pre-Micro RNA Signatures Delineate Stages of Endothelial Cell Transformation in Kaposi Sarcoma
|
How do bacteria regulate their cellular physiology in response to starvation ? Here , we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks . We have measured multiple cellular components , including RNA and proteins at deep genomic coverage , as well as lipid modifications and flux through central metabolism . Our study focuses on the physiological response of E . coli in stationary phase as a result of being starved for glucose , not on the genetic adaptation of E . coli to utilize alternative nutrients . In our analysis , we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation . Specifically , we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner . We have also developed , from dynamic models of gene expression , a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances . By comparing and contrasting our transcriptomic and proteomic data , we have identified several broad physiological trends in the E . coli starvation response . Strikingly , mRNAs are widely down-regulated in response to glucose starvation , presumably as a strategy for reducing new protein synthesis . By contrast , protein abundances display more varied responses . The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated .
Many global changes in cellular physiology occur during the growth of a typical laboratory culture of a microorganism , such as Escherichia coli , as it transitions from exponential growth to starvation where it eventually ceases dividing as nutrients become exhausted [1] . However , how these changes affect specific cellular components and processes is not fully known . Existing surveys , even if conducted at the genome scale , tend to have limited completeness , in at least two ways . First , most studies collect only one type of genome-scale data . For example , they either measure changes in gene expression , through RNA or protein levels , or they measure changes in metabolites . Second , technological limitations often prevent the detection of some subset of molecules in a category of interest . For example , small bacterial RNAs with key roles in regulation may be lost from a sample when using typical methods to purify “total” RNA from cells [2] . Furthermore , DNA microarray-based methods for profiling gene expression can only detect specific RNA sequences depending on the design of their probes , whereas RNA-seq transcriptomic methods theoretically recover all RNA species in a sample [3] . Similarly , in proteomics , 2-D gel electrophoresis approaches typically detect many fewer proteins than newer mass spectrometry based shotgun methods [4 , 5] . Moreover , while the short-term changes in cellular physiology that occur in a laboratory culture of E . coli have been the subject of intensive study , considerably less is known about the changes in cellular composition that occur during the long-term survival of E . coli and other non-spore-forming microbes under starvation , despite the likely prevalence of this condition in nature [6] . Most studies of this metabolic state have concentrated on the long-term survival of cells in rich medium [7] . Under these conditions , E . coli experience an ecological catastrophe in which 90–99% of the cells die within a few days due to pH and nutrient changes in the medium , and mutants emerge that continue to divide on the resources released from dead cells [8–10] Thus , these are studies of genetic adaptation to changed conditions rather than purely of changes in cellular physiology in stressed and starving , but genetically wild-type , cells . Finally , most genome-wide analyses of gene regulation focus on comparing differential changes across only two or three distinct environmental conditions or between two different time points . These studies reveal a snapshot of global physiological regulation but they do not provide insight into the underlying dynamics of regulation . By studying the dynamics of gene regulation over time , we can develop an understanding of how a cell’s physiology is regulated in the face of a natural environment that may undergo frequent changes . Here we performed a time course experiment of E . coli B REL606 growth and starvation up to two weeks . We used a chemically defined glucose-limited medium in which cells entered a starvation state but did not lose viability for at least one week . We collected genome-wide RNA and protein levels at multiple time points , as well as lipid-modification and central metabolic-flux data , all under identical , controlled experimental conditions . The resultant data set serves as a rich resource for computational models that span and integrate cellular sub-systems and for cataloguing and correlating the responses of specific genes and/or molecules across cellular subsystems during growth and long-term starvation . We analyzed these data using a novel , general approach for unbiased classification of expression time courses . We found that the mRNA pool was drastically reduced during starvation , possibly to limit new protein synthesis overall , and that some proteins declined rapidly in abundance , in proportion to their mRNAs , while others were buffered to rapid changes in their transcripts . Overall , we observed a pattern where starving E . coli cells employ transcriptional and translational/post-translational regulation to limit energy requirements while remaining capable of nutrient uptake and metabolism .
We grew multiple cultures of E . coli REL606 , from the same stock , under identical growth conditions of long-term glucose starvation , in the same medium . The samples were subsequently distributed to different laboratories that measured RNA , protein , lipids , and central metabolic flux ratios . Freezer stocks of the REL606 strain were revived for 24 h , diluted and preconditioned for another 24 h , and diluted again to initiate the experimental time course ( Fig 1A ) . Each biological replicate was performed on separate days . In a pilot experiment a growth curve was measured to determine informative time points for analysis ( Fig 1B ) . Time points spanning three hours to two weeks were collected and used to measure RNA via RNA-seq , proteins via LC/MS , lipids via MALDI-TOF MS and ESI MS , and central metabolic fluxes via 13C labeled glucose and GC-MS ( Fig 1C ) . In our conditions , the optical density at 600 nm ( OD600 ) changed little once cells entered stationary phase ( Fig 1B ) . Additionally , cell viability remained constant after entry to stationary phase at 24 h for up to one week . From one to two weeks , the number of viable cells per culture count decreased by 38% ( Fig 1B ) . We first assessed reproducibility of protein and RNA measurements . For both , we found that measurements from separate biological replicates correlated highly with each other . We saw Spearman correlations of 0 . 92 , 0 . 92 , and 0 . 95 between biological repeats of raw proteomics counts and correlations of 0 . 93 , 0 . 93 , and 0 . 94 for raw RNA-seq counts between the 3 h biological replicates ( S1 Fig ) . Furthermore , we also compared the overlap in protein IDs between the first three time points ( 3 , 4 , and 5 hrs ) , when the cells were exponentially dividing and the protein concentrations were more-or-less at steady state , and we found a high overlap among these time points . Each single time point yielded just over 2600 protein IDs , any pair yielded just over 2300 common protein IDs , and all three time points yield over 2100 overlapping protein IDs ( S2 Fig ) . Thus , our measurements were highly reproducible . We next compared how many different RNA and protein species we detected compared to previous 'multi-omic' studies ( Table 1 ) . Yoon et al . used 2D gels and microarrays to measure 60 significantly changing proteins and 4 , 144 mRNAs in E . coli REL606 , the same strain used in this study [11] . By comparison , at 3–4 h , we observed over 2 , 600 proteins , with ~1 , 200 that changed significantly at some point in the time course , along with 4 , 116 mRNAs , 85 tRNAs , and 89 other noncoding RNAs ( ncRNAs ) , a category that is largely made up of small RNAs . Even though the total number of proteins Yoon et al . observed at early exponential phase was not reported [11] , it was likely an order of magnitude less than our observations , if it followed the same pattern as the proteins found to have significant changes in expression . Taniguchi et al . measured protein and mRNA content of single cells using YFP fusions and FISH , resulting in the measurement of 1 , 018 proteins and 137 transcripts in an E . coli K12 strain [12] . Lewis et al . also measured ~1 , 000 proteins and RNA expression of 4 , 428 genes . Although these data sets were published separately , they were performed in the same lab and under similar conditions and thus were also comparable to a degree [13 , 14] . In summary , our proteomics measurements were far more complete than comparable studies , providing more than 1 , 000 additional protein observations than the most comprehensive other study , as many mRNAs as other studies , and additional data on tRNAs and ncRNAs . Our experiments also provided coverage comparable to or better than other experiments that focus on proteomics or RNA measurements alone . Using stable isotope labeling of amino acids ( SILAC ) , Soares et al . observed 2 , 053 proteins in at least 1 of 2 biological repeats , at a false discovery rate ( FDR ) of <1% [4] . We measured 2 , 658 proteins in at least 1 of 3 biological repeats with around 2 , 200 protein IDs per sample using the same FDR cutoff . A more recent study , using the filter-aided sample preparation ( FASP ) method , also observed around 2 , 200 proteins per sample , comparable to our recovery [5] . Additionally , using RNA-seq , we recovered as many mRNAs as microarray approaches do , with the added benefit of measuring 89 ncRNAs and 85 tRNAs from the same sample . As a point of reference , previous RNA-seq experiments on the E . coli K-12 strain identified 133 putative ncRNAs and 4 , 161 mRNAs [15] . Thus our recovery of both proteins and RNA represents the state of the art of the field , far outperforming recent comparative studies . As an added benefit of our study , we also simultaneously characterized lipid A and phospholipid composition in cell membranes and measured flux ratios in central metabolism , covering a wider range of cellular components than previous comparison studies . We next investigated changes in relative mRNA and protein abundance over time . Due to translational and post-translational regulation we expected differences in the response of mRNA transcripts and proteins after entry to stationary phase . mRNA counts at each time point were normalized via DESeq [16] , relative to the total pool of mRNA , tRNA , and ncRNA . Protein counts at each time point were normalized relative to the total protein count . To visualize changing mRNA and protein levels we compared and contrasted the general trends in the response of mRNA and proteins by way of K-means clustering . To simplify the analysis we focused on only those mRNAs and proteins that were changing significantly ( as measured by false discovery rate and fold-change cutoff , respectively ) throughout the time course , yielding a total of ~1900 significantly changing transcripts/proteins . To perform K-means clustering , an arbitrary choice for the number of clusters must be made such that the profiles are well separated into groups with unique and distinct behaviors . ( We also developed an alternative classification approach that does not depend on such an arbitrary choice , see below . ) We varied the number of clusters for both mRNA and protein profiles , and we found the best clustering performance , assessed by visual inspection , to be around 15 clusters for the mRNA profiles and 25 clusters for the protein profiles . Thus , the mRNAs appeared to respond in a more uniform manner than the proteins did . This finding is illustrated by the heat map of the cluster centroids of mRNA and protein ( Fig 2A and 2B , respectively ) . The vast majority of the differentially regulated mRNAs were down-regulated , while the protein response was much less uniform . Additionally , the mRNA profiles showed a clear separation between early and late time points with a transition period around 6–8 h . After this transitional period of entry to a starved state , the transcription profiles remained relatively constant , with only minor changes in expression . At two weeks some of the transcripts began changing again , perhaps signaling a further shift in cell state . As the cells ran out of glucose , overall demand for new protein synthesis was significantly decreased , demand for certain stress response proteins increased , and resources became limiting . New protein synthesis could be globally limited in at least three ways: by reducing the amounts of rRNAs , charged tRNAs , or mRNAs . To understand how these different RNA pools changed relative to each other , we calculated the relative amount of mRNA , tRNA , ncRNA , and rRNA present in both ribosome depleted and non-ribosome depleted samples ( S3 Fig ) . In the non-ribosomal depleted case the fraction of rRNA changed very little throughout the course of the experiment while the tRNA fraction increased and the mRNA fraction decreased . In the ribosome depleted samples ( in which we removed residual rRNA counts due to incomplete depletion before analysis ) , the tRNA fraction also increased as the mRNA fraction decreased , confirming that this effect was not due to sensitivity or sampling-bias issues resulting from rRNA dominating the RNA pool in the non-ribosome depleted sample . We would like to emphasize that the above clustering of the RNA and protein abundances were performed independently of each other . Therefore , we could not directly compare individual clusters between Fig 2A and 2B . The next section addresses the correlation between absolute and relative changes in abundance of individual proteins and their transcripts . While it has been observed that absolute levels of proteins do not necessarily correlate strongly with their corresponding transcripts , we expected at least a moderate correlation between absolute mRNA and protein levels at a given time point . We also expected a correlation within individual time courses between the relative levels of a protein and its transcript . To relate the relative levels of a protein to its transcript we had to account for the underlying dynamics of the time courses . We considered two limiting cases: At one extreme we assumed each protein had a degradation rate slower than the time scale of the experiment . At the other extreme we assumed each protein was degraded on a time scale that was fast compared to the time scale of the experiment . In the first limiting case proteins integrate their transcript levels over time . In the second limiting case ( relative ) protein levels track with their ( relative ) transcript level . Obviously , we expect that some proteins do not match either of these extreme cases but fall into an intermediate regime between the two . Plotted in Fig 2C and 2D are histograms of the Spearman correlation coefficients ( ρ ) calculated for proteins vs . the integrals of their transcripts ( integral regulation ) and proteins vs . their transcripts ( proportional regulation ) , respectively . Approximately 15% of the proteins correlated highly ( ρ>0 . 70 ) with the integrals of their transcripts whereas approximately 20% correlated highly with their transcript levels . There was little overlap between the two sets , as can be seen by the strong anti-correlation in the 2D histogram in Fig 2E of protein versus the integral and proportional levels of mRNA . Genes that were proportionally regulated were enriched for , among other things , locomotion and cell division . Genes that were integrally regulated were enriched for glycerol , alditol , and polyol metabolism . For a full list of proteins that were either proportionally or integrally related to their transcripts see S1 and S2 Tables , respectively . Approximately 65% of proteins did not fit one of these limiting models of how transcript and protein abundance were correlated; they may experience intermediate protein degradation rates or their expression and activity may be controlled by more complex post-translational modifications . To put proteins and RNA within a given sample on comparable absolute scales , we normalized protein counts using the APEX method [17] for absolute quantification , and we normalized mRNA counts to the length of each transcript . Both protein and mRNA levels were then averaged across all three biological replicates . Additionally , all proteins and mRNAs were scaled by the average of all proteins and mRNA . The strongest absolute correlation , across the time course , between mRNA and protein occurred at three hours ( Fig 2F , Spearman ρ = 0 . 71 , P = 10−224 ) . Absolute correlation between proteins and their corresponding transcripts were relatively strong for time points ≤8 h , with a correlation coefficient of ~0 . 71 . After 8 h , when cells had entered a starved state , the correlation was much weaker , with correlations around 0 . 3–0 . 4 ( S4 Fig ) . The correlation at three hours was somewhat higher than is usually observed for correlations between RNA and protein for other measured prokaryotes and eukaryotes , which typically have Spearman correlations around 0 . 5 between proteins and their transcripts [18–23] . Genes within an operon are co-transcribed as a single RNA and thus are likely to be under the same transcriptional control . Differences in translational efficiency between genes often lead to larger differences in protein expression in the same operon , as regulation via changes in subcellular localization , post-translational modifications , or control of degradation rates may differently impact the activities of each of these proteins [24–27] . We expected to see a high correlation between counts of RNAs for each gene within an operon , as the genes within an operon are under the same transcriptional control; however , we expected there to be less correlation between proteins within an operon , as the proteins are not guaranteed to be subject to the same translational/post-translational regulation . As a measure of correlation of gene expression within an operon we took the average of the pairwise Spearman correlation coefficient for all possible pairs of transcripts and proteins within an operon . Approximately eighty percent of transcripts had a mean pairwise correlation coefficient greater than 0 . 8 within an operon ( Fig 3A ) . On the other hand , less than fourteen percent of proteins had a mean pairwise correlation coefficient greater than 0 . 8 within an operon ( Fig 3B ) . Genes closer together within an operon were more likely to have correlated protein profiles ( see Fig 3C ) , which we took as evidence that distance between genes was a strong indicator of translational regulation . Also shown are a few examples of highly correlated transcripts and proteins for individual operons ( Fig 3D , 3E and 3F , respectively ) . Typical analysis of RNA expression data often involves performing a hierarchical clustering of profiles followed by a term enrichment of subsets of genes found in the emerging patterns . In this approach the patterning that comes from hierarchical clustering can be arbitrary , depending on the level of the hierarchy one chooses to focus on . Here , instead , we sought to sort the time courses into general behaviors in an unbiased manner . To accomplish this goal we fit each individual mRNA and protein to a piecewise continuous curve ( S5A Fig ) . This curve was defined by four free time parameters and three free amplitude parameters . To fit the curve we used a population-based differential evolution ( DE ) algorithm with the fitness function used in minimization scaled to the experimental error ( see Methods ) . Thus , our algorithm provided confidence intervals for our fit based upon the variability in biological replicates . To demonstrate the effectiveness of our fitting strategy we randomly selected five mRNA profiles and their respective fits ( S5B–S5E Fig ) . Green circles show the average of three biological replicates with their standard deviations ( green bars ) and the blue line and bar show the average and standard deviation of the population of fits , respectively . Both the data and fit were normalized to the average of the time course . We also plotted histograms of the time scale parameters we found by fitting the piecewise continuous curve to our data ( S6 Fig ) . The most informative time scales were t1 , the time to first inflection , and t2+t3+t4 , the time it takes for the profile to stop changing . The majority of proteins and their transcripts began changing before the 10 h mark ( or just after the cells enter a starved state ) . Once the profiles began to change it took >10 h before they stopped changing again . However , in this case the apparent long time scale of proteins and transcripts changing could be due to the low time resolution of our experiment after the cells had entered a starved state . As can be seen in S5B–S5E Fig , there was generally good agreement between the data and model for mRNAs . Thus , the fits gave us reasonable estimates of the distribution of time scales involved in the response . S5F Fig shows the distribution of t1 , the time to first inflection . Most of the mRNAs responded between 3–8 h , with a strong peak at around 6 h ( when cells began entry to a starved state ) . To better understand the regulation of cellular processes ( and mRNAs ) in our dataset , we sorted the mRNA profiles into five general categories , defined on the basis of our fitted parameters: up-regulated , down-regulated , transiently up-regulated , transiently down-regulated , or ambiguous . The confidence intervals for our fits allowed sorting individual mRNAs into these five categories with high confidence . The mRNAs in the categories down-regulated and up-regulated showed significant enrichment for GO terms . The average of the mRNAs in each of these terms is shown in Fig 4A and 4B . Terms enriched in the set of down-regulated transcripts represented translation , carboxylic acid biosynthetic process , and nitrogen compound biosynthetic process . These processes were likely down-regulated for energy conservation purposes in the face of limiting resources . Terms enriched in the set of up-regulated transcripts represented carbohydrate catabolic processes . To characterize the protein response we followed the same general strategy of fitting , classification , and GO enrichment as we had done for the RNA profiles . The distribution of the time to first inflection for the proteins was a little broader than for the mRNAs . However , the first-inflection times still mostly fell into the range of 3–8 h , and very few proteins had not responded by the time the cells entered a starved state . There were many proteins that were present for the duration of the time course , compared to the mRNAs where very few remained present for the entire duration of the experiment . Fig 4C shows the average abundance of the proteins in a given GO term that were enriched in the set of proteins that were being up-regulated . As in the case of down-regulated RNAs these proteins were likely down-regulated to conserve energy , and they included proteins involved in translation and locomotion . Up-regulated proteins were , like the up-regulated transcripts , involved in carbohydrate catabolism but also included terms involved in stress response and metabolism of glycerol . The average protein abundances for GO terms being down-regulated had a much wider distribution of decay times compared to the RNAs being down-regulated , likely due to differing protein degradation rates ( and/or thermodynamic stability ) ( Fig 4D ) . As a complementary approach we also averaged all proteins in a given KEGG pathway regardless of their behavior . Many pathways showed little to no differential regulation , on average , in their protein levels . Pathways that changed cohesively are plotted in Fig 4E and 4F , depending on whether they were down- or up-regulated , respectively . As in the previous term-enrichment analysis , we saw motility to be down-regulated , as well as other energy consuming processes involved in metabolism and biosynthesis . Interestingly , biosynthesis of siderophores was up-regulated , likely due to do increased demands for or reduced supply of iron . We used flux ratio analysis to measure the relative metabolic fluxes passing through different branches of central metabolism [28 , 29] . To measure flux ratios we used the FiatFlux software that fits a metabolic model to the amino acid labeling pattern [30] . Importantly , this analysis represents the integral of metabolism until the time at which the measurement was taken . As there was little ab-initio protein synthesis after the cells stopped growing ( after ~8 h ) , we did not include the flux ratios after this point , except for the two-week time point . Our major observation was that there was little change in flux ratios throughout growth , and for most of the experiment this initial labeling remained ( S7A–S7I Fig ) . Interestingly , we observed changes at two weeks in the flux ratio in P5P from G6P lower branch ( S7G Fig ) . Given that there is not expected to be any net synthesis of amino acids after growth ceased , we cannot use the steady-state approach to interpret these data . They do suggest , however , that either internal amino acid recycling or some de novo amino acid synthesis from recycling nutrients released by dead cells occurred after one week . Using negative-ion MALDI-TOF and ESI mass spectrometry ( MS ) , we analyzed lipid A and phospholipid profiles , respectively , of cells at each time point . Beginning before one week , we observed an appearance of an MS peak associated with the acylation of lipid A with a C16 chain ( Fig 5A and 5C ) . In the phospholipid analysis , a notable increase began around 6 h in the cyclopropanation of one unsaturated double bond within molecules of the major phospholipids , phosphatidylethanolamine ( PE ) and phosphatidylglycerol ( PG ) . This change was identified by the gradual relative increase of peaks at ~702 . 5 m/z and ~733 . 5 m/z , respectively . ( Representative data for PE is shown in Fig 5D . ) Both the modifications to lipid A and phospholipids continued to increase up to the two-week time point . In fact , the 702 . 5 m/z peak corresponding to cyclopropanation of phospholipid was barely detectable before six hours but became the predominant peak by the end of the time course . The enzymes relevant to the above lipid A and phospholipid modifications are lipid A palmitoyl transferase ( PagP ) and cycloproponated fatty acid synthase ( CFA ) , respectively [31 , 32] . PagP is known to be constitutively transcribed at low levels and remain latent in the outer membrane until enzyme activation [33] . It is also up-regulated by the transcriptional regulator , PhoP , under various stressful conditions encountered by a cell [34] . However , during our time course , transcript levels of PagP and PhoP did not change significantly . Furthermore , neither PagP nor PhoP was observed at the protein level . In the case of PagP , this could be due to the difficulty in detecting outer membrane beta-barrel proteins by our mass-spec proteomics method . With respect to phospholipid modification , CFA synthase protein levels increased between 3–6 h before decreasing again . This observation agreed with prior data showing that CFA synthase was important during the transition to stationary phase [32] . CFA synthase RNA levels increased again around one week , which was consistent with the activity observed in phospholipid modification , although it is not clear why we did not observe a corresponding increase in protein levels at this point ( Fig 5B ) .
We have collected a comprehensive E . coli time course and have developed computational techniques to analyze such data . Our computational techniques are general and can be applied to other time-course data collected in future studies . In particular , fitting piecewise continuous curves to expression profiles allowed us to reliably sort individual profiles into four basic groups , up-regulated , down-regulated , transiently up-regulated , or transiently down-regulated . Additionally , we have developed an unbiased approach to compare mRNA and protein profiles and to identify those proteins whose abundances followed their mRNA levels and those that were buffered against rapid mRNA changes . Our results provide a coherent picture of E . coli stationary phase , as summarized in Fig 6 . E . coli could survive for over a week when starved for glucose in a well-buffered minimal medium , with little change in cell viability ( Fig 6A ) . The fraction of mRNA relative to all RNA was down regulated after cells entered stationary phase ( Fig 6B ) . As cells ceased to divide , the demand for new protein synthesis declined . Reducing the overall pool of mRNA could contribute to limiting new protein synthesis . Upon entry to stationary phase , lipid A and phospholipids were modified by PagP and CFA synthetase , respectively ( Fig 6C ) . Modification of lipids continued gradually until eventually the lipid species that were rare during growth dominated at two weeks . All genes started to change in expression by 10 h , and mRNA expression clustered temporally into two regimes , before and after 10 h ( cells entered a starved state at around 8 h ) with some late changes in expression beginning around two weeks ( Fig 6D ) . We found that 20% of observed proteins were regulated in proportion to their transcripts ( Fig 6E ) , allowing for rapid down-regulation of the processes they were involved in . On the other hand , 15% of the observed proteins were integrally related to their transcripts ( Fig 6E ) and likely served to buffer against environmental changes . In addition to measuring and characterizing RNA and protein changes upon entry to stationary phase , we also demonstrated how a piecewise curve-fitting strategy allowed us to classify expression profiles into different categories . The enriched terms in the resulting classification were reasonably aligned with what was known about , or at least consistent with , cells coping with starvation ( Fig 6D ) . Importantly , this classification was accomplished in an unbiased manner , without any ad hoc assumptions about the number of clusters that should exist in the data . We found that , as cells entered a starved state , the total pool of mRNA was depleted compared to all other RNAs and many individual transcripts were down-regulated , possibly as part of a broader strategy to reduce the production of new protein . Reducing overall protein production could also be achieved by limiting the available ribosomes or by limiting the pool of available tRNA . The stringent response , activated in starving cells through the ppGpp alarmone , down-regulates new rRNA synthesis [35] . However , in our data , the relative fraction of rRNA within a cell changed little over time , and the tRNA fraction actually increased with time . Thus , new protein synthesis in starving cells may be limited more by the reduced mRNA pool than by reduced translational efficiency due to decreases in rRNA or tRNA abundance . Even if the total rRNA decreased over the time course , the total mRNA would have decreased more by a proportional amount . Said another way , the down-regulation of new rRNA synthesis by the stringent response may be most important for shutting down the production of ribosomes needed by new cells in an actively dividing culture , rather than for reducing the level of ribosomes in already existing cells . It has been suggested that the degradation rate of many proteins in E . coli is much slower than the doubling time during growth [36 , 37] . As a consequence , when cells cease to divide , such as in the case of glucose starvation , not all proteins can respond immediately to possible changes in transcript levels . In effect , the amounts of some proteins may be buffered against relatively fast changes in nutrient availability . At the same time certain proteins may need to be rapidly regulated to ensure survival upon starvation . We found that a subset of the proteome , ~20% of proteins , fell into the rapidly regulated category that may be degraded quickly—they maintained an abundance that was proportional to their transcripts . Another subset , ~15% of proteins , tended to be much more stable—they were proportional to the integrated abundance of their transcripts over the time scale of our experiment . For example , the abundance of several flagellar proteins was proportional to their transcript levels , whereas proteins involved in metabolism and energy production integrated their transcript levels over time . Turning off proteins involved in cell division and the flagellar machinery , both energy-intensive processes , needs to happen relatively quickly . By contrast , the proteins that were relatively stable were enriched for energy production terms . Thus , these proteins presumably persist so that if nutrients were to become available again the cell will be capable of using them to re-initiate growth . For proteins to track dynamically with their transcripts they must have a short half-life . For this reason , we can compare those terms enriched for proteins that dynamically correlate with their transcripts to the COG terms reported by Maier et al . [23] that have shorter than average half lives in M . pneumoniae . We found that those COG terms with shorter than average half-lives were generally consistent with terms that were enriched in highly dynamically correlated proteins and mRNAs . In particular , Maier et al . found that terms involved with energy production ( COG term C ) , metabolism ( COG terms H , I , G ) , protein turnover ( COG term O ) , and signaling ( COG term T ) had protein turnover rates significantly faster than the overall average . Among the terms that were significantly regulated in stationary phase , we saw that motility was down-regulated , likely because it places a high energy burden on cells [1] . Additionally , it has been shown that flagella in E . coli are down-regulated by the stringent response [38] . Other observed differential regulation is related to energy conservation ( shutting down expensive or unneeded pathways ) , catabolism ( breaking down non-essential components for food ) , stopping translation of new protein ( as there is no longer demand for protein from new cells ) , or a general stress response ( increasing nutrient influx or bolstering membrane integrity ) . We also found many uncharacterized genes ( both among the protein and the RNA profiles ) that were significantly up- or down-regulated upon entry to stationary phase . A subset of these proteins have computationally predicted functions [39] that were consistent with our findings for annotated genes . For instance , several uncharacterized proteins that were up-regulated are predicted to be involved in stress response and cell-wall biogenesis . Other predictions seem to be inconsistent with our observations for annotated genes or indicate that these genes regulate rather than take part in these processes . For example , some uncharacterized proteins that were up-regulated are predicted to be involved in translation , even though translation was heavily enriched in down-regulated genes . These uncharacterized genes might down-regulate the activity of ribosomes , for example . Lists of proteins and transcripts that were significantly regulated in our time course are provided in the Supplemental materials ( S3 and S4 Tables ) . Even though mRNA abundances within an operon were highly correlated ( as expected ) , in many cases their protein profiles were only weakly correlated . This finding could be due to different translation efficiencies between proteins [40] as well as differing degradation rates . In support of the former , we saw a tendency for proteins separated by a larger distance within a transcript to be less correlated than those located closer to one another . However , it was likely that different protein degradation rates also played a role in the low correlation between proteins within an operon . Indeed , many proteins coded by proximal regions of a transcript showed poor correlation in their profiles ( Fig 3C ) . Other explanations for this tendency of proteins nearby on the genome to be more highly correlated could be due to distance from the transcript start site or transcript length . Yet , our data did not show evidence for either of these scenarios . Distance from the transcript start site was not correlated with protein expression ( ρ = –0 . 02 , P = 0 . 65 ) and transcript length was only very weakly correlated with protein expression ( ρ = 0 . 12 , P = 0 . 003 ) . However , we cannot necessarily rule out other explanations for the observed intra-operon protein correlation vs . distance between genes . In addition to the expected disparities between RNA and protein levels , we also observed surprising changes in enzyme activity that did not correspond to the respective RNA-seq and proteomics analysis . For example , we saw striking levels of lipid modification late during the time course . These modifications were easily explained by their association with adaptation to stressful environments such as depleted nutrients and cations as well as increased acid resistance during starvation [32 , 34] . However , the stark differences in RNA , protein , and activity trends of the enzymes responsible for the lipid modifications , PagP and CFA synthase , highlight the fact that activation does not necessarily follow abundance measurements . In support of this idea , it has been shown that cylopropination by CFA synthase depends upon the concentration of bicarbonate , which could lead to a decoupling between protein levels and activation [41] . Metabolic fluxes were quite constant throughout the growth phase of the experiment , and these labeling patterns remained in place once growth ceased . At the two-week time point , however , the labeling patterns in histidine changed substantially , which during steady-state growth on glucose would have been interpreted as a change in the flux ratio corresponding to P5P from the G6P lower branch declining . This change so late in the experiment was unexpected , since we did not anticipate substantial turnover in cellular composition that late in stationary phase . The observation suggests that either internal amino acid recycling or some de novo amino-acid synthesis , possibly related to the moderate decline in the number of viable cells , occurs past the one week time point . A goal of systems biology has been to understand how phenotype originates from genotype . The phenotype of a cell is determined by complex regulation of processes including cell signaling , gene regulation , metabolism , and lipid biochemistry . Understanding the connection between phenotype and genotype is crucial to understanding disease and for synthetic engineering of biology . Even though computational models of individual component subsystems , such as flux models of metabolism [42–44] , have enjoyed a long history of success , they remain limited in their application . Much effort is currently being spent on understanding how to best integrate data from multiple subsystems . For example , there are many proposed approaches to combining gene expression with metabolic flux networks [20 , 45–52] while other studies have focused on integrative , whole-cell models [53 , 54] . Given the growing interest in integrative modeling approaches , there is a pressing need for studies that collect high quality genome-wide data across multiple cellular subsystems from the same biological samples . Our data set is a rich resource for comparing and contrasting the response of multiple cellular subsystems . Additionally , in the future we plan to use the techniques developed in this paper to measure the response of E . coli to several other environmental conditions , which will allow for more detailed models of regulation . Despite the completeness and quality of our data set , however , there were a few key limitations concerning our approach . Our analysis via RNA-seq and shotgun MS allowed for high confidence when comparing the relative levels of a particular transcript or protein over time . However , due to potential differences in detection efficiency between individual RNAs or peptides , care should be taken when comparing absolute abundances . In our analysis we used the APEX method to account for differences in protein detection efficiency . We normalized RNA by the length of a transcript as an estimate of RNA detection efficiency , for a particular experiment . This approach resulted in a correlation coefficient of ~0 . 71 between proteins and their transcripts , a finding on the high end for such correlation measurements . Previous reports on the correlation between mRNA and protein levels in E . coli and M . pneumoniae have yielded correlation coefficients of ~0 . 5 [18–23] . Thus , even straightforward means of correcting our experimental bias led to reasonable comparisons of levels between individual RNAs or proteins . Additional detection biases we did not account for , such as GC content bias in RNA-seq [55] , were likely responsible for some of the remaining unexplained variation [56] . The stepwise linear function we used for modeling works for a majority of our expression profiles . However , in some cases it over-fits the data and in other cases the function is unable to capture the underlying behavior . An example of a profile that may be under-constrained is a gene that is up- or down-regulated without further changes to expression . In this case the free time parameters t2 through t4 , along with amplitude parameter A2 , may be under-constrained . Even in this case , however , the parameters t1 , t2+t3+t4 , A1 , and A3 are still well constrained , providing enough information to reliably sort the profiles based upon behavior . Thus , since we used our model for classification and not for prediction purposes , any potential parameter over-fitting did not substantially affect our final results . More complicated temporal profiles , such as multiple peaks separated in time , could not be captured by our function . The presence of these more complicated behaviors was rare enough as to not warrant special consideration . In summary , our study provides both the most complete measurement , to our knowledge , of multiple cellular components in a changing environment , and novel computational approaches to analyze such data . Thus , this work represents an important step toward understanding how regulation of a cell’s physiology is coordinated , on a global , systems level , by interactions between multiple cellular subsystems .
E . coli B REL606 was inoculated from a freezer stock into 10 ml of Davis Minimal medium supplemented with 2 μg/l thiamine ( DM ) [57] and limiting glucose at 500 mg/l ( DM500 ) in a 50 ml Erlenmeyer flask . This culture was incubated at 37°C with 120 r . p . m . orbital shaking over a diameter of 1" . After overnight growth , 500 μl of the culture was diluted into 50 ml of prewarmed DM500 in a 250 ml flask and grown for an additional 24 h under the same conditions . On the day of the experiment , 500 μl of this preconditioned culture was added to ten 250 ml flasks , each containing 50 ml DM500 , to initiate the experiment . At each time point , aliquots of these cultures were removed as necessary to harvest a constant number of cells given the changes in cell density over the growth curve . Each sample was pelleted by centrifugation , washed with sterile saline ( 0 . 85% ( w/v ) NaCl ) , and then spun down again . After removing the supernatant , the resulting cell pellet was flash frozen using liquid nitrogen and stored at –80°C . Each of the three biological replicates was performed on a separate day . Samples for each type of cell composition measurement were taken from the same batch of flasks , except for those used for flux analysis , for which an additional batch was grown in 20% [U-13C] glucose . For graphs of OD600 and colony-forming units ( CFU ) , cultures were grown separately from the main batches used for harvesting cells but under identical conditions . The OD600 ( absorbance at 600 nm ) of a sample removed from the culture at each time point was measured relative to a sterile DM500 glucose blank . These samples were also diluted in sterile saline and plated on DM agar supplemented with 0 . 2 g/l glucose . After incubation at 37°C for 24 h colonies on these plates were counted to determine CFUs . Total RNA was isolated from cell pellets using the RNAsnap method [2] . After extraction , RNA was ethanol precipitated and resuspended in 100 μl H2O . Each sample was then DNase treated and purified using the on-column method for the Zymo Clean & Concentrator-25 ( Zymo Research ) . RNA concentrations were determined throughout the purification using a Qubit 2 . 0 fluorometer ( Life Technologies ) . DNase-treated total RNA ( ≤5 μg ) was then processed with the Gram-negative bacteria RiboZero rRNA removal kit ( Epicentre ) . After rRNA depletion , each sample was ethanol precipitated and resuspended in H2O again . A fraction of the RNA was then fragmented to ~250 bp using NEBNext Magnesium RNA Fragmentation Module ( New England Biolabs ) . After fragmentation , RNA was ethanol precipitated , resuspended in 20 μl ultra-pure water , and phosphorylated using T4 PNK ( New England Biolabs ) . After another ethanol precipitation cleanup step , sequencing library preparation was performed using the NEBNext Small RNA Library Pre Set for Illumina , Multiplex Compatible ( New England Biolabs ) . Samples were ethanol precipitated again after library preparation and separated on a 4% agarose gel . All DNA fragments greater than 100 bp were excised from the gel and isolated using the Zymoclean Gel DNA Recovery kit ( Zymo Research ) . Libraries were sequenced using an Illumina HiSeq 2500 at the Genomic Sequencing and Analysis Facility ( GSAF ) at the University of Texas at Austin to generate 2×101-base paired-end reads . For RNA-seq analysis , we implemented a custom analysis pipeline using the REL606 Escherichia coli B genome ( GenBank:NC_012967 . 1 ) as the reference sequence [58] . We updated annotations of sRNAs in this genome sequence using the Rfam 11 . 0 database [59] . Prior to mapping , we trimmed adapter sequences from Illumina reads using Flexbar 2 . 31 [60] . Mapping was carried out in single-end mode using Bowtie2 2 . 1 . 0 with the –k 1 option to achieve one unique mapping location per read [61] . The raw number of reads mapping to each gene was counted using HTSeq 0 . 6 . 0 [62] . The full computational pipeline is available at https://github . com/wilkelab/AG3C_starvation_tc_RNAseq . E . coli cell pellets were resuspended in 50 mM Tris-HCl pH 8 . 0 , 10 mM DTT . 2 , 2 , 2-trifluoroethanol ( Sigma ) was added to 50% ( v/v ) final concentration and samples were incubated at 56°C for 45 min . Following incubation , iodoacetamide was added to a concentration of 25 mM and samples were incubated at room temperature in the dark for 30 min . Samples were diluted 10-fold with 2 mM CaCl2 , 50 mM Tris-HCl , pH 8 . 0 . Samples were digested with trypsin ( Pierce ) at 37°C for 5 h . Digestion was quenched by adding formic acid to 1% ( v/v ) . Tryptic peptides were filtered through Amicon Ultra 30 kD spin filtration columns and bound , washed , and eluted from HyperSep C18 SpinTips ( Thermo Scientific ) . Eluted peptides were dried by speed-vac and resuspended in Buffer C ( 5% acetonitrile , 0 . 1% formic acid ) for analysis by LC-MS/MS . For LC-MS/MS analysis , peptides were subjected to separation by C18 reverse phase chromatography on a Dionex Ultimate 3000 RSLCnano UHPLC system ( Thermo Scientific ) . Peptides were loaded onto an Acclaim C18 PepMap RSLC column ( Dionex; Thermo Scientific ) and eluted using a 5–40% acetonitrile gradient over 250 min at 300 nl/min flow rate . Eluted peptides were directly injected into an Orbitrap Elite mass spectrometer ( Thermo Scientific ) by nano-electrospray and subject to data-dependent tandem mass spectrometry , with full precursor ion scans ( MS1 ) collected at 60 , 0000 resolution . Monoisotopic precursor selection and charge-state screening were enabled , with ions of charge >+1 selected for collision-induced dissociation ( CID ) . Up to 20 fragmentation scans ( MS2 ) were collected per MS1 . Dynamic exclusion was active with 45 s exclusion for ions selected twice within a 30 s window . Spectra were searched against an E . coli strain REL606 protein sequence database and common contaminant proteins ( MaxQuant using SEQUEST ( Proteome Discoverer 1 . 4; Thermo Scientific ) ) . Fully-tryptic peptides were considered , with up to two missed cleavages . Tolerances of 10 ppm ( MS1 ) and 0 . 5 Da ( MS2 ) , carbamidomethylation of cysteine as static modification , and oxidized methionine as dynamic modification were used . High-confidence peptide-spectral matches ( PSMs ) were filtered at <1% false discovery rate determined by Percolator ( Proteome Discoverer 1 . 4; Thermo Scientific ) . Flux ratios were obtained from the samples grown with 13C labeled glucose , using methods previously described [28 , 29] . Cell pellets were resuspended in 200 ml of 6 N HCl , hydrolyzed at 105°C overnight , and dried at 95°C for up to 24 h . To the hydrolyzed cell material we added 40 ml of dimethylformamide ( DMF ) and gently mixed until a “light straw” color was obtained . The DMF resuspension was transferred to a GC-MS vial with plastic insert and 40 ml of N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide with 1% tert-butyldimethyl-chlorosilane ( v/v ) ; vials were capped and baked at 85°C for 2 h , and samples were analyzed within 2 days of derivitization . Analysis of derivitized samples was performed on a Shimadzu QP2010 Plus GC-MS ( Columbia , MD ) with autosampler . The GC-MS protocol included: 1 mL of sample injected with 1:10 split mode at 230°C; an oven gradient of 160°C for 1 min , ramp to 310°C at 20°C/min , and hold at 310°C for 0 . 5 min; and flow rate was 1 mL/min in helium . A total of five runs were performed for each sample: a blank injection of DMF to waste , a blank injection of DMF to the column , and three technical replicates of each vial . Flux inference was performed using the FiatFlux software as described [29 , 30] . Each time point was analyzed separately , and the reported fluxes represent the integral of growth up until that point . For this reason , we do not display fluxes beyond 8 h , when growth ceased and there should not be any more net amino acid synthesis . We did , however , monitor the labeling patterns in all amino acids for the later time points . Although most patterns were unchanged , we did note that histidine labeling changed substantially at the final time point of two weeks . Lipid A and phospholipids were isolated from bacterial pellets containing 3–9×109 cells . Pellets were resuspended in 5ml 1:2:08 chloroform:methanol:water for 20 min and spun at 10 , 000×g for 10 minutes . Pellets containing lipid A were further purified by the Bligh/Dyer method as previously described [63] . Phospholipids in the supernatant were further purified by extractions as previously described [64] . Mass analysis of purified lipid A fractions was performed using a MALDI-TOF/TOF ( ABI 4700 Proteomics Analyzer ) mass spectrometer in the negative ion linear mode as previously described [63] . Phospholipid analysis was performed by liquid chromatography/ESI-mass spectrometry as previously described [64] . One of the three replicates used for lipid analysis was an additional independent biological replicate , prepared identically to all other replicates but not used for RNA-seq or proteomics analysis . We analyzed raw counts from the proteomics and RNA-seq experiments as follows . Initially , proteins with low counts ( <10 ) over the entire duration of the time course were filtered out . Each time point was then normalized to the read depth ( e . g . the sum of all counts for that particular time point ) . Only proteins with a fold change of ≥1 . 5 were considered for further analysis . Protein profiles were then normalized to the maximum value for a given protein time course . To estimate the absolute protein abundance we made use of the APEX normalization method [65] . To analyze relative RNA levels , raw RNA read counts per gene ( ignoring rRNAs ) were normalized within each sample using DESeq [16] . To identify RNAs that had changed significantly , we carried out a differential expression analysis between the 3 h time point and each subsequent time point , using DESeq , and we kept RNAs with a significant difference ( p < 0 . 05 ) at least one time point for further analysis . To compare absolute RNA abundances within a single time point , raw RNA counts were normalized by gene length . Finally , normalized RNA and protein profiles , both relative and absolute , were averaged across all three biological replicates . Clustering of protein profiles was performed using the python library scipy [66] . We used the k-means clustering algorithm with the number of protein clusters set to 25 and RNA clusters to 15 . To compare relative protein profiles with the integral of their relative transcript levels we integrated each of the transcript profiles , from the initial time to each additional time point , using the trapezoidal method implemented by the python library numpy [67] . We used a piecewise continuous curve to fit both RNA and protein profiles . This curve was defined by seven free parameters , four free time parameters , and three free amplitude parameters . To fit the profiles we used a custom implementation of a differential evolution ( DE ) algorithm [68] . Briefly , the DE algorithm initially generates an ensemble of random parameter guesses within a predefined range; subsequently , vectors of individual parameter sets ( sometimes called agents ) are mixed together at a predefined crossover rate , only those crossover events that yield a smaller error ( defined by a predefined cost function ) are kept , and the process is iterated until a convergence criterion is met . In our fits we used an ensemble of 15 agents with a crossover frequency of 0 . 75 and a mixing strength of 0 . 6 . The crossover frequency determines the probability that an agent will be changed at any given iteration and the mixing strength determines how large a change an agent undergoes if it was chosen to be altered . The crossover frequency and mixing strength were picked based upon an empirical study of the dependence of convergence efficiency on these parameters [69] for some standard optimization problems . The cost function is given by Fi=∑j ( di ( tj ) −si ( tj ) ) 2σi2 ( tj ) where di ( tj ) , σi ( tj ) , and si ( tj ) are the average of all experimental repeats of protein ( or mRNA ) i at time tj , the standard deviation of the experiments of the protein ( or mRNA ) i at time tj , and the average of the ensemble simulations i at time tj , respectively . Scaling by the standard deviation places a relatively lower weight on data points with relatively larger errors for a given protein or mRNA . Some of the profiles may be slightly over-fit by our curve ( e . g . profiles that are up-regulated or down-regulated once during the time course without further modulation of expression ) . Thus care needs to be exercised in the interpretation of some of the parameters . However , we found t1 to reliably represent the time to first inflection , the sum of t2 , t3 , and t4 was a decent proxy to how long it took an RNA/protein to reach a steady state after entering a starved state , and we could reliably sort the behavior into four categories based upon the amplitude parameters . The four categories we used were that of up-regulated , down-regulated , transiently up-regulated , and transiently down-regulated . Genes that were up ( or down ) regulated were those genes that increased ( or decreased ) at some point during the time course and did not decrease ( or increase ) at some later time . Genes that were transiently up ( or down ) regulated were those genes that increased ( or decreased ) at some point during the time course but decreased ( or increased ) at some later time . The sorting into categories was aided by our estimate of the distribution of parameters that allowed for a good fit within the population of fits . A fit was considered good if it was on average ( across the time course ) one standard deviation or less away from the experimental average . We used the DAVID database ( david . abcc . ncifcrf . gov ) to perform Gene Ontology term enrichment on each subset of sorted genes: up-regulated , down-regulated , transiently up-regulated , and transiently down-regulated . Specifically we made use of DAVID's API , instead of the web interface , to generate the GO-enrichment through a python script . GO terms were clustered based upon genes in a given term to reduce redundancy in the returned results . As a complementary approach we also enriched for KEGG pathway terms in the entire set of significantly changing proteins ( without presorting ) using the DAVID database API . The protein levels within each returned KEGG pathway were then averaged to see if there was any consistent response across the entire pathway . Those KEGG terms that gave inconsistent responses across proteins in that pathway returned a relatively flat average and were filtered out . All of the scripts used to perform the above analysis can be downloaded at https://github . com/marcottelab/AG3C_starvation_tc . Raw data are available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . hj6mr . Raw Illumina read data and processed files of read counts per gene and normalized expression levels per gene have been deposited in the NCBI GEO database ( accession GSE67402 ) [70] . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository ( accession PXD002140 ) [71] .
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Bacteria frequently experience starvation conditions in their natural environments . Yet how they modify their physiology in response to these conditions remains poorly understood . Here , we performed a detailed , two-week starvation experiment in E . coli . We exhaustively monitored changes in cellular components , such as RNA and protein abundances , over time . We subsequently compared and contrasted these measurements using novel computational approaches we developed specifically for analyzing gene-expression time-course data . Using these approaches , we could identify systematic trends in the E . coli starvation response . In particular , we found that cells systematically limit mRNA and protein production , degrade proteins involved in energy-intensive processes , and maintain or increase the amount of proteins involved in energy production . Thus , the bacteria assume a cellular state in which their ongoing energy use is limited while they are poised to take advantage of any nutrients that may become available .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation
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A new approach for the segregation of monaural sound mixtures is presented based on the principle of temporal coherence and using auditory cortical representations . Temporal coherence is the notion that perceived sources emit coherently modulated features that evoke highly-coincident neural response patterns . By clustering the feature channels with coincident responses and reconstructing their input , one may segregate the underlying source from the simultaneously interfering signals that are uncorrelated with it . The proposed algorithm requires no prior information or training on the sources . It can , however , gracefully incorporate cognitive functions and influences such as memories of a target source or attention to a specific set of its attributes so as to segregate it from its background . Aside from its unusual structure and computational innovations , the proposed model provides testable hypotheses of the physiological mechanisms of this ubiquitous and remarkable perceptual ability , and of its psychophysical manifestations in navigating complex sensory environments .
Humans and animals can attend to a sound source and segregate it rapidly from a background of many other sources , with no learning or prior exposure to the specific sounds . For humans , this is the essence of the well-known cocktail party problem in which a person can effortlessly conduct a conversation with a new acquaintance in a crowded and noisy environment [1] , [2] . For frogs , songbirds , and penguins , this ability is vital for locating a mate or an offspring in the midst of a loud chorus [3] , [4] . This capacity is matched by comparable object segregation feats in vision and other senses [5] , [6] , and hence understanding it will shed light on the neural mechanisms that are fundamental and ubiquitous across all sensory systems . Computational models of auditory scene analysis have been proposed in the past to disentangle source mixtures and hence capture the functionality of this perceptual process . The models differ substantially in flavor and complexity depending on their overall objectives . For instance , some rely on prior information to segregate a specific target source or voice , and are usually able to reconstruct it with excellent quality [7] . Another class of algorithms relies on the availability of multiple microphones and the statistical independence among the sources to separate them , using for example ICA approaches or beam-forming principles [8] . Others are constrained by a single microphone and have instead opted to compute the spectrogram of the mixture , and then to decompose it into separate sources relying on heuristics , training , mild constraints on matrix factorizations [9]–[11] , spectrotemporal masks [12] , and gestalt rules [1] , [13] , [14] . A different class of approaches emphasizes the biological mechanisms underlying this process , and assesses both their plausibility and ability to replicate faithfully the psychoacoustics of stream segregation ( with all their strengths and weaknesses ) . Examples of the latter approaches include models of the auditory periphery that explain how simple tone sequences may stream [15]–[17] , how pitch modulations can be extracted and used to segregate sources of different pitch [18]–[20] , and models that handle more elaborate sound sequences and bistable perceptual phenomena [10] , [21]–[23] . Finally , of particular relevance here are algorithms that rely on the notion that features extracted from a given sound source can be bound together by correlations of intrinsic coupled oscillators in neural networks that form their connectivity online [23] , [24] . It is fair to say , however , that the diversity of approaches and the continued strong interest in this problem suggest that no algorithm has yet achieved sufficient success to render the “cocktail party problem" solved from a theoretical , physiological , or applications point of view . While our approach echoes some of the implicit or explicit ideas in the above-mentioned algorithms , it differs fundamentally in its overall framework and implementation . It is based on the notion that perceived sources ( sound streams or objects ) emit features , that are modulated in strength in a largely temporally coherent manner and that they evoke highly correlated response patterns in the brain . By clustering ( or grouping ) these responses one can reconstruct their underlying source , and also segregate it from other simultaneously interfering signals that are uncorrelated with it . This simple principle of temporal coherence has already been shown to account experimentally for the perception of sources ( or streams ) in complex backgrounds [25]–[28] . However , this is the first detailed computational implementation of this idea that demonstrates how it works , and why it is so effective as a strategy to segregate spectrotemporally complex stimuli such as speech and music . Furthermore , it should be emphasized that despite apparent similarities , the idea of temporal coherence differs fundamentally from previous efforts that invoked correlations and synchronization in the following ways [29]–[33]: ( 1 ) coincidence here refers to that among modulated feature channels due to slow stimulus power ( envelope ) fluctuations , and not to any intrinsic brain oscillations; ( 2 ) coincidences are strictly done at cortical time-scales of a few hertz , and not at the fast pitch or acoustic frequency rates often considered; ( 3 ) coincidences are measured among modulated cortical features and perceptual attributes that usually occupy well-separated channels , unlike the crowded frequency channels of the auditory spectrogram; ( 4 ) coincidence must be measured over multiple time-scales and not just over a single time-window that is bound to be too long or too short for a subset of modulations; and finally ( 5 ) the details we describe later for how the coincidence matrices are exploited to segregate the sources are new and are critical for the success of this effort . For all these reasons , the simple principle of temporal coherence is not easily implementable . Our goal here is to show how to do so using plausible cortical mechanisms able to segregate realistic mixtures of complex signals . As we shall demonstrate , the proposed framework mimics human and animal strategies to segregate sources with no prior information or knowledge of their properties . The model can also gracefully utilize available cognitive influences such as attention to , or memory of specific attributes of a source ( e . g . , its pitch or timbre ) to segregate it from its background . We begin with a sketch of the model stages , with emphasis on the unique aspects critical for its function . We then explore how separation of feature channel responses and their temporal continuity contribute to source segregation , and the potential helpful role of perceptual attributes like pitch and location in this process . Finally , we extend the results to the segregation of complex natural signals such as speech mixtures , and speech in noise or music .
The critical information for identifying the perceived sources is contained in the instantaneous coincidence among the feature channel pairs as depicted in the C-matrices ( Fig . 1B ) . At each modulation rate , the coincidence matrix at time is computed by taking the outer product of all cortical frequency-scale outputs ( ) . Such a computation effectively estimates simultaneously the "average coincidence" over the time window implicit in each rate , i . e . , at different temporal resolutions , thus retaining both short- and long-term coincidence measures crucial for segregation . Intuitively , the idea is that responses from pairs of channels that are strongly positively correlated should belong to the same stream , while channels that are uncorrelated or anti-correlated should belong to different streams . This decomposition need not be all-or-none , but rather responses of a given channel can be parceled to different streams in proportion to the degree of the average coincidence it exhibits with the two streams . This intuitive reasoning is captured by a factorization of the coincidence matrix into two uncorrelated streams by determining the direction of maximal incoherence between the incoming stimulus patterns . One such factorization algorithm is a nonlinear principal component analysis ( nPCA ) of the C-matrices [35] , where the principal eigenvectors correspond to masks that select the channels that are positively correlated within a stream , and parcel out the others to a different stream . This procedure is implemented by an auto-encoder network with two rectifying linear hidden units corresponding to foreground and background streams as shown in Fig . 1B ( right panel ) . The weights computed in the output branches of each unit are associated with each of the two sources in the input mixture , and the number of hidden units can be automatically increased if more than two segregated streams are anticipated . The nPCA is preferred over a linear PCA because the former assigns the channels of the two ( often anti-correlated ) sources to different eigenvectors , instead of combining them on opposite directions of a single eigenvector [36] . Another key innovation in the model implementation is that the nPCA decomposition is performed not directly on the input data from the cortical model ( which are modulated at rates ) , but rather on the columns of the C-matrices whose entries are either stationary or vary slowly regardless of the rates of the coincident channels . These common and slow dynamics enables stacking all C-matrices into one large matrix decomposition ( Fig . 1B ) . Specifically , the columns of the stacked matrices are applied ( as a batch ) to the auto-encoder network at each instant with the aim of computing weights that can reconstruct them while minimizing the mean-square reconstruction error . Linking these matrices has two critical advantages: It ensures that the pair of eigenvectors from each matrix decomposition is consistently labeled across all matrices ( e . g . , source 1 is associated with eigenvector 1 in all matrices ) ; It also couples the eigenvectors and balances their contributions to the minimization of the MSE in the auto-encoder . The weight vectors thus computed are then applied as masks on the cortical outputs . This procedure is repeated at each time step as the coincidence matrices evolve with the changing inputs . The separation of feature responses on different channels and their temporal continuity are two important properties of the model that allow temporal coherence to segregate sources . Several additional perceptual attributes can play a significant role including pitch , spatial location , and timbre . Here we shall focus on pitch as an example of such attributes . Speech mixtures share many of the same characteristics already seen in the examples of Fig . 2 and Fig . 3 . For instance , they contain harmonic complexes with different pitches ( e . g . , males versus females ) that often have closely spaced or temporally overlapped components . Speech also possesses other features such as broad bursts of noise immediately followed or preceded by voiced segments ( as in various consonant-vowel combinations ) , or even accompanied by voicing ( voiced consonants and fricatives ) . In all these cases , the syllabic onsets of one speaker synchronize a host of channels driven by the harmonics of the voicing , and that are desynchronized ( or uncorrelated ) with the channels driven by the other speaker . Fig . 4A depicts the clean spectra of two speech utterances ( middle and right panels ) and their mixture ( left panel ) illustrating the harmonic spectra and the temporal fluctuations in the speech signal at 3–7 Hz that make speech resemble the earlier harmonic sequences . The pitch tracks associated with each of these panels are shown below them . Fig . 4B illustrates the segregation of the two speech streams from the mixture using all available coincidence among the spectral ( frequency-scale ) and pitch channels in the C-matrices . The reconstructed spectrograms are not identical to the originals ( Fig . 4A ) , an inevitable consequence of the energetic masking among the crisscrossing components of the two speakers . Nevertheless , with two speakers there are sufficient gaps between the syllables of each speaker to provide clean , unmasked views of the other speaker's signal [40] . If more speakers are added to the mix , such gaps become sparser and the amount of energetic masking increases , and that is why it is harder to segregate one speaker in a crowd if they are not distinguished by unique features or a louder signal . An interesting aspect of speech is that the relative amplitudes of its harmonics vary widely over time reflecting the changing formants of different phonemes . Consequently , the saliency of the harmonic components changes continually , with weaker ones dropping out of the mixture as they become completely masked by the stronger components . Despite these changes , speech syllables of one speaker maintain a stable representation of a sufficient number of features from one time instant to the next , and thus can maintain the continuity of their stream . This is especially true of the pitch ( which changes only slowly and relatively little during normal speech ) . The same is true of the spectral region of maximum energy which reflects the average formant locations of a given speaker , reflecting partially the timbre and length of their vocal tract . Humans utilize either of these cues alone or in conjunction with additional cues to segregate mixtures . For instance , to segregate speech with overlapping pitch ranges ( a mixture of male speakers ) , one may rely on the different spectral envelopes ( timbres ) , or on other potentially different features such as location or loudness . Humans can also exploit more complex factors such as higher-level linguistic knowledge and memory as we discuss later . In the example of Fig . 4C , the two speakers of Fig . 4A are segregated based on the coincidence of only the spectral components conveyed by the frequency-scale channels . The extracted speech streams of the two speakers resemble the original unmixed signals , and their reconstructions exhibit significantly less mutual interference than the mixture as quantified later . Finally , as we discuss in more detail below , it is possible to segregate the speech mixture based on the pattern of correlations computed with one “anchor” feature such as the pitch channels of the female , i . e . , using only the columns of the C-matrix near the female pitch channels as illustrated in Fig . 4D . Exactly the same logic can be applied to any auxiliary function that is co-modulated in the same manner as the rest of the speech signal . For instance , one may “look” at the lip movements of a speaker which open and close in a manner that closely reflects the instantaneous power in the signal ( or its envelope ) as demonstrated in [41] . These two functions ( inter-lip distance and the acoustic envelope ) can then be exploited to segregate the target speech much as with the pitch channels earlier . Thus , by simply computing the correlation between the lip function ( Fig . 5B ) or the acoustic envelope ( Fig . 5C ) with all the remaining channels , an effective mask can be readily computed to extract the target female speech ( and the background male speech too ) . This example thus illustrates how in general any other co-modulated features of the speech signal ( e . g . , location , loudness , timbre , and visual signals such as lip movements can contribute to segregation of complex mixtures ) . The performance of the model is quantified with a database of 100 mixtures formed from pairs of male-female speech randomly sampled from the TIMIT database ( Fig . 6 ) where the spectra of the clean speech are compared to those of the corresponding segregated versions . The signal-to-noise ratio is computed as ( 1 ) ( 2 ) where are the cortical representations of the segregated sentences and are the cortical representations of the original sentences and is the cortical representation of the mixture . Average SNR improvement was 6 dB for mixture waveforms mixed at 0 dB . Another way to demonstrate the effectiveness of the segregation is to compare the match between the segregated samples and their corresponding originals . This is evidenced by the minimal overlap in Fig . 6B ( middle panel ) across the distributions of the coincidences computed between each segregated sentence and its original version versus the interfering speech . To compare directly these coincidences for each pair of mixed sentences , the difference between coincidences in each mixture are scatter-plotted in the bottom panel . Effective pairwise segregation ( e . g . , not extracting only one of the mixed sentences ) places the scatter points along the diagonal . Examples of segregated and reconstructed audio files can be found in S1 Dataset . So far , attention and memory have played no direct role in the segregation , but adding them is relatively straightforward . From a computational point of view , attention can be interpreted as a focus directed to one or a few features or feature subspaces of the cortical model which enhances their amplitudes relative to other unattended features . For instance , in segregating speech mixtures , one might choose to attend specifically to the high female pitch in a group of male speakers ( Fig . 4D ) , or to attend to the location cues or the lip movements ( Fig . 5C ) and rely on them to segregate the speakers . In these cases , only the appropriate subset of columns of the C-matrices are needed to compute the nPCA decomposition ( Fig . 1B ) . This is in fact also the interpretation of the simulations discussed in Fig . 3 for harmonic complexes . In all these cases , the segregation exploited only the C-matrix columns marking coincidences of the attended anchor channels ( pitch , lip , loudness ) with the remaining channels . Memory can also be strongly implicated in stream segregation in that it constitutes priors about the sources which can be effectively utilized to process the C-matrices and perform the segregation . For example , in extracting the melody of the violins in a large orchestra , it is necessary to know first what the timbre of a violin is before one can turn the attentional focus to its unique spectral shape features and pitch range . One conceptually simple way ( among many ) of exploiting such information is to use as ‘template’ the average auto-encoder weights ( masks ) computed from iterating on clean patterns of a particular voice or instrument , and use the resulting weights to perform an initial segregation of the desired source by applying the mixture to the stored mask directly .
A biologically plausible model of auditory cortical processing can be used to implement the perceptual organization of auditory scenes into distinct auditory objects ( streams ) . Two key ingredients are essential: ( 1 ) a multidimensional cortical representation of sound that explicitly encodes various acoustic features along which streaming can be induced; ( 2 ) clustering of the temporally coherent features into different streams . Temporal coherence is quantified by the coincidence between all pairs of cortical channels , slowly integrated at cortical time-scales as described in Fig . 1 . An auto-encoder network mimicking Hebbian synaptic rules implements the clustering through nonlinear PCA to segregate the sound mixture into a foreground and a background . The temporal coherence model segregates novel sounds based exclusively on the ongoing temporal coherence of their perceptual attributes . Previous efforts at exploiting explicitly or implicitly the correlations among stimulus features differed fundamentally in the details of their implementation . For example , some algorithms attempted to decompose directly the channels of the spectrogram representations [42] rather than the more distributed multi-scale cortical representations . They either used the fast phase-locked responses available in the early auditory system [43] , or relied exclusively on the pitch-rate responses induced by interactions among the unresolved harmonics of a voiced sound [44] . Both these temporal cues , however , are much faster than cortical dynamics ( >100 Hz ) and are highly volatile to the phase-shifts induced in different spectral regions by mildly reverberant environments . The cortical model instead naturally exploits multi-scale dynamics and spectral analyses to define the structure of all these computations as well as their parameters . For instance , the product of the wavelet coefficients ( entries of the C-matrices ) naturally compute the running-coincidence between the channel pairs , integrated over a time-interval determined by the time-constants of the cortical rate-filters ( Fig . 1 and Methods ) . This insures that all coincidences are integrated over time intervals that are commensurate with the dynamics of the underlying signals and that a balanced range of these windows are included to process slowly varying ( 2 Hz ) up to rapidly changing ( 16 Hz ) features . The biological plausibility of this model rests on physiological and anatomical support for the two postulates of the model: a cortical multidimensional representation of sound and coherence-dependent computations . The cortical representation is the end-result of a sequence of transformations in the early and central auditory system with experimental support discussed in detail in [34] . The version used here incorporates only a frequency ( tonotopic ) axis , spectrotemporal analysis ( scales and rates ) , and pitch analysis [37] . However , other features that are pre-cortically extracted can be readily added as inputs to the model such as spatial location ( from interaural differences and elevation cues ) and pitch of unresolved harmonics [45] . The second postulate concerns the crucial role of temporal coherence in streaming . It is a relatively recent hypothesis and hence direct tests remain scant . Nevertheless , targeted psychoacoustic studies have already provided perceptual support of the idea that coherence of stimulus-features is necessary for perception of streams [27] , [28] , [46] , [47] . Parallel physiological experiments have also demonstrated that coherence is a critical ingredient in streaming and have provided indirect evidence of its mechanisms through rapidly adapting cooperative and competitive interactions between coherent and incoherent responses [26] , [48] . Nevertheless , much more remains uncertain . For instance , where are these computations performed ? How exactly are the ( auto-encoder ) clustering analyses implemented ? And what exactly is the role of attentive listening ( versus pre-attentive processing ) in facilitating the various computations ? All these uncertainties , however , invoke coincidence-based computations and adaptive mechanisms that have been widely studied or postulated such as coincidence detection and Hebbian associations [49] , [50] . Dimensionality-reduction of the coincidence matrix ( through nonlinear PCA ) allows us effectively to cluster all correlated channels apart from others , thus grouping and designating them as belonging to distinct sources . This view bears a close relationship to the predictive clustering-based algorithm by [51] in which input feature vectors are gradually clustered ( or routed ) into distinct streams . In both the coherence and clustering algorithms , cortical dynamics play a crucial role in integrating incoming data into the appropriate streams , and therefore are expected to exhibit for the most part similar results . In some sense , the distinction between the two approaches is one of implementation rather than fundamental concepts . Clustering patterns and reducing their features are often ( but not always ) two sides of the same coin , and can be shown under certain conditions to be largely equivalent and yield similar clusters [52] . Nevertheless , from a biological perspective , it is important to adopt the correlation view as it suggests concrete mechanisms to explore . Our emphasis thus far has been on demonstrating the ability of the model to perform unsupervised ( automatic ) source segregation , much like a listener that has no specific objectives . In reality , of course , humans and animals utilize intentions and attention to selectively segregate one source as the foreground against the remaining background . This operational mode would similarly apply in applications in which the user of a technology identifies a target voice to enhance and isolate from among several based on the pitch , timbre , location , or other attributes . The temporal coherence algorithm can be readily and gracefully adapted to incorporate such information and task objectives , as when specific subsets of the C-matrix columns are used to segregate a targeted stream ( e . g . , Fig . 3 and Fig . 4 ) . In fact , our experience with the model suggests that segregation is usually of better quality and faster to compute with attentional priors . In summary , we have described a model for segregating complex sound mixtures based on the temporal coherence principle . The model computes the coincidence of multi-scale cortical features and clusters the coherent responses as emanating from one source . It requires no prior information , statistics , or knowledge of source properties , but can gracefully incorporate them along with cognitive influences such as attention to , or memory of specific attributes of a target source to segregate it from its background . The model provides a testable framework of the physiological bases and psychophysical manifestations of this remarkable ability . Finally , the relevance of these ideas transcends the auditory modality to elucidate the robust visual perception of cluttered scenes [53] , [54] .
Sound is first transformed into its auditory spectrogram , followed by a cortical spectrotemporal analysis of the modulations of the spectrogram ( Fig . 1A ) [34] . Pitch is an additional perceptual attribute that is derived from the resolved ( low-order ) harmonics and used in the model [37] . It is represented as a ‘pitch-gram’ of additional channels that are simply augmented to the cortical spectral channels prior to subsequent rate analysis ( see below ) . Other perceptual attributes such as location and unresolved harmonic pitch can also be computed and represented by an array of channels analogously to the pitch estimates . The auditory spectrogram , denoted by , is generated by a model of early auditory processing [55] , which begins with an affine wavelet transform of the acoustic signal , followed by nonlinear rectification and compression , and lateral inhibition to sharpen features . This results in F = 128 frequency channels that are equally spaced on a logarithmic frequency axis over 5 . 2 octaves . Cortical spectro-temporal analysis of the spectrogram is effectively performed in two steps [34]: a spectral wavelet decomposition followed by a temporal wavelet decomposition , as depicted in Fig . 1A . The first analysis provides multi-scale ( multi-bandwidth ) views of each spectral slice , resulting in a 2D frequency-scale representation . It is implemented by convolving the spectral slice with complex-valued spectral receptive fields similar to Gabor functions , parametrized by spectral tuning , i . e . , . The outcome of this step is an array of FxS frequency-scale channels indexed by frequency and local spectral bandwidth at each time instant t . We typically used = 2 to 5 scales in our simulations ( e . g . , cyc/oct ) , producing copies of the spectrogram channels with different degrees of spectral smoothing . In addition , the pitch of each spectrogram frame is also computed ( if desired ) using a harmonic template-matching algorithm [37] . Pitch values and saliency were then expressed as a pitch-gram ( P ) channels that are appended to the frequency-scale channels ( Fig . 1B ) . The cortical rate-analysis is then applied to the modulus of each of the channel outputs in the freq-scale-pitch array by passing them through an array of modulation-selective filters ( ) , each indexed by its center rate which range over Hz in octave steps ( Fig . 1B ) . This temporal wavelet analysis of the response of each channel is described in detail in [34] . Therefore , the final representation of the cortical outputs ( features ) is along four axes denoted by . It consists of coincidence matrices per time frame , each of size x ( ( Fig . 1B ) . The exact choice of all above parameters is not critical for the model in that the performance changes very gradually when the parameters or number of feature channels are altered . All parameter values in the model were chosen based on previous simulations with the various components of the model . For example , the choice of rates ( 2–32 Hz ) and scales ( 1–8 cyc/oct ) reflected their utility in the representation of speech and other complex sounds in numerous previous applications of the cortical model [34] . Thus , the parameters chosen were known to reflect speech and music , but ofcourse could have been chosen differently if the stimuli were drastically different . The least committal choice is to include the largest range of scales and rates that is computationally feasible . In our implementations , the algorithm became noticeably slow when , , , and . The decomposition of the C-matrices is carried out as described earlier in Fig . 1B . The iterative procedure to learn the auto-encoder weights employs Limited-memory Broyden-Fletcher-Goldfarb-Shannon ( L-BFGS ) method as implemented in [56] . The output weight vectors ( Fig . 1B ) thus computed are subsequently applied as masks on the input channels . This procedure that is repeated every time step using the weights learned in the previous time step as initial conditions to ensure that the assignment of the learned eigenvectors remains consistent over time . Note that the C matrices do not change rapidly , but rather slowly , as fast as the time-constants of their corresponding rate analyses allow ( ) . For example , for the Hz filters , the cortical outputs change slowly reflecting a time-constant of approximately 250 ms . More often , however , the C-matrix entries change much slower reflecting the sustained coincidence patterns between different channels . For example , in the simple case of two alternating tones ( Fig . 2A ) , the C-matrix entries reach a steady state after a fraction of a second , and then remain constant reflecting the unchanging coincidence pattern between the two tones . Similarly , if the pitch of a speaker remains relatively constant , then the correlation between the harmonic channels remains approximately constant since the partials are modulated similarly in time . This aspect of the model explains the source of the continuity in the streams . The final step in the model is to invert the masked cortical outputs back to the sound [34] .
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Humans and many animals can effortlessly navigate complex sensory environments , segregating and attending to one desired target source while suppressing distracting and interfering others . In this paper , we present an algorithmic model that can accomplish this task with no prior information or training on complex signals such as speech mixtures , and speech in noise and music . The model accounts for this ability relying solely on the temporal coherence principle , the notion that perceived sources emit coherently modulated features that evoke coincident cortical response patterns . It further demonstrates how basic cortical mechanisms common to all sensory systems can implement the necessary representations , as well as the adaptive computations necessary to maintain continuity by tracking slowly changing characteristics of different sources in a scene .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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2014
|
Segregating Complex Sound Sources through Temporal Coherence
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Visceral leishmaniasis ( VL ) is a major problem worldwide and causes significant morbidity and mortality . Existing drugs against VL have limitations , including their invasive means of administration long duration of treatment regimens . There are also concerns regarding increasing treatment relapses as well as the identification of resistant clinical strains with the use of miltefosine , the sole oral drug for VL . There is , therefore , an urgent need for new alternative oral drugs for VL . In the present study , we show the leishmanicidal effect of a novel , oral antimalarial endoperoxide N-251 . In our In vitro studies , N-251 selectively and specifically killed Leishmania donovani D10 amastigotes with no accompanying toxicity toward the host cells . In addition , N-251 exhibited comparable activities against promastigotes of L . donovani D10 , as well as other L . donovani complex parasites , suggesting a wide spectrum of activity . Furthermore , even after a progressive infection was established in mice , N-251 significantly eliminated amastigotes when administered orally . Finally , N-251 suppressed granuloma formation in mice liver through parasite death . These findings indicate the therapeutic effect of N-251 as an oral drug , hence suggest N-251 to be a promising lead compound for the development of a new oral chemotherapy against VL .
Visceral leishmaniasis ( VL ) , also known as kala azar , is a disease with a worldwide distribution . It is endemic in more than 62 countries , with over 90% of cases occurring in Brazil , Ethiopia , India , Somalia , South Sudan Sudan [1] . An estimated 200 , 000 to 400 , 000 new cases are reported annually [1] , making VL a serious public health problem . It is caused by members of the Leishmania donovani complex , which consists of four species: L . archibaldi , L . chagasi , L . donovani , and L . infantum , which are distinguished by their vectors , reservoir hosts , and their associated pathology [2 , 3] . Parasites are transmitted to humans by the female phlebotomine sandfly . VL is typically presented as a chronic infection , characterized by irregular bouts of fever , weight loss , splenohepatomegaly , anemia , and a high mortality rate of almost 100% if left untreated [1] . At present , there is no licensed vaccine , therefore , chemotherapy remains the main form of control . Currently , treatment for VL relies on the use of drugs such as pentavalent antimonials , paromomycin , pentamidine , and amphotericin B . Most of these drugs are , however , either limited by their invasive methods of administration and/or their long periods of high dose treatment . Miltefosine remains the sole oral anti-leishmanial drug since it was approved for use in 2002 [4–6] . Recently , the absence of alternate oral drugs has become a major concern , particularly with recent reports of increasing treatment relapses as well as the identification of miltefosine-resistant clinical strains [7–9] . There is , therefore , an urgent need for the development of novel oral chemotherapeutics as alternatives to miltefosine for the effective treatment of VL cases . Recently , natural and synthetic endoperoxide compounds have received increasing attention due to their fast action and ability to target drug-resistant parasite strains [10–19] . This class of compound is known to be activated within host cells , as a result of the iron-mediated cleavage of the characteristic endoperoxide bridge . Moreover , as anti-infective agents , they are easily administered via the oral route . Previously , we synthesized a novel endoperoxide compound , 6- ( 1 , 2 , 6 , 7-tetraoxaspiro[7 . 11]nonadec-4-yl ) hexan-1-ol ( N-251 ) ( S1 Fig ) , that possesses antimalarial [20 , 21] , anti-Toxoplasma gondii [22] , anti-schistosomal [23–25] , anti-viral [26 , 27] activities in vitro when administered to mice orally . These findings suggest that N-251 possesses a broad spectrum of anti-infective activities . In the present study , we report on the in vitro leishmanicidal effects of N-251 against amastigotes of L . donovani , as well as the promastigotes of various L . donovani complex parasites from different geographical locations . In addition , we report on the in vivo efficacy of N-251 as an oral drug against L . donovani amastigotes . Results suggest that N-251 may be a promising lead compound for the development of a new oral chemotherapy against VL .
N-251 was chemically synthesized as described previously [20 , 21] . It was dissolved in dimethyl sulfoxide ( DMSO ) as 100 mM stock solutions stored at −20 °C . Miltefosine was purchased from Sigma-Aldrich ( MO , USA ) . Medium 199 was purchased from Nissui Pharmaceuticals Co . , Ltd , Japan . Dulbecco’s Modified Eagle’s Medium ( DMEM ) Roswell Park Memorial Institute ( RPMI ) medium were purchased from Sigma-Aldrich . HEPES Buffer ( 1 M ) was purchased from MP Biomedicals , LLC ( Ohio , USA ) . All reagents were maintained at 4°C unless otherwise stated . Five strains of L . donovani complex from different geographical locations with high VL burden ( Brazil , Nepal , India , Sudan Turkey ) were used in this study . These include L . chagasi PP75 ( MHOM/BR/74/PP75 ) , L . donovani D10 ( MHOM/NP/03/D10 ) , L . donovani DD8 ( MHOM/IN/80/DD8 ) , L . donovani KH ( MHOM/SU/43/KH ) ( ATCC 30503 ) , and L . infantum EP173 . Promastigotes were cultured in vitro in M-199 complete medium ( containing 25 mM HEPES at 25°C ) supplemented with 10% heat-inactivated fetal bovine serum ( Hi-FBS ) . For in vitro and in vivo infectivity assays , L . donovani D10 was used because it is constantly maintained in mice in our lab , therefore , highly infective . The usual procedure is to use freshly isolated parasites that have undergone 1–3 cycles of passages in M-199 complete medium to ensure high infectivity rates in our experiments . All animal experiments were reviewed and approved by the Animal Experiment Committee at the Graduate School of Agricultural and Life Sciences , University of Tokyo ( Ref . No . P16-254 ) . The experiments were performed in accordance with the Regulations for Animal Care Use of the University of Tokyo , which are based on the Law for the Humane Treatment and Management of Animals , Stards Relating to the Care and Management of Laboratory Animals Relief of Pain ( the Ministry of the Environment ) , Fundamental Guidelines for Proper Conduct of Animal Experiment Related Activities in Academic Research Institutions ( the Ministry of Education , Culture , Sports , Science Technology ) and the Guidelines for Proper Conduct of Animal Experiments ( the Science Council of Japan ) . At the end of the experiments , the animals were euthanized by exsanguination under anesthesia with isoflurane followed by cervical dislocation . The leishmanicidal effect of N-251 on L . donovani amastigotes was evaluated in murine macrophages . First , 4 x 104 RAW 264 . 7 macrophage cells in 200 μl of DMEM ( containing 1% penicillin/streptomycin , supplemented with 10% Hi-FBS ) were seeded in the wells of an 8-well chamber slide incubated at 37 °C in 5% CO2 for 2 hours to allow cell attachment . Next , stationary phase L . donovani promastigotes in fresh DMEM were added to the macrophages at a ratio of 50:1 ( parasites:macrophage ) and incubated for 6 h at 37 °C in 5% CO2 . During this period , the parasites invaded the macrophages and then transformed into amastigotes . Free promastigotes were subsequently removed by successive washes with DMEM . Infected macrophages were then treated with N-251 at concentrations ranging from 0 . 78 to 50 μM , and incubated for 24 , 48 and 72 h . Miltefosine was used as a reference drug control . Infected treated macrophages were then washed with 1x phosphate-buffered saline ( PBS ) and fixed with methanol for 10 min . Finally , the macrophages were stained with 5% Giemsa in PBS for 25 min and observed under a light microscope . Anti-leishmanial activity was evaluated by observing 300 macrophages within each treatment group . The percentage of infected macrophages was calculated using the following formula: [ ( Number of infected macrophages/300 macrophages observed ) x 100] . Infection index values were then calculated according to the following formula: [Percentage of infected macrophages x average number of intracellular amastigotes per infected macrophage] . Infection index values were then converted to percentage survival values relative to the untreated parasite population . IC50 values were eventually obtained by sigmoidal dose–response curve analysis using the scatter plot option of Microsoft Excel 2016 ( Microsoft Corporation , Washington , USA ) expressed as the mean of samples ± stard deviation ( SD ) from three independent experiments conducted in duplicates . The cytotoxicity of N-251 against murine macrophage cell lines was assessed using the Invitrogen alamar blue assay kit ( ThermoFisher Scientific , Japan ) according to the manufacturer’s instructions with modifications . Both RAW 264 . 7 and J774 cell lines were used in this assay . First , 5 × 103 cells were seeded into each well of a 96-well plate . Varying concentrations of N-251 , ranging from 0 . 195 μM to 200 μM , were then added to the cells and incubated for 48 h at 37 °C in 5% CO2 . Miltefosine was used as a positive control . Next , 10% alamar blue dye was added to all wells and the plate was incubated for another 24 h in darkness . After a total of 72 h , fluorescence intensity was measured at a wavelength of 600 nm using the SpectraMax Paradigm Multi-Mode Detection Platform ( Molecular devices LLC , CA , USA ) . All experiments were carried out 4 times in duplicates . Fluorescence intensity , which is directly proportional to the concentration of surviving parasites , was converted to percentage survival . Cytotoxic concentrations at 50% ( CC50 ) were eventually obtained by sigmoidal dose–response curve analysis using the scatter plot option of Microsoft Excel 2016 and expressed as mean of samples ± standard deviation ( SD ) . The selectivity index ( SI ) was calculated as the ratio of the CC50 obtained for both RAW 264 . 7 and J774 macrophage cells and the IC50 for Leishmania donovani D10 amastigotes . The effect of N-251 was also evaluated in logarithmic phase promastigotes of the L . donovani complex using the Invitrogen alamar blue assay kit . The experimental procedure carried out was essentially the same as described above . However , 5 × 104 promastigotes were seeded per well in this case . Treated and untreated promastigotes were incubated at 25 °C . Miltefosine was used a reference compound . IC50 values were also obtained as described above . To evaluate the anti-leishmanial efficacy of N-251 , L . donovani D10-infected mice , randomly allocated into experimental groups of 5 animals each , were treated orally with 68 mg/kg body weight of N-251 in olive oil . This dose was determined as the maximum concentration at which no toxicity ( ruffled fur , severe weight loss , reduced activity and death ) was observed in previous antimalarial studies [21] . Miltefosine ( 10 mg/kg body weight ) and olive oil were used as positive and negative controls , respectively . First , 6-week-old BALB/cA mice ( CLEA Japan , Inc . Tokyo , Japan ) were infected intraperitoneally with 1x108 stationary phase promastigotes . Four weeks post-infection , treatment was administered through a feeding gavage at 12-hour intervals for 14 consecutive days . Initially , treatment efficacy was expressed as Leishman-Donovan Units ( LDU ) , which was determined by sterilely harvesting and weighing the spleen and liver of euthanized mice . The macrophages in the spleen and liver were imprinted on glass slides and then fixed with methanol for 10 minutes . Imprints were then stained with 5% Giemsa in PBS for 25 mins and examined microscopically . LDU was calculated based on the formula: [ ( Number of Leishmania amastigotes per 1000 macrophage cells ) x organ weight ( g ) ] [28–33] . Percentage reduction was also calculated as follows: 100-[ ( LDU of treatment group/LDU of untreated group ) x 100] Determination of LDU was done before treatment at 1-day post-treatment . To further evaluate the anti-leishmanial efficacy of N-251 , post-treatment parasite burden levels were also assessed by limiting dilution analysis , modified for L . donovani in our laboratory . Briefly , portions of harvested mice liver and spleen were cut , weighed and homogenized in tissue grinders sterilely . The homogenate was suspended in a final volume of 2 ml of M-199 complete medium supplemented with 10% Hi-FBS and 1% penicillin/streptomycin . Five-fold serial dilutions ( ranging from 1 to 6 . 5 x 10−12 ) of the homogenate were made in M-199 complete medium plated ( 100 ul/well ) in 96-well flat-bottom tissue culture plates . Plates were stored in a humidified incubator at 25°C for 14 days after which wells were visually examined for growth with an inverted microscope . The presence or absence of motile promastigotes was recorded in each well . The final titer was the last dilution for which the well contained at least one parasite . The number of parasites per gram organ ( parasite burden ) was calculated as follows: parasite burden = ( geometric mean of reciprocal titers from each duplicate/ weight of homogenized cross section ) x 20 , where 20 is the reciprocal fraction of the homogenized organ inoculated into the first well . Percentage reduction was also calculated as follows: 100-[ ( parasite burden in treatment group/ parasite burden of control group ) x 100] . LDU and limiting dilution results were analyzed using Microsoft Excel 2016 are expressed as mean ± stard error of the mean ( SEM ) from 5 mice per group . Comparison of means was done using two-tailed Mann-Whitney U-test and differences were considered significant when p ≤ 0 . 05 . In addition to assessing post-treatment parasite burden levels , samples of harvested mice liver and spleen were also collected for histological studies . Tissue samples were fixed with formaldehyde for 48 hours and gradually dehydrated with increasing concentrations of ethanol . The tissues were then embedded in paraffin 5 μm thick and sections were cut using a microtome . Thin tissue sections on glass slides were stained with Hematoxylin and Eosin ( HE ) and analyzed by visualization under light microscope . The number of granulomas was determined with quantification of twenty microscope optic fields using a 40x objective of a light microscope . The experimental plan to evaluate the anti-leishmanial efficacy of N-251 has been summarized in Fig 1
To evaluate the leishmanicidal activity of N-251 , we used the amastigote form of L . donovani D10 parasites as the model parasite because the intracellular amastigote , which lives within the host macrophage , is the form of the parasite responsible for clinical symptoms of VL observed in the human host . Therefore , targeting this parasite form in vitro provides a more direct insight into the effect of N-251 in vivo . Stationary phase promastigotes and RAW 264 . 7 macrophages were incubated for 6 h to allow the invasion and transformation of promastigotes within the macrophages . Intracellular amastigotes were then challenged with 0–50 μM of N-251 for 24 , 48 and 72 h to investigate the dose-dependent leishmanicidal effect of N-251 . In addition , the IC50 of N-251 was determined at 72 h by microscopy . Our results clearly show the leishmanicidal effect of N-251 on intracellular amastigotes . When treated with varying concentrations of N-251 , L . donovani amastigotes were eliminated in a dose dependent manner ( S2 and S3 Figs ) at an IC50 of 6 . 69 ± 0 . 82 , ( Table 1 ) . Although most parasites had been cleared by 24 hours , a few macrophages were observed with about one or two intracellular parasites that were probably dead . By 48 and 72 hours , intracellular parasites were completely cleared from all macrophages ( Fig 2 ) . The leishmanicidal activity of N-251 was comparable to that of miltefosine ( reference drug control ) . The cytotoxicity of N-251 was evaluated on both RAW 264 . 7 and J774 murine macrophage cell lines with an alamar blue assay kit for 72 hours . The toxicity of the N-251 treatments was very low among both RAW 264 . 7 and J774 cells; within the concentration ranges tested , the IC50 values were 66 . 41 ± 4 . 15 μM and 138 . 25 ± 24 . 27 μM , respectively ( Table 1 ) . These IC50 values correspond to SI values of ≥10 for N-251 ( Table 1 ) , suggesting that N-251 is highly specific in its activity against L . donovani amastigotes , as biological efficacy cannot be attributed to cytotoxicity when the selectivity index is ≥10 . The SI values of miltefosine were about twice that of N-251 , which were quite comparable ( Table 1 ) . The results therefore suggest that N-251 selectively and specifically exhibits leishmanicidal activity by targeting L . donovani intracellular amastigotes within host macrophage cells , without having any cytotoxic effect on host cells . To examine the effect of N-251 on different L . donovani complex parasites , we screened N-251 against the promastigotes of five parasites belonging to the L . donovani complex . The parasites used in this study were selected specifically from different geographical locations known to be highly endemic for VL . They include: L . chagasi PP75 ( Brazil ) , L . donovani D10 ( Nepal ) [34 , 35] , L . donovani DD8 ( India ) , L . donovani KH ( ATCC 30503 ) ( Sudan ) and L . infantum EP173 ( Turkey ) . Our results clearly show the leishmanicidal effect of N-251 on various parasites of the L . donovani complex . When treated with N-251 , leishmanicidal activity was observed against all three L . donovani ( D10 , Dd8 KH ) parasites , as well as L . chagasi and L . infantum , in a dose dependent manner ( S4 Fig ) . The IC50 values obtained ranged from 6 . 12 ± 1 . 64 μM to 26 . 90 ± 2 . 51 μM ( Table 2 ) . The results shows that the activity of N-251 is reproducible in promastigotes of different L . donovani complex parasites including L . donovani D10 , which had its amastigote forms eliminated by N-251 in our in vitro infection assays . This suggests that N-251 will probably exhibit leishmanicidal activity against their respective amastigote forms , regardless of their geographic origin . The therapeutic efficacy of N-251 was evaluated in L . donovani-infected mice . Briefly , mice were initially infected intraperitoneally with 108 stationary phase promastigotes , followed by a 4-week incubation period . During this time , parasites invaded the neutrophils and mononuclear phagocytes within the spleen and liver , and a chronic infection was established [28 , 36 , 37] . Mice were then treated orally through feeding gavages at 12-hour intervals for 14 days . LDU was then determined in harvested spleen liver tissues before and after treatment . Before treatment , LDU in the liver and spleen were 857 . 73 ± 104 . 05 and 13 . 94 ± 0 . 30 , respectively . After treatment , in the vehicle control group that did not receive any drug , LDU increased by over 100% to 2214 . 34 ± 426 . 73 in the liver and 51 . 38 ± 16 . 24 in the spleen . In contrast , in N-251-treated mice , LDU decreased significantly ( p = 0 . 012 ) by 85 . 73% and 93 . 98% to 316 . 00 ± 161 . 14 and 3 . 09 ± 1 . 29 within the liver and spleen , respectively , relative to the untreated group at 6 weeks post-infection ( wpi ) . These results clearly show that after 14 days of treatment , parasites were significantly cleared by N-251 ( Fig 3a and 3b ) . Similarly , in miltefosine-treated mice , parasites were cleared at levels that were comparable to those obtained by N-251 . To further confirm the therapeutic effect of N-251 , we also evaluated its efficacy by limited dilution analysis to determine parasite burden levels post-treatment . Results here also showed that N-251 significantly cleared L . donovani parasites . After treatment , parasite burden in the untreated group were determined at 4 . 15 x 108 and 2 . 59 x 108 parasites/g organ in liver and spleen respectively . However , in N-251-treated mice , parasite burden decreased significantly ( p < 0 . 05 ) by 85 . 89% and 97 . 41% to 5 . 86 x 107 and 6 . 68 x 106 parasites/g organ in liver and spleen respectively ( Fig 3c and 3d ) . In vivo data obtained by both LDU and limiting dilution analyses therefore support our in vitro observation that N-251 has leishmanicidal activity against L . donovani amastigotes . Data herein therefore demonstrates the therapeutic effect of N-251 as an oral drug in L . donovani-infected mice . One of the major signs of histopathological damage known to be associated with L donovani infection in mice humans are granuloma formation in the liver [38] . Hence , we investigated efficacy of N-251 in suppressing tissue damage by VL . To accomplish this , samples of compound-treated untreated mice liver tissues were collected and processed for histological examinations . The results clearly showed that N-251 is able to suppress damage due to granuloma formation in L . donovani-infected liver . The untreated group revealed high levels of granuloma formation ( an average of about 4 per field ) within mice liver . In contrast , when mice were treated with N-251 , no granuloma formation was observed within the liver ( Fig 4 ) . These results demonstrate that N-251 significantly suppresses and improves the conditions of L . donovani-infected mice liver tissues as a result of significant parasite reduction .
The demand for novel , orally administered alternatives to miltefosine for the treatment of VL has become more urgent . This is particularly true as existing drugs are plagued with several limitations , such as their invasive means of administration and long duration regimens [39] . Currently , miltefosine is the sole orally-administered drug for the treatment of VL . Recently , the widespread use of miltefosine , coupled with recent reports of increasing treatment relapses , have raised concerns of possibility of the rapid emergence of resistance due to its long half-life [40 , 41] , thus , highlighting the urgent need for novel , oral antileishmanial drugs . In the present study , we showed that N-251 , a novel , synthetic , orally-administered endoperoxide , selectively and specifically kills L . donovani amastigotes with no toxicity to host cells . Even after a progressive infection was established in mice , N-251 eliminated intracellular amastigotes , resulting in the suppression of hyper-granuloma formation in mice liver . The formation of hepatic hyper-granulomas is known to be mostly elicited by the presence of intracellular amastigotes in Leishmania-infected hosts [38 , 42 , 43] . This therefore suggest that the significant reduction of intracellular amastigotes by N-251 observed in this study , may have resulted in the absence of granuloma , indicating the potential of N-251 as a lead compound for the development of an oral chemotherapy against VL . The cleavage of the endoperoxide bridge ( C-O-O-C , S1 Fig ) generates short-lived , cytotoxic oxyradicals in the presence of heme iron or free Fe2+ . Fenton degradation of the oxyradical intermediates can produce hydroxyl radicals ( OH ) that are highly reactive against a wide variety of molecules such as amino acids , enzymes , lipids and nucleic acids [44 , 45] . The released radicals then oxidize these molecules , thereby inhibiting their functions , which eventually leads to parasite death . This explains why endoperoxides exhibit a wide spectrum of anti-infective activity . N-251 is also characterized by a peroxide bridge within its structure that can be cleaved and activated in the presence of a heme iron or free Fe2+ [20 , 46 , 47] , suggesting that this is the basis for its antileishmanial activity observed in both promastigotes and amastigotes screened in this study . Leishmania are known to forage for iron from host macrophages for their growth for defense against the macrophage’s oxidative assault by providing iron to the antioxidant enzyme superoxide dismutase [19 , 48 , 49] . The accumulation of iron within the parasite therefore results in the selective killing of the parasite by N-251 . This also explains the 4-fold increase in activity observed against intracellular amastigotes relative to that of promastigotes . Some of the current antileishmanial drugs , including pentavalent antimonials and paromomycin , have been reported to exhibit a limited spectrum of antileishmanial activity , which is mostly dependent on the species of Leishmania , the geographical location , as well as the clinical presentation of the disease [50–54] . However , in this study , N-251 exhibited leishmanicidal effects against various L donovani complex parasites from different parts of the world . This therefore shows the reproducibility of N-251’s activity in different L . donovani complex parasites , suggesting that N-251 may exhibit leishmanicidal activity against different VL parasites , regardless of their geographical origin . In addition , in previous studies , N-251 was observed to be active against other non-Leishmania parasites such as Plasmodium , T . gondii and Schistosoma [20 , 21 , 23 , 47 , 55] . It is therefore quite clear that N-251 exhibits a wide spectrum of activity . In general , advantages of wide spectrum compounds include cost effectiveness in using one drug to treat different infectious diseases as well as serving as good options for Mass drug administration exercises in poor endemic regions [56 , 57] . This inherent characteristic of N-251 appears to be one of the several advantages it has over other existing antileishmanial drugs . Miltefosine , the only oral drug currently available against VL , affects parasites by the disruption of parasitic Ca2+ homeostasis via opening of the sphingosine-activated plasma membrane Ca2+ channel , together with the impairment of the acidocalcisomes [58] . However , it is limited by its teratogenicity and several side effects . There are also concerns that its use as a monotherapy can eventually lead to the rapid emergence of resistant parasites [40 , 59] . In fact , treatment relapses among VL patients [60] , as well as the identification of miltefosine-resistant clinical strains , have already been reported [61 , 62] . Our compound , N-251 , is also orally-administered and acts through a different mode of action from miltefosine , making it a good candidate for possible combination with miltefosine . According to the WHO , combination therapy is an important strategy to improve leishmaniasis therapy and also delay the emergence of resistance [63 , 64] , as can be seen in the case of malaria , tuberculosis , and HIV [65–67] . In future studies , we will therefore evaluate the combinatory effect of N-251 and miltefosine in experimental VL . In conclusion , results herein demonstrates the in vitro and in vivo antileishmanial effect of the novel orally administered synthetic endoperoxide , N-251 . Also , the broad-spectrum activity of N-251 against various parasites of L . donovani complex from different geographical locations was established . More importantly , this study highlights the importance of N-251 as an oral drug for monotherapy its possible combination with miltefosine . Finally , N-251 may be a promising lead compound for the development of new oral chemotherapy against visceral leishmaniasis .
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Visceral Leishmaniasis remains a serious health problem in many developing countries with thousands of new cases recorded annually . Novel oral therapies are required as existing drugs are limited by their invasive means of administration long duration of treatment regimens . Moreover , with miltefosine as the sole oral drug , there are concerns of the eventual development of parasite resistance with its continuous use . In this study , we report on the in vitro and in vivo leishmanicidal effect of orally administered N-251 on the Leishmania donovani complex parasites in mice . Our results suggest that N-251 may be a potential lead compound for the development of a new oral chemotherapy against VL
|
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2019
|
Oral activity of the antimalarial endoperoxide 6-(1,2,6,7-tetraoxaspiro[7.11]nonadec-4-yl)hexan-1-ol (N-251) against Leishmania donovani complex
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Rift Valley fever virus ( RVFV ) is a zoonotic arbovirus affecting livestock and people . This study was conducted in western Kenya where RVFV outbreaks have not previously been reported . The aims were to document the seroprevalence and risk factors for RVFV antibodies in a community-based sample from western Kenya and compare this with slaughterhouse workers in the same region who are considered a high-risk group for RVFV exposure . The study was conducted in western Kenya between July 2010 and November 2012 . Individuals were recruited from randomly selected homesteads and a census of slaughterhouses . Structured questionnaire tools were used to collect information on demographic data , health , and risk factors for zoonotic disease exposure . Indirect ELISA on serum samples determined seropositivity to RVFV . Risk factor analysis for RVFV seropositivity was conducted using multi-level logistic regression . A total of 1861 individuals were sampled in 384 homesteads . The seroprevalence of RVFV in the community was 0 . 8% ( 95% CI 0 . 5–1 . 3 ) . The variables significantly associated with RVFV seropositivity in the community were increasing age ( OR 1 . 2; 95% CI 1 . 1–1 . 4 , p<0 . 001 ) , and slaughtering cattle at the homestead ( OR 3 . 3; 95% CI 1 . 0–10 . 5 , p = 0 . 047 ) . A total of 553 slaughterhouse workers were sampled in 84 ruminant slaughterhouses . The seroprevalence of RVFV in slaughterhouse workers was 2 . 5% ( 95% CI 1 . 5–4 . 2 ) . Being the slaughterman , the person who cuts the animal’s throat ( OR 3 . 5; 95% CI 1 . 0–12 . 1 , p = 0 . 047 ) , was significantly associated with RVFV seropositivity . This study investigated and compared the epidemiology of RVFV between community members and slaughterhouse workers in western Kenya . The data demonstrate that slaughtering animals is a risk factor for RVFV seropositivity and that slaughterhouse workers are a high-risk group for RVFV seropositivity in this environment . These risk factors have been previously reported in other studies providing further evidence for RVFV circulation in western Kenya .
Rift Valley fever virus ( RVFV ) is a zoonotic arbovirus affecting livestock and people in Africa and the Arabian peninsula [1] . Epidemics of Rift Valley fever ( RVF ) are associated with greater than average rainfall , and are characterised by abortion in livestock and febrile illness in people [1 , 2] . RVFV outbreaks have not previously been reported in western Kenya since the initial discovery of the virus in the Rift Valley in 1931 , although epidemics have occurred in neighboring regions [3] . It has been suggested that the virus can be maintained in animal populations between epidemics and potentially spread to new areas through animal movement [4] . Previous work has documented low-levels of RVFV exposure in western Kenya , compared to high-levels in north-eastern populations , but exposure to RVFV in high-risk occupations in western Kenya has not been examined [4 , 5] . The climate of western Kenya is sub-tropical with consistently high temperatures and humidity , and predictable rain/dry season cycles . The study area is semi-humid to humid with greater than 1200mm annual rainfall . This differs from the semi-arid rangelands that cover the majority of Kenya [6] . The virus is transmitted between animals and from animals to people by mosquitoes , however the most common route of infection for people during epidemics is exposure to infected animals or their products , particularly abortion material when affected animals are shedding large amounts of virus [7 , 8] . Slaughterhouse workers are at risk of exposure to infected materials such as blood through cutting animals’ throats and handling animal parts [8–10] . Most people infected by RVFV suffer mild or subclinical disease , although a small percentage will suffer severe disease . Fever , nausea , and vomiting are the most commonly reported clinical signs in people [11 , 12] . Other signs include large joint arthralgia , diarrhea , jaundice , right upper quadrant pain , and backache [11–13] . Ocular manifestations , including uveitis and retinitis , occur in 1 . 5–3% of patients and can result in permanent vision loss [8 , 12] . Severe forms of the disease occur in up to 10% of patients and include a haemorrhagic form that is associated with high fatality and a meningoencephalitis form manifested by neurological symptoms which may continue after resolution of the infection [8 , 14 , 15] . Clinical diagnosis of RVF may be hindered because of the similar presentation to other endemic mosquito-borne illnesses , such as malaria or dengue [11 , 16] . Diagnosis of RVF is made by virus isolation or polymerase chain reaction ( PCR ) in the early stage of clinical disease [17] . Virus neutralisation assays are the gold standard of antibody detection , but the requirement for live virus makes their use limited [18] . Enzyme-linked immunosorbent assays ( ELISA ) for Immunoglobulin M and IgG can be used for diagnosis and surveillance of RVF , identifying recent and historic exposure , respectively [19] . This study aimed to determine the seroprevalence of RVF antibodies in a community in western Kenya where disease outbreaks have not previously been reported , but where a prior study documented RVFV circulation [4] . A concurrent study in slaughterhouse workers aimed to determine if there was an occupational risk of RVFV seropositivity in an area that has not experienced RVF outbreaks similar to reports in areas that experience epizootics [8] .
Ethical approvals for the ‘People , Animals and their Zoonoses’ ( PAZ ) project community and slaughterhouse worker studies were granted by the Kenya Medical Research Institute ( KEMRI ) Ethical Review Committee ( SCC Protocols 1701 and 2086 , respectively ) . Written , informed consent was obtained from all participants; for children between 5 and 17 a parent or legal guardian provided consent . Consent forms were in English and Kiswahili . The study was conducted in western Kenya in the Lake Victoria Basin region on the border with Uganda ( Fig 1 ) . The study area was within a 45-kilometre radius from Busia town where the project laboratory was located ( Fig 1 ) . The region is predominantly rural but has a high population density with approximately 500 people per square kilometre ( estimated from the Kenyan Human Population Census of 2009 ) . The predominant ethnic groups are Luhya , Luo , and Teso . It is estimated that more than 40% of homesteads are below the poverty line [20] . The mean homestead size is 5 persons ( estimated from the Kenyan Human Population Census of 2009 ) . Mixed subsistence farming is the predominant source of livelihood for 75 . 6% of homesteads [21] . The study population included every ruminant slaughterhouse worker in the study area . The location of slaughterhouses in the study area was obtained from the former District Veterinary Officers ( now County Directors of Veterinary Services ) who had oversight over meat inspection ( Fig 1 ) . There were 88 ruminant slaughterhouses identified in the study area . Inclusion criteria specified all workers , aged over 18 years and present at the slaughterhouse on the day of sampling . Due to the time required to process the samples on the day of collection , the number of workers recruited from each slaughterhouse was limited to 12 . The mean number of workers in the slaughterhouses was 9 and the mean number of animals slaughtered per week was 21 . In slaughterhouses with 12 workers or less , all willing participants were recruited . In slaughterhouses with greater than 12 workers , a random selection of 12 willing participants from the workers present on the day was sampled . On the day of sampling workers were assigned a number . This was written on a piece of paper and placed in a container . Numbers were selected from the container until twelve participants were chosen . A clinical officer from the project team , responsible for all medical examinations , could exclude participants for any underlying health condition where participation might affect them adversely , including third trimester pregnancy , under the age of eighteen , severe inebriation , aggression toward the project staff , and extreme old age ( over 85 years ) . This project investigated the current practices in slaughterhouses in western Kenya using two tools: 1 ) the foreman was asked questions related to the facilities and practices within the slaughterhouses as a unit; 2 ) individual workers were asked questions regarding knowledge , attitudes , hygiene practices , and health of the worker . Questionnaire data were recorded in a Palm operating system ( Palm OS ) personal digital assistant ( PDA ) using Pendragon Forms 5 . 1 ( Pendragon Software Corporation , Libertyville , IL , USA ) . Microsoft Access databases were used to manage data . Samples were collected from every participant who gave informed consent . A clinical officer collected 10mls of blood from each participant ( 10ml plain Becton , Dickinson and Company ( BD ) Vacutainer ) using a 21G or 23G BD Vacutainer Safetylok blood collection set and sterile technique . Sera from study participants were tested by indirect ELISA for presence of anti-RVFV IgG antibodies at Stanford University School of Medicine , Stanford , CA , as described previously [4 , 5 , 23] . Seropositive or seronegative results were compared to plaque reduction neutralization test ( PRNT ) -confirmed positive and negative controls for RVFV . The cut-off values used to determine positive readings were calculated by dividing the average positive control optical density ( OD ) value on each plate by two . Cut-off values used to determine negative readings were calculated by multiplying the average negative control OD value on each plate by two . Confidence intervals ( CI ) around apparent prevalence estimates were calculated using the epi . prev function in the EpiR package [24] of the R environment for statistical computing , version 3 . 0 . 2 ( http://cran . r-project . org/ ) . To account for the hierarchical nature of surveys in both the community and in slaughterhouse workers [25] , design-based adjustment was implemented using the svydesign procedure in the Survey package in R [26] . Sampling weights were calculated for the community sample by dividing the number of people per division ( from the Kenyan Human Population Census of 2009 ) by the number of people sampled in each division . Sampling weights for slaughterhouse were calculated by dividing the total number of workers by the number sampled in each slaughterhouse . Homestead and slaughterhouse were included as clustering variables in the community and slaughterhouse samples , respectively . The true prevalence estimate accounting for the RVFV IgG ELISA sensitivity and specificity , but without accounting for the complex survey design , were calculated using the truePrev function in the prevalence package [27] of R . The sensitivity and specificity of the test have been reported to be 100% and 95 . 3–100% respectively [4 , 5] . Multi-level logistic regression models were used to identify risk factors for RVFV seropositivity in community members and in slaughterhouse workers and estimate the strength of the relationship with the outcome . A multi-level mixed effects logistic regression model was used to account for the clustering of individuals within homesteads . A separate multi-level mixed effects logistic regression model was used to account for the clustering of workers within slaughterhouses . Univariable logistic regression was used to screen variables of interest , against disease seropositivity at the individual level . Variables were included from both the individual and homestead/slaughterhouse level . Variables screened were those that have been previously identified as risk factors associated with RVFV seropositivity for community members and slaughterhouse workers ( S1 Table ) . Multi-level logistic regression models were developed using glmer function in the lme4 package [28] . Group level variation in the final models was examined to assess the importance of the homestead/slaughterhouse in explaining individual risk of RVFV seropositivity . The Median Odds Ratio ( MOR ) was calculated for the final models . The MOR expresses the between group variance on the odds ratio scale , and therefore provides a measure of the between group variability in individual risk for an outcome that can be interpreted on the same scale that risk factors are interpreted [29 , 30] . The MOR is estimated using Eq 1 [30] . The intraclass correlation coefficient ( ICC ) was calculated for the final model . The ICC represents correlation in the probability of seropositivity at the homestead/slaughterhouse level . It was estimated using the latent variable method using Eq 2 [30] . Variance Inflation Factors ( VIFS ) were calculated to check for collinearity . VIFS >4 were considered a problem and the variable removed from the model . The Moran’s I statistic was calculated to check for spatial autocorrelation in homestead/slaughterhouse level residuals which can influence the stability of model co-efficients . The Moran’s I statistic measures if the outcome ( group level residual log odds of seropositivity ) is clustered or randomly distributed through space [31] . The Moran’s I statistic was calculated using the ape package [32] in R . A histogram of the group-level residuals was made to check for normality . Homesteads and slaughterhouses were georeferenced using a handheld GPS device ( Garmin eTrex ) . The locations were mapped using ArcGIS version 9 . 1 and version 10 . 2 . 2 ( ESRI , Redlands , CA , USA ) . For mapping purposes , homesteads and slaughterhouse were considered positive if one or more inhabitants/workers were seropositive for RVFV . The spatial scan statistic was used to determine if there was any evidence of clustering of the RVFV seropositive homesteads [33] . A Bernoulli model was used with 999 iterations in SatScan version 9 . 0 ( www . satscan . org ) . A kernel smoothing approach was used to map the intensity of positive homesteads and slaughterhouses using the sparr [34] package in R with a fixed bandwidth of 5km and correction for edge effects . A bandwidth of 5km was chosen because it is the approximate diameter of sublocations in the study area . The kernel intensity of seropositive homesteads/slaughterhouses was divided by the kernel intensity of the all homesteads/slaughterhouses in the study area creating a “risk” surface . This technique is not a test for clustering but produces spatially smooth risk maps that allow areas with the greatest risk for seropositivity to be identified .
A total of 1 , 861 individuals were sampled in 384 homesteads . Participating individuals were aged between 5 and 85 years with 969 ( 52% ) of participants aged below 20 years . Seventy-four percent of participants reported owning livestock including cattle ( 62% ) ; sheep ( 18% ) and goats ( 33% ) . Fifteen people were seropositive for RVFV hence the seroprevalence in the community was 0 . 8% ( 95% CI 0 . 5–1 . 3% ) . The survey-adjusted seroprevalence was 0 . 5% ( 95% CI 0 . 2–0 . 8% ) . The true prevalence accounting for the sensitivity and specificity of the diagnostic test was 0 . 1% ( 95% CI 0 . 0–0 . 2% ) . Using univariable logistic regression there was not a significant difference in seropositivity between genders ( Table 1 ) . There was a significant difference across age with only one seropositive individual in the 5–19 year age group giving a seroprevalence of 0 . 1% ( 95% CI 0 . 0–0 . 6% ) ; compared with 14 positives ( 1 . 6% , 95% CI 0 . 9–2 . 6% ) in the over 20 year age group . The youngest seropositive participant was aged between 10–14 years ( age data was collected in categories ) . Variables that have been previously described as being associated with RVF seropositivity were tested using univariable logistic regression analysis ( Table 1 ) . Variables that were significantly associated with RVFV seropositivity included: increasing age ( OR 1 . 2; 95% CI 1 . 1–1 . 4 , p < 0 . 001 ) , owning goats ( OR 3 . 1; 95% CI 1 . 1–8 . 7 , p = 0 . 033 ) ; slaughtering cattle at the homestead ( OR 3 . 6; 95% CI 1 . 1–11 . 5 , p = 0 . 029 ) and sheltering goats and sheep in the house ( OR 4 . 9; 1 . 1–22 . 3 , p = 0 . 039 ) . Other variables that have been associated with RVFV seropositivity in previous studies such as handling animal abortus , and assisting with animal birthing were positively associated with RVFV seropositivity but not significantly using the traditional level of 0 . 05 ( Table 1 ) . The small number of positive results in the community sample ( n = 15 ) limited the inclusion of all significant univariable effects in a multivariable model [35] . Instead , we focused only the effect of slaughtering animals , with control for the potential confounding effect of age . No further model selection was performed . Increasing age continued to predict seropositivity ( OR 1 . 2 95% CI 1 . 1–1 . 4 , p <0 . 001 ) and there was evidence of the positive effect of slaughtering cattle ( OR 3 . 3; 95% CI 1 . 0–10 . 5 , p = 0 . 047 ) ( Table 2 ) . The Moran’s I statistic demonstrated no evidence of residual spatial autocorrelation ( value = 0 . 004 , p-value = 0 . 417 ) . The histogram of the group level residuals had a normal distribution . The MOR was 1 and ICC less than 1% , indicating that very little of the variation in individual risk of seropositivity from the final model was associated with the factors operating at the homestead level . A significant spatial cluster was detected in the south of the study area . The relative risk ( RR ) of homesteads inside the cluster compared to outside was 45 . 57 ( p-value = <0 . 001 ) ( Fig 2 ) . The results of the kernel density mapping for RVFV in homesteads ( Fig 3 ) suggest the greatest risk for RVFV seropositivity in the community was to be near Lake Victoria in the southwest of the study area . A total of 553 slaughterhouse workers were sampled in 84 ruminant slaughterhouses . Four slaughterhouses refused to participate in the study . The majority of slaughterhouse workers were men ( 96 . 8% ) . The age of the slaughterhouse workers ranged from 18–82 years with a median age of 38 years . The roles in the slaughterhouse included flayers ( 74 . 7% ) , slaughtermen ( 11 . 6% ) , and cleaners/foremen ( 13 . 7% ) . There were 18 female slaughterhouse workers and the role of women within the slaughterhouse differed to men with only one female flayer ( 5 . 6% ) and the remainder were cleaners ( 94 . 4% ) . The number of workers seropositive for RVFV was 14 giving an apparent seroprevalence of RVFV in slaughterhouse workers of 2 . 5% ( 95% CI 1 . 5–4 . 2 ) . The survey-adjusted prevalence was 2 . 6% ( 95% CI 1 . 3–3 . 9 ) . The true prevalence accounting for the sensitivity and specificity of the test was 0 . 3% ( 95% CI 0 . 0–1 . 2% ) . Of the 18 female slaughterhouse workers , none were seropositive for RVFV ( Table 3 ) . Using univariable logistic regression the only variable associated with RVFV seropositivity in slaughterhouse workers was being the slaughterman ( OR 3 . 2 95% CI 1 . 0–10 . 5 , p = 0 . 055 ) . Due to the small number of positive samples , only three variables were included in the final multilevel model for RVFV seropositivity in slaughterhouse workers [35] . These were variables from the univariable analysis that had been previously reported as high risk for RVFV seropositivity and had p<0 . 2 . Age was included in the model since it is a common confounder [5] . No further model selection was performed . The final model included age , if the worker only slaughtered cattle and being the slaughter man ( Table 4 ) . Being the slaughter man was significantly associated with RVFV seropositivity in slaughterhouse workers after multi-level analysis ( OR 3 . 5; 95% CI 1 . 0–12 . 1 , p = 0 . 047 ) . The histogram of the group level residuals had a normal distribution . The MOR was 1 and ICC less than 1% , indicating that very little of the variance is associated with factors operating at the level of the slaughterhouse and most of the variation is at the individual level . The kernel density mapping for RVFV in slaughterhouses ( Fig 4 ) showed the areas of greatest risk for RVFV seropositivity in slaughterhouse workers to be through the center of the study area and along the border with Uganda .
There have been two Rift Valley Fever epidemics in East Africa in the past 20 years [3] . In both outbreaks there were no human cases recorded in western Kenya . Research efforts focusing on the inter-epidemic transmission of RVFV have reported the seroprevalence in people in high-risk areas to range from 6% ( 95% CI 2 . 7–11 . 8 ) to 20% ( 95% CI 14 . 0–29 . 2 ) [5] . In locations believed unaffected by RVFV outbreaks , the seroprevalence estimates range from 0% ( 95% CI 0–3 . 03% ) to 3% ( 95% CI 0 . 94–6 . 78% ) [4] . The apparent seroprevalence for RVFV in the community in this study was 0 . 8% ( 95% CI 0 . 5–1 . 3 ) . This is consistent with a previous report from Kabobo in the western highlands of Kenya ( 1%; 95% CI 0 . 03–5 . 45 ) [4] . The study area presented here is a semi-humid environment with high average annual rainfall ( >1200 mm per annum ) similar to that of Kabobo [6] . LaBeaud et al ( 2007 ) theorised that the different climatic conditions across regions would account for the difference in seroprevalence estimates [4] . Semi-arid regions with seasonal flooding events allow annual RVFV transmission resulting in high seroprevalence in contrast to semi-humid climates where only extensive rainfall allow RVFV transmission and hence low seropositivity [4] . The risk map demonstrated the highest risk for RVFV seropositivity in the community is in the south west of the study area bordering Lake Victoria . In addition there was a significant clustering of seropositive homesteads in this area ( p <0 . 001 ) . Previous studies have demonstrated an association between RVFV seropositivity in ruminants and proximity to water bodies [36 , 37] and RVFV outbreaks have been associated with flooding and the resulting increased vectors [38 , 39] . The southern area of the study site is the Nzoia and Yala rivers wetland zone which periodically floods in heavy rains [40] . The area around Lake Victoria is likely to be highly suitable for mosquito breeding that might allow for inter-epidemic transmission of RVFV from vectors . RVFV seropositivity in the community examined in this study was associated with increasing age ( OR 1 . 2 , 95% CI 1 . 1–1 . 4 , p<0 . 001 ) . This is consistent with findings by other researchers investigating risk factors for RVFV [5 , 41] . As concluded by LaBeaud et al ( 2015 ) [42] this is potentially the result of older people having more time to be exposed to infected materials and vectors since IgG antibodies for RVFV are considered to be lifelong [43] . In addition , young people are potentially less likely to be involved in risk practices such as handling livestock abortions and slaughtering [42] . The youngest seropositive individual was between 10–14 years ( more accurate age data was not collected ) and was potentially exposed to RVFV between 1998 and 2012 . There was a countrywide outbreak in 2006–2007 , however western Kenya was considered free of the disease at that time [44] . The low seroprevalence for RVFV in this study may indicate that there has not been a large amount of endemic RVFV circulation in this area in the past 20 years . Slaughtering cattle at the homestead was demonstrated to be a risk factor for RVFV seropositivity ( OR 3 . 3; 95% CI 1 . 0–10 . 5 , p = 0 . 047 ) . This is similar to previous studies that have demonstrated slaughtering animals and handling animals parts are risk factors for RVFV seropositivity [8 , 10 , 45] . Slaughtering animals during an RVFV outbreak has been reported to be a risk factor for severe disease and death [7] . The public health response to RVFV outbreaks in the past has been to ban slaughtering to reduce the human cases [7 , 9] . However , this results in significant economic losses to producers and other stakeholders in the value chain [46] . This may result in movement of infected animals away from the outbreak area spreading the virus to unaffected regions [9] . It is possible that during the last epidemic animals were moved into the study areas from affected areas . Movement of infected livestock is believed to have spread RVFV to the Arabian peninsula and Egypt [47] . The apparent seroprevalence of RVFV in slaughterhouse workers was 2 . 5% ( 95%CI 1 . 5–4 . 2 ) . These results are comparable to other studies that have been conducted in RVFV endemic areas . For example , in Egypt and Saudi Arabia , RVFV seroprevalence in slaughterhouse workers was 2% and 0 . 72% respectively [10 , 48] . The seroprevalence of RVFV antibodies in slaughterhouse workers ( 2 . 5% ) was higher than in the community population over 20 years old ( 1 . 6% ) , supporting the hypothesis that slaughterhouse workers are at higher risk for exposure to RVFV and that they may act as sentinels for RVFV , even in areas with low transmission [49] . The area with the highest spatial risk is in slaughterhouses located in the central region of the study area , along the main road networks into the study area , further indicating the possibility that animal cases may be imported from areas outside the study area . This highlights a difference in the risk profile between the community and slaughterhouse workers in this area suggesting that slaughterhouse workers risk is related to the movement of animals and not proximity to water . This hypothesis requires further investigation . The slaughterman who is directly responsible for slitting the animal’s throat is previously reported to be at risk for RVFV seropositivity in regions where epizootics occur [8 , 10] . It is likely that aerosolization of blood at slaughter is a means for transmission of RVFV [50] . The odds of being RVFV seropositive was significantly higher in slaughtermen ( OR 3 . 5 , 95% CI 1 . 0–12 . 1 , p = 0 . 047 ) compared to other roles in the slaughterhouse demonstrating that this is a high-risk position . Animals with RVF can present with abortion , hemorrhage , dyspnea , coughing , bloody discharges , anorexia , weakness [44] and it is possible that they might be removed from slaughter through antemortem inspection in order to protect workers from exposure . The impact of antemortem inspection on reducing RVFV exposure could not be determined from this study but should be considered for future investigations . The ELISA conducted in this study has been used in previous studies [4 , 5 , 23] . The sensitivity has been described to be 100% where confirmatory testing was carried out and the reported specificity ranged between 95 . 3–100% [4 , 5] . It was not possible to perform plaque neutralization confirmatory testing in this study . The true prevalence rates in both the community and slaughterhouse sample were substantially reduced when accounting for the vagaries of the diagnostic test , with the 95% confidence interval including zero . Genus-specific cross-reactivity is a known limitation with ELISAs . Although ELISAs are effective for general surveillance , the sensitivity and specificity are less than those for PRNT [5 , 23 , 41] . Participant exposure to other bunyaviruses , may have elicited a cross-reactive seropositive result , despite using antigenic proteins specifically derived from RVFV as a coating antigen . As a result , a fraction of our subjects may have been misclassified in terms of RVFV exposure . PRNTs were not performed to determine the species-specific origin of the IgG antibodies detected by ELISA , yet serum pools derived from samples confirmed as seropositive and seronegative for anti-RVFV IgG by PRNT were used as controls . Given the low risk of exposure to other Bunyaviridae and phlebovirsuses [51 , 52] , in the Busia region , we are confident that the results from this study are representative of RVFV exposure only . Our ability to explore a range of predictors of seropositivity was limited by the small numbers of RVFV seropositive individuals . The self reported questionnaire was focused on risk factors for zoonotic disease associated with animal contact and did not include questions related to mosquito exposure and clinical data relevant to RVFV . In addition recall bias may have influenced responses regarding exposures . Conclusions cannot be made about when and where the individuals encountered the virus since antibodies to RVFV are likely to be life-long [43] . It is possible that the seropositive people were infected with RVFV during travel outside the study area . It was not possible to test this hypothesis with the data available from this study . However , this is unlikely considering the clustered nature of the community sample and the plausibility of the identified risk factors .
This study investigated the epidemiology of RVFV in people in western Kenya . The study area was distinctly different from the regions of Kenya where RVFV outbreaks have been reported and where previous investigations have shown inter-epidemic transmission of RVFV . This study reported a low seroprevalence in the community and highlighted several previously identified risk factors in people including contact with animals and animal products . The study demonstrated that slaughterhouse workers are at a higher risk for exposure to RVFV and might be a sentinel for disease emergence . Methods to control infected animals being slaughtered such as antemortem meat inspection and education of workers should be implemented . The results suggest that RVFV virus is circulating in western Kenya . Improved surveillance in low risk areas is recommended particularly during countrywide outbreaks , to more accurately determine RVF burden .
|
Rift Valley fever virus ( RVFV ) is a zoonotic virus affecting livestock and people . Periodic outbreaks in Kenya are associated with greater than average rainfall , although outbreaks have not previously been reported in western Kenya . The virus is spread between animals and to people by mosquitos . Contact with infected animal tissues and products are also risk factors for transmission of RVFV to people . This study investigated the seroprevalence of RVFV in 1861 residents of western Kenya and compared this to the seroprevalence in 553 ruminant slaughterhouse workers . The seroprevalence of RVFV in people in western Kenya was less than 1% , which is consistent with previous reports from the region . Slaughterhouse workers were shown to be a higher risk group for RVFV seropositivity , with seroprevalence of 2 . 5% . The identification of plausible risk factors including slaughtering is consistent with reports from other regions . The results suggest that it is plausible that RVFV virus is circulating in western Kenya . Improved surveillance in low risk areas is recommended particularly during countrywide outbreaks .
|
[
"Abstract",
"Introduction",
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"Results",
"Discussion",
"Conclusion"
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] |
2017
|
The sero-epidemiology of Rift Valley fever in people in the Lake Victoria Basin of western Kenya
|
Podoconiosis , also known as mossy foot or endemic non-filarial elephantiasis , is a preventable form of lower-leg lymphoedema caused by prolonged ( typically barefoot ) exposure to soil derived from volcanic rocks . Acute adenolymphangitis ( also called ‘acute attack’ ) is a serious complication of podoconiosis resulting in significant symptoms and worsening disability . Despite the well-known morbidity associated with podoconiosis , to date there have been no studies looking at the impact , or burden , of podoconiosis on caregivers . This study explored the experiences and impact of acute attacks on the caregivers of those with podoconiosis in one endemic district of Ethiopia . This qualitative study was based in Wayu Tuka woreda ( district ) , Oromia , Western Ethiopia . 27 semi-structured interviews of those with podoconiosis and their caregivers were conducted in June 2018 . Here we report the findings from the caregiver’s interviews . Data were analysed using NVivo 12 . Directed content analysis , a qualitative approach related to thematic analysis , was used to analyse the results . This study highlights a previously unreported impact of acute attacks on the caregivers of those affected by podoconiosis . The findings demonstrate the significant social and financial pressures placed on podoconiosis-affected families which are exacerbated during acute attacks . This study also highlighted the emotional burden experienced by caregivers , the range of care activities placed on them and the limited support available . This study found a significant impact on the caregivers of those with podoconiosis , especially during acute attacks , in Wayu Tuka woreda . It also highlighted the limited support available to caregivers . Further research is needed to understand whether this impact applies to podoconiosis caregivers across Ethiopia , and beyond , and to establish if there are wider implications of this important consequence of podoconiosis , for example on the economy and caregivers’ mental and physical health .
Podoconiosis , also known as mossy foot or endemic non-filarial elephantiasis , is a preventable form of lower-leg lymphoedema caused by exposure to soil derived from volcanic rock [1 , 2] . It is most prevalent where volcanic soil is found together with altitudes >1000m above sea level and rainfall >1000mm/year [3] . Podoconiosis mainly affects poor , bare footed agricultural communities , who cannot afford protective footwear and who do not have access to clean water for washing [4 , 5] . Although podoconiosis is only found in individuals exposed to these volcanic soils , familial clustering is common , and a genetic link has been identified [6] . Disease progression can be halted by adhering to foot hygiene practices [7 , 8] . Like many Neglected Tropical Diseases ( NTDs ) , podoconiosis rarely causes mortality , but instead causes disabling changes to the lower legs , affecting mobility , quality of life and economic productivity [8 , 9] . Podoconiosis onset is most commonly reported between 16 and 45 years of age , affecting the most economically active groups [10 , 11] . Additionally , podoconiosis is highly stigmatised , with evidence of those affected being excluded , insulted or shunned by their communities [1 , 2 , 12 , 13] . Podoconiosis has been identified in 32 countries , making it a health problem of global concern [14] . In 2011 , it was recognised as one of the NTDs by the World Health Organisation ( WHO ) , under the category of ‘other tropical conditions’ [15] . Ethiopia is thought to have the highest number of podoconiosis cases of any country; here it is highly endemic with recent mapping suggesting 1 , 537 , 963 adults are living with podoconiosis and a further 34 . 9 million are at risk of the disease [3 , 16] . Podoconiosis is endemic in 345 of Ethiopia’s 839 woredas [3 , 4] . Acute adenolymphangitis , or acute dermatolymphangioadenitis ( ADLA ) , known commonly as ‘acute attacks’ , is the term used to describe recurrent inflammatory episodes in lymphoedema [17] . ADLA is most likely triggered by skin lesions acting as entry points for bacteria , fungi or viruses , although often no clear cause is identified [18 , 19] . Episodes are typically characterized by hot , painful and reddened swelling of the leg , chills , malaise , anorexia and sometimes lower leg skin peeling , and are one of the most serious complications of podoconiosis; resulting in worsening disability [8 , 20] . ADLA incidence varies across the studies conducted to date in Ethiopia , ranging from 5 . 5 to 23 . 3 episodes per person per year , with each episode lasting on average 6 . 42 days [21 , 22] . As ADLA leaves individuals’ bed-bound , it is estimated that anywhere between 24 and 149 . 5 working days are lost per year [8 , 21 , 22] . While there is no panacea for ADLA , previous research in Ethiopia observed that those participants who never walked barefoot and who engaged in daily foot washing had lower odds of developing ADLA [21] . The GoLBeT study , a randomised controlled trial conducted in Northern Ethiopia , evaluated the impact of a simple lymphoedema care package ( including foot washing and shoe wearing ) on the incidence of ADLA in those with podoconiosis [8] . It found that incidence of ADLA was lower in the intervention group at 19 . 3 episodes per person year compared to 23 . 9 episodes per person year in the control group [8] . Additionally , the duration of attacks was found to be shorter in the intervention group [8] . State-provided or private care is not available or is unaffordable to the majority living with disabilities in LMICs , and most care is therefore provided by unpaid family or friends; often referred to ‘family caregivers’ or ‘informal caregivers’ [23] . In 2015 the WHO defined a caregiver as ‘a person who provides care and support to someone else; such support may include: helping with self-care , household tasks , mobility , social participation and meaningful activities; offering information , advice and emotional support , as well as engaging in advocacy , providing support for decision making and peer support , and helping with advance care planning; offering respite services; and engaging in activities to foster intrinsic capacity [24] . ’ Whilst caregiving may be rewarding , can strengthen family ties or be part of honouring the person needing care; many caregivers struggle with the burden of caregiving [25] . ‘Caregiver burden’ is broadly summarised as ‘the extent to which caregivers perceive that caregiving has had an adverse effect on their emotional , social , financial , physical , and spiritual functioning [25] . ’ We have used this definition of ‘caregiver burden’ throughout this study . NTDs , such as podoconiosis , are widely known to cause morbidity and disability more frequently than mortality; typically , YLD ( years lived with disability ) greatly outweigh YLL ( years of life lost ) in DALY ( disability-adjusted life years ) calculations for NTDs [26 , 27] . Despite evidence of high disability amongst those affected by NTDs , and the disproportionate impact of NTDs on LMICs , there is a surprising dearth of literature on the burden of NTDs on caregivers in these regions . A review of NTDs and mental health in 2012 found no papers on the impact of NTDs on caregivers [28] . A 2014 review of the literature on the burden of caregiving for those affected by disability or disease in LMICs , highlighted a lack of published data , with most research conducted focusing on HIV/AIDS and mental ill-health [23] . An economic evaluation of podoconiosis in 2006 reported that caregivers lost an average of 9 work days per quarter through supporting relatives with podoconiosis to clinics or caring for them at home , equivalent to an opportunity cost of 4224 birr ( approximately $350 ) [11] . Beyond this , no study to date has explored the burden , or impact , of podoconiosis and ADLA on caregivers .
We used qualitative research methodology to allow participants to describe their experiences in their own words in order to draw out unanticipated or culturally relevant answers [29 , 30] . Content analysis , related to thematic analysis , was used . A cross-sectional exploratory study , in one geographical area , was used to allow for holistic , in-depth investigation in a small , real-life context . Ethical approval was obtained from the Research Governance and Ethics Committee ( RGEC ) at Brighton and Sussex Medical School ( BSMS ) in January 2018 and from Wollega University Institutional Review Board ( IRB ) in March 2018 prior to initiation of the study . The study was conducted at Konchi clinic , in Wayu Tuka woreda which lies in East Wollega zone in the state of Oromia , Western Ethiopia . The clinic sits just off the main tarmac road , approximately 16km from Nekemte , and 7km from Gute , the main town in Wayu Tuka . A study in 2016 reported a high incidence of ADLA in Wayu Tuka woreda , with participants on average experiencing 23 . 3 episodes annually , making this an ideal setting in which to conduct this study [21] . Konchi clinic is run by an order of Indian Catholic nuns who provide , amongst other services , free leg washing to those affected by podoconiosis . Caregivers were identified by those affected by podoconiosis who attended the clinic for leg washing , using snowball sampling [29] . Snowball sampling relies on participants to invite or identify others who may be willing to participate in the study [31] . No relationship existed between the principal researcher ( CP ) and clinic staff , those attending leg-washing services or caregivers prior to recruitment . Those affected by podoconiosis were selected using purposive sampling , specifically , maximum variation sampling was used to recruit participants with podoconiosis from across the disease spectrum , in order to understand the impact and experiences of those with all stages of the disease [32] . Participants were provided with a Participant Information Sheet ( PIS ) , in Afaan Oromo , informing them of the aims and purpose of the study . This was read to participants who were unable to read . Participants were recruited ( face-to-face ) if they met the inclusion criteria ( see Table 1 ) . Informed , written consent was obtained from all adult participants . Informed , written assent was obtained from participants over 12 years of age and the informed written consent of their parent or guardian was also obtained . A thumb print was obtained from those who could not write . A total of 13 caregivers were included in the study . No one who was approached declined recruitment . Data was collected via semi-structured interviews , using a topic guide developed by the principal researcher that was translated into Afaan Oromo . A meeting was held at Wollega University , before recruitment began , with the qualitative interviewer , research assistant , study supervisor and the principal researcher to discuss the topic guides in detail , agree and adjust the translated versions of the topic guides and agree definitions and local terms for ADLA and caregivers . Locally , ADLA is referred to ‘dhukuba muddaa’ or ‘nyaataa muddaa’ . A primary caregiver was defined as ‘a friend/relative/member of the community who provides the majority of care to the participant . Care activities include assistance with personal care e . g . washing , dressing , toileting and eating , household activities e . g . food preparation , cooking , shopping , laundry , collecting water and cleaning , medical care e . g . obtaining and administering medication , accompanying to appointments , wound care , monitoring patient’s medical condition , physical assistance e . g . assisting the person to walk , moving or carrying the person , transport/mobility outside of the home , emotional support or assistance [23 , 33] . The topic guide included open questions around the impact of acute attacks on daily life , support available to caregivers , understanding of symptoms , prevention and causation and access to healthcare . Face to face interviews were conducted in Aafan Oromo by an experienced , qualitative researcher working as a lecturer in the School of Public Health at Wollega University . Interviews were audio recorded on two password-protected devices to ensure they were accurately transcribed , and a high-quality recording was obtained . Interviews ranged from 15–30 minutes in duration . Data collection took place at Konchi clinic from 5-7th June 2018 . Those with podoconiosis either waited to be interviewed or returned at an appointed time on subsequent days . Caregivers were asked by their affected relative to attend the clinic for interview at a convenient or pre-arranged time . Interviews were held in an office at the clinic and were conducted until data saturation was reached . Caregivers were interviewed separately to the person they cared for . Data saturation was determined by the interviewer , following discussion of each interview’s content with the principal researcher . Interviews were first transcribed to Microsoft Word in Afaan Oromo from the audio recordings by an audio-typist at Wollega university . The Afaan Oromo transcripts were then read , sections of the audio recordings listened to for confirmation of the transcript , and verbally translated into English by the study’s research assistant ( GT ) . The principal researcher ( CP ) directly transcribed the English verbal translation to Microsoft Word , aiding data immersion [31] . Finally , one third of the translated interviews were checked by an impartial lecturer in Public Health from Wollega University , who listened to the audio recording in Afaan Oromo whilst reading the English transcription . This was to establish the accuracy of the translations . On return to the UK , interviews were uploaded to NVivo 12 which was used to aid the coding process . Directed content analysis , a qualitative approach related to thematic analysis , was used to analyse the results [31 , 34] . Data were completely coded ( by CP ) to broad themes that were created from previous research and which were used to structure the interview topic guides e . g . access to healthcare , impact on livelihood , education and household income [31 , 34] . After this , the codes were refined and revised , and themes specific to the data were identified [34] , see Fig 1 .
Support from friends , neighbours and healthcare staff was evident in those attending the leg washing clinic in Wayu Tuka , however the absence of support was a consistent theme reported by participants in this study . This contradiction , particularly in relation to the support offered by healthcare staff , most likely relates to the support patients expected and what the clinic staff were able to offer . The clinic had not received any funding for podoconiosis care for a number of years at the time of the study and all care was provided by charitable donations to the order . Caregivers have been found to report lower burdens when they receive more social support , yet with changes in traditional societies , support mechanisms are thought to be declining in LMICs [23 , 39] . This would require further evaluation in this context . One relatively novel finding was the borrowing of money from neighbours and friends to pay medical costs; money loaning was reported by nearly all participants in the study . This appeared to be outside of known money loaning schemes such as Idir , Equub and Senbat , which operate in this region of Ethiopia . Whilst money loaning has not been directly reported in studies on podoconiosis in Ethiopia , a study in Cameroon found podoconiosis patients spent 142 USD per year on direct healthcare costs , of which 34USD was borrowed from friends , family and community groups [36] . Although money loaning in this study was reported as the social norm , one participant reported knowing people in debt who had killed themselves . This again highlights the need for appropriate healthcare financing of healthcare costs in those affected by podoconiosis . Whist the literature on caregivers in LMICs is minimal , what literature there is highlights a clear need for greater support for caregivers . Longitudinal burden measurements of the caregivers of those with bipolar disorder in southern Ethiopia concluded that more needed to be done from a health policy perspective to lessen the economic and family burden of caregivers [40] . While this study has shed light on an important yet neglected issue , there are a number of limitations . Firstly , a significant limitation to this study was that the main research assistant , who translated the interviews into English , was not experienced in qualitative research and therefore could not conduct the interviews . Whilst reasonable adjustment for this was made ( the research assistant was present at the clinic during interviews and was involved in translation of topic guides , PIS and all other materials related to the study and so was highly familiar with the content ) it is possible that some nuance of the interviews was lost as they were not translated by the interviewer . Secondly , a clear error in the study was the absence of field notes . No notes about behaviour or emotions were documented . At times participants became tearful , and whilst this was acknowledged at the time , it was not documented and is a disappointing gap in the data . Thirdly , whilst the study identified some novel and , potentially valuable , findings , due to the case-study design these are not generalisable to the whole of Ethiopia . Additionally , this study only recruited those linked to the free leg washing clinic and therefore does not reflect the experiences of those without access to this vital service . The study does however highlight areas of potential future research and supports findings from studies in other regions of the country . Finally , the ethical approval for this study was only for those aged 12 years or over . Participants under the age of 12 were turned away from participating in this study which highlights that children are indeed acting as primary caregivers in Wayu Tuka . It was also reported in the interviews that children were kept from school to attend sick relatives . Further research into the role of children as caregivers would be useful to evaluate the full extent of this issue and its impact . In conclusion , acute attacks have a debilitating impact on the caregivers of those with podoconiosis in Wayu Tuka woreda . In the context of podoconiosis and NTDs , this burden on caregivers appears to be a largely unrecognised and novel finding yet supports data from caregiver studies in other contexts and is perhaps unsurprising [23] . Whilst this is a case-study , in one small corner of Ethiopia , and has limited generalisability , it highlights some gaps in the current literature and in national policy which may be important . Reducing the incidence of ADLA is a key priority in alleviating caregiver burden and lessons can be taken from the GoLBeT study [8] . Beyond this , further research is needed to further evaluate this important finding . Validation of a caregiver burden assessment scale for use across all NTDs , including podoconiosis , would be a useful tool to formally measure caregiver burden . The Family Burden Interview Schedule ( FBIS ) or the Burden Assessment Schedule ( BAS ) , both developed in India to assess burden in the caregivers of those with mental illness , may be suitable for adaptation [41 , 42] . Secondly , an economic evaluation of caregivers in podoconiosis , including consideration of mental and physical health consequences on economic productivity would be helpful in quantifying the wider implications of caregiver burden . In addition to this , the development of a cost-effective intervention to support caregivers and an evaluation of the role of children as caregivers in podoconiosis is needed . Finally , improved health financing to prevent podoconiosis-affected families falling into debt and alleviating caregiver burden is warranted . A possible mechanism for is to include expanding fee waiver certificates to all Ethiopians with an NTD or from an NTD-affected family [43] .
|
Podoconiosis is a foot condition , common in the highlands of Ethiopia , caused by exposure to volcanic soil . It can be prevented by wearing shoes and adhering to foot hygiene practices . Podoconiosis causes swelling of the lower legs and is a disabling and stigmatised condition . It is made worse by ‘acute attacks’ , during which the leg becomes painful , swollen and red . Often the person affected cannot work and is bedbound during these episodes . This study identified a previously unreported burden on the caregivers of those with podoconiosis during acute attacks in one endemic district of rural Ethiopia . Specifically , we identified a significant social and financial pressure placed on podoconiosis-affected families in meeting healthcare costs , covering daily expenses such as children’s education costs and progressing within their communities . This study also highlighted the emotional burden experienced by caregivers , the range of care activities placed on caregivers and the limited support available . These findings warrant further research in other contexts but highlight an important wider social consequence of podoconiosis .
|
[
"Abstract",
"Introduction",
"Methods",
"Discussion"
] |
[
"children",
"medicine",
"and",
"health",
"sciences",
"legs",
"feet",
"tropical",
"diseases",
"geographical",
"locations",
"social",
"sciences",
"parasitic",
"diseases",
"health",
"care",
"age",
"groups",
"neglected",
"tropical",
"diseases",
"ethiopia",
"body",
"limbs",
"africa",
"families",
"musculoskeletal",
"system",
"elephantiasis",
"emotions",
"health",
"economics",
"podoconiosis",
"mental",
"health",
"and",
"psychiatry",
"economics",
"people",
"and",
"places",
"finance",
"psychology",
"anatomy",
"biology",
"and",
"life",
"sciences",
"population",
"groupings"
] |
2019
|
The impact of acute adenolymphangitis in podoconiosis on caregivers: A case study in Wayu Tuka woreda, Oromia, Western Ethiopia. ‘If she was healthy, I would be free.’
|
Zika virus ( ZIKV ) , a member of the Flaviviridae family , is the most recent emerging arbovirus with pandemic potential . During infection , viruses trigger the host cell stress response , leading to changes in RNA translation and the assembly of large aggregates of stalled translation preinitiation complexes , termed stress granules ( SGs ) . Several reports demonstrate that flaviviruses modulate the assembly of stress granules ( SG ) . As an emerging pathogen , little is known however about how ZIKV modulates the host cell stress response . In this work , we investigate how ZIKV modulates SG assembly . We demonstrate that ZIKV negatively impacts SG assembly under oxidative stress conditions induced by sodium arsenite ( Ars ) , a treatment that leads to the phosphorylation of eIF2α . By contrast , no measurable difference in SG assembly was observed between mock and ZIKV-infected cells treated with sodium selenite ( Se ) or Pateamine A ( PatA ) , compounds that trigger eIF2α-independent SG assembly . Interestingly , ZIKV infection markedly impaired the phosphorylation of eIF2α triggered in Ars-treated infected cells , and the abrogation of SG assembly in ZIKV-infected cells is , at least in part , dependent on eIF2α dephosphorylation . These data demonstrate that ZIKV elicits mechanisms to counteract host anti-viral stress responses to promote a cellular environment propitious for viral replication .
Zika virus ( ZIKV ) is a positive-sense , single-stranded RNA virus that belongs to the genus Flavivirus of the family Flaviviridae , which also includes yellow fever ( YFV ) , West Nile ( WNV ) , dengue ( DENV ) and Japanese encephalitis viruses ( JEV ) [1] . The genome of ZIKV encodes a large polyprotein precursor that is co- and post-translationally processed by viral and cellular proteases into three structural proteins [capsid ( C ) , precursor of membrane ( prM ) , and envelope ( E ) ] and seven nonstructural proteins [ ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B and NS5 ) ] that are involved in virus replication , which takes place in the cytoplasm of the host cell [2] . Like other Flavivirus members , ZIKV relies mainly on arthropods such as mosquitoes or ticks for transmission and thus is classified as an arthropod-borne virus ( arbovirus ) . The main arthropod vectors of ZIKV are Aedes sp . mosquitoes ( A . aegypti or A . albopictus ) [3] . Along with the vector-borne transmission , other routes of ZIKV transmission have been demonstrated , including sexual transmission , transplacental and perinatal transmission and blood transfusion [4] , raising the concern about the global spread of the disease . ZIKV was first isolated from a rhesus monkey in the Zika Forest ( Uganda ) in 1947 [5] . For more than 50 years , ZIKV was rarely reported to cause disease in humans and was commonly associated with mild illness . In 2007 , there was an outbreak in the Federated States of Micronesia [6] , followed by outbreaks in French Polynesia in 2013–14 , in which severe neurological complications were reported [7] . Since then , ZIKV is considered to be the most recent emerging arbovirus with pandemic potential [8] . In 2015 , autochthonous transmission of ZIKV was confirmed in the northeastern region of Brazil [9] . A dramatic increase in reported cases of microcephaly in the affected Brazilian regions suggested an association between ZIKV infection and fetal malformations [10] and neurological disorders in adults , including Guillain-Barré syndrome and meningoencephalitis [11] . In February 2016 , the World Health Organization declared a public health emergency of international concern regarding neurological disorders associated with the rapid emergence of ZIKV in Oceania and the Americas [12] . In response to conditions of environmental stress , eukaryotic cells activate kinases ( HRI , GCN2 , PKR and PERK ) that phosphorylate eIF2α ( eukaryotic initiation factor 2 alpha ) to ease cellular injury or , alternatively , to induce apoptosis . Phosphorylation of eIF2α reduces global translation by impairing the formation of the ternary complex eIF2-GTP-tRNAMet , allowing cells to conserve resources and to initiate a reconfiguration of gene expression to effectively manage stress conditions [13] . Protein synthesis arrest triggers the assembly of stress granules ( SG ) , that are large ribonucleoprotein ( mRNP ) aggregates formed by stalled translation preinitiation complexes [14 , 15] . The major components of SG are untranslated mRNAs , eukaryotic translation initiation factors ( eIF4E , eIF4G , eIF4A , eIF2 ) , the 40S ribosomal subunit and RNA-binding proteins such as the poly ( A ) binding protein ( PABP ) , T-cell intracellular antigen 1 ( TIA-1 ) , TIA-1-related protein ( TIAR ) , and Ras GTPase activating protein-binding protein 1 ( G3BP1 ) [16] . Distinct cell host processes are interrupted or co-opted during viral infection , leading to the activation of cell stress responses on many levels . SG assembly lowers the cytosolic availability of components of the cellular translation machinery and functions as a platform that connects stress and antiviral innate responses , implying an overall antagonistic relationship between viruses and SGs [17] . In this sense , viruses have evolved a plethora of strategies to guarantee their replication by preventing or blocking SG assembly in infected cells , for example by co-opting RNA granule factors and/or blockage of activation of eIF2α kinases , such as PKR [18] . Cellular stress responses are essential in eliciting immune detection and in the cell’s ability to shut down viral gene expression in response to viral infection . So far , little is known about how ZIKV modulates stress responses in infected cells . Recently , it was shown that ZIKV infection triggers a potent repression of host cell translation initiation , while viral protein synthesis remains unaffected [19] . The interplay between viral replication and the cellular stress response may contribute to the exacerbated pathogenesis seen in the current epidemic . Elucidation of the interaction of viral components with host factors involved in SG assembly will provide new insight into the pathology of ZIKV infection . In this work , we investigated how ZIKV infection modulates SG assembly .
Green African monkey kidney ( Vero ) ( ATCC ) cells and human osteosarcoma-derived U2OS containing G3BP1-GFP ( a kind gift from Dr . Paul Anderson and Nancy Kedersha , Harvard Medical School [20] ) cells were maintained at 37°C and 5% CO2 atmosphere in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) ( HyClone ) and 1% penicillin/streptomycin ( Life Technologies ) . Cell viability was evaluated by trypan blue exclusion cytotoxicity assay [21] To produce viral stocks , Vero cells were infected with ZIKV strain PRVABC59/2015 at a multiplicity of infection ( MOI ) of 0 . 01 and incubated for 3 days at 37°C . Viral supernatants were then harvested , centrifuged at 300 x g for 10 minutes at 4°C and filtered on a 45 μm syringe filter . Viral titers were determined by plaque forming assay using culture media supplemented with carboxymethylcellulose ( Sigma ) as described previously [22] . A stock with a viral titer of 2 x 107 was used in the experiments . For immunofluorescence assays , 7 . 5 x 104 Vero or U2OS cells were seeded on 18 mm diameter coverslips the day prior infection . For Western blotting analysis , 7 . 5 x 104 Vero or U2OS cells were seeded in each well of a 12-well plate . Then , cells were incubated for 1 hour with ZIKV diluted in DMEM at an MOI of 0 . 5 [23] . After this period , the viral inoculum was removed by aspiration and cells were incubated in complete culture media for the periods specified in each experiment . Vero cells were seeded on 18 mm coverslips and infected as described above . Viral RNA was labeled as described in [24] . Briefly , cells were treated for 30 minutes with 1 μg/mL Actinomycin D ( Sigma ) to block host cellular transcription . Then , cells were transfected with 10 mM 5-bromourudine 50-triphosphate ( BrUTP ) ( Sigma ) using Lipofectamine 2000 reagent ( Invitrogen ) . After 1 hour , cells were fixed and processed for indirect immunofluorescence analysis . Stress was induced using 500 μM sodium Ars ( NaAsO2; Sigma-Aldrich ) for 1 h [24] , 300 nM Pateamine A ( a kind gift from Jerry Pelletier , McGill University ) for 1 h , 1 mM sodium selenite ( Na2SeO3; Sigma-Aldrich ) for 2 h and 2mM dithiothreitol ( DTT; Invitrogen ) for 1 h [25] . The eIF2α-dephosphorylation inhibitors Salubrinal ( Sigma-Aldrich ) and Sal003 ( a kind gift from Colin Crist , McGill University ) were used at final concentrations ranging from 5 to 75 μM by the time described in each experiment . Goat anti-TIAR ( Santa Cruz Biotechnology ) was used for indirect immunofluorescence microscopy at a dilution of 1:500; rabbit anti-eIF4G ( Santa Cruz Biotechnologies ) was used for indirect immunofluorescence at 1:500; mouse anti-Zika NS1 ( BioFront Technologies ) was used at 1:500 for indirect immunofluorescence and 1:1 , 000 for Western blotting; mouse anti-BrUTP ( Enzo Life Sciences ) was used for indirect immunofluorescence at 1:100; rabbit anti-phospho eIF2α ( Ser51 ) ( Cell Signaling Technology ) was used for indirect immunofluorescence and 1:500 and for Western blotting at 1:1 , 000; mouse anti-eIF2α ( Cell Signaling Technology ) was used for Western blotting at 1:1 , 000; and mouse anti-actin ( Abcam ) was used for Western blotting at 1:10 , 000; rabbit anti-GADD34 ( Thermo Fisher Scientific ) was used for western blotting at 1:1000; rabbit anti-PERK antibody ( Cell Signaling Technology ) was used for western blotting at 1:1000 . Horseradish peroxidase-conjugated secondary antibodies were purchased from Rockland Immunochemicals and used at 1:5 , 000 , and AlexaFluor secondary antibodies were purchased from Life Technologies and used at 1:500 . Cells were lysed in NP40 lysis buffer ( 50 mM Tris pH 7 . 4 , 150 mM NaCl , 0 . 5 mM EDTA , 0 . 5% NP40 ) . Equal amounts of protein were separated by SDS-PAGE and transferred to a nitrocellulose membrane ( Bio-Rad ) . Blocking was performed using 5% nonfat milk in Tris-buffered saline with 0 . 1% Tween 20 ( TBST ) for 1 hour at room temperature . Membranes were probed with the indicated primary and appropriate horseradish peroxidase-conjugated secondary antibodies . For detection of total and phosphorylated forms of proteins , samples were run in duplicate gels and transferred to independent membranes for western blotting . Membranes were probed for actin and protein levels were normalized in both membranes for the downstream densitometry analysis [26] . Proteins were detected using Western Lightning Plus-ECL ( PerkinElmer ) . For quantitation , the pixel intensity of each band was determined using the ImageJ program ( NIH ) and then normalized to the indicated control . Cells were prepared for indirect immunofluorescence as described previously [27] . Briefly , cells were fixed in 4% paraformaldehyde and permeabilized with 0 . 2% Triton X-100 . To prevent nonspecific binding , the cells were blocked using Roche Blocking Solution for 30 minutes at room temperature . Primary antibodies were applied followed by incubation with the appropriate secondary antibody in blocking solution . Stained cells were mounted in ProLong Gold Antifade Reagent with DAPI ( Life Technologies ) . Laser scanning confocal microscopy was performed using a Leica DM16000B microscope equipped with a WaveFX spinning disk confocal head ( Quorum Technologies ) using a 40X objective lens . Images were acquired with a Hamamatsu ImageEM EM-charges coupled device ( CCD ) camera and collected as Z-stacks that were rendered for image reconstruction using the Imaris software ( v . 8 . 1 . 3 , Bitplane , Inc . ) . Twenty-four hours after infection , Vero cells were treated with 500 μM Ars for 1 h , 2mM DTT for 1 h or 50 nM PatA for 1 h or U2OS cells were treated with 1 mM Se for 2 h and then processed for immunofluorescence as described above . Infected cells were identified by detection of viral protein NS1 or BrUTP labeled RNA , and SG-positive cells were defined as having at least 3 SG as determined by colocalized G3BP1 and TIAR or eIF4G and TIAR puncta . At least 150 cells were analyzed per condition in 10 to 15 fields in 3 independent experiments and the data are presented as the percentage of cells containing SG . All experiments were performed in triplicate , and the data are presented as the mean ± standard deviation ( SD ) . A p-value <0 . 05 in a two-way ANOVA test was considered statistically significant . GraphPad Prism 6 ( Graphpad Software Inc . ) was used to conduct statistical analyses and create graphs .
ZIKV is a positive-strand RNA virus that replicates in the cytoplasm but little is known about redistribution of host proteins in ZIKV infected cells . Sequestering SG components to sites of viral replication is a strategy used by viruses to impair SG assembly in infected cells [18] . To determine whether ZIKV replication altered the distribution of SG markers , Vero cells were infected with ZIKV with an MOI of 0 . 5 and 6 , 12 and 24 hours after , nascent viral RNA was labeled with BrUTP and detected by indirect immunofluorescence . In ZIKV-infected cells , viral protein or RNA was not detectable to 12 hpi . TIAR was evenly distributed throughout mock-infected cells ( Fig 1A and 1B ) , and eIF4G was distributed homogeneously in the cytoplasm ( Fig 1A and 1B ) . However , in infected cells , TIAR was still found in both the cytoplasm and nucleus but also concentrated in foci in the perinuclear region ( Fig 1A and 1B ) , colocalizing with the ZIKV RNA ( Fig 1A ) and viral nonstructural protein , NS1 ( Fig 1B ) . No change in eIF4G distribution was observed between mock and infected cells ( Fig 1A and 1B ) . These findings suggest that ZIKV infection induces the redistribution of TIAR to sites of viral RNA replication . Cleavage of proteins that nucleate SG assembly has been reported to be a strategy employed by viruses to overcome cellular stress response [28] . We next evaluated whether ZIKV replication alters the levels of SG markers . Cells were mock infected or infected with ZIKV and at 24 hpi cells lysates were collected and analyzed by SDS-PAGE followed by Western blotting . No alteration was observed in the levels of G3BP-1 , TIAR and PABP between mock and infected cells ( Fig 1C ) . These results indicate that ZIKV infection does not induce changes in the levels of SG-nucleating proteins . Several viruses , including many members of Flaviviridae family , have the ability to modulate SG assembly to keep the cell environment favorable to their own replication [29 , 30] . We investigated whether ZIKV can interfere with the assembly of SG in infected cells . Vero cells were infected with ZIKV or mock-infected and treated with sodium arsenite ( Ars ) at 24 hours post-infection to induce cellular stress . Ars is an oxidative agent that rapidly induces SG assembly through phosphorylation of eIF2α [31] . SG assembly was determined by indirect immunofluorescence of TIAR and eIF4G and infected cells were identified by the presence of the viral protein NS1 . In the absence of stress , mock-infected ( blue arrows ) and ZIKV-infected cells ( red arrows ) exhibited SG assembly at a rate of 0 . 61% and 0% , respectively ( Fig 2A , top panels and Fig 2B ) , indicating that ZIKV infection does not induce the assembly of SG . In mock-infected cells , Ars treatment induced abundant SG assembly as expected , with 81 . 4% of the cells presenting TIAR and eIF4G co-localized in cytoplasmic puncta ( Fig 2A and 2B ) . In contrast , ZIKV infected cells presented SG at a rate of only 21 . 6% ( Fig 2A , bottom panel and Fig 2B ) . Similar results were observed when U2OS cells were used in place of Vero cells ( S1 Fig ) . These results indicate that ZIKV infection blocks the assembly of type I SGs . Pateamine A ( PatA ) is a natural product isolated from a marine sponge that disrupts the translation initiation by hyperactivating the eIF4A helicase and disrupting the eIF4F complex , leading to the assembly of SG in an eIF2α-independent manner [32] . To test whether ZIKV infection was also capable of blocking PatA-induced SGs , Vero cells were mock-infected or infected with ZIKV and at 24 hpi were treated with PatA . SG assembly was determined by colocalized puncta of TIAR and eIF4G and infected cells were identified by the presence of the viral protein NS1 . PatA treatment , as expected , induced a robust SG assembly in 97 . 2% of the mock-infected cells ( Fig 3A , top panels , and 3B ) . Interestingly , ZIKV infection did not impair PatA-induced SG assembly , as 97 . 5% of the infected cells presented TIAR and eIF4G puncta ( Fig 3A , top panels , and 3B ) . Sodium selenite ( Se ) promotes the assembly of type II SG that differ from canonical SGs in their morphology , composition and mechanism of assembly , mainly by disrupting the eIF4F complex formation through 4EBP1 [33] . To test whether ZIKV infection alters Se-induced SG assembly , U2OS cells ( S1 Fig ) stably expressing GFP-G3BP1 were mock-infected or infected with ZIKV and at 24 hpi were treated with Se . U2OS cells were used in place of Vero cells due to the high toxicity of Se to the latter ones . SG assembly was determined by colocalized puncta of TIAR and G3BP-1 and infected cells were identified by the presence of the viral protein NS1 . Similarly to PatA-induced SG , no significant difference was observed in the assembly of SG between mock and infected cells treated with Se ( Fig 3A , bottom panels , and 3B ) . These findings indicate that ZIKV infection blockage of SG assembly is eIF2α-dependent . Many viruses modulate p-eIF2α levels during replication to assure viral protein synthesis and avoid cellular stress responses . For example , coronaviruses can induce GADD34 expression to enhance PP1 activity and consequently the dephosphorylation of eIF2α [34] , and herpesviruses encode a viral protein that mimics the function of GADD34 [35] . We examined the phosphorylation status of eIF2α in ZIKV infected untreated or Ars treated cells . Protein lysates were analyzed by Western blotting using an antibody specific for eIF2α phosphorylation at S51 . As shown in Fig 4A and 4B , little phosphorylation of eIF2α was detected in mock-infected and untreated Vero cells , with a slight increase in p-eIF2α in ZIKV-infected cells . As expected , high levels of eIF2α phosphorylation ( 40-fold increase ) were observed in extracts of mock-infected cells treated with Ars . However , in ZIKV-infected and Ars treated cells , levels of eIF2α phosphorylation were consistently lower ( 10 . 5-fold increase ) . ZIKV replication was confirmed by the detection of the viral protein NS1 in cell extracts . The amount of total eIF2α was similar under all conditions tested ( Fig 4A ) , indicating that ZIKV replication does not alter its expression . To further confirm that ZIKV-infected cells exhibit lower levels of p-eIF2α under arsenite treatment , phosphorylation of eIF2α was also analyzed by IF/LSCM . As shown in Fig 4C , phosphorylation of eIF2α is strongly induced in the cytoplasm of non-infected cells ( blue arrows ) . In contrast , in ZIKV-infected cells , the phospho-eIF2α signal is visibly weaker ( red arrow ) . Upon arsenite treatment , the fluorescence intensity of p-eIF2α in infected cells was in average 30% lower in ZIKV-infected cells in comparison to mock-infected cells ( Fig 4D ) . These results indicate that ZIKV infection impairs eIF2α phosphorylation triggered by oxidative stress . Our findings show that ZIKV infection blocks SG assembly and phosphorylation of eIF2α triggered by Ars , an HRI activator . To investigate whether this blockage is dependent on the eIF2α kinase activated upon stress , Vero cells were mock-infected or infected with ZIKV and at 24 hpi were treated with DTT , an endoplasmic reticulum ( ER ) stressor that activates PERK . SG assembly was determined by TIAR puncta and infected cells were identified by the presence of NS1 . DTT treatment induced SG assembly in 81 . 8% of the mock-infected cells ( Fig 5A and 5B ) . In contrast , only 28 . 6% of ZIKV-infected cells presented SG ( Fig 5A and 5B ) . The blockage of SG assembly correlates with lower levels of p-eIF2α upon DTT treated in ZIKV-infected cells ( Fig 5C , lane 4 ) when compared to mock-infected cells ( Fig 5C , lane 2 ) . Interestingly , the activation of PERK in DTT-treated cells , demonstrated by an increased PERK mobility , was similar in mock and ZIKV-infected cells ( Fig 5C , compare lanes 2 and 4 , position 1: activate PERK; position 2: inactive PERK ) , suggesting that the reduced levels of p-eIF2α in infected cells are a result of an interference downstream the activation of the eIF2α kinases . Our results suggest that ZIKV infection might abrogate SG assembly by blocking eIF2α phosphorylation . To test this further , Vero cells were infected with ZIKV and at 24 hpi , the levels of GADD34 , a PP1A cofactor , were evaluated . Our results show that are GADD34 levels are significantly higher in ZIKV-infected cells as compared to uninfected cells ( Fig 6A and 6B ) . To evaluate the role of GADD34/PP1A activity on ZIKV-infected cells , we treated cells with salubrinal and its derivative sal003 , small molecules that selectively inhibit the PP1/GADD34-mediated dephosphorylation of phospho-eIF2α [36 , 37] . Vero cells were treated with 75 μM of salubrinal or 10 μM of sal003 for 3 h prior to the addition of Ars to the cells . The phosphorylation status of eIF2α was evaluated by western blotting analysis ( Fig 6C ) . In cells treated with salubrinal prior to Ars-induced stress , ZIKV-infected cells present higher levels of phospho-eIF2α as compared to mock-infected control ( Fig 6C , compare lanes 6 and 8 ) . Similar results were obtained with sal003 [37] ( S2 Fig ) . The assembly of SG in the distinct conditions was monitored by indirect immunofluorescence . SGs were induced in 23 . 1±6 . 5% of ZIKV-infected cells treated with Ars . This value increased to 47 . 8±7 . 0% in cells that were treated with salubrinal prior to Ars-induced stress and was not significantly different from mock-infected cells ( Fig 6D and 6E ) . No significant difference was observed between control or salubrinal pre-treated mock-infected cells ( Fig 6E ) . Hence , inhibiting eIF2α dephosphorylation reduces the ability of ZIKV infection to block Ars-induced SG assembly . These results indicate that eIF2α dephosphorylation is differentially modulated during ZIKV replication and that this feature can contribute to ZIKV-mediated blockage of SG assembly . To further confirm the importance of modulating eIF2α for ZIKV replication , Vero cells were infected with ZIKV and after 1 hpi , salubrinal was added to culture media in increasing concentrations . After 24 h , supernatants of each condition were collected and viral titer was determined by plaque forming assay and cells were lysed and lysates were processed by SDS-PAGE followed by Western blotting . Treatment of cells with salubrinal led to a dose-dependent decrease in the production of infectious particles released to the culture media ( Fig 7A , bar graph and 7B ) . Cells treated with 75 μM of salubrinal produce only 4 . 9% of the infectious viral particles produced by control cells ( Fig 7A , bar graph and 7B ) . Salubrinal had no toxic effects on treated cells ( Fig 7A , line graph ) . Finally , a dose-dependent decrease in NS1 expression was observed in salubrinal treated cells ( Fig 7C ) .
The relationship between viruses and the cellular stress response is a multifaceted and complex phenomenon that depends on the structural and genetic characteristics of the virus and the host cell [38] . Infection by several types of RNA and DNA viruses results in changes in the cellular environment as viral replication co-opts several cellular pathways , including nutrient , energy and macromolecular synthesis , to produce infectious particles . In this process , viruses trigger the host cell stress response , which can lead to the assembly of SGs [17] . Since viral replication relies on the host translational machinery , most viruses suppress the stress response pathway and SG assembly at some point of their replicative cycle [18] . Interactions between stress proteins and viral components have been described in a large variety of experimental models at different stages of the viral lifecycle , depending on the type of virus and host cell [29 , 39] . ZIKV has emerged as a global public health threat over the last decade . Many aspects of the molecular mechanisms involved in the pathogenesis of this emerging virus remain unclear and require further investigation . In this work , we described that ZIKV replication does not induce SG assembly in Vero cells ( Fig 1 ) . This contrasts with the results recently published by Roth and colleagues [19] that describe the assembly of SG-like structures on Huh-7 cells infected with ZIKV . It is possible that those distinct findings are due to the usage of distinct cell lines . In our work , we also show that ZIKV and blocks SG assembly triggered by treatment of cells with Ars ( Fig 2 ) and DTT ( Fig 5 ) . These finds are similar to the ones described recently by Roth and colleagues , in which they describe that flaviviruses block SG assembly independently of the eIF2α kinase activated by stress [19] . Interestingly , during the review process of this manuscript , Basu and colleagues [40] reported that ZIKV-mediated blockage of SG assembly was specific for oxidative stress induced by arsenite . The reasons why these differences were observed remain to be determined . Several reports have shown that members of the Flaviviridae family modulate SG assembly in infected cells . The 3’ stem loop from the viral minus strand of WNV and DENV captures TIA-1 and TIAR to promote viral genome RNA synthesis and inhibit SG assembly [29 , 41] . JEV capsid protein interaction with Caprin-1 leads to the sequestration of several SG components , such as G3BP1 and USP10 , in the perinuclear region of infected cells , resulting in impairment of SG assembly [30] and bovine viral diarrhea virus ( BVDV ) blocks Ars-mediated SG assembly [42] . TIA1 and TIAR are recruited to tick-borne encephalitis virus ( TBEV ) sites of replication [43] . Finally , hepatitis C virus ( HCV ) replication leads to oscillating SG assembly/disassembly in infected cells through controlling the phosphorylation of eIF2α and co-opting TIA-1 , TIAR and G3BP1 [44 , 45] . More recently , Roth and colleagues [19] demonstrated that DENV and ZIKV uncouple translation suppression from the stress response by a mechanism that is yet to be identified . We demonstrate that ZIKV infection did not lead to a blockage in PatA or Se-induced SG ( Fig 3 ) . The assembly of SG triggered by both molecules is independent of the phosphorylation of eIF2α , suggesting that ZIKV blocks stress granules assembly mainly via eIF2α signaling . Interestingly , this does not seem to be a general feature of flaviviral infections , as it has been demonstrated by Roth and colleagues that DENV inhibits SG assembly induced by hippuristanol , an inhibitor of eIF4A RNA binding [19] . The phosphorylation of eIF2α is a key regulator of mRNA translation initiation , and the level of phospho-eIF2α is modulated by the activities of kinases and phosphatases [46] . Oxidative stress induced by Ars culminates on eIF2α phosphorylation by HRI [31] , which prevents the recycling of the eIF2-GTP-tRNAMet ternary complex , leading to polysome disassembly and consequent translational arrest and SG assembly [14] . Regulation of protein synthesis by eIF2α phosphorylation plays an important role in the cellular defense against viral infection , thus viruses evolved diverse strategies to prevent it . Our results show that ZIKV attenuates eIF2α phosphorylation triggered by Ars ( Fig 4 ) and DTT ( Fig 5 ) and this ability is , at least in part , a consequence of modulating its dephosphorylation , as supported by the observation that treatment of cells with salubrinal reverses the ZIKV-mediated blockage of SG assembly induced by Ars ( Fig 6 ) . Similar to the finding of Wang and colleagues using coronavirus [34] , we demonstrated that ZIKV infection induces a moderate increase in GADD34 expression ( Fig 6A and 6B ) . Recently , Buchman and colleagues [47] described a mechanism by which trehalose modulates p-eIF2α levels and stress granule assembly/disassembly by enhancing the expression of GADD34 and CReP . The increase in the cellular levels of the PP1 phosphatase subunits could lead to faster dephosphorylation of p-eIF2α and disassembly of SGs , thereby rendering the cells able to recover more quickly from stress . It is possible that the enhanced levels of GADD34 found in ZIKV-infected cells play a similar role in response to stress . Treatment of cells with salubrinal causes an accumulation of phospho-eIF2α through an inhibition of PP1/GADD34-mediated dephosphorylation of eIF2α without increasing eIF2α kinase activity [36] . Modulation of PP1 activity by viral infection was demonstrated for human cytomegalovirus [48] , African swine fever virus [49] , Newcastle disease virus [50] , papillomavirus [51] and herpes simplex virus [52] . ICP34 . 5 is a protein homologous to GADD34 encoded by HSV that is essential for HSV replication in some cell types . It binds cellular PP1 and promotes eIF2α dephosphorylation , ensuring viral replication despite activation of PKR [35] . Treatment of HSV-infected cells with salubrinal inhibits viral replication in a dose-dependent manner and leads to higher phospho-eIF2α levels [36 , 52] . Similarly , our results demonstrate that ZIKV replication is severely impaired in salubrinal-treated cells ( Fig 7 ) , indicating that ZIKV relies to some extent on eIF2α dephosphorylation for its replication . These findings are distinct from the model proposed by Roth and colleagues [19] , in which modulation of SG assembly in ZIKV-infected cells was independent of eIF2α dephosphorylation promoted by elevated GADD34 levels . Cell type-specificities can be responsible for those contrasting results . It remains to be determined whether treatment with salubrinal has secondary effects on the infected cells that could act in synergy with the PP1/GADD34 inhibition and the mechanism by which ZIKV modulates this activity . In conclusion , our work provides new insights into the ZIKV biology by demonstrating that ZIKV inhibits SG assembly in a phospho-eIF2α dependent way . This ability may reflect one of the many strategies that ZIKV has evolved to control the host stress response and demonstrate that ZIKV elicits mechanisms to counteract host anti-viral stress responses to promote a cellular environment propitious for viral replication . Elucidation of the interaction of viral components with host factors involved in SG assembly may provide new insights into the pathology of ZIKV infection and lead to the identification of novel targets for therapeutic intervention .
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Zika virus ( ZIKV ) is transmitted to humans primarily through mosquito bites , but there have also been cases of sexual , perinatal , and suspected blood transfusion transmission . It has been associated with fetal malformations and neurological disorders in adults . The rising concern about this pathogen led the World Health Organization to declare it as a public health emergency of international concern regarding neurological disorders . There is an urgent global scientific effort underway to better understand ZIKV biology and define interactions that occur between the virus and the host cell . We evaluated how ZIKV infection counteracts the assembly of dynamic aggregates of RNA and proteins called stress granules ( SGs ) . We observed that ZIKV blocks SG assembly induced by sodium arsenite ( Ars ) , but not by sodium selenite or Pateamine A . We demonstrate that this difference is related to the ability of ZIKV to modulate the dephosphorylation of eIF2α via its phosphatase . Our work demonstrates that ZIKV prevents a host stress response in order to maintain a cellular environment propitious for viral replication .
|
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"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
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2017
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Zika virus inhibits eIF2α-dependent stress granule assembly
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Impaired nitric oxide ( NO˙ ) -cyclic guanosine 3' , 5'-monophosphate ( cGMP ) signaling has been observed in many cardiovascular disorders , including heart failure and pulmonary arterial hypertension . There are several enzymatic determinants of cGMP levels in this pathway , including soluble guanylyl cyclase ( sGC ) itself , the NO˙-activated form of sGC , and phosphodiesterase ( s ) ( PDE ) . Therapies for some of these disorders with PDE inhibitors have been successful at increasing cGMP levels in both cardiac and vascular tissues . However , at the systems level , it is not clear whether perturbation of PDE alone , under oxidative stress , is the best approach for increasing cGMP levels as compared with perturbation of other potential pathway targets , either alone or in combination . Here , we develop a model-based approach to perturbing this pathway , focusing on single reactions , pairs of reactions , or trios of reactions as targets , then monitoring the theoretical effects of these interventions on cGMP levels . Single perturbations of all reaction steps within this pathway demonstrated that three reaction steps , including the oxidation of sGC , NO˙ dissociation from sGC , and cGMP degradation by PDE , exerted a dominant influence on cGMP accumulation relative to other reaction steps . Furthermore , among all possible single , paired , and triple perturbations of this pathway , the combined perturbations of these three reaction steps had the greatest impact on cGMP accumulation . These computational findings were confirmed in cell-based experiments . We conclude that a combined perturbation of the oxidatively-impaired NO˙-cGMP signaling pathway is a better approach to the restoration of cGMP levels as compared with corresponding individual perturbations . This approach may also yield improved therapeutic responses in other complex pharmacologically amenable pathways .
Signal transduction via the nitric oxide ( NO˙ ) -cyclic guanosine 3' , 5'-monophosphate ( cGMP ) pathway is involved in multiple and diverse biological responses , including smooth muscle relaxation , inhibition of platelet aggregation , and neural communication [1–6] . This pathway is composed of several molecular species acting in two opposing limbs , the cGMP-synthetic limb and the cGMP-degradative limb ( see Fig 1 ) . The proper function of these two limbs is crucial in controlling these biological responses . Within the cGMP-synthetic limb , NO˙ binds to soluble guanylyl cyclase ( sGC ) to catalyze the production of cGMP from guanosine-5'-triphosphate ( GTP ) , whereas in the cGMP-degradative limb , cyclic nucleotide phosphodiesterase ( PDE ) converts cGMP to GMP . Impaired function of either or both limbs of the NO˙-cGMP signaling pathway has been reported in many cardiovascular disorders , including heart failure and pulmonary arterial hypertension . Importantly , increased oxidative stress associated with malfunction of the NO˙-cGMP signaling pathway has been implicated in the pathobiology of several diseases [7 , 8] . During oxidative stress , the pathway’s unresponsiveness can be explained by several mechanisms , among which sGC insensitivity to NO˙ ( tolerance ) is decisive . Elevated reactive oxygen species ( ROS ) may promote sGC insensitivity through either non-heme ( cysteine ) oxidation of sGC [9–14] , S-nitrosation of sGC [15] , heme oxidation of sGC [16] , or oxidation of NO˙ , such as via enhanced peroxynitrite ( ONOO- ) formation [17] . Potentially , there are several determinants of this oxidatively-adapted pathway , including oxidatively inactivated sGC itself , oxidized NO˙ , and PDE . The pharmacological challenge is how best to deploy potential therapeutic options that focus on these determinants under increased oxidative stress in a way that optimizes restoration of the function of this pathway . Investigating the complexity of biological systems using combinatorial perturbations is a rational strategy for predicting function and phenotype [18] , understanding network mechanisms [19–22] , and identifying new and more promising therapeutic targets for human diseases [23 , 24] . In theory , using a combination of drugs that can perturb different components of a system could be a more effective strategy than treating a disease with a single drug [25] . Indeed , the most complex diseases , such as cardiovascular diseases , cancer , diabetes mellitus , neurodegenerative diseases , and asthma , are multifactorial diseases . Systems-based interventions using multi-component drug combinations have been used increasingly to treat these complex diseases , although these approaches have largely been developed empirically in the clinical setting . The main purpose of model-based drug discovery is to revisit classical pharmacology logically in order to replace the one-gene , one-protein , and one-mechanism perspective with a systems-oriented paradigm to improve the therapeutic index of potential drugs targeting these complex diseases [26 , 27] . Relevant principles have emerged from different studies of combination therapies that do not always yield predicted outcomes . For example , the combination of niacin ( vitamin B3 ) with a statin [5-hydroxy-3-methylglutaryl-coenzyme A ( HMG-CoA ) reductase] leads to an incremental decrease in low-density lipoprotein ( LDL ) cholesterol concentration and an increase in high-density lipoprotein ( HDL ) cholesterol concentration [28] . Combinations of drugs that perturb five different targets in the HIV life cycle have turned AIDS from a lethal infection into a manageable chronic disease [29] . Another interesting combination is that of nitroglycerin and N-acetylcysteine ( NAC ) , which can potentiate the effects of nitroglycerin in the treatment of acute myocardial ischemia [30] . The combination of β2-adrenergic receptor activators with muscarinic receptor blockers is useful for the treatment of chronic obstructive pulmonary disease [31] . Opposing and independent regulatory mechanisms within the NO˙-cGMP pathway determine the biological level of cGMP in the steady-state . Model-based approaches have facilitated our understanding of these regulatory mechanisms for cGMP formation [32–34] . Thus far , modeling has been used to study two distinct limbs of the NO˙-cGMP signaling pathway separately [33–39]; however , here , we will build this model as an integrated system that also includes oxidative inactivation . We then pose the question of whether a combination of two or three agents with orthogonal therapeutic actions ( and toxicities ) , used at lower concentrations than when used alone , will enhance cGMP formation beyond that of single agents in the presence of oxidative stress . In this study , we pursued this question using a dynamical model of the NO˙-cGMP signaling pathway in the presence of hydrogen peroxide . Impaired activation of NO˙-cGMP signaling has been observed in several cardiovascular disorders , including heart failure [40] and pulmonary arterial hypertension [41] , due , in part , to excess oxidants . Current treatments for these disorders that involve this pathway includes nitrovasodilators and phosphodiesterase inhibitors . Thus , this theoretical approach , were it to demonstrate benefit , may offer initial strategies for optimal drug combinations for the treatment of these ( and other ) disorders in which NO˙-cGMP signaling is dysfunctional using approved agents . Previous studies have used models of cell signaling networks to evaluate the action of drug pairs as compared with corresponding individual drugs [42] . Modeling drug action using ordinary differential equations could be challenging without sufficient information about the integrated network kinetics of drug action . To address this challenge , Araujo and colleagues investigated an interesting concept , perturbation simulation , on the epidermal growth factor receptor ( EGFR ) signaling network . They found that pairwise perturbations of reaction rates was more effective at restoring optimal function of the network than individual perturbation of corresponding reaction rates [43] . We expanded this approach to a practically remediable pathway and examined how hydrogen peroxide ( H2O2 ) -induced oxidant stress affects the key reaction steps of the NO˙-cGMP signaling pathway to diminish cGMP levels , and then developed a combinatorial approach to perturb the oxidatively-impaired NO˙-cGMP signaling pathway and restore cGMP levels toward normal . In addition , in contrast to [43] , we also examined the consequences of lesser degrees of inhibition in combination modeling to infer lower dose-dependent toxicity . Lastly , in contrast to [43] , we performed cell-based experiments to validate the modeling strategy . To do so , all rate constants were perturbed individually , in pairs , or in trios by step-wise ten-fold changes in their values to their original values , which is analogous to concentration-dependent inhibition of a given reaction by a specific inhibitor . Our goal was to identify an optimal perturbation that augments the cGMP levels toward normal during oxidative stress . Using a single perturbation , we found that the potential therapeutic targets , including the oxidation of sGC , NO˙ dissociation from sGC , and cGMP degradation by PDE , had a profound effect on enhancing cGMP accumulation as compared with other reaction steps . Using combined perturbations , we were able to identify an optimal triple perturbation that increases cGMP levels beyond that observed with the corresponding individual or paired perturbations that comprise it . Importantly , these theoretical findings were confirmed in cell-based experiments in which a combination of a nitric oxide donor ( S-nitroso-N-penicillamine ) , an antioxidant ( N-acetyl-L-cysteine ) , and a phosphodiesterase type 5-inhibitor ( sildenafil ) significantly improved the cyclic GMP output of the pathway in the setting of oxidant stress ( hydrogen peroxide ) in pulmonary artery vascular smooth muscle cells .
Once NO˙ is generated in source cells ( endothelial cells ) in the vasculature , it diffuses into vascular smooth muscle cells and binds to sGC , a ferrous iron hemoprotein receptor , to generate the NO˙-sGC complex . Either sGC alone or the NO˙-sGC complex , whose specific activity is ~200 times greater than sGC alone [45] , can convert GTP to the second messenger molecule , cGMP , which is degraded by cyclic nucleotide PDE ( s ) to GMP . However , under oxidative stress conditions , sGC is also oxidized by H2O2 and thereby desensitized ( Fig 1 ) . The biological reactions comprising this system were modeled using ordinary differential equations and mass action kinetics involving 12 molecular species and 13 rate constants ( S1 Table ) . The simulation time intervals were selected to monitor cGMP dynamics between 0 and 200 seconds ( based on the cGMP dynamics in S1A Fig for pulmonary artery vascular smooth muscle cells ) . Next , an oxidant ( 500 μM H2O2 ) was added to the system to alter the dynamics of all molecular species , including cGMP , as compared with control . One prior experimental study showed that both cGMP-degrading enzymes and sGC desensitization cooperatively accounted for the diverse patterns of cGMP responses to NO˙ . Two different temporal dynamic signatures of cGMP were reported within platelets and astrocytes that have high and low levels of PDEs , respectively [32 , 46 , 47] . In our system under control conditions , the cGMP concentrations increased abruptly to a peak concentration within 40 seconds , and then decreased to baseline within 200 seconds ( mirroring the experimental dynamics of S1A Fig ) . When the system was exposed to H2O2 , the cGMP levels decreased by ~6-fold ( Fig 2A ) . In this model , we proposed that H2O2 impairs activation of sGC and its generation of cGMP [48]; however , conflicting results have been reported by others [49] . To restore cGMP generation to the normal level , there are several potential therapeutic targets , including oxidatively inactivated sGC , the NO˙-activated form of sGC , and PDE . Therapies for some diseases with PDE inhibitors have been successful at increasing cGMP levels in both cardiac and vascular tissues . However , to predict which one of these potential targets would be most effective at increasing cGMP levels , we perturbed either the synthetic limb ( k3 ) or the degradative limb ( k10 ) of the pathway in the absence or the presence of H2O2 , and then evaluated cGMP dynamics . We found that targeting these two reaction steps can significantly increase the cGMP levels as compared with control if there is sufficient unoxidized sGC available . However , under significant oxidative stress , targeting these two reaction steps cannot be an effective strategy for restoring cGMP levels to normal ( Fig 2B and 2C ) . To evaluate more systematically the role of any given reaction in cGMP formation , we compared cGMP dynamics by reducing each of the thirteen rate constants to 10% of its original value ( simulating significant reaction inhibition ) in the presence of H2O2 . We found that the cGMP levels were not restored toward control levels by decreasing k1 ( oxidizing sGC ) , k3 ( desensitizing sGC ) , or k10 ( degrading cGMP ) to 10% of their original values ( Fig 2D ) . This observation suggested that under oxidative stress , targeting either the synthetic limb or the degradative limb of the pathway alone is not an effective approach for restoring cGMP to normal levels . The relative involvement of both synthetic and degradative components of the NO˙-cGMP signaling pathway led us to propose that these components could exert autonomous effects on cGMP accumulation . This concept raised the possibility that combined perturbations may have more profound effects on cGMP levels than single perturbations . Addressing this concept , the NO˙-cGMP pathway was perturbed using all possible single , paired , or triple perturbations in the presence of H2O2 , and then the time-integrated cGMP ( cGMPT ) levels were calculated ( Fig 3 ) . We found that the simultaneous perturbation ( ρ ) of several rate processes along with the perturbation of k1 ( ρk1 ) yielded the highest cGMPT levels relative to other perturbations . This finding suggested that targeting the primary driver of pathway dysfunction ( ρk1 ) along with other potential therapeutic targets might be a better approach for increasing cGMP levels ( even ) beyond control levels under oxidative stress . We next perturbed the proposed rate constants , including ρk1 , ρk3 , ρk10 , or all possible combinations of these three rate constants , in the presence of H2O2 . Modeling a fractional linear reduction of values for these rate constants , we created a vector of eleven different values for each wherein the maximum value for each rate constant was its original value ( S1 Table ) and the minimum value was 0 . 1 , 0 . 3 , or 0 . 5 of its original value for a rate constant in single , paired , or triple perturbations , respectively ( lesser minimal values were used with greater combined perturbations to attempt to demonstrate efficacy at combined doses that might limit dose-dependent toxicities ) . Rate constants that were not varied under each set of modeling conditions were maintained at their full values . The cGMP dynamics was then calculated using the range of rate constants ( Fig 4 ) . These results suggested that under oxidative stress , decreasing dissociation of NO˙ from the NO˙-sGC complex ( ρk3 ) is the most sensitive reaction step for increasing cGMP levels as compared with the use of an anti-oxidant ( ρk1 ) and PDE inhibitor ( ρk10 ) . Furthermore , perturbation of k1 ( ρk1 ) is the best strategy by which to increase cGMP levels beyond perturbation of either k3 ( ρk3 ) or k10 ( ρk10 ) . Subsequently , we used the Bliss model [50] ( which , based on probability theory , assumes two inhibitors work through independent mechanisms of action , and assumes that the two inhibitors do not interfere or compete with each other ) to evaluate the power of paired perturbations . Under oxidative stress , optimal parameters were perturbed either individually or in pairs in order to compare the effects of perturbations on cGMP levels . The effects of single perturbations on cGMP levels were used to calculate the Bliss model , as indicated by eq ( 15 ) . As depicted in Fig 5 , paired perturbations of optimized single parameters increased cGMP levels beyond the Bliss model predictions . To assess whether these differences are additive or non-additive , we used an isobologram analysis . We examined the combined effects on cGMPT when two or three rate constants were perturbed simultaneously . The isobologram ( contour plot ) [51–56] was used to quantify the combined effects ( Fig 6 ) , wherein we observed that combined perturbations act additively to increase cGMP levels in this system . We studied cGMP dynamics using human embryonic kidney ( HEK ) 293 cells and human pulmonary artery vascular smooth muscle ( PAVSM ) cells . PAVSM contain abundant PDE5 compared with HEK293 cells ( 35 ) , thereby ensuring that both the cGMP-synthetic and degradative limbs determine the cGMP levels ( S1 Fig ) . Thus , in PAVSM , the rapid accumulation of cGMP is followed by its equally rapid reduction ( S1A Fig ) . In HEK293 cells , which contain lower amounts of PDE5 ( 35 ) as compared with PAVSM cells , the cGMP-synthetic limb of the pathway primarily determines the cGMP levels ( S1B Fig ) . The cells were pretreated with either H2O2 or buffer for 30 minutes . Time points were selected to capture cGMP dynamics . When the HEK293 cells were exposed to H2O2 at 500 μM , we found that NO˙-stimulated cGMP production was significantly reduced as a function of time ( S1B Fig ) . This result suggested that H2O2 blocked the cGMP-synthetic limb of the pathway , which plays a predominant role in determining the cGMP levels in HEK293 cells ( as confirmed by the absence of a biphasic response in cGMP dynamics compared with the PAVSM cells in S1 Fig ) . In order to determine the validity of the combinatorial modeling described above , we measured cGMP in PAVSM cells treated with various combinations of agents that act on different steps in the pathway of Fig 1 . Agents were chosen because they have been used in human studies , and because they affect each of the limbs of the pathway in Fig 1 . As shown in Fig 7A , we first showed that the addition of a NO . -donor , S-acetyl-N-penicillamine ( SNAP ) , increased the cGMP produced by 58% over vehicle-treated control cells; hydrogen peroxide treatment , however , abrogated this increase . When cells were treated with hydrogen peroxide and the reducing agent , N-acetyl-L-cysteine ( NAC ) , cGMP levels increased to ~2-fold above control . The addition of sildenafil , a PDE5 inhibitor ( the primary PDE isoform found in PAVSM responsible for cGMP degradation ) , to SNAP and NAC in the presence of hydrogen peroxide further increased cGMP levels to ~3 . 2-fold above vehicle-treated control cells . With these baseline measurements , we next explored key comparative combinations of agents that mimicked the optimal modeled combinations , as shown in Fig 7B . Here , cGMP responses are reported as the % of the maximal response ( to sildenafil and SNAP in the absence of hydrogen peroxide ) owing to variation from experiment to experiment . We observed that the combinations of NAC and SNAP , sildenafil and SNAP , and sildenafil , NAC , and SNAP each increased cGMP in the presence of hydrogen peroxide , and that the relative magnitude of the increases was consistent with the modeled data in Fig 3 . The use of NAC inhibits reactions 1 ( and possibly 13 ) , the use of SNAP ‘inhibits’ reaction 3 indirectly by driving reaction 2 , and the use of sildenafil inhibits reaction 10 as a competitive inhibitor of PDE5 and indirectly inhibits reaction 12 by limiting the formation of the catalytic complex and hence substrate turn-over . Thus , as in Fig 3 , the magnitude of increase in cGMP was of the following order: inhibition of reactions 1 + 3 < inhibition of reactions 3 + 10 ( or 12 ) < inhibition of reactions 1 + 3 + 10 , which is similar to the experimental reaction order we observed in the data of Fig 7 .
Impaired activation of NO˙-cGMP signaling pathway has been observed in cardiovascular disorders and other common disease states . There are multiple enzymatic determinants of cGMP production in this pathway , including sGC itself , the oxidatively inactivated form of sGC , the NO˙-activated form of sGC , enzymatic sources of NO˙ , and PDE . Therapies for these disorders with PDE inhibitors have been successful at enhancing cGMP levels in cardiac and vascular tissue with attendant improvement in lusitropy and vasodilation , respectively . However , PDE is only one of the enzymatic determinants of cGMP formation . In this systems-level approach , we used all possible single , paired , or triple perturbations to propose a combined perturbation that was more effective in cGMP accumulation than any single perturbation . The optimal number of the perturbations was three owing to there being only three key processes that determine independently the cGMP levels , i . e . , cGMP synthesis , cGMP degradation , and oxidative inactivation of sGC . By having this modeled information , one can improve experimental design , curb cost , and save time in performing the experiments necessary for gaining useful results . Alternatively , one could randomly target any given component of this pathway either individually or in combination with other components of the pathway . Yet another approach is the maximal damage targeting strategy [57] , theoretically a better approach relative to the random targeting of a pathway . However , using either the random targeting or the maximal damage targeting approach , we might overlook the optimal perturbations among many combinations that may never have been tested . PDEs are essential enzymes within normal cells that degrade the phosphodiester bond in the second messenger molecules cAMP and cGMP . PDEs are , therefore , important regulators of signal transduction mediated by these second messenger molecules . As with many drugs that affect molecular pathways involved in ( many ) different signaling pathways , the side effects of PDE inhibitors are dose-dependent [58]; thus , to reduce the dose of a PDE inhibitor and then combine it with other potential drugs that have non-overlapping mechanisms of action and toxicities may significantly improve the overall therapeutic index of the treatment strategy . One of the rationales for using combination therapy is to block redundant pathways that exist extensively within the molecular networks whose functions are modified in human diseases . To overlook this network property may limit the potential for reformulating existing drugs that can be used in combination with higher efficacy and fewer toxicities . Our results show how the combined perturbations of the NO˙-cGMP signaling pathway represent a useful strategy for increasing cGMP levels . A model-based analysis suggests that the combinatorial perturbation of biological networks is a promising approach by which to identify drug combinations with higher efficacy and perhaps lower toxicity ( rational polypharmacy ) [42] . Further work on other specific pathways will be required to validate the general approach .
Enzyme immunoassay ( EIA ) : human pulmonary artery vascular smooth muscle ( PAVSM ) cells and growth media were obtained from Lonza Inc . ( Walkersville , MD . , USA ) . Confluent cells were pre-treated with phenol-red free DMEM ( supplemented with 10% fetal calf serum ) in the presence of absence of 10 mM N-acetyl-L-cysteine ( NAC ) , a thiol reducing agent and antioxidant , to reverse mildly oxidized critical sulfhydryl groups in sGC [14] and , possibly , to prevent the oxidation of NO to NOx; and/or 100 nM sildenafil , a PDE5 inhibitor , for 90 min followed by 500 μM H2O2 for 30 min . Cells were next treated with either phosphate-buffered saline or 100 μM S-nitroso-N-acetylpenicillamine ( SNAP ) , a NO . -donor , for 10 min . PAVSM cells were rinsed in ice-cold phosphate buffered saline and then solubilized in ice-cold 6% trichloroacetic acid . Samples were stored at -80° until the day of the assay . Samples were processed and cGMP and protein were measured as previously described [14] . cGMP formation was measured by immunoassay according to the cGMP Assay ( Cayman Chemical Co . , Ann Arbor , MI ) . H2O2 , trichloroacetic acid , NAC , and sildenafil were purchased from Sigma-Aldrich ( St . Louis , MO ) . Phenol-red-free DMEM was obtained from Gibco , Life Technologies , Grand Island , NY and fetal calf serum was from Atlanta biologicals , Flowery Branch , GA . Assuming mass-action kinetics , the reaction scheme ( Fig 2 , S1 Table ) was deconstructed into 12 ordinary differential equations ( ODEs ) : d [H2O2]/dt = −k1 [ H2O2] * [sGC] ( 1 ) d [sGC]/dt = − k1 [H2O2] * [sGC]−k2 [NO . ]* [sGC] +k3 [NO . −sGC]−k4 [ sGC] * [GTP] + ( k8 +k5 ) [sGC−GTP] ( 2 ) d [sGC−H2O2]/dt = k1 [ H2O2] * [sGC] ( 3 ) d [NO . ]/dt = −k2 [ NO . ] * [sGC]+k3 [NO . −sGC]−k13[NO . ] ( 4 ) d [NO . −sGC]/dt = k2 [ NO . ] * [sGC]−k3 [NO . −sGC]−k6 [GTP]* [NO . −sGC] + ( k9+k7 ) [NO . −sGC−GTP] ( 5 ) d [GTP]/dt = −k4 [ GTP] * [sGC]+k5 [sGC−GTP]−k6 [GTP]* [NO . −sGC] + k7 [NO . −sGC−GTP] ( 6 ) d [sGC−GTP]/dt = k4 [ GTP] * [sGC]− ( k5+k8 ) [sGC−GTP] ( 7 ) d [NO . −sGC−GTP]/dt = k6 [ GTP] * [NO . −sGC]− ( k7+k9 ) [NO . −sGC−GTP] ( 8 ) d [cGMP]/dt = k8 [ sGC−GTP] +k9 [NO . −sGC−GTP]−k10[cGMP]*[PDE] +k11[cGMP−PDE] ( 9 ) d [PDE]/dt = −k10 [ cGMP] *[PDE]+ ( k11 +k12 ) [cGMP−PDE] ( 10 ) d [cGMP−PDE]/dt = k10 [ cGMP] *[PDE]− ( k11 +k12 ) [cGMP−PDE] ( 11 ) d [GMP]/dt = k12 [cGMP−PDE] ( 12 ) to simulate the dynamics of the molecular species within the NO˙-cGMP signaling pathway . The ODEs were solved using a numerical ODE solver ( ode15s ) . All mathematical modeling and simulations were performed using the SimBiology toolbox in MatLab ( Version 8 , 2012b , MathWorks , Natick , MA ) . The parameter values for this model include 13 rate constants and 12 initial concentrations ( S1 Table ) , which were chosen or estimated from the literature , as indicated in the Table . The system dynamics were assessed in the absence or presence of H2O2 . In the presence of H2O2 ( 500 μM ) , the NO˙-cGMP pathway was perturbed by assuming the presence of an effective inhibitor of a given reaction ( s ) sufficient to impair the reaction kinetics ( ρk = 0 . 1k ) . We perturbed all possible individual ( 13 ) , pairs ( 78 ) , or trios ( 286 ) of reactions in the model . The total number of perturbations ( up to triple perturbations ) was computed by inserting the total number of rate constants ( q = 13 ) and the maximum number of perturbations ( p = 3 ) into following equation: Cqp=∑i=1pq ! /[ ( q−i ) ! × i ! ( 13 ) Thus , the total possible number of perturbations ( for triple perturbations ) is 13C3 = 377 . To assess the relative role of each perturbation , cGMP dynamics were illustrated ( Fig 3 ) . The time-integrated cumulative cGMP ( cGMPT ) level is defined as: cGMPT = ∫0T[cGMP] ( t ) dt , T= 200sec ( 14 ) Perturbation of k1 , k3 , or k10 alone can induce a dose-dependent cGMPT response in the presence of H2O2 . We varied the rate constants by fractional linear decrements . To depict the matrix response of cGMPT , two vectors of rate constant values were combined in 11×11 matrices where the value of each rate constant is depicted along each axis ( Fig 6 ) . The contour plots were used to evaluate additive and non-additive effects . Thus , the combined actions were either additive ( Fig 6 ) or synergistic if the isobole is a straight line or a convex line , respectively . Likewise , 11×11×11 matrix of all model parameters was constructed for triple perturbations and three-dimensional contour plots analyzed accordingly .
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Developing drugs for a well-defined biochemical or molecular pathway has conventionally been approached by optimizing the inhibition ( or activation ) of a single target by a single pharmacologic agent . On occasion , drug combinations have been used that generally target multiple pathways affecting a common phenotype , again by optimizing the extent of inhibition of individual targets , semi-empirically adjusting their doses to minimize toxicities as they are manifest . Here , we present a computational approach for identifying optimal combinations of agents that can affect ( inhibit ) a well-defined biochemical pathway , doing so at minimal combined concentrations , thereby potentially minimizing dose-dependent toxicities . This approach is illustrated computationally and experimentally with a well-known pathway , the nitric oxide-cyclic GMP pathway , but is readily generalizable to rational polypharmacy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"neurochemistry",
"chemical",
"compounds",
"oxides",
"pathology",
"and",
"laboratory",
"medicine",
"oxidative",
"stress",
"cardiovascular",
"anatomy",
"cgmp",
"signaling",
"neuroscience",
"toxicology",
"cell",
"signaling",
"toxicity",
"pharmaceutics",
"arteries",
"pulmonary",
"arteries",
"hydrogen",
"peroxide",
"blood",
"vessels",
"neurochemicals",
"nitric",
"oxide",
"chemistry",
"oxidation",
"biochemistry",
"signal",
"transduction",
"cell",
"biology",
"anatomy",
"biology",
"and",
"life",
"sciences",
"chemical",
"reactions",
"physical",
"sciences",
"drug",
"therapy",
"peroxides"
] |
2016
|
Systems Pharmacology and Rational Polypharmacy: Nitric Oxide−Cyclic GMP Signaling Pathway as an Illustrative Example and Derivation of the General Case
|
The accuracy of machine learning tasks critically depends on high quality ground truth data . Therefore , in many cases , producing good ground truth data typically involves trained professionals; however , this can be costly in time , effort , and money . Here we explore the use of crowdsourcing to generate a large number of training data of good quality . We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting . We investigate the accuracy , speed and other quality metrics when this task is performed by students for academic credit , Amazon MTurk workers , and Master Amazon MTurk workers . We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students , but with no significant difference between the two MTurk worker types . Furthermore , the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker . We provide best practices to assess the quality of ground truth data , and to compare data quality produced by different sources . We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality , especially in the context of high throughput plant phenotyping . We also provide several metrics for assessing the quality of the generated datasets .
Crop genetics include basic research ( what does this gene do ? ) and efforts to effect agricultural improvement ( can I improve this trait ? ) . Geneticists are primarily concerned with the former and plant breeders are concerned with the latter . A major difference in the perspectives between these groups is their interest in learning which genes underlie a trait of interest: whereas geneticists are generally interested in what genes do , breeders can treat the underlying genetics as opaque , selecting for useful traits by tracking molecular markers , or directly , via phenotypic selection [1] . Historically , the connections between plant genotype and phenotype were investigated through forward genetics approaches , which involve identifying a trait of interest , then carrying out experiments to identify which gene is responsible for that trait . With the advent of convenient mutagens , molecular genetics , bioinformatics , and high-performance computing , researchers were able to associate genotypes with phenotypes more easily via a reverse genetics approach: mutate genes , sequence them , then look for an associated phenotype . However , the pursuit of forward genetics approaches is back on the table , given the even more recent availability of inexpensive image data collection and storage coupled with computational image processing and analysis . In addition , the potential for breeders to computationally analyze phenotypes is enabled , thus allowing for the scope and scale of breeding gains to be driven by computational power . While high-throughput collection of forward genetic data is now feasible , we must now enable the analysis of phenotypic data in a high-throughput way . The first step in such analysis is to identify regions of interest as well as quantitative phenotypic traits from the images collected . Tang et al . [2] described a model to extract tassel out of one single corn plant photo through color segmentation . However , when images are taken under field conditions , classifying images using the same processing algorithm can yield sub-optimal results . Changes in illumination , perspective , or shading , as well as occlusion , debris , precipitation , and vibration of the imaging equipment can all result in large fluctuations in image quality and information content . Machine learning ( ML ) methods have shown exceptional promise in extracting information from such noisy and unstructured image data . Kurtulmuş and Kavdir [3] adopted a machine learning classifier , support vector machine ( SVM ) , to identify tassel regions based on the binarization of color images . An increasing number of methods from the field of computer vision are recruited to extract phenotypic traits from field data [4 , 5] . For example , fine-grained algorithms have been developed to not only identify tassel regions , but also identify tassel traits such as total tassel number , tassel length , width , etc . [6 , 7] A necessary requirement for training ML models is the availability of labeled data . Labeled data consist of a large set of representative images with the desired features labeled or highlighted . A large and accurate labeled data set , the ground truth , is required for training the algorithm . The focus of this project is the identification of corn tassels , in images acquired in the field . For this task , the labeling process includes defining a minimum rectangular bounding box around the tassel . While seemingly simple , drawing a bounding box does requires effort to ensure accuracy [8] , and a good deal of time to generate a sufficiently large training set . Preparing such a dataset by a single user can be laborious and time consuming . To ensure accuracy , such a generated set should ideally be proofed by several people , adding more time , labor , and expense to the task . One solution to the problem is to take a large cohort of untrained individuals to perform the task , and to compile and extract some plurality or majority of their answers as a training set . This approach , also known as crowdsourcing , has been used successfully many times to provide image-based information in diverse fields including astronomy , zoology , computational chemistry , and biomedicine , among others [9–17] . Crop genetics research has a long history of “low-tech” crowdsourcing . Groups of student workers are sent into fields to identify phenotypes of interest , with the rates of success often a single instance among thousands of plants . Students in the social sciences also regularly participate in experiments to learn about the research process and gain first-hand experience acting as participants . To manage these large university participant pools , cloud based software , such as the Sona system ( www . sona-systems . com ) , are routinely used to schedule experiment appointments and to link to web-based research materials before automatically granting credit to participants . University participant pools provide a unique opportunity for crowdsourcing on a minimal budget because participants are compensated with course credit rather than money . More recently , crowdsourcing has been available via commercial platforms , such as the Amazon Mechanical Turk , or MTurk , platform ( https://www . mturk . com/ ) . MTurk is a popular venue for crowdsourcing due to the large number of available workers and the relative ease with which tasks can be uploaded and payments disbursed . Methods for crowdsourcing and estimates of data quality have been available for years , and several recommendations have emerged from past work . For example , collecting multiple responses per image can account for natural variation and the relative skill of the untrained workers [18] . Furthermore , a majority vote of MTurk workers can label images with similar accuracy to that of experts [19] . Although those studies were limited to labeling categorical features of stock images , other studies have shown success with more complex stimuli . For example , MTurk workers were able to diagnose disease and identify the clinically relevant areas in images of human retinas with accuracy approaching that of medical experts [11] . Amazon’s MTurk is a particularly valuable tool for researchers because it provides incentives for high quality work . The offering party has the ability to restrict their task to only workers with a particular work history , or a more general criterion known as ‘Master Turk’ status . The Master title is a status given to workers by Amazon based on a set of criteria that Amazon believes to represent the overall quality of the worker; note that Amazon does not disclose those criteria . The time and cost savings of using crowdsourcing to label data are obvious , but crowdsourcing is only a viable solution if the output is sufficiently accurate . The goal of the current project was to test whether crowdsourcing image labels ( also called tags ) could yield a sufficient positive-data training set for ML from image-based phenotypes in as little as a single day . We focus on corn tassels for this effort but we anticipate our findings to extend to other similar tasks in plant phenotyping . In this project , we recruited three groups of people for our crowdsourcing tassel identification task , from the two online platforms Sona and MTurk . The first group consisted of students recruited for course credit , or the Course Credit group . The second group consisted of paid Master-status Mechanical Turk workers , ( the Master MTurkers group ) , and the third group consisted of paid non-master Mechanical Turk workers ( the non-Master MTurkers group ) . The accuracy of the different groups’ tassel identification was evaluated against an expert-generated gold standard . These crowdsourced labeled images were then used as training data for a “bag-of-features” machine learning algorithm . We found that performance of Master and non-Master MTurkers was not significantly different; however both groups performed better than the Course Credit group . At the same time , using the labeling data from either course credit , MTurk or Master MTurk did not make any significant difference in the performance of the machine learning algorithm when trained on sets generated by any of these groups . We conclude that crowdsourcing via MTurk can be useful for establishing ground truth sets for complex image analysis tasks in a short amount of time , and that MTurkers’ and expert MTurkers’ performance exceed that of students working for course credit . At the same time , perhaps surprisingly , we also show that the differences in labeling quality do not significantly affect the performance of a machine learning algorithm trained by any of the three groups .
Research involving human participants was approved by the Institutional Review Board at Iowa State University under protocol 15-653 . The software for this study is available from: https://github . com/ashleyzhou972/Crowdsource-Corn-Tassels The data for this study are available from: https://doi . org/10 . 6084/m9 . figshare . 6360236 . v2 The overall scheme of the work is depicted in Fig 1 . Course Credit , Master MTurkers , MTurkers , and an expert , all labeled corn tassels in a set of 80 images . First , the labeling performance was assessed against the gold standard . Then , each set of labeled images was also used to train a bag-of-features machine learning method . The trained methods were each tested against a separate expert-labeled training set , to assess how differently the ML method performed with different training sets . The Course Credit group included 30 participants , which were recruited using the subject pool software Sona from the undergraduate psychology participant pool at Iowa State University . Recruited students were compensated with course credits . The master MTurkers included 65 master-qualified workers recruited through MTurk . The exact qualifications for master status are not published by Amazon , but are known to include work experience and employer ratings of completed work . Master MTurkers were paid $8 . 00 to complete the task and the total cost was $572 . 00 . Finally , the non-master MTurkers pool included 66 workers with no qualification restriction , recruited through the Amazon Mechanical Turk website . Due to the nature of Amazon’s MTurk system , it is not possible to recruit only participants who are not master qualified . However , the purpose of including the non-master MTurkers was to evaluate workers recruited without the additional fee imposed by Amazon for recruitment of Masters MTurkers . Non-master MTurkers were also paid $8 . 00 to complete the task and the total cost was $568 . 00 . Note that the costs include Amazon’s fees . Of the 30 students recruited , 26 completed all 80 images . Of the 65 Master MTurkers recruited , 49 completed all images . Of the 66 non-master MTurkers recruited , 51 completed all images . Data collected from participants who did not complete the survey were not included in subsequent analyses . A brief cropping task was initially administered to Sona and master MTurkers groups as a pilot study to test the viability of this project and task instructions . Each participant was presented with a participant-specific set of 40 images randomly chosen from 393 total images . The accuracy of participant labels helped designate Easy and Hard status for each image . Forty images were classified as “easy to crop” , and 40 as “hard to crop” , based on accuracy results of the pilot study . An expert who made gold standard boxes made adjustments to the Easy/Hard classifications based on personal experience . These 80 images were selected for the main study . As opposed to the pilot study , participants in the main study each received the same set of 80 images , with image order randomized separately for each participant . The results of the pilot study indicated that at least 40 images could be processed without evidence of fatigue so the number of images included in the main experiment was increased to 80 . The pilot study also indicated , via user feedback , that a compensation rate of $8 . 00 for the set of 80 images was acceptable to the MTurk participants . To expedite the pilot study , we did not include regular MTurkers . Our rationale was that feasibility for a larger study could be assessed by including master MTurkers and Sona only . We define a gold standard box for a given tassel as the box with the smallest area among all bounding boxes that contain the entire tassel , a minimum bounding box . Gold-standard boxes were generated by the expert , a trained and experienced researcher . The expert cropped all 80 images then computationally minimized the boxes to be minimum bounding . These images were used to evaluate the labeling performance of crowdsourced workers , and should not be confused with the ‘ground truth’ which were used to refer the labeled boxes used in training the ML model . We selected the images randomly from a large image pool obtained as part of an ongoing maize phenomics project . The field images focused on a single row of corn captured by cameras set up as part of the field phenotyping of the maize Nested Association Mapping [20] , using 456 cameras simultaneously , each camera imaging a set of 6 plants . Each camera took an image every 10 minutes during a two week growing period in August 2015 [21] . Some image features varied , for example , due to weather conditions and visibility of corn stalks , but the tassels were always clearly visible . Images were presented on a Qualtrics webpage ( www . qualtrics . com ) and Javascript was used to provide tassel annotation functionality . After providing Informed Consent , participants viewed a single page with instructions detailing how to identify corn tassels and how to create a minimum bounding box around each tassel . Participants were first shown an example image with the tassels correctly bounded with boxes ( Fig 2 ) . Below the example , participants read instructions on how to create , modify , and delete bounding boxes using the mouse . These instructions explained that an ideal bounding box should contain the entire tassel with as little additional image detail as possible . Additional instructions indicated that overlapping boxes and boxes containing other objects would sometimes be necessary and were acceptable as long as each box accurately encompassed the target tassel . Participants were also instructed to only consider tassels in the foreground , ignoring tassels that appear in the background . After reading instructions , participants clicked to progress to the actual data collection . No further feedback or training were provided . The exact instructions are provided in the Supplementary Materials . For each image , participants created a unique bounding box for each tassel by clicking and dragging the cursor . Participants could subsequently adjust the vertical or horizontal size of any drawn box by clicking on and dragging a box corner , and could adjust the position of any drawn box by clicking and dragging in the box body . Participants were required to place at least one box on each image before moving on to the next image . No upper limit was placed on the number of boxes . Returning to previous images was not allowed . The time required to complete each image was recorded in addition to the locations and dimensions of user-drawn boxes . Consider any given participant-drawn box and gold standard box as in the right panel of Fig 3 . Let PB be the area of the participant box , let GB be the area of the gold standard box , and let IB be the area of the intersection between the participant box and the gold standard box . Precision ( Pr ) is defined as IB/PB , and recall ( Rc ) is defined as IB/GB . Both Pr and Rc range from a minimum value of 0 ( when the participant box and gold standard box fail to overlap ) to a maximum value of 1 ( full overlap of boxes ) . As an overall measure of performance for a participant box as an approximation to a gold standard box , we use F1 , the harmonic mean of precision and recall: F 1 = 2 × Pr × Rc Pr + Rc . Each participant-drawn box was matched to the gold standard box that maximized F1 across all gold standard boxes within the image containing the participant box . If more than one participant box was matched to the same gold standard box , the participant box with the highest F1 value was assigned the Pr , Rc , and F1 values for that match , and the other participant boxes matching that same gold standard box were assigned Pr , Rc , and F1 values of zero . In the usual case of a one-to-one matching between participant boxes and gold standard boxes , each participant box was assigned the Pr , Rc , and F1 values associated with its matched gold standard box . To summarize the performance of a participant on a particular image , F1 values across participant-drawn boxes were averaged to obtain a measure referred to as Fmean . This provides a dataset with one performance measurement for each combination of participant and image that we use for subsequent statistical analysis .
As described in Methods , precision and recall were calculated for each participant-drawn box . Density of precision recall pairs by group based on 61 , 888 participant-drawn boxes are shown in the heatmap visualization of Fig 4 . High value precision-recall pairs are more common than low value precision-recall pairs in all three groups . Perfect recall values were especially common because participants tended to draw boxes that encompassed the minimum bounding box , presumably to ensure that the entire tassel was covered . Fig 4 ( D ) shows the distribution of Fmean for the three groups . We used a linear mixed-effects model analysis to test for performance differences among groups with the Fmean value computed for each combination of image and user as the response variable . The model included fixed effects for groups ( Master MTurker , non-Master MTurker , course credit ) , random effects for participants nested within groups , and random effects for images . The mixed procedure available in SAS software was used to perform this analysis with the Kenward-Roger method [22] for computing standard errors and denominator degrees of freedom . The analysis shows significant evidence for differences among groups ( p-value < 0 . 0001 ) . Furthermore , pairwise comparisons between groups ( Table 1 ) show that both Master and non-Master MTurkers performed significantly better than undergraduate students performing the task for course credit . There was no significant performance difference between Master and non-Master MTurkers . Next we wanted to understand whether there is any change of time taken to annotate over the task given , whether there is a significant difference between the groups , and specifically if any change indicated fatigue . Participants took a median time of 26 . 43 seconds to complete an image , with the median time for the Master MTurker group at 30 . 02 seconds , non-Master MTurkers at 29 . 40 seconds , and the course credit student group at 16 . 86 seconds . The course credit group generally spent less time than either MTurker group . It is worth noting that there is a large variance in time spent on each image , with the longest time for a single image at 15 , 484 . 63 seconds , and the shortest being 0 . 88 seconds . The very long image annotation time was probably due to the participant taking a break after cropping part of the image and then coming back later to finish that image . There is a general downward trend in the time spent on each image over time . The trend is shown in Fig 5 , via linear regression on log time with fixed effects for group , question ordinal index and group×question ordinal index , and random effects for user and image . The trend is statistically significant in all three groups , with similar effect sizes . As participants complete questions , the average time spent per question is reduced by about 1% , as shown by Table 2 . By looking at the interaction term between participant group and question index , we were able to conclude that the reduced time effect is not significantly different between the Master MTurker and non-Master MTurker group ( p = 0 . 6003 ) , but is different between the course credit group and Master MTurker group ( p = 0 . 0431 ) . This difference is weakened in terms of course credit versus non-Master MTurker , with a p-value of 0 . 1086 . We also analyzed the change in accuracy , as measured by Fmean as the test progresses . Fig 5 ( B ) shows that Fmean decreased slightly as the task progressed . The decreases are statistically significant ( p<0 . 05 ) for all three groups . However , the effect sizes ( average decrease in Fmean per round of image ) for both MTurker groups are almost negligible , with Master MTurk group showing a 0 . 00008 decrease per image and Non-master group showing a 0 . 00027 decrease . Decrease in Fmean for the course credit group is only slightly more noticeable , at 0 . 00095 , and on a scale of 0-1 is unlikely to affect training . To summarize the effect of image order , there was a subtle decline in Fmean and a larger decrease in image completion time as the survey progressed . Another question of interest was whether image accuracy correlates with image completion time . Indeed , there tended to be a slight increase in accuracy as time spent on an image increased . Although the correlation is statistically significant in all three groups , the effect sizes are too small to conclude that spending more time on a single image has an important positive effect on accuracy for that image . In conclusion , all three groups of participants spent less time on each image as the survey progressed , possibly due to increasing familiarity in the task . Although their performance in the task also decreases slightly over time , the effects were almost negligible . This fatigue effect , while significant , is minor . Did the annotators spend more time on more difficult images ? To answer this question , we obtained the Best Linear Unbiased Predictor ( BLUP ) [23] of each image in the above analyses to assess whether each image contributes to increased or decreased accuracy and time . BLUPs can be viewed as predictions of random effects , in our case , one prediction of the eighty images . Fig 6 is a scatter plot , with each point representing an image . The horizontal axis shows the BLUPs with regard to logtime . The higher the BLUP , the more this particular image contributes to increased time spent on each question . Similarly , the vertical axis shows the BLUPs with regard to Fmean . Images with higher BLUPs tended to be processed more accurately . We also obtained a difficult / easy classification of all eighty images from our expert who manually curated the gold standard boxes , as they are shown by the two different colors on the plot . It is interesting to observe that longer time spent annotating an image correlates positively with accuracy . Indeed , the linear regression fit shown as the red line on the plot has a slope estimate of 0 . 1003 ( p = 0 . 00136 ) , and an adjusted R2 of 0 . 1127 , suggesting weak correlation . Furthermore , the images that our expert considered difficult did not take participants longer to complete , nor did they yield significantly lower accuracy . The images were shown to participants in a random order , eliminating the possibility that fatigue contributes to the longer time it takes to complete easy images . Since previous analysis showed that participants tend to spend less time on images shown to them later ( Fig 5A ) , this may suggest ordering the images so that more difficult images are shown to the participants first . In that way , a surveyor may take advantage of the fact that participants tend to spend more time on each image in the beginning , to obtain more accurate results . Automatic tassel detection is an important prerequisite for fast computation of quantitative traits . We can automatically detect tassels in images using a classifier trained with data derived from crowdsourcing . Although our results above show that paid Master MTurkers and non-Master MTurkers tend to provide higher quality tassel bounding boxes than students working for course credit , the differences in quality we have detected may not necessarily translate into better training of a classification algorithm . We therefore examined how the performance of a classification algorithm varies as the data used to train the classifier varies across participants . The algorithm consists of two stages . The first stage involves extracting features from a set of training images using a bag-of-features method [24] . Each training image corresponds to a single box within one of our original images . The training images ( i . e . , boxes ) are selected so that each contains either a tassel or no tassel . Each training image is then represented by a vector of frequencies , with one frequency for each feature . The second stage of the algorithm involves training a support vector machines ( SVM ) classifier using the frequency vectors associated with the training images , as well as the status of each training image: whether it contains a tassel or not . For each of the 126 participants in our study , we constructed a set of training images using the participant-drawn boxes as the positive set ( i . e . , the training images containing a tassel ) , together with a constant negative set of 600 images ( corresponding to boxes that contain no tassel ) . The number of training images in the positive set varied across participant , with a median of 457 . The classification algorithm was then separately trained using each of the 126 training datasets . We applied the 126 participant-trained classifiers to a test set of 600 tassel images and 600 non-tassel images . Performance was calculated as the mean of the true positive rate and the true negative rate of the classification . Overall , the algorithm achieved a classification performance of 0 . 8811 , averaged over all participants . For the master and non-master MTurker groups , the average performances were 0 . 8851 and 0 . 8781 , respectively . For the course credit group , it was 0 . 8795 . We performed a linear model analysis of the 126 performance measures to test for group differences . The F test yielded a p-value of 0 . 7325 , indicating no detectable differences among the average performances of the three groups . We also trained a classifier based on the ground truth data that the our expert curated . This classifier achieved an accuracy of 0 . 91 , slightly higher than the performance of the classifiers trained from the crowdsourced labels .
Machine learning methods have revolutionized processing and extracting information from images , and are being used in fields as diverse as public safely , biomedicine , weather , military , entertainment , and , in our case , agriculture . However , these algorithms still require an initial training set created by expert individuals before structures can be automatically extracted from the image and labeled . This project has identified crowdsourcing as a viable method for creating initial training sets without the time consuming and costly work of an expert . Our results show that straightforward tasks , such tassel cropping , do not benefit from the extra fee assessed to hire master over non-master MTurkers . Performance between the two groups was not significantly different , and non-master MTurkers can safely be hired without compromising data quality . The MTurk platform allows for fast collection of data within a day instead of one to two weeks . While MTurk may be one of the most popular crowdsourcing platforms , many universities possess a research participant pool that compensates students with class credit instead of cash for their work . However , in our study the undergraduate student participant pool did not perform as well as either of the MTurker groups . While it is possible that MTurk workers are simply more conscientious than college students , it is also possible that monetary compensation is a better motivator than course credit . In addition to the direct monetary reward , both groups of MTurkers were also motivated by either working towards or maintaining the “master” status . Such implicit motivational mechanisms might be useful in setting up a long-term crowdsourcing platform . The distinction in labeling performance between MTurkers and students does not matter when considering the actual outcome of interest: how well the machine learning algorithm identifies corn tassels when supplied with each of the three training sets . The accuracy of the ML algorithm used here was not affected by the quality of the training set provided , which were manually-labeled through crowdsourcing . Therefore , a student participant pool with a non-monetary rewards system provides the opportunity for an alternate model by lowering overall image tagging cost . This would allow additional features to be tagged or a larger number of responses to be sourced with existing funding levels and further database expansion . Indeed , there are many crowdsourcing projects that do not offer monetary reward . For example , the Backyard Worlds: Planet 9 project hosted by NASA for search of planets and star systems in space [9] , the Phylo ( http://phylo . cs . mcgill . ca/ ) game for multiple sequence alignment [25] and fold . it ( http://fold . it ) [12] for protein folding . These projects attract participants by offering the chance to contribute to real scientific research . This concept has been categorized as citizen science , where nonprofessional scientists participate in crowdsourced research efforts . In addition to the attraction of the subject matter , these projects often have interactive and entertaining interfaces to quickly engage the participants’ interests and attention . Some of them were even designed as games , and competition mechanisms such as rankings provide extra motivation . Another important purpose of such citizen science projects is to educate the public about the subject matter . Given the current climate regarding Genetically Modified Organisms ( GMOs ) , crowdsourcing efforts of crop phenomic and phenotypic research could potentially be a gateway to a better understanding of plant research in the general public . A recent effort has shown that non-experts can be used for accurate image-based plant phenomics annotation tasks [26] . However , the current data points to the challenge of non-monetary reward in sustaining a large-scale annotation effort . Phenomics is concerned with the quantitative and qualitative study of phenomes , where all possible traits of a given organism vary in response to genetic mutations and environmental influences [27] . An important field of research in phenomics is the development of high-throughput technology analogous to high-throughput sequencing in genetics and genomic studies , to enable the collection of large-scale data with minimal efforts . Many phenotypic traits could be recorded with images , and databases such as BioDIG [28] make the connection of such image data with genomic information , providing genetics researchers with tools to examine the relationship between the two types of data directly . Hence , the computation and manipulation of such phenomic image data becomes essential . In plant biology , maize is central for both basic biological research as well as crop production ( reviewed in [29] ) . As such , phenotypic information derived from ear ( female flowers ) and tassel ( male flowers ) are key to both the study of genetics and crop productivity: flowers are where meiosis and fertilization occur as well as the source of grain . To add a new features such as tassel emergence , size , branch number , branch angle and anthesis to the systems such as BioDIG , the specific tassel location and structure should be located , and our solution to this task is to use crowdsourcing combined with machine learning to reduce cost and time of such a pipeline , while expanding its utility . Our findings , and the suggested crowdsourcing methods can be generally applied to other phenomic analysis tasks . It is worthy to note that differences in quality of training sets may not translate into significant differences in classification , as was in our study . However , this may vary between different classification algorithms , and different training sets . We hope our study will help establish some best practices for researchers in setting up such a crowdsourcing study . Given the ease and relatively low cost of obtaining data through Amazon’s Mechanical Turk , we recommend it over the undergraduate research pool . That being said , student research pools would be a suitable method for obtaining proof of concept or pilot data to support a grant proposal .
|
Food security is a growing global concern . Farmers , plant breeders , and geneticists are hastening to address the challenges presented to agriculture by climate change , dwindling arable land , and population growth . Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency . However , the terabytes of image data require new computational methods to extract useful information . Machine learning algorithms are effective in recognizing select parts of images , but they require high quality data curated by people to train them , a process that can be laborious and costly . We examined how well crowdsourcing works in providing training data for plant phenomics , specifically , segmenting a corn tassel—the male flower of the corn plant—from the often-cluttered images of a cornfield . We provided images to students , and to Amazon MTurkers , the latter being an on-demand workforce brokered by Amazon . com and paid on a task-by-task basis . We report on best practices in crowdsourcing image labeling for phenomics , and compare the different groups on measures such as fatigue and accuracy over time . We find that crowdsourcing is a good way of generating quality labeled data , rivaling that of experts .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2018
|
Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning
|
Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation , and provides an upper bound for the utility of genetic risk prediction models . Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants ( SNP heritability ) across a broad phenotypic spectrum . Here , we present a computationally and memory efficient heritability estimation method that can handle large sample sizes , and report the SNP heritability for 551 complex traits derived from the interim data release ( 152 , 736 subjects ) of the large-scale , population-based UK Biobank , comprising both quantitative phenotypes and disease codes . We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population , and identify phenotypes whose heritability is moderated by age ( e . g . , a majority of physical measures including height and body mass index ) , sex ( e . g . , blood pressure related traits ) and socioeconomic status ( education ) . Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank , and underscores the importance of considering population characteristics in interpreting heritability .
The heritability of a trait refers to the proportion of phenotypic variance that is attributable to genetic variation among individuals . Heritability is commonly measured as either the contribution of total genetic variation ( broad-sense heritability , H2 ) , or the fraction due to additive genetic variation ( narrow-sense heritability , h2 ) [1] . A large body of evidence from twin studies has documented that essentially all human complex traits are heritable . For example , a recent meta-analysis of virtually all twin studies published between 1958 and 2012 , encompassing 17 , 804 traits , reported that the overall narrow-sense heritability estimate across all human traits was 49% , although estimates varied widely across phenotypic domains [2] . Over the past decade , the availability of genome-wide genotyping has enabled the direct estimation of additive heritability attributable to common genetic variation ( “SNP heritability” or hSNP2 ) [3–5] . These estimates do not capture non-additive genetic effects such as dominance or epistasis , and provide a lower bound for narrow-sense heritability because they also do not capture contributions ( e . g . , from rare variants ) that are not assayed by most genotyping microarrays and are not well tagged by genotyped variants . Nevertheless , estimates of SNP heritability can provide important information about the genetic basis of complex traits such as the proportion of phenotypic variation that could be explained by common-variant genome-wide association studies ( GWAS ) . However , heritability is not a fixed property of a phenotype but depends on the population in which it is estimated . As a ratio of variances , it can vary with population-specific differences in both genetic background and environmental variation [1] . For example , twin data have documented variations in the heritability of childhood IQ by socioeconomic status ( SES ) [6] , highlighting that different environment may have different relative contributions to the variance of a phenotype . In addition , heritability estimates for a range of complex phenotypes have been shown to vary according to the sex and age distributions of the sampled populations [2] . Identifying variables that may affect the heritability of complex traits has implications for the design of GWAS , highlighting subgroups and environmental conditions in which common-variant contributions may be diminished or magnified . To date , however , studies of complex trait heritability and the effect of modifying variables have produced mixed results likely due to sample size limitations and population-specific differences in genetic and environmental variance that may be operating in different cohorts . The UK Biobank ( http://www . ukbiobank . ac . uk ) provides a unique opportunity to estimate the heritability of traits across a broad phenotypic spectrum in a single population sample . The UK Biobank is a large prospective population-based cohort study that enrolled 500 , 000 participants aged 40–69 years between 2006 and 2010 [7] . The study has collected a wealth of phenotypic data from questionnaires , physical and biological measurements , and electronic health records as well as genome-wide genotype data . However , this rich data source also presents analytic challenges . For example , with the large sample size , existing heritability estimation methods such as genome-wide complex trait analysis ( GCTA ) [3–5] and LD ( linkage disequilibrium ) score regression [8] become computationally expensive and memory intensive , and thus can be difficult to apply . Here we implemented a computationally and memory efficient approach to estimate the heritability for 551 complex traits derived from the interim data release ( 152 , 736 subjects ) of the UK Biobank , comprising both quantitative phenotypes and disease categories . We then examined how heritability estimates are modified by three major demographic variables: age , sex and socioeconomic status ( SES ) . Our results underscore the importance of considering population characteristics in estimating and interpreting heritability , and may inform efforts to apply genetic risk prediction models for a broad range of human phenotypes .
We report the heritability for 551 traits that were made available to us through the interim data release of the UK Biobank ( downloaded on Mar 3 , 2016 ) and had sufficient sample sizes to achieve accurate heritability estimates ( standard error of the heritability estimate smaller than 0 . 1; 15 disease codes excluded ) using a computationally and memory efficient heritability estimation method ( see Methods and S1 Text ) . The 551 traits can be classified into 11 general phenotypic domains as defined by the UK Biobank to group individual data fields into broadly related sets: cognitive function ( 5 traits ) , early life factors ( 7 traits ) , health and medical history ( 60 traits ) , hospital in-patient main diagnosis ICD-10 codes ( 194 traits ) , life style and environment ( 88 traits ) , physical measures ( 50 traits ) , psychosocial factors ( 40 traits ) , self-reported cancer codes ( 9 traits ) , self-reported non-cancer illness codes ( 79 traits ) , sex-specific factors ( 14 traits ) , and sociodemographics ( 5 traits ) . ICD-10 ( the International Classification of Diseases , version-10 ) is a medical classification list published by the World Health Organization ( WHO ) , which contains thousands of diagnostic codes . Fig 1 shows the percentage of each domain that makes up the 551 traits we analyzed . Using the top-level categories and chapters of the self-reported disease and ICD-10 coding tree , we can further break down self-reported non-cancer illness codes and ICD-10 codes into different functional domains ( S1 Fig ) . We note that since we only analyzed disease codes that had prevalence greater than 1% in the sample , distribution of the disease traits across functional domains was skewed . For example , we investigated a large number of gastrointestinal and musculoskeletal traits , while diseases that have low prevalence in the sampled population such as psychiatric disorders were not well represented . Table 1 lists the top heritable traits in each domain ( the most heritable trait and traits with heritability estimates greater than 0 . 30 ) . S1 Table and S2 Table show the heritability estimates , standard error estimates , sample sizes , covariates adjusted , prevalence in the sample ( for binary traits ) and other relevant information for all the traits we analyzed . Common genetic variants appear to have an influence on most traits we investigated , although heritability estimates showed heterogeneity within and across trait domains . Complex traits that exhibited high SNP heritability ( larger than 0 . 40 ) included human height ( 0 . 685+/-0 . 004 ) , skin color ( very fair/fair vs . other , 0 . 556+/-0 . 008 ) , ease of skin tanning ( very/moderately tanned vs . mildly/occasionally/never tanned , 0 . 454+/-0 . 006 ) , comparative height at age 10 ( taller than average , 0 . 439+/-0 . 007; shorter than average , 0 . 405+/-0 . 008 ) , rheumatoid arthritis ( 0 . 821+/-0 . 046 ) , hypothyroidism/myxedema ( 0 . 814+/-0 . 017 ) , malignant neoplasm of prostate ( 0 . 426+/-0 . 093 ) , and diabetes diagnosed by doctor ( 0 . 414+/-0 . 016 ) , among others . On the other end of the spectrum , traits such as duration of walks/moderate activity/vigorous activity , frequency of stair climbing , ever had stillbirth , spontaneous miscarriage or termination , painful gums , stomach disorder , fracture , injuries to the head/knee/leg , and pain in joint had zero or close to zero heritability estimates , indicating that their phenotypic variation is largely determined by environmental factors , or there is widespread heterogeneity or substantial measurement error in these phenotypes . SNP heritability estimates for several phenotypes , including diseases with known immune-mediated pathogenesis ( rheumatoid arthritis , psoriasis , diabetes , hypothyroidism ) , were markedly reduced when the major histocompatibility complex ( MHC ) region was excluded from analysis ( S4 Table ) , and thus need to be interpreted with caution ( see Discussion ) . A substantial fraction of the phenotypes we examined were based on self-report illness codes or diagnostic ( ICD-10 ) codes , which may be noisy and have low specificity . However , the SNP heritability estimates for 14 pairs of self-reported illness and ICD-10 codes that represent the same or closely matched diseases were largely consistent and had a Pearson correlation of 0 . 78 ( Table 2 ) , indicating that both phenotypic approaches captured useful and comparable variations in these phenotypes . Heritability analysis stratified by sex identified a number of traits whose heritability showed significant difference in males and females after multiple testing correction ( Fig 2 ) . For example , the analyses of diastolic and systolic blood pressure , and self-reported hypertension and high blood pressure provided consistent evidence that the heritability of blood pressure related traits and diseases is significantly higher in females than in males . A majority of physical measures showed decreasing heritability with age ( S3 Table ) . More specifically , 33 out of 50 physical measures had a significant decreasing trend in heritability estimates after accounting for multiple testing correction ( mean slope of the 33 traits -0 . 0035 , i . e . , heritability estimates decrease by 3 . 5 percent per decade ) . The age-varying SNP heritability estimates and their standard errors for 12 traits that showed both significant slopes and significantly different heritability estimates between the first ( 40–49 years ) and last age range ( 64–73 years ) are shown in Fig 3A . S2 Fig shows the mean and standard deviation of the 12 traits in each age range . When we stratified heritability by the Townsend deprivation index , a proxy for SES , education ( has college or university degree or not ) was the only trait on which SES had a significant moderating effect after accounting for multiple testing correction . Fig 3B shows that the heritability of education increases with increasing SES .
Estimating the heritability of complex , polygenic traits is an important component of defining the genetic basis of human phenotypes . In addition , heritability estimates provide a theoretical upper bound for the utility of genetic risk prediction models [9] . We calculated the common-variant heritability of 551 phenotypes derived from the interim data release of the UK Biobank , and confirmed that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population . Two aspects of our work are particularly notable . First , we developed a computationally and memory efficient method that enabled us to calculate the most extensive population-based survey of SNP heritability to date . Second , we found that the heritability for a number of phenotypes is moderated by major demographic variables , demonstrating the dependence of heritability on population characteristics . We discuss each of these advances and the limitations of the biobank data and our analyses below . Classical methods to estimate SNP heritability , such as GCTA ( also known as the GREML method ) , rely on the restricted maximum likelihood ( ReML ) algorithm [3–5] , which can give unbiased heritability estimates in quantitative trait analysis and non-ascertained case-control studies , and is statistically efficient when the trait is Gaussian distributed [10] . However , ReML is an iterative optimization algorithm , which is computationally and memory intensive , and thus can be difficult to apply when analyzing data sets with hundreds of thousands of subjects . An alternative and widely used SNP heritability estimation method is LD score regression , which is based on GWAS summary statistics and an external reference panel for the LD structure [8] . The approach can thus be easily applied to complex traits on which large-scale GWAS results are available , and allows meta-analysis of heritability estimates from different studies . Recently , LD score regression has been extended to partition heritability by functional annotation [11] , and to estimate the genetic correlation between two traits [12 , 13] . However , when applying LD score regression to novel phenotypes in a large cohort , conducting GWAS is often time-consuming . In the present study , we implemented a computationally and memory efficient moment-matching method for heritability estimation , which is closely related to the Haseman-Elston regression [14–16] and phenotype-correlation genetic-correlation ( PCGC ) regression [10] , and produces unbiased SNP heritability estimates for both continuous and binary traits . The moment-matching method is theoretically less statistically efficient than the ReML algorithm ( i . e . , produces larger standard error on the point estimate ) when analyzing quantitative traits , but the power loss is expected to be small [17] and is less of an issue given large sample sizes , such as in the UK Biobank . The moment-matching method is also mathematically equivalent to LD score regression if the following conditions are satisfied: ( 1 ) the out-of-sample LD scores estimated from the reference panel and the in-sample LD scores estimated from individual-level genotype data are identical; ( 2 ) the intercept in the LD score regression model is constrained to 1 ( i . e . , assuming that there is no confound and population stratification in the data ) ; and ( 3 ) a particular weight is used in the LD score regression ( more specifically , the reciprocal of the LD score , which is close to the default setting in the LD score regression software ) [18] . Here , since we have constrained our analysis to a white British ( Caucasian ) sample and have accounted for potential population stratification by including top PCs of the genotype data as covariates , the two methods should produce similar estimates . See Box 1 for an empirical comparison between the moment-matching method , LD score regression and GCTA . Using the moment-matching method , we found that a large number of traits we examined display significant heritability . For traits whose heritability has been intensively studied , our estimates are generally in line with prior studies . For example , twin and pedigree studies have estimated the heritability of human height and body mass index ( BMI ) to be approximately 80% and 40–60% [see e . g . , 19–21] , respectively , although recent studies have shown that heritability may be overestimated in family studies due to , for instance , improper modeling of common environment , assortative mating in humans , genetic interactions , and suboptimal statistical methods [10 , 22–25] . Using genome-wide SNP data from unrelated individuals , it has been shown that common SNPs explain a large proportion of the height and BMI variation in the population , although SNP heritability estimates are lower than twin estimates [4 , 5 , 26] . Specifically , the first GCTA analysis estimated the SNP heritability of human height to be 0 . 45 using relatively sparse genotyping data ( approximately 300 , 000 SNPs ) and showed that the estimate could be higher if imperfect LD between SNPs and causal variants are corrected [4] . A more recent study leveraging whole-genome sequencing data and imputed genetic variants concluded that narrow-sense heritability is likely to be 60–70% for height and 30–40% for BMI [27] . Here , we estimated the SNP heritability of human height and BMI to be 0 . 685+/-0 . 004 and 0 . 274+/-0 . 004 , respectively , which are comparable to the expected range . The SNP heritability estimates of other complex traits of interest , such as age at menarche in girls ( 0 . 239+/-0 . 007 ) , diastolic ( 0 . 184+/-0 . 004 ) and systolic ( 0 . 156+/-0 . 004 ) blood pressures , education ( has colleague or university degree or not , 0 . 294+/-0 . 007 ) , neuroticism ( 0 . 130+/-0 . 005 ) , smoking ( ever smoked or not , 0 . 174+/-0 . 006 ) , asthma ( 0 . 340+/-0 . 010 ) and hypertension ( 0 . 263+/-0 . 007 ) were also more modest and lower than twin estimates , as expected [2] . Heritability is , by definition , a ratio of variances , reflecting the proportion of phenotypic variance attributable to individual differences in genotypes . Because the genetic architecture and non-genetic influences on a trait may differ depending on the population sampled , heritability itself may vary . Examples of this have been reported in the twin literature . In one well-known study , Turkheimer and colleagues [6] reported that the heritability of IQ is moderated by SES in a sample of 320 7-year-old twin pairs of mixed ancestry . In that study , the heritability of IQ was essentially 0 at the lowest end of SES but substantial at the highest end . Subsequent studies of the moderating effects of SES on the heritability of cognitive ability and development using twin designs have produced mixed results [28–33] . In our analysis , using SNP data , we observed no moderating effect of SES ( as measured by the Townsend deprivation index ) on the heritability of cognitive traits ( including fluid intelligence ) , possibly due to the age range of participants in the UK Biobank ( middle and old age ) in contrast to many previous studies targeting childhood or early adulthood , and the cross-national differences in gene-by-SES interaction on intelligence as shown by a recent meta-analysis [34] . In addition , the brief cognitive tests available in the UK Biobank may have had limited sensitivity for capturing individual differences in IQ ( see discussion below ) . On the other hand , the heritability of education showed significant interactions with SES , with increasing heritability at higher SES levels . Prior evidence has suggested that education has substantial genetic correlation with IQ and may be a suitable proxy phenotype for genetic analyses of cognitive performance [35]; thus our results may indirectly support earlier studies of the SES moderation of IQ heritability . With two exceptions , significant sex differences we observed indicated greater heritability for women compared to men . Our results are consistent with findings from some twin studies but not others . For example , we found that women exhibited significantly greater heritability for measured waist circumference and blood pressure . Twin studies have also reported greater female heritability for waist circumference [36] but no substantial sex difference in heritability of blood pressure [2 , 37] . A substantial difference between the heritability of rheumatoid arthritis ( RA ) in males compared to females was observed , although the MHC region has a large impact on the SNP heritability estimates of autoimmune diseases , and thus this finding needs to be interpreted with caution ( see discussion below ) . While RA is known to be more common in women , a twin analysis found no sex difference in heritability among Finnish and UK twin pairs , though power was limited in that analysis [38] . Intriguingly , greater heritability was observed among men for the personality trait of miserableness , a component of neuroticism , suggesting that environmental factors may be more influential for this trait among women or that measurement error differs by sex . We examined age effects on heritability for a subset of variables and found that a number of physical measures indexing body size , adiposity , height , as well as systolic blood pressure and lung function , showed declining heritability with age . Age-related declines in heritability may reflect the cumulative effect of environmental perturbations over the lifespan . Prior twin studies of age effects on the heritability of anthropometric traits in adults have had inconsistent results [39–41] . Haworth and colleagues showed that the heritability of BMI increases over childhood [42] . A recent meta-analysis of 32 twin studies documented a non-monotonic relationship between BMI heritability and age ( from childhood to late adulthood ) , with a peak around age 20 and decline thereafter [43] . An age-related decline in indices of body size may reflect a decreasing contribution of genetically-regulated growth processes over the lifespan . However , we were unable to assess the entire trajectory of heritability due to the age range ( 40–73 years ) of the UK Biobank participants . Some but not all studies have also suggested varying or declining heritability with age for blood pressure , lung function and age at first birth [39 , 44–50] . Our results should be interpreted in light of the limitations associated with the biobank data . First , the UK Biobank is restricted to middle and old age groups , which may be subject to sample selection bias . For example , older and physically/cognitively impaired subjects may be underrepresented in the study , which may have an impact on the heritability estimates stratified by age . Mortality selection can also alter the results of genetic analyses as shown by recent analyses [51] . In addition , the UK Biobank participants comprised a relatively high proportion of well-educated , skilled professionals [52] , potentially leading to the underrepresentation or restricted range of certain traits such as smoking relative to other cohorts . Therefore , our heritability estimates may be specific to this UK population and may not generalize to other settings or ancestry groups . Second , although the UK Biobank has collected a wealth of phenotypes , measurements associated with a particular phenotypic domain may not be comprehensive . For example , only five cognitive tests were included in the UK Biobank . The reasoning task ( fluid intelligence test ) was brief and had a narrow range; the reaction time was averaged from a small number of trials; and the visual memory test ( pairs-matching test ) had a significant floor effect ( a large number of participants made zero or very few mistakes , and thus the scores do not fully reflect individual differences ) . In addition , all cognitive tests had relatively low reliability across repeat measurements [53] . These noisy measurements may thus downwardly bias heritability estimates of cognition . The Townsend deprivation index , which we used to stratify phenotypes , was calculated based on the national census output area of each participant in which their postcode was located at the time of recruitment , and thus can only serve as a proxy for SES . Third , the phenotypes were limited to those for which we had sufficient data to estimate heritability with adequate precision . Therefore , diseases with low prevalence in the sampled population were not well represented in our analysis . We expect to analyze traits with lower prevalence ( e . g . , 0 . 5% ) when the genetic data for all UK Biobank participants becomes available . We also assumed in our analysis that the population prevalence of a binary trait is identical to the observed sample prevalence , but diseases such as schizophrenia and stroke are naturally under-ascertained and thus their sample prevalence is often lower than population prevalence . In addition , we note that since we used medical history to define cases and controls , the prevalence of many diseases we investigated reflected lifetime prevalence , which may be different from cross-sectional prevalence used in other studies . We also binarized categorical ( multinomial or ordinal ) variables to facilitate analysis , but this might not optimally represent variation in these variables with respect to heritability . Fourth , a substantial fraction of the phenotypes we examined were based on self-report or diagnostic ( ICD-10 ) codes , which may or may not validly capture the phenotypes they represent . For example , a recent UK Biobank study shows that 51% of the participants who reported RA were not on RA-relevant medication , a proxy measure of valid diagnosis [54] . However , our head-to-head comparison of the heritability estimates between self-reported illness and ICD-10 codes showed largely consistent results , indicating that both phenotypic approaches at least captured comparable variations in these phenotypes . Prior research evaluating phenotypes derived from electronic health records ( EHR ) indicate that greater phenotypic validity can be achieved when diagnostic codes are supplemented with text mining methods [55–58] . The specificity of the disease codes might also be improved by leveraging the medication records in the UK Biobank . Methodologically , our SNP heritability estimation approach , despite its superior computational and memory performance compared to existing methods , also has several limitations . First , heritability estimation always relies on a number of assumptions on the genetic architecture . For example , the moment-matching method we used here , as well as the established GCTA and LD score regression approaches , implicitly assumes that the causal SNPs are randomly spread over the genome , which is independent of the MAF spectrum and the LD structure , and the effect sizes of causal SNPs are Gaussian distributed and have a specific relationship to their MAFs . Although it has been shown that SNP heritability estimates are reasonably robust to many of these modeling assumptions [59] , the estimates can be biased if , for instance , causal SNPs are rarer or more common than uniformly distributed on the MAF spectrum , or are enriched in high or low LD regions across the genome . For example , the heritability estimates for some autoimmune diseases such as psoriasis and RA dropped dramatically when the MHC region ( chr6:25-35Mb ) was removed when constructing the genetic similarity matrix , indicating , as expected , that causal variants for these diseases are disproportionally enriched in the MHC region . S4 Table lists all the traits whose heritability estimates decreased by 0 . 2 or more when the MHC region was taken out , and thus need to be interpreted with caution . Methods to correct for MAF properties and region-specific LD heterogeneity of causal variants have been proposed [27 , 59 , 60] . For example , we can stratify MAF and LD structure into different bins , compute a genetic similarity matrix within each bin , and fit a mixed effects model with multiple variance components [27 , 60] . This approach can give heritability estimates that are more robust to properties of the underlying genetic architecture , but has the downside of increased computational burden and reduced statistical power . A different direction to explore is to estimate SNP heritability using imputed data ( in contrast to the genotype data here ) , which might capture more genetic variation from rare variants , or common variants that are not well tagged by the genotyped SNPs , and thus lead to increased heritability estimates . Second , heritability analysis models , including the one we employed in the present study , typically assume that genetic and environmental effects are independent , i . e . , no gene-by-environment ( GxE ) interaction exists . This is certainly a simplification of the real world where GxE interactions are expected for many complex traits . Recent computational studies have also shown that ignoring GxE interactions in heritability analysis can produce biased estimates [25] . However , modeling GxE would require collecting relevant environmental variables for each phenotype and more sophisticated statistical modeling approaches , e . g . , incorporating multiple random effects in the heritability analysis model [5 , 61] . Due to the limited measurements of environment collected by the UK Biobank , and the extensive analyses we have conducted across the phenotypic spectrum , explicitly modeling the environmental factors and GxE interactions is not feasible . We therefore took an alternative approach to examine the moderating effects of three major demographic variables on heritability estimates by stratifying samples . Of note , consistent heritability estimates across different levels of the stratifying variable do not completely eliminate the potential existence of GxE interactions . Specifically , recent studies have identified genetic heterogeneity in human traits such as BMI and fertility [62 , 63] , indicating that the genetic architecture of a trait may be different across environments ( i . e . , the genetic correlation of a trait in different environments may be significantly smaller than 1 ) even if the overall heritability estimates are similar . Dissection of common and unique environmental influences and their interactive effects with genetics on different complex traits , and the shared and unique genetic effects across environments are important future directions to explore . Lastly , as reviewed in [64] , a number of empirical genetic similarity measurements computable from genome-wide SNP data have been proposed , which , when utilized in heritability analysis , can give different estimates with different interpretations . In addition , recent studies have argued that estimation error associated with genetic similarity measurements and the ill-posedness of the empirical genetic similarity matrix may produce unstable and unreliable SNP heritability estimates [65] . However , this is an area under active investigation and debate [64–67] . Here , as the first study to screen all UK Biobank variables and provide an overview of the distribution of SNP heritability across different trait domains , and to examine the effect of potential modifying variables on heritability estimates , we used a straightforward and classical modeling approach that is most widely used . To obtain more insights into the genetic architecture and find the most appropriate and robust model for each individual trait , more systematic investigation is needed . In sum , using a computationally and memory efficient approach , we provide estimates of the SNP heritability for 551 complex traits across the phenome captured in the population-based UK Biobank . We further identify phenotypes for which the contribution of genetic variation is modified by demographic factors . These results underscore the importance of considering population characteristics in interpreting heritability , highlight phenotypes and subgroups that may warrant priority for genetic association studies , and may inform efforts to apply genetic risk prediction models for a broad range of human phenotypes .
This study utilized deidentified data from the baseline assessment of the UK Biobank , a prospective cohort study of 500 , 000 individuals ( age 40–69 years ) recruited across Great Britain during 2006–2010 [7] . The protocol and consent were approved by the UK Biobank’s Research Ethics Committee . The UK Biobank collected phenotypic data from a variety of sources including questionnaires regarding mental and physical health , food intake , family history and lifestyle , a baseline physical assessment , computerized cognitive testing , linkage with health records , and blood samples for biochemical and DNA analysis . Details about the UK Biobank project are provided at http://www . ukbiobank . ac . uk . Data for the current analyses were obtained under an approved data request ( Ref: 13905 ) . The interim release of the genotype data for the UK Biobank ( downloaded on Mar 3 , 2016 ) comprises 152 , 736 samples . Two closely related arrays from Affymetrix , the UK BiLEVE Axiom array and the UK Biobank Axiom array , were used to genotype approximately 800 , 000 markers with good genome-wide coverage . Details of the design of the arrays and sample processing can be found at http://biobank . ctsu . ox . ac . uk/crystal/refer . cgi ? id=146640 and http://biobank . ctsu . ox . ac . uk/crystal/refer . cgi ? id=155583 . Prior to the release of the genotype data , stringent quality control ( QC ) was performed at the Wellcome Trust Centre for Human Genetics , Oxford , UK . Procedures were documented in detail at http://biobank . ctsu . ox . ac . uk/crystal/refer . cgi ? id=155580 . We leveraged the QC metrics made available by the UK Biobank and removed samples that had mismatch between genetically inferred sex and self-reported sex , samples that had high genotype missingness or extreme heterozygosity not explained by mixed ancestry or increased levels of marriage between close relatives , and one individual from each pair of the samples that were 3rd degree or more closely related relatives . We restricted our analysis to subjects that were self-reported white British and confirmed by principal component analysis ( PCA ) to be Caucasians . We further filtered out genetic markers that had high missing rate ( >1% ) , low minor allele frequency ( <1% ) , significant deviation from Hardy-Weinberg equilibrium ( p<1e-7 ) , and subjects that had high missing genotype rate ( >1% ) . 108 , 158 subjects ( age 40–73 years; female 52 . 84% ) and 486 , 175 SNPs remained for analysis after QC . S4 Fig shows the age distribution of the subjects that passed QC . The genetic similarity matrix was computed using all genotyped autosomal SNPs . All genetic analyses were performed using PLINK 1 . 9 ( https://www . cog-genomics . org/plink2 ) [68] . We analyzed every trait available to us that had a sufficient sample size to produce a heritability estimate with its standard error smaller than 0 . 1 . The traits can be classified into the following 11 domains as defined by the UK Biobank: cognitive functions , early life factors , health and medical history , life style , physical measures , psychosocial factors , sex-specific factors and sociodemographics . For continuous traits , we excluded samples that were more than 5 standard deviations away from the population mean to avoid extreme outliers and data recording errors . We only analyzed binary traits that had prevalence greater than 1% in the sample , so that we had enough statistical power to get reliable heritability estimates . We typically binarized categorical variables at a meaningful threshold close to the median and then analyzed them as binary traits . For the specific cutoff-points used to binarize each categorical variable , see S1 Table . We also analyzed a large number of self-reported illness codes and hospital in-patient diagnosis codes . Self-reported cancer and non-cancer illness codes were obtained through a verbal interview by a trained nurse at the UK Biobank assessment center on past and current medical conditions . Hospital in-patient diagnoses were obtained through medical records and were coded according to the International Classification of Diseases version-10 ( ICD-10 ) . Disease codes for each domain ( self-reported cancer , self-reported non-cancer illness , and ICD-10 ) were organized in a hierarchical tree structure; codes closer to the root of the tree are often less specific and have larger prevalence , while codes closer to the leaves are more specific but have lower prevalence . We analyzed every disease code that had prevalence greater than 1% in the sample . 15 ICD-10 codes were excluded due to small sample sizes and large standard errors ( >0 . 1 ) on heritability estimates ( N70-N77 Inflammatory diseases of female pelvic organs; O20-O29 Other maternal disorders predominantly related to pregnancy; O80-O84 Delivery; N50 Other disorders of male genital organs; N80 Endometriosis; N81 . 1 Cystocele; N81 . 2 Incomplete uterovaginal prolapse; N83 Noninflammatory disorders of ovary—Fallopian tube and broad ligament; N83 . 2 Other and unspecified ovarian cysts; N84 . 1 Polyp of cervix uteri; N92 . 1 Excessive and frequent menstruation with irregular cycle; N93 Other abnormal uterine and vaginal bleeding; O68 Labour and delivery complicated by foetal stress [distress]; O70 . 1 Second degree perineal laceration during delivery; O80 Single spontaneous delivery ) . We also employed a data-driven approach to determine if a disease is sex-specific . More specifically , if the sample prevalence of a disease in males was more than 100 times larger than the sample prevalence in females , we defined the disease as male-specific and the analysis was restricted to males . The same approach was used to find female-specific diseases . See S2 Table for all the disease codes we analyzed . We consider the linear random effect model y = g + e , where an N-dimensional trait y is partitioned into the sum of additive genetic effects g and unique ( subject-specific ) environmental effects e . The covariance structure of y is cov[y]=σg2K+σe2I , where K is the empirical genetic similarity matrix for each pair of individuals estimated from genome-wide SNP data [4 , 5] , I is an identity matrix , σg2 and σe2 are the total additive genetic variance captured by genotyped common SNPs and the variance of unique environmental factors across individuals , respectively . SNP heritability is then defined as hSNP2=σg2/ ( σg2+σe2 ) =σg2/σp2 , which measures the total phenotypic variance σp2 that can be explained by total additive genetic variance tagged by genotyped SNPs , and is a lower bound for the narrow-sense heritability h2 . When covariates need to be incorporated into the model , i . e . , y = Xβ + g + e , where X is an N × q covariate matrix and β is a vector of fixed effects , an N × ( N − q ) matrix U always exists , which satisfies UTU = I , UUT = P0 , UTX = 0 , and P0 = I − X ( XTX ) −1XT . Applying UT to both sides of the model removes the covariate matrix [69 , 70] . To obtain unbiased estimates of σg2 and σe2 , we used a computationally efficient moment-matching approach [70 , 71] , which is closely related to the Haseman-Elston regression [14–16] and phenotype-correlation genetic-correlation ( PCGC ) regression [10] , and mathematically equivalent to the LD score regression under certain conditions [8 , 18] . Specifically , we regress the empirical estimate of the phenotypic covariance onto the matrices K and I: vec[yyT]=σg2vec[K]+σe2vec[I]+ϵ , where vec[⋅] is the matrix vectorization operator that converts a matrix into a vector by stacking its columns , and ϵ is the residual of the regression . The ordinary least squares ( OLS ) estimator of this multiple regression problem can be explicitly written as σ^g2=1vKyT ( K−τI ) y and σ^e2=1vKyT ( κI−τK ) y , where τ = tr[K]/N , κ = tr[K2]/N , and vK = N ( κ − τ2 ) . SNP heritability is then estimated as h^SNP2=σ^g2/ ( σ^g2+σ^e2 ) =σ^g2/σ^p2 . To estimate the sampling variance of h^SNP2 , we follow Visscher et al . [17] and make two assumptions: ( 1 ) the off-diagonal elements in the empirical genetic similarity matrix K are small , such that K ≈ I and V=cov[y]=σg2K+σe2I≈σp2I; and ( 2 ) the phenotypic variance σp2 can be estimated with very high precision . We thus have var[σ^g2]=2vK2tr[ ( K−τI ) V ( K−τI ) V]≈2σp4/vK , and var[h^SNP2]≈2/vK . This estimator coincides with existing results in the literature [17] . We note that the calculation of the variance of σ^g2 relies on an additional assumption that the trait y is Gaussian distributed and thus may be suboptimal for binary traits . However , Visscher and colleagues have empirically shown that this sampling variance approximation is accurate for both continuous and binary traits when the sample size is large [17] ( also see S3 Fig ) . We note that for large sample size N , the N × N genetic similarity matrix K and residual forming matrix P0 can be very large , making the computation memory intensive . We have developed a memory efficient algorithm that can iteratively load columns ( or block columns ) of K into the memory to compute the SNP heritability estimate , and does not need to explicitly compute P0 or any other N × N matrices . See S1 Text for details . Matlab and Python implementations of the algorithm are available at https://github . com/chiayenchen/mmhe . For binary traits , the above calculation gives a heritability estimate on the observed scale , which is dependent on prevalence of the trait in the population . We transformed this heritability estimate to the underlying liability scale under the assumption of a classical liability threshold model [72 , 73] , which makes heritability estimates independent of prevalence and thus comparable across traits . Specifically , heritability estimate on the liability scale can be obtained using a linear transformation of the heritability on the observed scale: h^SNP , L2=ch^SNP2 , where c = P ( 1 − P ) /φ ( t ) 2 , P is the population prevalence , t = Φ−1 ( 1 − P ) is the liability threshold , Φ is the cumulative distribution function of the standard normal distribution , and φ is the density function of the standard normal distribution [3 , 74] . Since the UK Biobank is not designed to be ascertained for particular diseases , we assumed that population prevalence is identical to sample prevalence . The sampling variance of the heritability estimate can be transformed accordingly: var[h^SNP , L2]=c2var[h^SNP2] . In all heritability analyses , we included genotyping array , UK Biobank assessment center , age at recruitment and top 10 principal components ( PCs ) of the genotype data as covariates . Other covariates such as sex and handedness ( e . g . , when analyzing the grip strength of the left/right hand ) were adjusted where appropriate . See S1 Table for the set of covariates we included in the model when estimating the heritability for each trait . To compute PCs of the genotype data , we performed pairwise linkage disequilibrium ( LD ) based SNP pruning at R2>0 . 02 and excluded SNPs in the major histocompatibility complex ( MHC ) region ( chr6:25-35Mb ) and chromosome 8 inversion ( chr8:7-13Mb ) . Top PCs were then computed using flashPCA [75] on the pruned data , which employs an efficient randomized algorithm and is thus scalable to large data sets with hundreds of thousands of individuals . To examine how heritability estimates are modified by sex , we estimated heritability for each non-sex-specific trait in males and females separately . For binary traits , sample prevalence was calculated in each sex . To test if heritability estimates are significantly different by sex , we assumed that the two SNP heritability estimates to be contrasted , h^A and h^B , are independent and approximately Gaussian distributed , and computed the z-score of their difference: z= ( h^A−h^B ) /se^A2+se^B2 , where se^A2 and se^B2 are standard error estimates of h^A and h^B , respectively . A p-value can then be computed as p = 2 ∙ Φ ( −|z| ) , where Φ is the cumulative distribution function of the standard normal distribution . To examine whether SNP heritability estimates vary with age , we used a sliding window approach and estimated heritability for every age range of 10 years ( i . e . , 40–49 years , 41–50 years , … , 64–73 years ) by stratifying the samples . For binary traits , sample prevalence was calculated in each age range separately . We assessed whether heritability estimates exhibited a linear trend with age by fitting a regression model , h^k2=α+age¯kγ+ϵk , where h^k2 is the heritability estimate in the k-th age range , age¯k is the mean of the age range , α is an intercept , γ is the slope and ϵk is the residual of the regression , and testing whether γ is significantly different from zero . We weighted heritability estimates by the inverse of their standard errors when fitting the regression model , and thus put more emphasis on estimates with better precision . We only analyzed physical and cognitive measures , and did not consider disease codes and medical history in age stratification analyses because age at recruitment does not reflect disease onset . Similarly , we used a sliding window approach to estimate the SNP heritability for each trait from the bottom 1/3 quantile to the top 1/3 quantile of the Townsend deprivation index at recruitment , a measure of material deprivation within the population of a given area . For binary traits , sample prevalence was calculated in each SES bin separately . For traits that do not reflect the status of participants at the time of recruitment ( e . g . , medical history and early-life factors ) , we have implicitly made an assumption that the SES of participants had not changed dramatically throughout their lives . To account for multiple testing in our stratification analyses , we corrected the p-values using the effective number of independent traits we analyzed . Specifically , for each stratification analysis ( sex , age and SES ) , we calculated the Pearson correlation coefficient for each pair of the traits using their overlapping samples . The correlation between traits that had no sample overlap , e . g . , male- and female-specific factors , was set to zero . We then conducted a principal component analysis ( PCA ) to the constructed phenotypic correlation matrix , and estimated the effective numbers of independent traits that explained 99% of the total phenotypic variation in sex , age and SES stratification analyses to be 400 , 31 and 440 , respectively . Finally , we multiplied uncorrected p-values by the corresponding effective number of independent traits to obtain corrected p-values .
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Heritability of a trait refers to the proportion of phenotypic variation that is due to genetic variation among individuals . It provides important information about the genetic basis of complex traits and indicates whether a phenotype is an appropriate target for more specific statistical and molecular genetic analyses . Recent studies have leveraged the increasingly ubiquitous genome-wide data and documented the heritability attributable to common genetic variation captured by genotyping microarrays for a wide range of human traits . However , heritability is not a fixed property of a phenotype and can vary with population-specific differences in the genetic background and environmental variation . Here , using a computationally and memory efficient heritability estimation method , we report the heritability for a large number of traits derived from the large-scale , population-based UK Biobank , and , for the first time , demonstrate the moderating effect of three major demographic variables ( age , sex and socioeconomic status ) on heritability estimates derived from genome-wide common genetic variation . Our study represents the first comprehensive heritability analysis across the phenotypic spectrum in the UK Biobank .
|
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"Results",
"Discussion",
"Materials",
"and",
"methods"
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2017
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Phenome-wide heritability analysis of the UK Biobank
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Cockayne syndrome ( CS ) is a photosensitive , DNA repair disorder associated with progeria that is caused by a defect in the transcription-coupled repair subpathway of nucleotide excision repair ( NER ) . Here , complete inactivation of NER in Csbm/m/Xpa−/− mutants causes a phenotype that reliably mimics the human progeroid CS syndrome . Newborn Csbm/m/Xpa−/− mice display attenuated growth , progressive neurological dysfunction , retinal degeneration , cachexia , kyphosis , and die before weaning . Mouse liver transcriptome analysis and several physiological endpoints revealed systemic suppression of the growth hormone/insulin-like growth factor 1 ( GH/IGF1 ) somatotroph axis and oxidative metabolism , increased antioxidant responses , and hypoglycemia together with hepatic glycogen and fat accumulation . Broad genome-wide parallels between Csbm/m/Xpa−/− and naturally aged mouse liver transcriptomes suggested that these changes are intrinsic to natural ageing and the DNA repair–deficient mice . Importantly , wild-type mice exposed to a low dose of chronic genotoxic stress recapitulated this response , thereby pointing to a novel link between genome instability and the age-related decline of the somatotroph axis .
A prevailing hypothesis to explain the molecular basis of ageing is Harman's “free-radical theory of ageing” , which states that endogenous reactive oxygen species ( ROS ) , which result from cellular metabolism , continually damage biomolecules [1] . In line with this hypothesis , it has been shown that increased resistance to oxidative stress ( e . g . , by improved antioxidant defense ) extends the lifespan of Caenorhabditis elegans , Drosophila , and rodents [2–4] , whereas hypersensitivity to oxygen considerably reduces the lifespan of nematodes [5] . A key macromolecule at risk for ROS-mediated damage is nuclear DNA [1] , which is evident from the wide range of oxidative DNA lesions that accumulate gradually in rodents and humans with advancing age [6 , 7] . In humans , the causative role of DNA damage in ageing is supported by a variety of progeroid disorders with defects in DNA repair pathways [8 , 9] . One such condition is Cockayne syndrome ( CS ) ( affected genes: CSA or CSB ) , a photosensitive disorder that originates from a defect in transcription-coupled repair ( TCR ) , which specifically removes DNA lesions that obstruct RNA polymerases , allowing resumption of transcription and promoting cellular survival from DNA damage . TCR of helix-distorting DNA damage is a dedicated subpathway of the multi-step “cut-and-patch” nucleotide excision repair ( NER ) system , and is designated TC-NER [10] to distinguish it from the so-called global genome NER ( GG-NER ) subpathway that operates genome-wide to eliminate distorting damage . Available evidence suggests that CS cells are also defective in TCR of non–helix distorting DNA lesions that block transcription such as transcription-blocking oxidative DNA lesions [11 , 12] , which are normally genome-wide removed by base excision repair . We will use the term TCR when referring to transcription-coupled repair in general . CS patients present with growth failure ( cachectic dwarfism ) , progressive neurological abnormalities ( including delayed psychomotor development , mental retardation , microcephaly , gait ataxia , sensorineural hearing loss , retinal degeneration ) , along with impaired sexual development , kyphosis , osteoporosis , and severely reduced lifespan ( mean age of death: 12 . 5 y ) [13 , 14] . A related yet distinct disorder is trichothiodystrophy ( TTD ) ( affected genes: XPB , XPD , or TTDA ) . TTD patients are partially defective in TCR as well as in the GG-NER , and share the symptoms associated with CS . In addition , these patients have a partial defect in transcription itself , causing additional symptoms such as ichthyosis and brittle hair and nails [15] . Many of the CS and TTD features are progressive and resemble premature ageing . Because patients develop some but not all aspects of normal ageing in an accelerated manner , CS and TTD are considered “segmental progeroid syndromes” [8] . Mouse mutants for CS-A and CS-B reliably mimic the UV sensitivity of CS patients and show accelerated photoreceptor loss , reduced body weight , and mild neurologic abnormalities [16 , 17] . Similarly , mice homozygous for a causative TTD point mutation in the Xpd gene faithfully mirror the symptoms in TTD patients [9] , whereas complete inactivation of NER ( by concurrent inactivation of the Xpa gene ) dramatically aggravates the CS features of partially NER-defective TTD mice [9] . These observations , together with the notion that DNA lesions can provoke a permanent cell cycle arrest or apoptosis , led us to propose that ageing can result from ( oxidative ) DNA lesions that interfere with transcription and/or replication causing cell death or cellular senescence , ultimately leading to the loss of tissue homeostasis and the onset of age-related diseases [18–20] . Here we report that mice with engineered mutations in both Csb and Xpa genes display many CS features in a dramatic form , including postnatal growth attenuation , progressive kyphosis , ataxia , retinal degeneration , motor dysfunction , and premature death . Importantly , full genome transcriptome analysis of the Csbm/m/Xpa−/− mouse liver at the age of 15 d uncovered a systemic response seen also in wild-type ( wt ) mice exposed to chronic oxidative stress . These findings disclose a novel link between DNA damage , compromised genome maintenance , and the somatotrophic axis that determines lifespan and shed new light on the etiology of Cockayne syndrome and natural ageing .
TCR-defective Csbm/m mutant mice [16] were intercrossed with GG-NER-defective Xpc−/− [21] and GG/TC-NER-defective Xpa−/− [22] animals to investigate whether an increase in the endogenous burden of unrepaired DNA damage , as provoked by the inactivation of GG-NER , enhances the phenotype , including progeroid features . Analysis of UV-induced repair synthesis and RNA synthesis recovery ( indicative for GG-NER and TC-NER capacity , respectively ) confirmed complete inactivation of NER in Csbm/m/Xpa−/− and Csbm/m/Xpc−/− animals ( Figure 1A ) . As expected on the basis of previous work , Xpa−/− cells display the highest UV sensitivity , whereas Xpc−/− and Csbm/m cells show intermediate sensitivities ( Xpa−/− > Csbm/m > Xpc−/− > wt; see Figure 1B ) . Interestingly , inactivation of GG-NER in Csbm/m mouse embryonic fibroblasts ( MEFs ) ( as in Csbm/m/Xpa−/− and Csbm/m/Xpc−/− cells ) renders cells more UV-sensitive than already completely NER-deficient Xpa−/− MEFs . We attribute this enhanced sensitivity to the absence of CSB-mediated TCR of UV-induced lesions that do not form a substrate for NER . Thus , the repair defect in the double mutant appears to be more severe than that of the single mutants . We could not detect a similar increased sensitivity to ionizing radiation in double-mutant cells above that of Csbm/m cells [12] ( unpublished data ) , supporting the notion that MEFs in culture are already under high oxygen stress [23 , 24] . As evident from their overall appearance and weight ( Figure 1C–1E ) , Csbm/m/Xpa−/− and Csbm/m/Xpc−/− pups ( hybrid C57BL/6Jx129ola genetic background ) displayed a strikingly attenuated growth , resulting in pronounced dwarfism . Whereas the number of double mutant pups was ∼3-fold below that expected for Mendelian inheritance ( Table S1 ) , embryonic day 18/5 ( E18 . 5 ) Csbm/m/Xpa−/− and Csbm/m/Xpc−/− embryos were present at Mendelian frequency , pointing to considerable lethality during or shortly after birth . Importantly , double-mutant embryos were morphologically and histologically indistinguishable from wt and single-mutant embryos ( Figure 1F and unpublished data ) , indicating that the growth defect was postnatal and did not reflect impaired embryonic development per se . In the third week of life , however , Csbm/m/Xpa−/− and Csbm/m/Xpc−/− pups developed progressive cachexia ( evident from the weight loss after day 15; see Figure 1E ) , ultimately resulting in death before postnatal day 22 . Neither moistening of food pellets ( to facilitate intake of solids ) nor removal of wt or single-mutant pups from the litter ( to reduce competition for breast milk ) improved the physical condition or the lifespan of Csbm/m/Xpa−/− and Csbm/m/Xpc−/− pups . Necropsy revealed milk or solid food in the stomach , indicating that insufficient access to supplied nutrition was not the underlying cause of growth retardation , weight loss , and early death . Importantly , progressive growth retardation , cachexia , and short life expectation ( ∼12 . 5 y ) are also observed in human patients with CS [13] . Combined inactivation of Xpa and Xpc rendered mice without any overt phenotype ( unpublished data ) , leading us to conclude that the dramatic phenotype of Csbm/m/Xpa−/− and Csbm/m/Xpc−/− pups results from a combined GG-NER/TC-NER/TCR defect . Further analysis of the Csbm/m/Xpa−/− phenotype , performed in an isogenic C57BL/6J background , revealed a near-normal size of the skull at day 11 and 21 ( autoradiographs shown in Figure 2A ) , implying that the ( postnatal ) growth defect is restricted to the trunk , and to a lesser extent , the extremities . All 21-d-old double-mutant animals showed kyphosis ( abnormal curvature of the spinal column , Figure 2A , middle left and bottom right ) , which was also observed in younger Csbm/m/Xpa−/− pups , indicating that it is not determined by terminal illness . The normal appearance of the spine in 11-d-old double-mutant pups excluded a prenatal developmental defect and further pointed to an extremely accelerated onset of kyphosis , a feature observed in naturally aged ( 2-y old ) C57BL/6J mice ( see Figure 2A , bottom left panel ) . Two-dimensional images of proximal end-to-mid-diaphysis micro–computed tomography ( micro-CT ) scans of fixed tibiae from 10- , 15- , and 20-d-old wt and Csbm/m/Xpa−/− mice revealed retarded , yet steady , longitudinal as well as radial ( perimeter ) growth , along with a thinner bone cortex and a less developed growth plate ( Figure 2B ) . In line with this observation , we observed a reduction in tibia length ( Figure 2C ) . Notably , whereas Csbm/m/Xpa−/− pups lose weight in the third week of life , bone growth proceeds , resulting in relatively large extremities , a representative feature of CS and TTD [13] . Motor coordination problems , manifesting as tremors and abnormal posture of the hind limbs ( flexion rather than extension in tail suspension test ) , became evident around day 10 in Csbm/m/Xpa−/− mice ( unpublished data ) . Foot print analysis revealed a disturbed gait from day 15 onwards . Whereas wt and single-mutant animals maintained a straight path with regular alternating strides , Csbm/m/Xpa−/− mice demonstrated a nonuniform alternating left-right step pattern and unevenly spaced shorter strides ( Figure 2D ) . Despite their runted size , the front base width of Csbm/m/Xpa−/− animals was significantly greater than that of wt and single-mutant littermates , which likely illustrates an attempt to maintain balance ( Figure 2D ) . These data are consistent with the profound early postnatal ataxia and abnormal cerebellar development in Csbm/m/Xpa−/− mice [25] and the progressive neurodegeneration observed in human CS patients [26] . We next examined the retinas of 15-d-old Csbm/m/Xpa−/− pups for the presence of apoptotic cells , because retinal degeneration is a prominent neurological feature of CS patients [27] and adult CS mice ( T . Gorgels , I . van der Pluijm , R . Brandt , G . Garinis , H . van Steeg , et al . , unpublished data ) . At this age , cell loss occurs as part of the normal development of the retina . Yet , as shown by terminal deoxynucleotidyltransferase-mediated dUTP nick end labeling ( TUNEL ) ( Figure 2E ) and caspase-3 staining ( unpublished data ) , the number of apoptotic cells in the outer nuclear layer ( ONL ) and inner nuclear layer ( INL ) of the retinas of Csbm/m/Xpa−/− pups was significantly increased ( analysis of variance [ANOVA] , S-N-K posthoc test , p < 0 . 05 ) , as compared to wt and single-mutant littermates ( Figure 2E ) . Thus , the Xpa defect enhanced the apoptotic sensitivity of photoreceptor cells in Csbm/m mice , thereby pointing to DNA damage as a trigger for age-related retinal degeneration . Because 15-d-old Csbm/m mice still have wt levels of apoptotic cells , spontaneous photoreceptor loss in the Csbm/m mouse initiates in the second/third month of life . Visual inspection and histological analysis of most internal organs of 15-d-old Csbm/m/Xpa−/− mice did not reveal any obvious pathological abnormalities ( unpublished data ) , with the exception of substantial loss of abdominal fat . Because we did not find any sign of infections , necrosis , or abnormal cellular proliferation ( as determined by bromodeoxyuridine [BrdU] staining ) in the gastrointestinal tract of 15- and 21-d-old Csbm/m/Xpa−/− animals , intestinal malfunction is an unlikely cause of the growth defect ( Figure 3A ) . In addition , the liver had a normal histological appearance ( Figure 3B ) , whereas neither BrdU ( Figure 3C ) , proliferating cell nuclear antigen ( PCNA ) ( Figure 3D ) , and Ki67 staining ( unpublished data ) , nor TUNEL ( Figure 3E ) and caspase-3 staining ( unpublished data ) revealed any significant difference between Csbm/m/Xpa−/− and wt livers . This finding indicates that aberrant cell proliferation or apoptosis in the liver does not likely contribute to the Csbm/m/Xpa−/− phenotype . Moreover , inactivation of the p53 tumor-suppressor gene failed to rescue the mutant phenotype , because Csbm/m/Xpa−/−/p53−/− triple-mutant pups appeared indistinguishable from Csbm/m/Xpa−/− pups ( unpublished data ) . Thus , the precise etiology of the overall physical deterioration and the cause of death of Csbm/m/Xpa−/− mice remain unknown . The spontaneous , age-related and ionizing radiation ( IR ) –induced loss of post-mitotic photoreceptor cells in Csbm/m mice underscores the relevance of DNA repair in the removal of ( oxidative ) DNA damage for the long-term survival of terminally differentiated cells in the retina ( T . Gorgels , I . van der Pluijm , R . Brandt , G . Garinis , H . van Steeg , et al . , unpublished data ) . To test whether Csbm/m/Xpa−/− animals are more sensitive to genotoxic insults than are single-mutant Csbm/m and Xpa−/− animals , we next examined if the additional Xpa defect further enhances the IR sensitivity of the Csbm/m retina . To this end , we exposed 18-d-old Csbm/m/Xpa−/− pups and wt and single-mutant littermates to γ rays ( 10 Gy ) and quantified the number of apoptotic cells in the retina by TUNEL staining 20 h after exposure . As shown in Figure 4 , the number of apoptotic cells in the ONL of untreated ( 19-d-old ) Csbm/m/Xpa−/− pups further increased , as compared to 15-d-old double-mutant animals ( see Figure 2E ) . Whereas IR exposure did not increase the frequency of apoptotic photoreceptors in the ONL of wt and Xpa−/− animals , Csbm/m mice already show a tendency to increased photoreceptor loss , as characteristic for mature Csbm/m animals ( T . Gorgels , I . van der Pluijm , R . Brandt , G . Garinis , H . van Steeg , et al . , unpublished data ) . In contrast , the retinas of IR-exposed Csbm/m/Xpa−/− animals showed an almost 2-fold increase in the level of TUNEL-positive photoreceptor cells ( Student's t-test; p = 0 . 021 ) . Taken together , these findings not only further point to unrepaired DNA damage ( likely originating from oxidative stress ) as the underlying trigger for photoreceptor loss , but importantly , also show that inactivation of Xpa further enhances the sensitivity of Csbm/m mice to genotoxic stress . To investigate whether a disturbance in growth and metabolism could explain the pronounced accelerated organismal deterioration seen in Csbm/m/Xpa−/− mice , we evaluated the liver transcriptome of 15-d-old wt , single-mutant , and double-mutant mice ( n = 4 mice ) . At this age , the Csbm/m/Xpa−/− pups have not yet become cachectic . Two-tailed t test analysis Affymetrix full mouse genome arrays revealed 1 , 865 genes with significantly changed expression patterns between wt and Csbm/m/Xpa−/− livers ( p ≤ 0 . 01 , 1 . 2-fold change up- or down-regulated , Table S2 ) , a number that significantly exceeds the ∼80 genes that are expected to occur by chance under these selection criteria . Among the set of 1 , 865 genes , we identified those gene ontology ( GO ) –classified biological processes with a significantly disproportionate number of responsive genes relative to those printed on microarrays ( false detection rate ≤ 0 . 10 ) . This unbiased approach revealed processes implicated in the derivation of energy from oxidation of organic compounds , homeostasis of energy reserves , cell growth and maintenance , and the redox status of the cell . Subsequent analysis of these processes led us to identify the following four results . ( 1 ) A profound attenuation of the somatotroph axis , as evidenced by the consistent down-regulation of genes encoding main components of the GH/IGF1 axis ( e . g . , IGF1 , Igfbp3 , Igfbp4 , Igfals , Ghr ) , as well as lactotroph ( e . g . , Prlr ) and thyrotroph functions ( e . g . , Dio1 ) in Csbm/m/Xpa−/− livers , in addition to a decrease in the expression of several genes associated with a variety of mitogenic signals ( e . g . , Esr1 , Fgf1 , Fgfr3 , Fgfr4 ) ( Table 1 and Table S2 ) . ( 2 ) An extensive suppression of catabolic metabolism in the Csbm/m/Xpa−/− liver , as evident from the significant down-regulation of key genes involved in glycolysis , the tricarboxylic acid cycle , and oxidative phosphorylation pathways ( Table 1 and Table S2 ) , coupled with a significant up-regulation of genes associated with glycogen synthesis ( e . g . , Gyg1 and Gys2 ) and down-regulation of glycogen phosphorylase ( Pygl ) , suggesting that the Csbm/m/Xpa−/− liver stores glucose into glycogen , rather than burn it for energy derivation . These changes were further accompanied by the broad down-regulation of genes associated with electron transport and oxidative phosphorylation ( e . g . , several cytochrome P450 monooxygenases , the NADH dehydrogenase complex , and the NADPH-dependent oxidative metabolism ) ( Table 1 and Table S2 ) and the significant down-regulation of several genes associated with peroxisomal biosynthesis ( Table 1 ) . Apparently , the complete catabolic metabolism is restrained in the Csbm/m/Xpa−/− liver . ( 3 ) A broad up-regulation of genes associated with fatty acid synthesis and transport ( several genes listed in Table 1 and Table S2 ) , the up-regulation of the receptor for the adipocyte hormone leptin ( Lepr ) , and the central fat regulator peroxisome proliferator-activated receptor-gamma ( Pparγ ) . Thus , similar to their reserved glucose utilization and enhanced glycogen synthesis , Csbm/m/Xpa−/− mice attempt to store rather than burn fat . ( 4 ) An up-regulation of genes encoding key enzymatic and nonenzymatic low–molecular mass scavengers and antioxidant defense enzymes ( e . g . , Sod1 , Prdx2 and 3 , Txnip , Ephx1 , Hmox1 and five components of the glutathione system ) ( Table 1 ) , suggesting that Csbm/m/Xpa−/− mice try to minimize the induction of ( DNA ) damage by counteracting ROS . None of these genes were identified as significantly differentially expressed in the livers of Csbm/m or Xpa−/− littermate controls ( Table 1 ) . Quantitative real-time PCR ( Q-PCR ) evaluation of the expression levels of key genes involved in the somatotroph axis , energy metabolism , and antioxidant defense in the livers of Csbm/m/Xpa−/− mice , and wt , Csbm/m , and Xpa−/− littermates , as well as further biochemical analysis ( see below ) , confirmed the validity of the microarray data ( Figure 5A , upper left panel ) . Next we analyzed whether the onset of the aforementioned transcriptional changes paralleled the progressive postnatal growth attenuation as well as the weight loss observed later . Consistent with the normal embryonic development , the expression levels of genes involved in the somatotroph axis ( Ghr , Igf1 , Prlr ) , antioxidant defense ( Gstt2 , Hmox1 , Ephx1 ) , and oxidative metabolism ( Gck , Gyg1 , Cs , Ndufs8 ) did not differ significantly between wt and Csbm/m/Xpa−/− livers at postnatal day 1 ( Figure 5B ) . In contrast , during the first 2 wk of life , wt mice exhibited , as expected , a robust up-regulation in Igf1 , Ghr , and Prlr gene expression , a response that was virtually absent in Csbm/m/Xpa−/− animals ( Figure 5B , left panels ) ; this explains well the severe growth retardation of double-mutant pups after birth . Analysis of Gstt2 , Hmox1 , and Ephx1 mRNA levels revealed that the up-regulation of the antioxidant defense system in the Csbm/m/Xpa−/− liver already initiated before postnatal day 10 , and thus occurs well ahead of the initiation of the physiological decline ( i . e . , weight loss ) ( Figure 5B , middle panels ) . When comparing mRNA levels of key genes in glycolysis ( Gck ) , tricarboxylic acid cycle ( Cs ) , and mitochondrial oxidative phosphorylation ( Ndufs8 ) , we noticed that beginning postnatal day 10 , Csbm/m/Xpa−/− livers do not show the prominent up-regulation of these catabolic genes seen in the wt liver ( instead , expression levels continued to decline ) , whereas they up-regulate glycogen synthesis ( Gyg1 ) ( Figure 2B , right panels ) . In agreement , the enzymatic activity of citrate synthase was significantly lower ( p ≤ 0 . 01 ) in the livers of 15-d-old Csbm/m/Xpa−/− mice ( 119 ± 15 mU/mg protein ) , as compared to wt littermate controls ( 70 ± 13 mU/mg protein ) . We next determined the expression levels of aforementioned genes in the kidney , heart , and spleen of the same set of animals used in the microarray experiment . Expression levels markedly mirrored the deviant expression patterns observed in the liver , whereas mRNA levels in Csbm/m and Xpa−/− tissues were not significantly different from wt animals ( Figure 5A ) . Thus , attenuation of the GH/IGF1 axis and down-regulation of metabolism , along with the enhanced antioxidant/detoxification response , represents a systemic rather than liver-specific response of the Csbm/m/Xpa−/− pups to the DNA repair defect . Interestingly , when 96-wk-old wt livers were tested for expression levels of this same set of Csbm/m/Xpa−/− responsive genes , we noticed a remarkable resemblance ( Figure S1 ) . The previous result prompted us to investigate whether and to which extent the gene expression changes in the Csbm/m/Xpa−/− mouse liver overlap with those observed in a natural aged liver . To this end , we first compared the full mouse liver transcriptome of adult 16- , 96- and 130-wk-old wt C57Bl/6J mice ( n = 4 ) with that of adult 8-wk-old wt C57Bl/6J mice ( n = 4 ) ( Tables S3–S5 ) . Using the same analytical method as applied to the Csbm/m/Xpa−/− mouse livers , we identified homeostasis of energy reserves , oxidative metabolism , along with cell growth and maintenance to be significantly overrepresented in 96- and 130-wk-old wt mice , but not in 16-wk-old animals ( Table S6 ) . These findings fit well with previous studies , suggesting the repression of oxidative metabolism to represent a conserved response shared by highly diverged species [28] . Next we implemented a previously described method [29] to evaluate the extent of genome-wide similarity between the liver transcriptomes of 2-wk-old Csbm/m/Xpa−/−mice and wt animals of various ages . We first classified all significantly differentially expressed genes in the Csbm/m/Xpa−/− liver transcriptome as having increased or decreased expression ( as compared to wt ) , and we asked how many of these genes respond in a similar direction in the 16/8 wk , 96/8 wk , and 130/8 wk datasets . If the Csbm/m/Xpa−/− liver resembles an aged liver , one expects the Spearman's rank correlation coefficient ( r , +1 . 0 or −1 . 0 in case of perfect similarity or dissimilarity , respectively , and 0 . 0 in case of no correlation ) to increase with age . Notably , whereas the liver transcriptome of Csbm/m/Xpa−/− mutant mice was dissimilar to that of 16-wk-old wt mice ( Spearman's r = −0 . 28 ) , as it was with 15-d-old littermates , this turned into a positive correlation when the comparison was made between the Csbm/m/Xpa−/− and 96-wk-old mouse liver transcriptomes ( r = +0 . 15 ) and even more with the 130-wk-old wt mouse group ( r = + 0 . 44 , p ≤ 0 . 0001 ) ( Figure 6A ) . Comparable results were obtained when the same approach was applied over the whole mouse transcriptome ( including all Affymetrix probe sets with signals above the detection cutoff value; see Materials and Methods ) , thus avoiding any initial preselection or introduction of bias . Using the same approach , we did not find a significant correlation between the liver transcriptomes of 15-d-old Csbm/m or Xpa−/−mice and aged wt mice . The genome-wide resemblance between the short-lived Csbm/m/Xpa−/− mice and the 130-wk-old mice was substantially higher ( >90% ) when the comparison was restricted to those functional categories that were significantly overrepresented in the double-mutant and 130-wk-old mice , such as the GH/IGF1 axis , oxidative metabolism ( i . e . , glycolysis , Krebs and oxidative phosphorylation ) , cytochrome P450 electron transport , and peroxisomal biogenesis ( Figure 6B and 6C and Table S7 ) . Despite the occurrence of dissimilarities between the liver transcriptome of Csbm/m/Xpa−/− pups and aged wt mice ( the latter animals showing over-representation of genes involved in the immune and inflammatory responses , ATP biosynthesis , and protein glycosylation , along with a lack of the anti-oxidant response ) , these findings strongly underline the genome-wide parallels between the Csbm/m/Xpa−/− repair mutants and natural ageing , thereby validating the progeria in the double mutant pups . In agreement with the down-regulation of Igf1 gene expression in the liver ( the main source of circulating IGF1 [30] ) , we observed a significant reduction ( p < 0 . 004 ) in serum IGF1 levels in Csbm/m/Xpa−/− mice ( Figure 7A ) together with significantly lower blood glucose levels ( p < 0 . 04 ) ( Figure 7B ) . Following an initial reduction of ∼30% ( p < 0 . 04 ) in 7- and 10-d-old Csbm/m/Xpa−/− mice , blood glucose levels started to drop at day 15 , gradually reaching low levels in 17-d-old Csbm/m/Xpa−/− mice ( ∼3 mM ) , contrasting the steady blood glucose levels ( ∼ 9 mM ) in littermate controls ( Figure 7B ) . The presence of milk and food in the stomach of the double-mutant pups along with the normal appearance of the intestinal epithelium ( Figure 3A ) indicates that the hypoglycemia is not due to food intake . Even more , the suppression of the somatotroph axis and subsequent decreased IGF1 production in 15-d-old Csbm/m/Xpa−/− mice appeared not to originate from a pituitary dysfunction as histological examination ( Figure 3F ) and TUNEL staining of sections from the pituitary pars distalis , intermedia , and nervosa did not reveal any abnormalities ( unpublished data ) . Moreover , serum GH levels in 15-d-old Csbm/m/Xpa−/− mice ( 15 . 2 ± 4 . 2 ng/ml , n = 8 ) did not differ significantly from wt littermates ( 12 . 8 ± 2 . 8 ng/ml , n = 6 ) . Interestingly , the normal serum GH levels together with the significant systemic down regulation of GH receptor gene expression likely point to growth hormone resistance in 15-d-old Csbm/m/Xpa−/− mice . Periodic acid-Schiff ( PAS ) staining of liver sections from 10- to 20-d-old pups and naturally aged mice revealed enhanced accumulation of glycogen in unusually large vesicles in Csbm/m/Xpa−/− pups and 96-wk-old mice when compared to wt littermates and 8-wk-old wt mice ( Figure 7C ) . This observation fits our microarray data , suggesting that both the Csbm/m/Xpa−/− and naturally aged mice store rather than use glucose . Overnight fasting of Csbm/m/Xpa−/− pups and littermate controls resulted in a near-to-complete depletion of liver glycogen ( Figure 7D ) , indicating that the glycogen accumulation is not due to inability to split glycogen into its constitutive glucose monomers . Consistent with the broad up-regulation of genes associated with fatty acid synthesis ( Table 1 ) , Oil Red O staining of liver sections from 15-d-old pups and naturally aged mice revealed enhanced accumulation of triacylglycerides in both compared to control littermates and 8-wk-old mice ( Figure 7C ) , indicating hepatic steatosis . This and the absence of adipose tissue suggest that Csbm/m/Xpa−/− mice display generalized lipodystrophy ( loss and abnormal redistribution of body fat ) [31] . To test whether the presence of endogenous ( oxidative ) DNA damage can provoke the somatotrophic drop and enhanced antioxidant potential , wt C57BL/6J mice ( n = 6; 4-wk-old ) were fed ad libitum for 9 wk with standard food containing subtoxic levels of an oxidative DNA damage-inducing agent [di ( 2-ethylhexyl ) phthalate ( DEHP ) , 1500 ppm] [32] . Neither body weight nor appetite and food intake of DEHP-exposed animals deviated from that of untreated control animals . As shown in Figure 8 , subsequent analysis revealed suppression of the expression of genes associated with the somatotroph axis ( Igf1 , Igfbp3 , Ghr , and Dio1 ) and oxidative metabolism ( Gck , Cs , and Ndufs8 ) , along with the up-regulation of glycogenin 1 ( Gyg1 , Figure 5A ) in DEHP-exposed animals . Consistent with the ability of DEHP to generate ROS-induced DNA damage in the liver , we also noticed a significant up-regulation of genes associated with the antioxidant and detoxification responses ( Hmox1 , Ephx1 , Gsr , Sod1 , Gstt2 ) . These findings suggest that the accumulation of unrepaired ( oxidative ) DNA damage is likely one of the causes underlying the observed suppression of the GH/IGF1 and oxidative metabolism in Csbm/m/Xpa−/− mice ( Text S1 ) .
Csbm/m mice exhibit several CS features ( e . g . , attenuated growth , blindness , neurological dysfunction ) , but their phenotype is overall milder than the human syndrome [16] despite the fact that the truncation in the N-terminal part ( mimicking a mutant allele of CS-B patient CS1AN ) completely inactivates the protein and TC-NER [16] . Although the severity of clinical features in humans does not seem to correlate with the severity of the molecular defect [33] , the absence of the complete spectrum of CS features in the Csbm/m mouse model is likely to originate from human-mouse differences ( i . e . , adaptation to stress , tolerance to DNA damage/genome instability ) , rather than from the nature of the Csbm/m mutation . This idea is supported by our observations that XpdTTD and XpdXPCS mice ( all carrying causative point mutations ) also fail to show the severe CS features associated with XPCS and TTD [9 , 34] . Yet , the present study reveals that inactivation of GG-NER or complete abrogation of NER ( through inactivation of Xpc or Xpa , respectively ) in TCR-deficient Csbm/m mice dramatically aggravates the Csbm/m mouse phenotype . Because animals were not exposed to exogenous genotoxic agents , we attribute this effect to enhanced levels of unrepaired endogenous ( oxidative ) DNA damage . In further support of this , we have shown that Csbm/m/Xpc−/− and Csbm/m/Xpa−/− MEFs , as well as Csbm/m/Xpa−/− retinal photoreceptor cells , are more sensitive to environmental genotoxic insults ( i . e . , UV light and ionizing radiation ) than their single mutant counterparts . A comparable phenotypic deterioration has been noticed when Xpa was inactivated in XpdTTD [9] , XpdXPCS [34] , compound heterozygous XpdTTD/XPCS animals ( carrying causative mutations for TTD and combined XP/CS ) [35] or XpgdeltaEx15 mice [36] . Importantly , Csbm/m/Xpa−/− mice appeared normal at birth , indicating a normal intra-uterine development and ruling out that this condition is in fact an embryonic developmental disorder . Instead , after birth , the Csbm/m/Xpa−/− pups displayed progressive kyphosis , cachexia , photoreceptor loss , and motor dysfunction , all common postnatal manifestations of CS [13] , as well as of natural mammalian ageing [37–39] . Also similar to human CS patients ( average age 12 . 5 y ) , Csbm/m/Xpa−/− pups fail to grow into adulthood and die before weaning . The relation between ( residual ) repair capacity , time , and severity of a particular phenotype is well illustrated by the retinal photoreceptor loss in the Csbm/m mouse models . Whereas ageing C57Bl/6J mice lose about 5%–10% of their rods and cones in 30 months , TCR-deficient Csbm/m mice have already lost about 50% of their photoreceptor cells by the age of 16 months . This spontaneous retinal degeneration in Csbm/m mice originates from enhanced apoptotic sensitivity of photoreceptor cells ( T . Gorgels , I . van der Pluijm , R . Brandt , G . Garinis , H . van Steeg , et al , unpublished data ) , evolving in the first 1 or 2 months after weaning . Interestingly , further crippling of NER in Csbm/m animals by inactivation of Xpa accelerates the onset of photoreceptor loss , which becomes visible as early as postnatal day 15 , and progressively increases thereafter . The strong correlation between the severity of the repair deficiency and the onset of photoreceptor loss , as well as the enhanced ionizing radiation hypersensitivity of photoreceptor cells of Csbm/m/Xpa−/− mice ( as compared to age-matched Csbm/m animals ) , well support the hypothesis that ( oxidative ) DNA damage likely underlies the retinal degeneration . Full genome transcriptome analysis of the Csbm/m/Xpa−/− mouse liver , aiming at unraveling the etiology of the severe double-mutant phenotype , led us to identify significant genome-wide parallels between the 2-wk-old Csbm/m/Xpa−/− and 130-wk , but not 16-wk-old , wt animals at the fundamental level of gene expression . This resemblance was largely attributable to the substantial down-regulation of genes associated with processes implicated in oxidative energy and growth metabolism that were previously revealed by others to represent a conserved transcriptional response in ageing [28] . The down-regulation of genes associated with the GH/IGF1 growth axis in the liver , the systemic reduction in GH receptor mRNA levels , and the impaired Igf1 gene expression in liver and other tissues ( resulting in low serum IGF1 levels ) likely underlie the postnatal growth defect in Csbm/m/Xpa−/− pups . These changes were not due to reduced GH serum levels or pituitary abnormalities . A steady decline in the GH/IGF1 somatotroph axis was also observed in rodents and humans during natural ageing [40] . Furthermore , Csbm/m/Xpa−/− pups failed to up-regulate metabolism; instead , they displayed a sharp systemic reduction in the expression levels of genes involved in glycolysis , tricarboxylic acid cycle ( including decreased citrate synthase activity ) , and oxidative respiration , which coincided with the onset of weight loss ( cachexia ) . In addition , Csbm/m/Xpa−/− pups up-regulated genes associated with glycogen and fatty acid synthesis , leading to increased hepatic glycogen storage and fat accumulation ( steatosis ) and pronounced hypoglycemia . Simultaneously , subcutaneous fat tissue was virtually absent . Given that in mammals , the GH/IGF1 signaling pathway is one of the major regulators of energy homeostasis to integrate metabolism with growth [30 , 41 , 42] , it is tempting to speculate that reduced IGF1 signaling is responsible for the postnatal metabolic shift and growth defect seen in Csbm/m/Xpa−/− mice . Interestingly , several CS patients have been previously reported with hypoglycemia and low IGF1 serum levels [43 , 44] , low metabolic rate [45] , and abnormal fat deposition [46] . Paradoxically , however , the systemic suppression of the somatotrophic axis and energy metabolism , along with the up-regulation of antioxidant defenses , low IGF1 serum levels , and low blood glucose levels observed in the Csbm/m/Xpa−/− mouse , are all associated with increased longevity rather than with the short lifespan of this mouse model . In lower paradigms for lifespan extension ( C . elegans , D . melanogaster ) , genetic interference in the insulin-signaling pathway can prolong life multi-fold [47 , 48] . In mammals , IGF1-deficient , Ames and Snell dwarf mice ( characterized by defects in the development of the anterior pituitary due to mutations in the Prop-1 and Pit1 loci and diminished levels of GH , thyroid stimulating hormone , and prolactin hormone ) combine hypoglycemia , low body temperature , and increased storage of carbohydrates and lipids [40 , 42] with up-regulation of antioxidant defense capacity and extended lifespan [49 , 50] . Conversely , GH-overexpressing transgenic mice display reduced lifespan and antioxidant responses [51] . These findings have also been recently confirmed by our identification of genome-wide parallels between the extremely short-lived DNA repair mutants ( Csbm/m/Xpa−/− and Ercc1−/− ) and the extremely long-lived Ames and Snell dwarfs and growth hormone receptor knockout ( Ghr−/− ) mice ( Garinis et al . manuscript in preparation ) . Last but not least , IGF1 plasma levels decline with age in humans and rodents [52–54] . Along with this hormonal shift , ageing cells surmount an intricate antioxidant defense response [55 , 56] that is thought to prevent the detrimental consequences of oxidative stress . Interestingly , the progressive , age-related decrease in the somatotroph axis has been suggested to confer a selective advantage by postponing the onset of age-related disease and prolonging lifespan through the reduction of toxic free radicals [40] . How would repair-deficient mice benefit from such a response ? During development , the mitogenic action of GH and IGF1 fuels cellular metabolism , thereby promoting tissue growth and function [40 , 57 , 58] . A high metabolic activity , however , leads to higher oxygen consumption [40] and may also increase the ROS burden through the parallel increase of mitochondrial electron transport , peroxisomal fatty acid metabolism , and/or microsomal cytochrome P-450 enzymes [59] . Despite antioxidant defense and DNA repair , oxidative DNA damage will still accumulate , leading to transcriptional stress , impaired replication , cellular senescence , malfunction or death and eventually to progressive loss of tissue homeostasis and organismal decline ( Figure 9 , model ) . We hypothesize that complete abrogation of NER ( by inactivation of Xpa ) renders TCR-deficient Csbm/m mice unable to adequately cope with the increased burden of DNA damage in the transcribed strand of active genes . This triggers an adaptive response; i . e . , reduction of metabolic activity through down-regulation of the GH/IGF1 axis to relieve the pressure on their genome . We interpret this as an attempt to limit the deleterious effects of arrested transcription , such as cellular senescence and death causing accelerated ageing . As a consequence , the initially normal growth becomes arrested soon after birth , leading to severe growth retardation . This scenario provides a plausible explanation for the growth defect in CS patients . However , this response is unable to fully compensate for the repair defect , and thus damage still accumulates to critical levels and triggers apoptosis and/or senescence , thereby leading to ageing-associated pathology such as neurodegeneration ( as illustrated by the photoreceptor cells in Csbm/m/Xpa−/− mice ) . The conceptual link between DNA damage and the systemic adaptive response is supported by our observation that chronic exposure of wt mice to a sub-toxic dose of DEHP ( a pro-oxidant that enhances the DNA damage load; [60] ) triggers a response similar to that observed in ( untreated ) Csbm/m/Xpa−/− mice . Although DEHP at much higher concentrations has been previously documented to affect the endocrine function of the pituitary , proteome analysis revealed that synthesis of prolactin and growth hormone appears unaffected in DEHP-treated rats [61] . This suggests that the observed suppression of genes associated with the somatotroph axis and oxidative metabolism in the liver of DEHP-exposed mice is triggered by DNA damage in the liver , rather than by a pituitary defect or hypothalamic defect . As one would predict , other short-lived NER mouse models ( e . g . , Xpg and Xpf mice [62 , 63] ) or NER mutant mice with a milder progeroid phenotype could also show accelerated attenuation of the somatotrophic axis in response to their DNA repair defect . Indeed , Ercc1−/− animals , carrying a combined NER/crosslink DNA repair defect and a lifespan of only a few weeks , demonstrate a remarkable genome-wide similarity in liver gene expression profiles with Csbm/m/Xpa−/− mice ( L . Niedernhofer , G . Garinis , A . Raams , A . Lalai , A . Rasile Robinson , et al . , unpublished data ) , whereas XpdXPCS/Xpa−/− and compound heterozygous XpdTTD/XPCS/Xpa−/− mice contain lower serum IGF1 levels [35] . Furthermore , XpdTTD mice , which manifest accelerated ageing in many ( but not all ) organs and tissues , have recently been shown to display features related to a caloric restricted–like phenotype and suppression of the GH/IGF1 axis in a limited set of organs and tissues , stressing the segmental nature that is characteristic of all progeroid syndromes and the systemic nature of the response [64] . Finally , proper glucose homeostasis and normal IGF1 levels were recently shown to require activity of Sirt6 , a chromatin deacetylase that may promote DNA repair [65] . Because ROS-mediated DNA damage appears to be the underlying cause of the Csbm/m/Xpa−/− progeria , it is tempting to speculate that one can attenuate the premature onset of age-related features by directly counteracting the harmful byproducts of metabolism ( ROS ) and , consequently , DNA damage . An antioxidant-based nutraceutical intervention pilot study with Csbm/m/Xpa−/− mice , aiming at extending lifespan and delaying onset of pathology , yielded promising results ( I . van der Pluijm , R . Brandt , J . Hoeijmakers , G . van der Horst , unpublished data ) .
The generation and characterization of NER-deficient Xpa−/− , Xpc−/− , and Csbm/m mice has been previously described [16 , 22 , 66 , 67] . p53 −/− mice [68] were kindly provided by T . Jacks ( Massachusetts Institute of Technology , Cambridge , Massachusetts , United States ) . Unless stated otherwise , all mice were kept in a C57BL/6J genetic background . In the DEHP exposure study , 4-wk-old male wt mice ( C57BL/6J; n = 6 ) were put on a DEHP ( 1500 ppm; Sigma , St . Louis , Missouri , United States ) containing diet or on a regular diet for 9 wk . Animals were screened daily for discomfort and weighed once a week . Food consumption was registered by weighing the food . In the ionizing irradiation exposure study , 18-d-old Csbm/m/Xpa−/− and littermate control animals ( n = 4–6/genotype ) were exposed to 10 Gy , killed 20 h after exposure , and their eyes were further processed . Additional information on the isolation and processing of the eyes is provided in the Text S1 . As required by Dutch law , all animal studies were approved by an independent Animal Ethical Committee ( Dutch equivalent of the Institutional Animal Care and Use Committee ) . Further information on mouse crossing , genotyping , housing , and macroscopic examination is described in the Text S1 . UV sensitivity was determined as described [69] . Sparsely seeded Petri dish cultures were exposed to different doses of UV ( 254 nm , Phillips TUV lamp ) . After 4 d , the number of proliferating cells was estimated from the amount of radioactivity incorporated during a 2-hr pulse with [3H] thymidine . Cell survival was expressed as the percentage of radioactivity in exposed cells in relation to the radioactivity in untreated cells . UV-induced GG repair was assayed using the unscheduled DNA synthesis ( UDS ) method described in [70] . In brief , coverslip-grown cells were exposed to 16 J/m2 of 254-nm UV light and labeled with [3H] thymidine . Repair capacity was quantified by grain counting after autoradiography . RNA synthesis recovery was measured according to [71] . In short , coverslip-grown cells were exposed to 10 J/m2 of 254-nm UV light , allowed to recover for 16 hr , labeled with [3H] uridine , and processed for autoradiography . The relative rate of RNA synthesis was expressed as GUV/GC ( percentage ) , where GUV and GC represent the number of grains over UV-exposed and nonexposed nuclei , respectively . Ionizing radiation sensitivity of immortalized MEFs was determined using a colony assay . Cells were plated in 6-cm-diameter dishes at various densities . After 16 h , cells were exposed to a single dose of ionizing radiation ( 137Cs source; dose range of 0 to 8 Gy . Cells were grown for another 5 to 14 d , and after fixation and staining , colonies were counted . All experiments were performed in triplicate . Detailed histopathological examination was performed on all organs and tissues . Paraffin-embedded tissues were sectioned at 5 μm and stained with haematoxylin/eosin ( HE ) solution . Liver sections were stained with PAS or Oil Red O ( cryosections ) to detect glycogen and triglycerides , respectively . Detailed information on the immunohistochemical procedures is described in Text S1 . Apoptotic cells were detected using a TUNEL assay as described by the manufacturer ( Apoptag Plus Peroxidase In Situ Apoptosis Detection Kit , Chemicon , Temecula , California , United States ) . For retinal evaluation , eyes were marked nasally with Alcian blue ( 5% Alcian blue in 96% ethanol ) , enucleated , fixed in 4 % paraformaldehyde in 0 . 1 M phosphate buffer , washed in PBS , and embedded in paraffin . Horizontal sections ( 5 μm thick ) of the retina were cut , and sections in the middle of the retina were selected by Alcian blue marking and proximity of the optic nerve . Sections were stained for degenerating cells by TUNEL according to the manufacturer's instructions ( Apoptag Plus Peroxidase In Situ Apoptosis Detection Kit , Chemicon ) . For quantification , the number of TUNEL-positive cells in the INL and ONL were counted in six whole sections per mouse . Differences between the genotypes were tested for statistical significance using multivariate ANOVA , followed by a posthoc test of Student-Newman-Keuls ( S-N-K ) . Significance was set at p < 0 . 05 . Serum IGF1 and GH levels were determined with the active mouse/rat IGF1 enzyme-linked immunosorbent assay ( ELISA ) and active mouse/rat GH ELISA kits respectively , as described by the manufacturer ( Diagnostic Systems Laboratories Inc . , Texas , United States ) . Blood glucose was measured using a Freestyle mini blood glucose measurement device ( Abbott Diabetes Care , Amersfoort , The Netherlands ) . Mice were anaesthetized by intraperitoneal injection of ketalin and rompun ( 120 and 7 . 5 μg/g body weight , respectively ) . Lateral films were taken at 2× magnification using a CGR Senograph 500T x-ray system operated at 30 kV and 32 mAS [9] . Formalin fixed tibiae from wt and mutant mice were scanned from proximal end to mid-diaphysis , using a SkyScan 1072 microtomograph ( SkyScan , Antwerp , Belgium ) with a voxel size of 8 . 82 μm . Scans were processed , and 2-D images of the bones were obtained . Footprint analysis was performed by painting the hind and fore paws of the mice with different colors of water-soluble nontoxic paints . Animals were allowed to walk along a 30 × 7 cm walled runway , lined with paper , into a darkened , enclosed space . Tests were performed in duplicate at day 15 and 19 . Footprint patterns were analyzed for ( 1 ) stride length , measured as the average distance between each stride; ( 2 ) front base width; and ( 3 ) hind base width , measured as the average distance between contralateral footprints [72] . Standard procedures were used to obtain total RNA ( Qiagen , Valencia , California , United States ) from the liver of wt , Xpa−/− , Csbm/m , and Csbm/m/Xpa−/− mice ( n = 4 ) at postnatal day 15 and from the liver of 8- , 16- , 96- , and 130-wk-old mice ( n = 4 ) . Synthesis of double stranded cDNA and biotin-labeled cRNA was performed according to the instructions of the manufacturer ( Affymetrix , Santa Clara , California , United States ) . Fragmented cRNA preparations were hybridized to full mouse genome oligonucleotide arrays ( 430 V2 . 0; Affymetrix ) . Q-PCR was performed with a DNA Engine Opticon device ( Bio-Rad Laboratories B . V . , Veenendaal , The Netherlands ) . Detailed information on microarray hybridization , microarray data analysis , gene ontology classification , and analysis of overrepresented biological themes , as well as on Q-PCR data analysis and used primer pair sequences is described in Text S1 . Microarrays were complied with the Minimum Information for Microarray Experiments ( MIAME , E-MEXP-835 and E-MEXP-839 ) .
|
Normal metabolism routinely produces reactive oxygen species that damage DNA and other cellular components and is thought to contribute to the ageing process . Although DNA damage is typically kept in check by a variety of enzymes , several premature ageing disorders result from failure to remove damage from active genes . Patients with Cockayne syndrome ( CS ) , a genetic mutation affecting one class of DNA repair enzymes , display severe growth retardation , neurological symptoms , and signs of premature ageing followed by an early death . Whereas mouse models for CS exhibit relatively mild deficits , we show that concomitant inactivation of a second DNA repair gene elicits severe CS pathology and ageing . Moreover , a few days after birth , these mice undergo systemic suppression of genes controlling growth , an unexpected decrease in oxidative metabolism , and an increased antioxidant response . Similar physiological changes are also triggered in normal mice by chronic exposure to DNA-damaging oxidative stress . From these findings , we conclude that DNA damage triggers a response aimed at limiting oxidative DNA damage levels ( and associated tissue degeneration ) to extend lifespan and promote healthy ageing . Better understanding of the ageing process will help to delineate intervention strategies to combat age-associated pathology .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"geriatrics",
"mammals",
"diabetes",
"and",
"endocrinology",
"computational",
"biology",
"mus",
"(mouse)",
"genetics",
"and",
"genomics"
] |
2007
|
Impaired Genome Maintenance Suppresses the Growth
Hormone–Insulin-Like Growth Factor 1 Axis in Mice with Cockayne Syndrome
|
Plants produce cytokinin ( CK ) hormones for controlling key developmental processes like source/sink distribution , cell division or programmed cell-death . Some plant pathogens have been shown to produce CKs but the function of this mimicry production by non-tumor inducing pathogens , has yet to be established . Here we identify a gene required for CK biosynthesis , CKS1 , in the rice blast fungus Magnaporthe oryzae . The fungal-secreted CKs are likely perceived by the plant during infection since the transcriptional regulation of rice CK-responsive genes is altered in plants infected by the mutants in which CKS1 gene was deleted . Although cks1 mutants showed normal in vitro growth and development , they were severely affected for in planta growth and virulence . Moreover , we showed that the cks1 mutant triggered enhanced induction of plant defenses as manifested by an elevated oxidative burst and expression of defense-related markers . In addition , the contents of sugars and key amino acids for fungal growth were altered in and around the infection site by the cks1 mutant in a different manner than by the control strain . These results suggest that fungal-derived CKs are key effectors required for dampening host defenses and affecting sugar and amino acid distribution in and around the infection site .
Plant pathogens have evolved sophisticated strategies to manipulate host biological processes during infection [1 , 2] . ( Hemi ) biotrophic pathogens produce and secrete effector proteins and metabolites to hijack cellular metabolism of the infected tissues to their own benefit . For instance , the bacterial pathogen Xanthomonas oryzae pv . oryzae produces Transcription Activator Like effectors that specifically induce the expression of genes coding for sugar transporters and thus enhance bacterial nutrition [3] . The virulence factors can also participate to the inhibition of plant defenses that lead to cell death , thus contributing to maintaining infected cells alive [4] . Plant-pathogen interactions shaped the pathogen virulence arsenal and the host immune response system . A first layer of plant defenses is induced by the perception of pathogen- or microbe-associated molecular patterns , like flagellin from bacteria or chitin from fungi [5] . These basal defense responses consist of an early accumulation of reactive oxygen species ( ROS ) , a thickening of the cell wall , and production of metabolites/enzymes with antimicrobial activities . To limit these defenses triggered by chitin perception by the plant’s chitin receptor , fungal pathogens like Magnaporthe oryzae secrete chitin-binding effectors that enable escape from the host recognition system [6] . Pathogens also interfere with other steps of plant immunity like signaling cascades following recognition and transcriptional regulators of host defenses [2 , 7] . Plant pathogens manipulate components of hormonal pathways , whether the corresponding hormones are involved in disease resistance ( i . e . salicylic acid , jasmonic acid and ethylene [8] ) or in the control of plant developmental processes ( i . e . auxin , cytokinins and gibberellic acid [8–11] ) . Pathogens can affect hormonal homeostasis by targeting/secreting enzymes involved in hormone metabolism or by producing hormonal/mimicking compounds and thereby inhibit defenses , modify nutrient flows and/or induce symptom development . For instance , the fungal pathogens Ustilago maydis and Magnaporthe oryzae secrete respectively a chorismate mutase and a monooxygenase affecting salicylic acid or jasmonic acid homeostasis during infection and then contributing to their virulence [12 , 13] . Plant pathogens can also directly produce hormones or compounds with similar biological activities . The bacterial pathogen Pseudomonas syringae produces coronatine to mimic jasmonic acid , which actively opens stomata and counteracts salicylic acid accumulation [14 , 15] . As a consequence , these combined effects on several processes required for infection facilitate host invasion . In the case of pathogenic fungi , there is , however , no direct evidence that hormonal compounds produced are required for virulence . Fungi and bacteria produce hormonal compounds that are chemically identical or very close to plant hormones involved in plant developmental processes such as cytokinins ( CKs ) [16–22] . CKs are adenine derivatives that differ in their side chains ( aromatic or isoprenoid ) . In plants , isoprenoid CKs are mainly synthesized through a de novo biosynthesis pathway from adenosine phosphate . In this pathway , Isopentenyl transferase ( IPT ) enzymes perform the first step of biosynthesis . However , another minor CK biosynthesis pathway involving tRNA modification is also described in plants and yeast . This minor pathway requires tRNA-Isopentenyl Transferases ( tRNA-IPT ) , enzymes that perform tRNA modification leading to CK production after tRNA degradation [23–25] . In plants , CKs were originally studied for their effects on cell division/differentiation [26] . CKs also modulate nutritional source/sink distribution and programmed cell-death processes like xylem differentiation , senescence and hypersensitive response [27–30] . Tumor-forming pathogens are striking examples of microbes which are able to produce CKs , like the fungi U . maydis [31] and Claviceps purpurea [32] or the protist Plasmodiophora brassicae [33] . Morrison et al . , ( 2015a ) [31] have recently shown that CK accumulation in U . maydis infected tissues is correlated to the virulence of this pathogen . However , in this latter case the evidence that these hormones are produced by the pathogen is still unclear . Likewise , the pathogenic bacterium Rhodococcus fascians secretes CKs to interfere with host CK signaling pathways by affecting the transcriptional regulation of key cell cycle genes leading to tumor development [34] . In addition to having potential roles in the virulence of tumor-inducing pathogens , CKs have long been suspected to participate to virulence of pathogens that do not trigger tumors or organ deformations . However , it has not been demonstrated that the CKs produced by this type of pathogens are key virulence factors . Because CKs delay plant senescence by limiting oxidative burst and maintaining photosynthesis activity [35] , they are at the cross-road of several pathways of interest for manipulation by pathogens that need to keep host cells alive in order to drain nutrients for their own growth . Consistent with this role , CKs are accumulated in “green islands” , which are tissues corresponding to photosynthetically active zones maintained around pathogenic lesions caused by ( hemi ) biotrophic pathogens [36–39] . CK compounds have been measured in several different fungal species ( even in non-plant interacting ones [40] ) and CK secretion was mostly observed in the case of ( hemi ) biotrophic microbes [32 , 41] . For instance , Hu & Rijkenberg ( 1998 ) immuno-detected CKs in and around fungal hyphae of Puccinia recondita f . sp . tritici during wheat infection [42] . The hemibiotrophic fungus Magnaporthe oryzae responsible for the rice blast disease , also produces CKs in vitro [43] but the CK biosynthesis pathway in this fungus has not been established . Moreover , the role of CKs in the virulence of filamentous fungi that , like Magnaporthe , do not form tumors has never been demonstrated . In this study we identified a gene coding for a putative tRNA-Isopentenyl Transferase ( tRNA-IPT ) in the genome of M . oryzae and in all Ascomycete genomes tested . Mutants deleted for this gene , named Cytokinin Synthesis 1 ( CKS1 ) , were generated and were found to be impaired in CK production confirming that the tRNA degradation pathway is involved in fungal CK production in Magnaporthe as was suggested for other fungi by Morrison et al . , ( 2015b ) [40] . Deleted cks1 mutants were not affected in their in vitro growth or asexual development in standard minimal conditions however they showed severely reduced virulence on rice . Remarkably , the CK-deficient mutant was not able to maintain the levels of several key nutrients at the infection site and induced early and strong plant defenses , suggesting that fungal CKs may contribute to metabolite mobilization and to rice defense inhibition . Our work confirms the view that , in M . oryzae , and possibly in many other fungi , this putative tRNA-IPT , contributes to CK production . Since CKS1 is required for the full virulence of Magnaporthe , we propose that CKs from fungal pathogens could act as effectors combining functions in defense inhibition and nutrient mobilization .
To investigate the role of CKs in the virulence of the blast fungus M . oryzae , homologs of plant and yeast genes involved in CK metabolism or signaling were searched in the M . oryzae genome using BLASTp . Potential orthologous genes of some important CK biosynthesis , degradation and signaling component coding genes were identified ( S1 Table ) , further extending previous reports that the CK signaling and metabolic pathways are present in M . oryzae . As previously mentioned , independently of the de novo biosynthesis pathway , CKs can be also produced through tRNA degradation . In this second pathway the first step of CK production involves tRNA-Isopentenyl transferases ( tRNA-IPT ) , as previously described in A . thaliana ( AtIPT2 and AtIPT9 ) and Saccharomyces cerevisiae ( MOD5 ) [23–25 , 44 , 45] . A putative tRNA-IPT protein , coded by MGG_04857 , was identified in M . oryzae which shared 36% identity with AtIPT2 or MOD5 and the sequences of the different known interaction sites were highly conserved ( S1A Fig ) . No other gene containing the coding sequence of the IPT domain was identified , suggesting that there is only one orthologous gene in M . oryzae . We modeled the three-dimensional structure of MGG_04857 ( S1B Fig ) by protein threading , a method based on fold recognition [46] and using the experimentally determined MOD5 structure as a template . The model produced suggested a conserved structure , but also confirmed a high conservation of primary sequences of interaction sites i . e . the ATP , DMAPP as well as tRNA binding sites ( S1B Fig [47 , 48] ) and indicated that MGG_04857 shows all the features of a bona fide tRNA-IPT . Taken together , these results support the hypothesis that the blast fungus can produce CKs [43] , potentially perceive them , and suggest that MGG_04857 could act as a key enzyme in fungal CK production . To test the involvement of the putative tRNA-IPT coded by the MGG_04857 gene in fungal CK production , knock-out mutants of this gene , later called cks1 ( see below ) , were generated by homologous recombination in the M . oryzae GY11 genetic background ( S2 Fig ) . In addition , a complemented strain ( cks1CKS1 ) was built by transformation of the cks1 mutant strain with a construct carrying the genomic sequence of MGG_04857 under control of its own promoter . Gene disruption and complementation were confirmed by PCR on genomic DNA and qRT-PCR and showed that the expression of MGG_04857 was similar between wild type and cks1CKS1 isolates , but not detected in the cks1 mutants ( S2E Fig ) . Since yeast MOD5 mutants are known to display altered phenotypes [49] , we measured the growth of cks1 mutants under normal and environmental stress . The in vitro growth of the cks1 mutant was not different from the wild type or cks1CKS1 strains under standard minimal growth conditions ( S3A Fig ) . Moreover , the development of infection structures like the appressorium was not impaired by the cks1 mutation on glass slides ( S3B Fig ) or on rice leaf surface ( S3C Fig ) . This suggests that the early steps of fungal growth are not significantly modified by the cks1 mutation . By contrast , the cks1 mutant showed slight but significant and reproducible reduced growth when grown under 1mM H2O2 ( S4A Fig ) . This phenotype could be complemented by the addition of the CK kinetin to the medium ( S4B Fig ) , suggesting that this defect could be related to a reduced CK production due to the MGG_04857 deletion . To investigate the role of MGG_04857 in CK production , CK levels were determined by liquid chromatography-positive electrospray ionization tandem mass spectrometry ( LC ( ESI+ ) -MS/MS ) [19 , 50] both in the culture supernatant and mycelia of the different strains ( Table 1 ) . Four major isoprenoid CKs , cisZR ( cis-zeatin riboside ) , iPR ( isopentenyl adenosine ) , cisZNT ( cis-zeatin nucleotide ) and iPNT ( isopentenyladenine nucleotide ) were detected in mycelia and supernatant of the wild type GY11 . The riboside forms , cisZR and iPR , were the most abundant CKs in mycelia while the nucleotide precursor forms , cisZNT and iPNT , were the main secreted ones . The other CK forms , trans-zeatin and dihydrozeatin were not detected . Thus , in the minimal medium , cisZR and cisZNT are the major CKs produced and secreted by the wild-type strain . This is consistent with CK measurements made on several other fungi where cis-zeatin forms were the main CKs detected [40] . The cks1 mutant was not able to produce and/or secrete any detectable CKs whereas the complemented strain , cks1CKS1 , produced CKs at similar levels as the wild type strain GY11 did . Thus , MGG_04857 appears to be required for CK biosynthesis in the rice blast fungus ( therefore called CKS1 for Cytokinin Synthesis 1 ) and is likely coding for an active tRNA-IPT protein , although this activity needs to be established . To test for involvement of fungal CKs in the interaction between rice and the blast fungus , we inoculated mutant and control strains on the susceptible rice cultivar Nipponbare . The cks1 mutant strain was less virulent than cks1CKS1 or GY11 wild-type strains as shown by a reduction of disease symptoms ( Fig 1A ) . The CK-deficient strain produced less grayish , sporulating lesions per leaf ( Fig 1B ) and these lesions were smaller than those caused by control strains ( Fig 1C ) . These results suggest that leaf penetration ( reflected by lesion number ) and invasion ( reflected by lesion size ) of the CK-deficient strain are both impaired , although not completely abolished . Impaired penetration was confirmed by microscopic observation of individual interaction sites which showed that cks1 failed to penetrate as efficiently as cks1CKS1 during the early times of infection ( <24 h , Fig 1D ) . This is likely due to defective steps after appressorium formation since this developmental stage was unaffected by the cks1 mutation ( S3C Fig ) . As a test for biological activity of fungal-produced CKs , we performed qRT-PCR and quantified the expression of rice Response Regulator ( RR ) genes OsRR6 and OsRR1 ( Fig 2 ) that were previously described to be transcriptionally regulated by CKs [51 , 52] and can thus be used as CK bio-sensors [43] . During early contact of fungal conidial spores with the plant surface ( 2h , 4h , 6h ) , and at a stage where all strains showed similar growth in vitro or on the plant surface ( S3C Fig ) , the two CK response markers tested had significantly lower expression in plants inoculated with cks1 than with cks1CKS1 strains . Similar results were found for all other RR genes tested ( S5A Fig ) . This result supports the hypothesis that fungal CKs affect host CK signaling pathway and is consistent with the report made by Jiang et al . , ( 2013 ) [43] that conidia , the first M . oryzae cells in contact with the host , can produce CKs . To test if CKs could restore the virulence of the mutant strain , we exogenously applied the CK kinetin at 24h after inoculation ( Fig 3 ) since the delay of penetration of the mutant is noticed at this time ( Fig 1D ) . Kinetin was applied in the same conditions as previously described [43] . Kinetin treatment fully restored the virulence of cks1 since the number and the size of lesions caused by cks1 were similar to those caused by the cks1CKS1 complemented strain ( Fig 3 ) . Similar results were obtained with an exogenous application of cis-zeatin ( S6 Fig ) , which is the major cytokinin produced by M . oryzae ( 92% of CKs in supernatant; Table 1 ) . These data strongly support the hypothesis that the reduced virulence of the cks1 mutant is directly due to a defect in CK production . To evaluate the effect of fungal-derived CKs on early host basal defense responses , we measured ROS accumulation . Compared to cks1CKS1 , the accumulation of H2O2 ( as revealed by DAB staining ) was much more pronounced in response to the cks1 mutant ( Fig 4A ) and extended well beyond penetration sites ( punctuated brown spots in complemented mutant controls ) since the DAB staining was visible throughout all leaf tissues . In order to test if the cks1 mutant was able to infect the host more efficiently if ROS production was impaired , we treated inoculated plants with DPI ( Diphenylene Iodonium ) , an inhibitor of flavor-enzymes like the NAD ( P ) H oxidase involved in H2O2 production [55] . The virulence of cks1 mutant was partially restored when plants were treated with DPI ( Fig 4B and 4C ) . This suggests that the capacity of the cks1 mutant to invade the host cell is restored , although only partially , when ROS production is reduced . We measured the expression of some well-established rice defense-marker genes during infection with the different strains . It first appeared that the defense-marker genes were differentially expressed earlier ( 2 to 6 hpi; Fig 5A and S5B Fig ) in plants infected with cks1—before fungal penetration that mostly occurs after 24h ( Fig 1D ) . After penetration of the fungus ( >24 hpi ) and very strikingly , many defense-markers tested also showed a stronger induction in plants infected with cks1 as compared to those infected with the control cks1CKS1 ( e . g . CHI , PBZ1 , PR10 and PR5 in Fig 5B ) . In the next step , we tested whether this over-induction of defense-markers by cks1 was affected when the cks1 virulence was complemented by exogenous application of the CK kinetin . This was the case for most genes that initially showed an over-induction ( e . g . CHI , PBZ1 and PR5; Fig 5B ) . It indicates that complementation by exogenous cytokinin treatment reverts penetration and growth of the fungus as well as plant it brings back defense-marker expression to normal levels . Altogether these results support the hypothesis that the loss of virulence and the over-induction of defense associated with the cks1 mutation are linked to an absence of CK production by the mutant strain . The enhanced oxidative burst and defense-genes expression is consistent with the reduced virulence phenotype observed with the cks1 mutant . This suggests that fungal CKs normally produced by M . oryzae may inhibit key plant defense reactions . To further support this hypothesis , we tested the capacity of the cks1 isolate to infect rice mutants which are defective for the chitin receptor , CEBiP , and the master transcriptional regulator NH1 that are both known to be immuno-depressed [57 , 59] . The fungal cks1 mutant was more virulent on cebip and nh1 rice mutants than on the wild-type plants ( Fig 6 ) . The restoration of virulence of the cks1 mutant on rice mutant plants impaired for defense responses strongly supports the hypothesis that the cks1 mutants have the capacity to infect rice as long as defenses are inhibited . CKs affect different key metabolic processes in plants , like Calvin-Benson or tricarboxylic acid cycles and mediate source/sink modifications [29] . Therefore , we hypothesized that the lack of virulence of the CK-deficient strain could be also due to a reduced capacity to exploit or drain nutrient resources . Under this hypothesis , we reasoned that high levels of fertilization could enhance cks1 virulence . Indeed high fertilization levels were previously shown to increase amino-acid and sugar contents in rice leaves [60] as well as rice blast susceptibility [61] . The protocol described in [61] was used to fertilize plants 24h before inoculation with cks1 ( Fig 7 ) . Under high fertilization regime , plants were more susceptible to the cks1 mutant ( Fig 7A ) , supporting the hypothesis that this mutant is able to infect rice tissues but likely requires external complementation with essential nutrients . We then evaluated if the over-induction of defense-markers , observed in plants infected with the cks1 mutant ( Fig 5B ) , was still visible when the virulence was reversed by fertilization . Quite remarkably , the over-induction of defense-marker genes tested did not revert under high fertilization ( Fig 7B ) in cks1-infected plants , despite the fact that the virulence of this mutant strain was restored ( Fig 7A ) . The data strongly support the idea that the over-induction of defense-markers by cks1 is not due to an arrest of growth itself ( which would subsequently trigger enhanced plant defense ) but rather to another defect that leads to reduced virulence , likely in CK production . The virulence of the cks1 mutant despite high expression of defense is likely compensated by high nutrient availability under high fertilization . Previous metabolomic analysis of plants infected by the rice blast fungus showed that some amino acids ( glutamate and aspartate ) as well as sugars ( fructose and glucose ) are nutrients which are drained towards the infection site [62] . We therefore tested whether CKS1 is required to efficiently maintain nutrient levels and/or modify nutrient fluxes at and around the penetration site . In order to test this hypothesis , after local deposition of a droplet of spore suspension of the different strains on the leaf surface , we measured the levels of different sugars and amino acids in the blast-infected and the neighboring non-infected zones ( Fig 8 and S7 Fig ) . During the infection by the cksCKS1 control strain , glucose ( Fig 8A ) and fructose ( S7 Fig ) contents progressively increased in and around the infected zone , which is consistent with previous observations that the accumulation of sugar is associated with successful pathogen invasion [63] . Initially , glucose and fructose contents were significantly higher 48 hpi in plants infected with the cks1 mutant compared to plants infected with the control strain , an observation that can be related to enhanced induction of defense ( see Discussion ) . By contrast , at later time points ( after 48hpi ) , soluble sugars contents were significantly lower in plants infected with the cks1 mutant . Given the known effects of CKs on maintaining photosynthesis active [64] , this observation supports the idea that CKs produced by Magnaporthe could contribute to maintain sugar production during infection . For most amino acids , there were no strong differences between the tissues infected by cks1 mutant and those infected by cks1CKS1 except for aspartate and glutamate ( Fig 8A ) . Away from the infected zone , the concentration of these two amino acids transiently decreased at 48hpi in plants infected with the control strain and increased at the site of infection at 72 hpi , suggesting that these amino acids can be drained towards the infection site or accumulated . By contrast , their level remained almost stable during infection with the cks1 mutant . This suggests that the cks1 mutant strain is not able to drain or consume aspartate and glutamate as efficiently as the control strain during infection .
The pathogenic fungus Magnaporthe oryzae produces and secretes CKs [43] but its biosynthesis pathway had remained unknown . Moreover , the involvement of CKs in virulence of pathogenic fungi that do not induce tumors was still undetermined . Recently , a cluster including two genes ( including one coding for a IPT-LOG ) involved in the de novo CK biosynthesis pathway , was characterized in the ergot fungus Claviceps purpurea [32] . In mutants deleted for these two genes , CK production was partially affected but virulence was not . In the present study , we identified a gene in the rice blast fungus , CKS1 , required for CK biosynthesis . The protein encoded by this gene presents all the features of a tRNA-IPT enzyme , the type of which is known in plants and yeast , and suspected in many fungi , to perform the first step of one of the CK biosynthesis pathways [25 , 40 , 45] . Phylogenetic analysis of tRNA-IPT protein sequences suggests that this gene is highly conserved among Ascomycete fungi ( S8 Fig ) and beyond [47 , 65] . Our work sets the basis for functional analysis of this pathway in several other plant associated fungi known to produce CKs [41 , 66 , 67] . We generated a cks1 mutant strain and demonstrated that this strain does not produce any of the CK types secreted by the wild type GY11 and cks1CKS1 complemented strain ( Table 1 ) . Moreover , the CKS1 protein is the only one found to contain an IPT domain in the rice blast fungal genome ( S1 Table ) . These results suggest that the CK biosynthesis pathway controlled by CKS1 is probably the only one in the rice blast fungus . Nucleotide forms , which are known to be precursors to riboside , free base and glycosylated CKs [44] , seem to be the major type of CKs secreted by control strains ( cks1CKS1 and GY11 ) . This contrasts with the yeast Δmod5 mutant which was found to still produce CKs , and suggested that tRNA turnover is not mainly involved in yeast CK production [68] . However , the Δmod5 mutant was grown on medium composed of yeast extract that already contains hormonal compounds . Thus , the free CK production by yeast observed in that study could have come from the recycling of CK compounds provided by the medium . Like the yeast Δmod5 mutant , the M . oryzae cks1 mutant had no obvious pleiotropic effects under standard growth conditions in minimal medium ( S3 Fig ) . By contrast , the growth of cks1 was affected under oxidative stress and this could be reverted by exogenous CKs ( S4 Fig ) . This result suggests that CKs play a role in fungal processes , like in yeast , for which MOD5 has primary roles in translation and is required for antifungal drug resistance [49] . These processes may participate to the loss of virulence of the cks1 mutants ( see below ) . In plants , CK signaling is mediated by a multistep phosphorelay system involving Histidine Kinase Receptors , Histidine Phospho-transfer proteins and Response Regulators [53 , 69] . This kind of transduction system is widespread among organisms [70–73] . Several studies mentioned its involvement in osmoregulation in yeast and in hyphal growth of Neurospora crassa [74] . Based on protein sequence homology , putative orthologous genes to those of the plant CK signaling pathway were found in the M . oryzae genome ( S1 Table ) . Two of these genes , MoSLN1 ( coding for an histidine kinase receptor ) and MoSSK1 ( coding for type-A response regulator ) , were previously shown to be required for full virulence of M . oryzae [75 , 76] . This suggests that CKs could also be perceived by the blast fungus in order to trigger a signal potentially required for its virulence . However , the involvement of these proteins in CKs perception and/or in CKs signaling transduction in response to plant or fungal-derived CKs remains to be established . In addition to becoming deficient in CK production , the Magnaporthe cks1 mutant was less virulent than wild-type GY11 or cks1CKS1 control strains since its capacity for penetration and invasion was strongly impaired ( Fig 1 ) . Mutations affecting fungal invasion are still scarce in M . oryzae as most mutations affect appressorium formation and penetration only [77] . Only very recent studies reported the role of protein effectors in virulence of Magnaporthe during host invasion [78] . The characterization of the CK deficient cks1 strain demonstrates that CKs play a key role in virulence of the rice blast fungus . Testing whether CKs play similar roles in other pathogenic fungi is now possible since CKS1 homologs exist in most of them ( S8 Fig ) . An exogenous supply of kinetin ( Fig 3 ) or cis-zeatin ( S6 Fig ) post inoculation restored the virulence of the CK-deficient strain , suggesting that the lack of virulence of these mutants was due to their inability to produce CKs . Moreover , exogenous application of kinetin reverted part of the over-induction of defense by the cks1 mutant ( Fig 5B ) . This suggests that although other defects due to the deletion of the tRNA-IPT gene may exist , they are not responsible for this over-induction of defense . The demonstration that these fungal-derived CKs are secreted in planta is technically challenging because of the presence of plant CKs and the difficulty to localize such small metabolites . However the observation that the expression of plant CK responsive genes is differentially affected by the cks1 strain before penetration in the plant tissue ( Figs 2 and 5A ) suggests that the CKs produced by Magnaporthe are also secreted and detected by the plant . The plant receptors and pathways engaged in CK detection remain to be identified and CK mutants in rice will have to be produced to address this question . Several observations support the hypothesis that the impaired virulence of cks1 is the consequence of an inability of the fungus to manipulate the plant defense pathways and metabolic fluxes rather than a consequence of a self-triggered growth arrest ( caused by the cks1 loss-of-function ) that would in turn trigger enhanced defense . First , enhanced defense is already visible before fungal penetration ( Fig 5A and S5B Fig ) at a time where the cks1 and control strains were indistinguishable in terms of growth ( S4C Fig ) . Second , reduced fungal growth could be partially restored by manipulating the plant in three ways: ( i ) by reducing plant defense chemically ( Fig 4 ) ( ii ) by impairing immunity genetically in two independent mutants ( Fig 6 ) , ( iii ) by modifying plant fertilization ( Fig 7A ) . Third , the restoration of virulence under high fertilization ( Fig 7A ) did not affect the over-induction of defense responses ( Fig 7B ) . This indicates that the arrest of fungal growth can be compensated by high N-fertilization , although the associated over-induction of defense response cannot , presumably because CKs are still not produced . For these reasons , we propose that the impaired virulence of the cks1 mutant is the consequence of the absence of CK production that would normally dampen defenses and modify nutrient fluxes for the pathogen’s benefit . Since the cks1 mutant is also more susceptible to oxidative stress in vitro ( S4 Fig ) , this may further reduce its global capacity to grow into plant tissues , especially if the plant ROS production is enhanced ( Fig 4A ) . In that sense the cks1 mutant is similar to the des1 mutant which is required for dampening ROS-mediated plant defense [54] . The transient and elevated sugar content in cks1 compared to the cks1CKS1 strain at 48 hpi ( Fig 8A ) is consistent with previous studies in other pathosystems that showed that an early and strong accumulation of soluble sugars , providing energy for the establishment of host defenses [63 , 79 , 80] . By contrast , the lower contents of glucose , fructose and sucrose found after 72 hpi with the cks1 strain suggests that photosynthesis is reduced in plants infected by the CK-deficient strain . We propose that Magnaporthe-derived CKs contribute to prevention of photosynthesis breakdown during infection process , for instance by limiting oxidative stress generated through photorespiration and , in consequence , allowing the establishment of the biotrophic phase . Among the different amino acids quantified , the contents of two key amino acids ( glutamate and aspartate ) , which are known to be essential for M . oryzae [62] , were differently affected between mutant and control strains following infection ( Fig 8A ) . In Arabidopsis , CKs were described to alter transcription of genes like glutamate dehydrogenase , asparagine synthetase and aspartate aminotransferase [81] . The fungal-derived CKs could also alter transcription of these genes to re-channel these amino acids towards fungal hyphae . Furthermore , CKs were previously shown to modify amino acid uptake through fungal cell membranes [82]; therefore , cks1 mutants could also be affected in their capacity to import these molecules into fungal hyphae . Altogether , these possible effects of CKs could explain why aspartate and glutamate contents were differently affected during infection by cks1 strain and the complemented cks1CKS1 strain . These results suggest that fungal CKs could be involved in pathogen nutrition during infection as hypothesized for many plant/fungi interactions by Greene ( 1980 ) [16] . Our results also suggest that the lack of virulence of cks1 mutants is partially due to an inability to limit plant defense responses like the oxidative burst ( Fig 4 ) and transcription of defense-related genes ( Fig 5A ) . The enhanced oxidative burst could lead to the stronger induction of defense markers observed during mutant infection and could participate to the strengthening of the cell wall to limit fungal penetration [83 , 84] . This is consistent with the ROS scavenging activity of CKs demonstrated in transgenic tobacco by Pogány et al . , ( 2004 ) [85] . Quite paradoxically , exogenously applied CKs have been shown to enhance , in combination with salicylic acid , rice defense marker genes expression and phytoalexin biosynthesis [43 , 86] . This synergistic effect depends on key defense transcriptional regulators like OsWRKY45 , a pivotal factor in biotic and abiotic stress responses [87] . Similarly , in Arabidopsis , specific recognition of bacterial CKs by plant CK receptors leads to a stronger induction of plant defenses and host resistance , involving plant CK Response Regulators and the transcription factor TGA3 [88] . In this context , how the CKs produced by Magnaporthe oryzae can act as negative regulators of defenses remains to be elucidated . A tight temporal and spatial production of CKs by the blast fungus could be central to avoiding enhanced activation of defenses . CKs play a key role in plant-microorganism communication [89] and it seems to be particularly true in plant symbiotic relations with fungi and bacteria [90 , 91] . Our work , showing that Magnaporthe requires CK production to be fully virulent , extends the key role that these hormones play in the interactions between plants and microbes to pathogenic fungi that do not trigger organ deformation . Our work suggests that CKs produced by M . oryzae act like classical effectors during host invasion as they significantly reduce defenses ( e . g . [78] ) . Moreover , our study shows that fungal CKs can divert and attract plant nutrients essential for fungal growth , much like the TAL effectors from bacteria [3] . Therefore , CKs , like other hormones , could represent metabolic effectors with several biological functions . Given their central role in several metabolic processes , plant hormones represent ideal factors to be acquired as effectors by pathogens . Accordingly , evolution probably led the rice blast fungus to include CKs into its weapons for successful colonization of rice plants .
Based on published studies on plant CK metabolisms ( cited in S1 Table ) , sequences from plant proteins were used to perform BLASTp on the Magnaporthe proteome at http://www . broadinstitute . org/annotation/genome/magnaporthe_grisea/Blast . html ? sp=Sblastp . We used an E-value of 1e-3 , with the comparison Matrix BLOSUM62 and gapped alignment . Similarly , BLASTp were performed on the yeast proteome available at http://www . yeastgenome . org/cgi-bin/blast-sgd . pl with default parameters . Hit proteins from yeast and M . oryzae were used to BLASTp back on Magnaporthe proteome to ensure Best Blast Mutual Hits were identified . Protein structure predictions were realized with the on-line platform I-TASSER ( http://zhanglab . ccmb . med . umich . edu/I-TASSER/ ) . The primary sequences of proteins of interest were submitted ( At2g27760 and MGG_04857 ) and secondary structures were predicted . Based on secondary structure predictions and primary sequences , the most similar proteins whose 3D structure was determined by NMR or X-ray crystallography were used as template for the model prediction . The model presented in S1B Fig was obtained with the MOD5 yeast protein ( PDB accessions: 3epjA and 3ephA ) as a template . Similar models were obtained with 3foz ( E . coli ) and 3a8t ( Humulus lupulus ) proteins as templates . Afterwards , alignments between the protein of interest and templates were generated by the use of different threading programs ( MUSTER , FFAS-3D , SPARKS-X , HHSEARCH I , Neff-PPAS , HHSEARCH , pGenTHREADER , wdPPAS , PROSPECT2 ) . Finally , the 10 best alignments were used to generate five structural models characterized [47] . The quality of structures used as templates were evaluated by Qmean server as well as the models obtained ( http://swissmodel . expasy . org/qmean/cgi/index . cgi ) . We analyzed results obtained for symptom quantification ( Figs 1B , 3B and 4C ) using a generalized linear model with a quasi-Poisson error structure . Significance was determined using a Chi² test . In each experiment , three biological replicates composed of 10 plants were analyzed per strain/condition . The size of the lesion trait was analyzed using a mixed model , with the “leaf” factor as random error , to compare the invasion of the different strains in the different conditions ( Figs 1C and 3C ) . Boxplots represent data distribution using the median ( indicated by the black line ) and approximate quartiles . Each experiment was replicated at least three times . Gene expression data were analyzed using a Student t-test on four biological replicates , with each replicate composed of five to six plants ( Figs 2 , 5 , 7B and S5 ) . A student t-test was also used to analyze data presented in figs 1D , 6 , 7A , 8 , S3 , S4 , S6 and S7 . Transformation was performed as described by Ribot et al . , ( 2013 ) [92] . Protoplasts of GY11 were prepared as described previously [93] . For the knock-out cks1 mutant , 1 . 2kb upstream and 1 . 2 kb downstream regions of the gene of interest were amplified by PCR using genomic GY11 DNA ( 100 ng ) as template . The strategy used for constructing the gene replacement cassettes is derived from Kämper ( 2004 ) [94] and presented in the S2 Fig . Primers used are shown in S2 Table . Growing colonies were transferred to Tanaka-Hygromycin or Tanaka-Basta plates for assessing resistance . Resistant colonies were further grown on rice agar media for 7–10 days at 26°C , purified by single-spore isolation , tested for resistance , and stored at -20°C . At least 2 independent transgenic fungal lines were isolated for each plasmid . Resistant colonies were characterized by PCR using a Phire Plant Direct PCR kit ( Thermo Scientific , Waltham , MA , USA ) . Fungal isolates were grown in 50mL of minimal Tanaka liquid medium without yeast extract ( 10g/L Glucose , 2 g/L NaNO3 , 2 g/L KH2PO4 , 0 . 5 g/L MgSO4-7H2O , 0 . 1 g/L CaCl2-2H2O , 4 mg/L FeSO4-7H2O , 1mg/L Thiamine , 5μg Biotin and microelements as Tanaka-B medium [95] ) on rotary shaker for 10 days at 26°C . Yeast extract was excluded because this compound already contains hormones , including CKs , which leads to misinterpretation between CKs really produced compared to those that are just taken from the media and metabolized by the fungus . Fungal cultures were centrifuged for 10 min at 2000×g , the supernatant was collected and quantified . Mycelia were rinsed with sterile Tanaka liquid medium , centrifuged for 10 min at 2000×g and pressed with absorbent paper to accurately quantify the fungal biomass . Samples were then frozen and lyophilized . CK extraction and measurements were performed as previously described [19 , 50] . Fungal isolates were grown on rice flour agar for spore production [96] . For the determination of interaction phenotypes and cytology analysis , a suspension of fungal conidiospores ( 5×104 sp/mL ) was spray-inoculated on the leaves of 3-week-old plants . For gene expression analysis , an inoculum of 2×105 sp/mL was used . Radial mycelial growth was measured on minimal Tanaka solid medium ( 20g/L agar added to the recipe mentioned above ) , during 13 days at 26°C . Nipponbare plants ( O . sativa ssp . japonica ) were grown during three weeks as described previously [97] . In standard conditions , nitrogen fertilization was performed for three weeks and inoculation was done 4 days after fertilization . For the high/low nitrogen experiments , plants were fertilized for two weeks as in standard experiment , and in the third week , plants were fertilized ( or not ) one day before infection , as described in [61] . Amino acids and sugar contents were quantified as described in Gravot et al . , ( 2010 ) [98] in leaf tissue of plants locally inoculated with a drop ( 15μL ) of inoculum at 20 000 sp/mL . One centimeter corresponding to the inoculation site , and one centimeter above and below was sampled to quantify amino acids on 4 replicates composed of three leaf fragments ( see S7 Fig for a picture of the experimental setting ) . RNA extraction was performed as mentioned in Delteil et al , 2012 [57] . Quantitative PCR was performed using LC 480 SYBR Green I Master Mix ( Roche , Basel , Switzerland ) and a LightCycler 480 instrument ( Roche ) . Amplification was performed as follows: 95°C for 10 min; 40 cycles of 95°C for 15 s , 60°C for 20 s and 72°C for 30 s; then 95°C for 5 min and 40°C for 30 s . In Figs 5 and 7 , in order to compare genes with different expression levels , for a given gene , all values were normalized using the average value of this gene across the different conditions . Kinetin ( Sigma ) and cis-zeatin ( OlChemIm ) were diluted in 50% ethanol to prepare a stock solution of 50 mM . The solution sprayed contained 50 μM of CKs ( replaced by ethanol 50% in mock solution ) , Tween at 0 . 02% final , diluted in water . Kinetin was applied to plants as described before [43] with slight modifications . Diphenylene iodonium ( DPI; SIGMA D23926 ) treatment was performed at 0 . 5 μM final concentration , prepared from a 50× stock solution diluted in 50% DMSO . Mock treatment was performed with the equivalent volume of 50% DMSO/water . The volume sprayed was calculated to saturate the leaf surface ( 5mL for 10 three week-old plants ) . Inoculated leaves were harvested , fixed and stained as described by Ballini et al . , ( 2013 ) [61] . In order to show H2O2 accumulation , we performed a DAB staining as mentioned in Faivre-rampant et al . , ( 2008 ) [97] .
|
The role of plant-like hormonal compounds produced by fungal pathogens during infection has not been elucidated . Here we identified a conserved gene in most fungi , required for cytokinin production by the rice blast fungus and for its full virulence . Fungal-derived cytokinins are likely potent inhibitors of plant immunity . They are also needed to maintain elevated sugar contents at the site of infection and to drain or consume essential amino acids at , and around , the infection site . Thus , cytokinins represent the second example , after the bacterially-produced coronatine , of plant hormones hijacked by pathogens to successfully invade plant tissues . These findings also suggest that this invasion strategy could be widely conserved among fungi .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"anatomy",
"fungal",
"genetics",
"plant",
"physiology",
"hormones",
"cereal",
"crops",
"fungi",
"plant",
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"rice",
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"organisms",
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"crops",
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"pathology",
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"methods",
"cytokinins",
"mycology",
"grasses",
"crop",
"science",
"leaves",
"agriculture",
"biochemistry",
"plant",
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"defenses",
"rice",
"blast",
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] |
2016
|
Cytokinin Production by the Rice Blast Fungus Is a Pivotal Requirement for Full Virulence
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Regulatory T ( Treg ) cells are known for their role in maintaining self-tolerance and balancing immune reactions in autoimmune diseases and chronic infections . However , regulatory mechanisms can also lead to prolonged survival of pathogens in chronic infections like leprosy and tuberculosis ( TB ) . Despite high humoral responses against Mycobacterium leprae ( M . leprae ) , lepromatous leprosy ( LL ) patients have the characteristic inability to generate T helper 1 ( Th1 ) responses against the bacterium . In this study , we investigated the unresponsiveness to M . leprae in peripheral blood mononuclear cells ( PBMC ) of LL patients by analysis of IFN-γ responses to M . leprae before and after depletion of CD25+ cells , by cell subsets analysis of PBMC and by immunohistochemistry of patients' skin lesions . Depletion of CD25+ cells from total PBMC identified two groups of LL patients: 7/18 ( 38 . 8% ) gained in vitro responsiveness towards M . leprae after depletion of CD25+ cells , which was reversed to M . leprae-specific T-cell unresponsiveness by addition of autologous CD25+ cells . In contrast , 11/18 ( 61 . 1% ) remained anergic in the absence of CD25+ T-cells . For both groups mitogen-induced IFN-γ was , however , not affected by depletion of CD25+ cells . In M . leprae responding healthy controls , treated lepromatous leprosy ( LL ) and borderline tuberculoid leprosy ( BT ) patients , depletion of CD25+ cells only slightly increased the IFN-γ response . Furthermore , cell subset analysis showed significantly higher ( p = 0 . 02 ) numbers of FoxP3+ CD8+CD25+ T-cells in LL compared to BT patients , whereas confocal microscopy of skin biopsies revealed increased numbers of CD68+CD163+ as well as FoxP3+ cells in lesions of LL compared to tuberculoid and borderline tuberculoid leprosy ( TT/BT ) lesions . Thus , these data show that CD25+ Treg cells play a role in M . leprae-Th1 unresponsiveness in LL .
The human immune system strives to maintain the delicate balance between preventing host susceptibility to various pathogens and limiting immunopathology due to an exacerbated immune response to infections . Sub-populations of T-cells previously identified as suppressor T-cells and later as Treg cells are the major players in the regulatory network of the immune system [1] , [2] . Although the idea of suppressor T-cells was a key topic of research already in the 70's and 80's it was not successfully established because of poor cellular characterization , and it took until mid-1990's before Treg cells were recognized as a different lineage [1] . More recently , studies clearly demonstrated the suppressive ability of this sub-population contributing to the re-acceptance of suppressor T-cell as a different T-cell lineage [3] , [4] . Characterization of this T-cell sub-population has continued and currently the thymus-derived Treg cells ( tTreg cells ) and peripherally derived Treg cells ( pTreg cells ) [5] are the two widely accepted categories of Treg cells [1] , [6] , [7] . Both T-cell subtypes play a role in limiting immune reactions in autoimmune diseases and chronic infections [8]–[11] . In addition , CD39+ Treg cells have also been reported as a subset of the CD4+ CD25highFoxP3+ Treg cells in association with chronic infections like tuberculosis ( TB ) [12] , hepatitis B ( HBV ) and in graft rejections [13] , [14] and the ability of CD8+ CD39+ Treg cells to suppress antigen specific CD4+ proliferation clearly demonstrated the importance of this sub-population [15] . Leprosy is a chronic infectious disease leading to more than 200 , 000 new cases every year [16] . The remarkable inter-individual variability in clinical manifestations of leprosy closely parallels the hosts' abilities to mount effective immune responses to M . leprae . This is clear from the well-known immunological and clinical spectrum in those who progress to disease ranging from polar T helper 1 ( Th1 ) to Th2 responses . TT and BT show more dominant Th1 responses which limit M . leprae growth resulting in clinical paucibacillary ( PB ) leprosy whereas , BL/LL patients demonstrate dominant Th2 responses as well as more permissive growth of M . leprae resulting in clinical multibacillary ( MB ) leprosy . TT/BT patients in general show high cellular responses and low antibody titers to M . leprae antigens , and develop localized granuloma with often no detectable bacilli in their lesions . The LL/BL patients at the opposite pole are incapable to generate M . leprae specific Th1 cell responses , show high antibody titers to M . leprae antigens , and poor granuloma formation with numerous bacilli in their lesions . The borderline states of leprosy are immunologically unstable . The different outcomes of infection in leprosy are most likely caused by host defense mechanisms [17] . However , the mechanism underlying the M . leprae-specific T-cell anergy in LL patients is still not completely understood . In chronic bacterial or viral infections , evidence exists that Treg cells suppress effector T-cells ( Teff cells ) in order to limit damage to the host caused by the immune responses against pathogens [18] . In this situation , the regulatory activity of Treg cells may lead to prolonged survival of pathogens in the host [9] , [19] . As evidenced in a previous study , higher levels of CD4+CD25+FoxP3+ Treg cells were observed in active TB patients in the periphery compared to latently infected individuals and healthy controls [20] , [21] . Also , an increased number of Treg cells expressing FoxP3 , cytotoxic T-lymphocyte antigen 4 ( CTLA-4 ) and glucocorticoid-induced tumour-necrosis-factor-receptor-related protein ( GITR ) were reported in lymphnodes from children with tuberculosis lymphadenitis [22] . Similarly , in leprosy , higher numbers of Treg cells in PBMC from BL and LL patients stimulated with M . leprae cell wall antigen ( MLCWA ) were observed compared to TT/BT forms , indicating the possibility that Treg cells may have a role in persistence of M . leprae bacteria as well as unresponsiveness of Th1 cells in BL/LL patients [23] . Recently , the mechanism of action of FoxP3 in CD4+CD25+ T cells derived from BL/LL leprosy patients was shown to result from increased molecular interactions of FoxP3 with Histone deacetylases ( HDAC7/9 ) in the nucleus of CD4+CD25+ T cells derived from BL/LL patients [24] . In the presence of pathogens , Treg cells can also be induced by certain macrophages as evidenced by the anti-inflammatory , CD163+ macrophages , known as type 2 macrophages ( mφ2 ) , that exert a suppressive effect on Th1 responses [25] , [26] . On the other hand , IL-10 induced phagocytosis of M . leprae by mφ2 without induction of microbicidal activity in LL lesions has been described [27] indicating the role of IL-10 producing Treg cells in the persistence of the pathogen within the host . Similarly , the presence of higher IL-10 expression correlated with increased CD163 and indoleamine 2 , 3-dioxygenase ( IDO ) proteins in tissues and sera of LL patients further evidenced their potential [28] . In this study , we have investigated the functional role of CD25+ Treg cells in M . leprae unresponsiveness of LL patients as well as the frequency of CD25+ and FoxP3+ cells in the PBMC of leprosy patients . Additionally , lesions of LL and TT/BT patients were assessed for the presence of FoxP3+ cells and CD163+ macrophages ( mφ2 ) .
Ethical approval of the study protocol was obtained from the National Health Research Ethical Review committee , Ethiopia ( NERC # RDHE/127-83/08 ) and the Nepal Health Research Council ( NHRC #751 ) . Participants were informed about the study objectives , the required amount and kind of samples and their right to refuse to take part or withdraw from the study at anytime without consequences for their treatment . Written and Informed consent was obtained from study participants before enrollment . The following HIV-negative individuals were recruited on a voluntary basis: newly diagnosed , non reactional leprosy patients from Ethiopia ( ALERT hospital , Addis Ababa , Ethiopia ) classified as LL ( n = 40 ) and TT/BT ( n = 16 ) and healthy endemic controls from health centers in Addis Ababa ( EC; n = 5 ) ; Treated , non reactional LL ( n = 6 ) and TT/BT ( n = 9 ) patients and EC ( n = 10 ) from Anandaban Hospital , ( Kathmandu , Nepal ) ; and non-endemic Dutch healthy controls ( NEC; n = 13 ) . Leprosy was diagnosed based on clinical , bacteriological and histological observations and classified by a skin biopsy evaluated according to the Ridley and Jopling classification [17] by qualified microbiologists and pathologists . All patients were enrolled before treatment was initiated . EC were assessed for the absence of clinical signs and symptoms of tuberculosis and leprosy . Individuals working in health facilities were excluded as EC . PBMC were isolated by Ficoll-Hypaque density gradient method , cells were washed and suspended in 20% fetal calf serum ( FCS ) in AIM-V ( Invitrogen , Carlsbad , CA ) and kept cool on ice , counted and frozen using a cold freshly prepared freezing medium composed of 20% FCS , 20% dimethyl sulphoxide ( DMSO ) in AIM-V . Cells were kept at −80°C for 2–3 days and transferred to liquid nitrogen until use . During thawing , cells were transported in liquid nitrogen to a water bath ( 37°C ) for 30 to 40 seconds until thawed half way and resuspended in 10% FCS in AIM-V ( 37°C ) containing 1/10 , 000 benzonase until completely thawed , washed 2 times ( 5–7 minutes each ) and counted . The percentage viability obtained was >75% and cells were incubated with anti-CD25 magnetic beads or used for FACS analysis . Frozen PBMC were thawed , washed and incubated with 20 µl of the CD25 micro beads II , human ( Miteny Biotec , Bergisch Gladbach , Germany ) in 80 µl MACS buffer ( Phosphate-buffered saline ( PBS ) with 0 . 5% Bovine serum albumin ( BSA ) and 2 mM EDTA ) for 20 minutes at 4°C . Cells were washed and added to MS column attached to Magnetic Cell Sorter ( MACS ) ( Milteny Biotec ) where CD25− cells were collected as flow through and the CD25+ population was collected by detaching the column from the magnetic cell sorter . Cells were washed with MACS buffer and resuspended in AIM-V medium . The purity of the CD25− and CD25+cell populations was >80% ( supplementary figure S2A and S2B ) . Total PBMC ( 150 , 000 cells/well ) , CD25− cells ( 150 , 000 cells/well ) or CD25− cells with proportionally added CD25+ cells ( 10 , 000 and/or 25 , 000 ) were added in triplicate into 96 well U bottom tissue culture plates and cultured with M . leprae whole cell sonicate ( WCS; 10 µg/ml ) , phytohaemagglutinin ( PHA; 1 µg/ml ) or AIM-V medium at 37°C with 5% CO2 and 70% humidity . After 6 days , supernatants were collected and kept frozen until used in ELISA . Irradiated armadillo-derived M . leprae whole cells were probe sonicated with a Sanyo sonicator to >95% breakage . This material was kindly provided by Dr . J . S . Spencer through the NIH/NIAID “Leprosy Research Support” Contract N01 AI-25469 from Colorado State University ( now available through the Biodefense and Emerging Infections Research Resources Repository listed at ( http://www . beiresources . org/TBVTRMResearch Materials/tabid/1431/Default . aspx ) . IFN-γ levels were determined by ELISA ( U-CyTech , Utrecht , The Netherlands ) [29] . The cut-off value to define positive responses was set beforehand at100 pg/ml . The assay sensitivity level was 40 pg/ml . Values for unstimulated cell cultures were typically <40 pg/ml . After depletion , the total PBMC , CD25− or CD25+ populations ( 25 , 000 to 200 , 000 cells ) were stained for CD3 ( clone SK7 , PerCP; Becton , Dickinson and Company , New Jersey , USA ) , CD4 ( clone SK3 , FITC; BD ) and CD25 ( PE; MACS ) to check the purity . Frozen PBMC of patients and healthy controls ( 2×106 cells/ml ) were thawed , washed and treated with benzonase ( 10 U/ml , Novagen , Merck4Biosciences , Merck KGaA , Darmstadt , Germany ) for 2 hours prior to in vitro stimulation with PMA ( 20 ng/ml ) /ionomycine ( 500 ng/ml ) in the presence of 1 µg/ml anti CD28 ( Sanquin , the Netherlands ) and 1 µg/ml anti CD49d ( BD Biosciences , Eerbodegem , Belgium ) . After 4 hours , Brefeldin A ( Sigma Aldrich ) was added at 3 µg/ml and cells were left for an additional 16 hours in the incubator at 37°C with 5% CO2 and 70% humidity . After live/dead staining with Vivid ( Invitrogen , Life technologies , Merelbeke , Belgium ) , surface staining was performed for 30 minutes at 4°C with the labeled antibodies directed against: CD14- and CD19-Pacific Blue , CD3-PE-TexasRed ( all Invitrogen , Life technologies ) , CD8-Horizon V500 , CD4-Pe-Cy7 , CD25-APC-H7 ( all BD Biosciences ) , and CD39-PE ( Biolegend , ITK Diagnostics , Uithoorn , The Netherlands ) . Samples were washed , fixed and intracellular staining was performed using the intrastain kit ( Dako Diagnostics , Glostrup , Denmark ) with IFN-γ -Alexa700 ( BD Biosciences ) , IL-10 APC ( Miltenyi Biotec GmbH , Bergisch Gladbach , Germany ) , and FoxP3 PE-Cy5 ( eBioscience , Hatfield , UK ) labeled antibodies . Cells were acquired on a FACS LSR Fortessa with Diva software ( BD Biosciences , The Netherlands ) and analyzed with FlowJo version 9 . 4 . 1 ( Tree Star , Ashland , OR , USA ) . The full gating strategy for live CD4+ CD3+ cells or CD8+ CD3+ cells ( supplementary Figure S1A and S1B ) was performed in compliance with the most recent MIATA [30] guidelines according to the following procedure: events were first gated using a forward scatter area ( FSC-A ) versus height ( FSC-H ) plot to remove doublets . Subsequently , the events were subjected to a lymphocyte gate using a side scatter ( SSC ) followed by a live/dead gating . Then , live CD3+ cells were gated and CD14+ and CD19+ events were excluded from analysis using a dump channel . Finally , CD3 live cells were separated in to CD4+ and CD8+ . After the gates for each function were created , we used the Boolean gate platform to identify all functions within each cell preparation using the full array of possible combinations . Skin biopsies taken from leprosy lesions of LL ( n = 10 ) and TT/BT ( n = 4 ) patients were fixed in formalin and embedded in paraffin . Tissue sections with 4 µm thickness were prepared using a microtome ( LEICA RM 2165 ) . The prepared tissues sections were stained for hematoxylin and eosine ( H & E; images are shown in supplementary figure S3 ) and also used as previously described [31] for immunofluorescence staining . Tissue sections were deparaffinised and rehydrated using graded concentrations of ethanol to distilled water . Antigen retrieval was performed in boiling Tris-EDTA buffer ( 10 mM Tris Base , 1 mM EDTA Solution , 0 . 05% Tween 20 , pH 9 . 0 ) for 12 minutes . After two hours of cooling at room temperature in antigen retrieval buffer , slides were washed twice in distilled water and twice in PBS , blocked for 15 min with 5% goat serum in PBS , washed again with PBS and stained with primary antibodies for FoxP3 ( 1∶100 , mouse anti-human IgG1 Abcam; Cambridge , UK ) , CD8 ( 1∶100 mouse anti-human IgG2b , Abcam ) , CD68 ( mouse anti-human IgG2a AbD serotec/Bio-Rad; Veenendaal , The Netherlands ) , CD163 ( 1∶400 , mouse anti-human IgG1 , Leica; Rijswijk , The Netherlands ) and CD39 ( 1∶100 , mouse anti-human IgG2a , Abcam ) . Two antibodies were used per tissue section: FoxP3 with CD68 , CD163 , CD39 or CD8; CD68 with CD163 and CD39 with CD163 . After overnight incubation at room temperature in the dark , sections were washed and incubated for 1 hour in the dark with secondary antibodies; goat-anti-mouse IgG1 coupled with Alexa 488 ( 1∶200 ) ( Invitrogen , Bleiswijk The Netherlands ) , goat-anti-mouse IgG2a or goat-anti-mouse IgG2b with Alexa 546 ( 1∶200 ) ( Invitrogen ) . Tissue sections were then washed three times with PBS and mounted with Vectashield ( DAPI , 4′ , 6-diamidino-2-phenylindole; Vector Laboratories , Brussels , Belgium ) . Immunofluorescence of skin sections was examined and images were taken from 5 different fields per section using a Leica-TCS-SP5 confocal laser scanning microscope ( Leica Microsystems , Mannheim , Germany ) . Nucleated cells that positively stained for the specific marker were counted from five different fields per section by two laboratory persons independently . Average counts for each marker per section were compared for all samples . Differences in cytokine concentrations were analyzed with the two-tailed Mann-Whitney U test or Wilcoxon signed rank test for non-parametric distribution using GraphPad Prism version 5 . 01 for Windows ( GraphPad Software , San Diego California USA; www . graphpad . com ) P-values were corrected for multiple comparisons . The statistical significance level used was p<0 . 05 .
To analyse the role of CD25+ cells in the production of IFN-γ , PBMC from Ethiopian LL patients ( n = 17 ) and Dutch healthy controls ( n = 12 ) were depleted of CD25+ cells and cell subsets with and without re-added CD25+ cells were stimulated with M . leprae WCS in 6 days culture . PBMC from treated Nepali LL ( n = 6 ) , BT ( n = 9 ) patients and EC ( n = 10 ) were depleted for CD25+ cells but only the total PBMC and CD25−cell subset were stimulated with M . leprae WCS . When compared according to clinical classification , there was a trend of higher IFN-γ production in PB compared to MB samples . IFN-γ production of total PBMC ( undepleted fraction ) from LL patients in response to M . leprae ( WCS ) was significantly lower ( p = 0 . 001 ) compared to responses by PBMC from TT/BT patients , whereas IFN-γ responses to PHA were high in both groups ( Fig . 1 ) . These data further confirm the M . leprae-specific lack of cell mediated immunity ( CMI ) in LL patients . Analysis of IFN-γ production in response to M . leprae ( WCS ) by CD25− cells alone or CD25− cells ( 150 , 000 cells per well ) supplemented with the CD25+ fraction ( 10 , 000 or 25 , 000 cells/well ) discriminated two groups of LL patients: those that produced IFN-γ in response to M . leprae after CD25+ cell depletion and those that did not ( Fig . 2A , 2B and 2E ) . Among the 18 LL Ethiopian patients , 7 ( 38% ) responded to M . leprae WCS after depletion of CD25+ cells whereas they lacked any response in total PBMC . IFN-γ production in response to PHA in both groups was not affected by the depletion of or enrichment with CD25+ cells . In the LL patient group , in which recovery of IFN-γ responses was observed to M . leprae WCS after depletion of CD25+ cells , this could be reversed proportionally by the addition of CD25+ cells ( Fig . 2A ) . In the patient group in which CD25+ cell depletion did not reverse anergy to M . leprae , there was no effect observed by addition of CD25+ cells to the depleted fraction ( Fig . 2B ) . In similar analysis of treated leprosy patients ( LL and BT ) and endemic controls from a Nepali population , PBMC responded to M . leprae WCS in the presence of CD25+ cells and a slight increase in IFN- γ levels after CD25+ cell depletion was also observed ( Fig . 2C ) . Similarly , healthy Dutch controls ( n = 8 ) responding to M . leprae WCS before depletion of CD25+ cell showed a slight increase after depletion ( Fig . 2D left panel ) as well , while other NEC ( n = 5 ) remained unresponsive after CD25+ cell depletion ( Fig . 2D right panel ) . For cell subset analysis , PBMC from Ethiopian LL ( n = 13 ) , TT/BT ( n = 5 ) and EC ( n = 7 ) and Dutch healthy controls ( NEC; n = 4 ) were stained for surface and intra-cellular markers . The frequency of FoxP3+ CD8+CD25+ cells was significantly higher in PBMC of LL patients compared to TT/BT patients ( p = 0 . 02 ) ( Fig . 3 ) . Although not statistically significant ( p = 0 . 05 ) , we also observed a higher frequencies of FoxP3+ CD4+ CD25+ T-cell in the LL group compared to the TT/BT patients ( Fig . 3 ) . In contrast , analysis of the frequency of IL-10 producing CD4+ CD25+ or CD8+CD25+ T-cell showed no significant differences between patients and healthy controls . The frequency of IL-10 production in CD4+ CD25+ or CD8+CD25+ T-cell in general was very low in all groups . Confocal analysis of two-colour immunofluorescence was used to localize specific cell markers in skin biopsies of Ethiopian LL ( n = 10 ) and TT/BT ( n = 4 ) leprosy patients . Higher number of CD68+ cells in LL lesions ( p = 0 . 02 ) ( Fig . 4A , 5A and B ) indicated the presence of more infiltrating macrophages compared to TT/BT ( Fig . 5C and D ) . In addition , CD68+ CD163+ cells ( mφ2 ) and FoxP3+ cells were present to a larger extent in LL patients' lesions ( p = 0 . 02 ) compared to TT/BT ( Fig . 4B , 4C , 5C and 5D ) . With respect to the numbers of CD68+ CD163+ cells ( mφ2 ) and FoxP3+ cells , differences were observed among the LL patients which could be explained by variations in the time elapsed since skin lesions were noticeable or by influence of other host factors . Although we found significantly higher frequency of CD8+FoxP3+ in PBMC , we could not clearly detect CD8+FoxP3+ in the skin lesions indicating CD4+FoxP3+ cells could play a regulatory role in these tissues . In addition , skin lesions were stained with CD39 combined with FoxP3 to localize CD39+FoxP3+ regulatory T-cells . However , in most skin tissues , CD39+ cells were not detected except for two LL skin tissues in which CD39 and FoxP3 positivity was observed simultaneously in macrophage-like shaped cells ( Fig . 4E ) . Thus , these results indicate the induction of more FoxP3+ but not CD39+ Treg cells in LL patients' skin lesions probably by the presence of type 2 macrophages .
Decreased M . leprae-specific T-cell mediated immunity is the hall mark of lepromatous multibacillary leprosy and can be assessed by in vitro unresponsiveness to M . leprae ( antigens ) or clonal anergy [2] , [23] , [32] . In this study , we confirm the M . leprae-specific unresponsiveness by the absence of IFN-γ responses to M . leprae WCS . Several studies have investigated the possible causes leading to hyporesponsiveness in LL patients such as formation of foamy macrophages in presence of IL-10 [27] , cholesterol dependent dismantling of HLA-DR raft in macrophages of BL/LL [33] and other factors , including Treg cells . Some of these studies on Treg cells have shown their presence and role either in the periphery or in skin lesions through measuring Treg associated markers , mainly CD25 , TGF-β , CTLA4 , IL-10 , and FoxP3 [23] , [24] , [34] , [35] . Recently , Teles et al . showed higher expression of IFN-γ and the downstream vitamin D-dependent antimicrobial pathway related genes including CYP27B1 and VDR ( Vitamin D receptor ) in TT/BT as well as an increased IL-10 expression induced by IFN-β in LL lesions [36] . Some reports have revealed the limitations of the available Treg markers due to their lack of specificity [37]–[39]: CD25 , for example , is expressed on activated T and B cells and is not exclusively found on Treg cells . However , noting that CD25 is still a crucial marker for Treg cells in the unstimulated situation , we performed depletion of CD25+ cells from unstimulated PBMC to isolate the Treg cells and demonstrated their involvement in M . leprae-specific unresponsiveness in LL patients . The BL/LL patients are known for their poor CMI and this is commonly assessed by measuring IFN-γ responses to M . leprae WCS . The total PBMC of the LL patients were analysed along with the CD25+ depleted and enriched fraction for their IFN-γ responses to M . leprae WCS and was negative . However , the depletion of CD25+ cells from total PBMC of LL patients showed an enhanced pro-inflammatory response as measured by the level of IFN-γ in response to M . leprae WCS in some but not all patients . Two distinct groups of LL patients were identified after depletion of CD25+ cells; 38% ( 7/18 ) of the LL patients showed enhanced IFN-γ responses in the CD25− population while the remaining 62% of the LL patients did not respond to M . leprae WCS at all . The recovered IFN-γ production in the first group was reversed by addition of CD25+ cells , clearly indicating that this CD25+ cell population conferred the unresponsiveness in these LL patients . However , we did not stain the CD25+ cell populations with FoxP3 which could have allowed more detailed characterization as CD25high FoxP3 or CD25low FoxP3 sub-populations which might have explained differences between the responders and non-responders . Nonetheless , the presence of non-responding LL patients after depletion of CD25+ cells indicates that CD25+ Treg cells do not represent the sole factor responsible for T-cell anergy in LL leprosy . As the Th1 arm is responsible for killing and clearing bacilli , there could have been enormous damage to tissues in BL/LL patients where high load of bacilli and antigens are available . However , the presence of Treg in these patients represents one important factor that can avoid tissue damage but , on the other hand , creates a convenient environment for bacilli to survive through suppression of Th1 response . In addition , the significant IFN-γ production observed in treated LL patients in our study before depletion of CD25+ T cells showed how treatment and thereby the level of bacillary load can influence the Th1 response and Treg . Similar findings were reported for TB patients with recovered IFN-γ production and reduced number of Treg cells after treatment [21] , [40] . The slight increases observed in IFN-γ production after depletion of CD25+ T cells in treated LL and BT patients and in EC tested in the depletion experiments could also indicate the regular presence of Treg cells to maintain homeostasis in the host . However , the overall ratio of CD25+ Treg cells to effector T cells will be crucial in determining the outcome of M . leprae infection in the host . Previous studies which aimed at identifying potential factors for M . leprae-specific unresponsiveness in LL used the addition of IL-2 [2] , [41]–[43] or anti-DQ monoclonal antibodies [44] or offered isolated antigenic fractions of M . leprae . Interestingly , each of the studies similarly identified two groups of LL patients , in one of which M . leprae unresponsiveness could be reversed . This indicated that the unresponsive phenotype in LL patients is likely mediated through the collective effects of various molecules . The more recent observation of cholesterol-dependent dismantling of HLA-DR raft and an increased membrane fluidity in BL/LL patients which causes a major defect in antigen presentation provides additional evidence for the presence of multiple different factors leading to T-cell anergy [33] . Thus , M . leprae specific unresponsiveness/anergy in LL patients very likely is a complex phenomenon mediated by multiple host and pathogen associated factors , one of which is represented by Treg cells . Several studies have reported on the ex vivo frequency of Treg cells in peripheral blood of LL and TT/BT patients in unstimulated or M . leprae antigens stimulated PBMC [23] , [35] . Attia et al . showed , elevated frequencies of circulating Treg cells ( CD4+CD25highFoxP3+ ) in TT patients [35] whereas Palermo et al . , showed that PBMC stimulated with M . leprae antigen for 6 days in culture had significantly higher number of Treg cells ( CD4+ CD25+FoxP3+ ) in LL patients [23] . Recently , Saini et al . , further confirmed the importance of Tregs in LL non-responsiveness by measuring TGF-β producing CD4+ CD25+FoxP3+ cells in stimulated PBMC culture [45] . In this study , we analysed the frequency of Treg cells in PBMC briefly activated with PMA/ionomycin . The frequency of CD4+ CD25+FoxP3+ cells was higher in LL compared to BT but not statistically significant ( Fig . 3 ) . However , with the visible difference observed between LL and BT and with the evidences from previous studies , their presence and role in BL/LL patients cannot be denied . For example , the recent molecular analysis of FoxP3 in CD4+CD25+ T cells nuclei has revealed that the FoxP3 interaction with histone deacetylases drives the immune suppression by CD4+ CD25+ Tregs in BL/LL unlike in other forms of leprosy [24] . On the other hand , the frequency of CD8+ CD25+FoxP3+ cells found in this study was significantly higher in LL ( Fig . 3 ) . This suggests that FoxP3+ CD8+ CD25+ Treg cells may also play a role in unresponsiveness in LL although not specifically analyzed for their functional role in our depletion experiments . Although lower in frequency compared to the CD4+ CD25+FoxP3+ , Saini et al . , also reported higher numbers of CD8+ CD25+FoxP3+ in LL compared to BT but without induction of TGF-β [45] . Most studies focused on CD4+ CD25+FoxP3+ in leprosy [23] , [35] . In contrast one study on LL lesions showed the presence of increased numbers of CD8+ T cells with suppressive type in LL indicating the importance of CD8+ Treg cells in leprosy [46] . In addition few other studies identified CD8+ Treg as a potential suppressive sub-population [47] , [48] . Recent evidence from an in vitro study also revealed CD8+ Treg cells ( CD8+ LAG-3+ FoxP3+CTLA-4+ ) induced by matured plasmacytoid dendritic cells ( pDC ) with suppression activity on allo-reactive T memory cells [49] . In our opinion , the CD8+ Treg population is not sufficiently studied in leprosy and we believe further analysis of this population in all forms of leprosy in periphery and lesionary tissues will be vital . The low IL-10 frequency measured by FACS analysis in all groups did not allow detection of significant differences among groups as expected in view of the crucial role of IL-10 as an anti-inflammatory cytokine in the unresponsiveness in LL patients [27] , [36] . This could be due to the short PMA/ionomycin stimulation inherent to the procedure for ex vivo determination of the frequency of CD25+ cells . However , 6 days stimulation of PBMC from BL patients with M . leprae induced high levels of IL-10 [50] . Although , it will not be easy to generalize or conclude on frequencies and numbers of CD4+ CD25+FoxP3+ Treg cells in different forms of leprosy since the experimental procedures used in each study vary , most of the studies including ours , point to the presence of increased numbers of Treg cells in LL patients either in periphery as well as lesions . Detailed characterization of Treg cell subsets in large cohorts of leprosy patients as well as the ratio to effector T cells may provide additional insights in this area . The dominant presence of CD163+ macrophages in LL lesions [27] , [28] and the significantly higher expression of IL-10 and CTLA4 in LL tissues have been reported previously [25] . The role of Treg cells ( FoxP3+ GITR+ CD25+ ) and their induction by CD163+ anti-inflammatory human macrophages was demonstrated in vitro since CD4+ T-cells gained a potent regulatory/suppressor phenotype and functions after activation by mφ2 [25] . In the current study , we show the presence of significantly higher number of CD68+ CD163+cells ( mφ2 ) in the vicinity of FoxP3+ cells in LL lesions compared to TT/BT lesions . These findings support the involvement of both cell types in the induction and/or maintenance of M . leprae directed Treg cells in LL lesions . Since a suppressive effect of CD4+CD39+FoxP3+ Treg cells was described in TB patients [12] , we also analysed the frequency of CD39+FoxP3+ cells in PBMC but observed no differences between LL and TT/BT patients except for few LL skin lesions , in which macrophage-shaped CD39+ cells were observed . A recent study has shown that CD39 expression on macrophages has an important role in self-regulation mechanism during inflammation [51] . These cells may also play a similar role in LL patients but this has to be further analysed . In summary , this study clearly show that CD25+ Treg cells play a role in unresponsiveness in LL , and that there are two subtypes of M . leprae unresponsive LL patients . Furthermore , the co-existence of Treg cells with mφ2 in LL lesions further supports the potential role of these regulatory cell subsets at the site of infection .
|
Leprosy is a curable infectious disease caused by Mycobacterium leprae ( M . leprae ) that affects the skin and peripheral nerves . It is manifested in different forms ranging from self-healing , tuberculoid leprosy ( TT ) with low bacillary load and high cellular immunity against M . leprae , to lepromatous leprosy ( LL ) with high bacillary load and high antibody titers to M . leprae antigens . However , LL patients have poor cell mediated response against M . leprae leading to delayed clearance of the bacilli . A possible explanation for this bacterial persistence could lie in the presence of more regulatory cells at infection sites and in peripheral blood . This study shows the recovery of the cell mediated response by depletion of CD25+ cells in a subset of LL patients , while another patient subset was not affected similarly . Moreover , an increased frequency of FoxP3+ T cells together with anti-inflammatory macrophages was observed in LL patients' skin biopsies . Thus , these data show that CD25+ Treg cells play a role in M . leprae-unresponsiveness in leprosy patients .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
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"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"biology",
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"life",
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"immunology",
"microbiology"
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2014
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T-Cell Regulation in Lepromatous Leprosy
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TNFα overexpression has been associated with several chronic inflammatory diseases , including psoriasis , lichen planus , rheumatoid arthritis , and inflammatory bowel disease . Paradoxically , numerous studies have reported new-onset psoriasis and lichen planus following TNFα antagonist therapy . Here , we show that genetic inhibition of Tnfa and Tnfr2 in zebrafish results in the mobilization of neutrophils to the skin . Using combinations of fluorescent reporter transgenes , fluorescence microscopy , and flow cytometry , we identified the local production of dual oxidase 1 ( Duox1 ) -derived H2O2 by Tnfa- and Tnfr2-deficient keratinocytes as a trigger for the activation of the master inflammation transcription factor NF-κB , which then promotes the induction of genes encoding pro-inflammatory molecules . In addition , pharmacological inhibition of Duox1 completely abrogated skin inflammation , placing Duox1-derived H2O2 upstream of this positive feedback inflammatory loop . Strikingly , DUOX1 was drastically induced in the skin lesions of psoriasis and lichen planus patients . These results reveal a crucial role for TNFα/TNFR2 axis in the protection of the skin against DUOX1-mediated oxidative stress and could establish new therapeutic targets for skin inflammatory disorders .
Tumor necrosis factor α ( TNFα ) is a multifunctional cytokine that mediates key roles in acute and chronic inflammation , antitumor responses , and infection . TNFα binds TNF receptor 1 ( TNFR1 , also known as TNFRSF1A or P55 ) and TNFR2 ( also known as TNFRSF1B or P75 ) for stimulation of two opposing signaling events [1] . In general , TNFR1 signaling results in the trigger of a cascade that can result in apoptosis [2] . This is dependent upon the cell type , the state of activation of the cell , and the cell cycle . In contrast , a TNFR2 signal induces cell survival pathways that can result in cell proliferation [2] . Enhanced TNFα synthesis is associated with the development of autoimmune/chronic inflammatory diseases , including psoriasis , lichen planus , rheumatoid arthritis , and inflammatory bowel disease ( IBD ) . The inhibition of TNFα activities in these diseases has been remarkably successful [3] , [4] . Paradoxically , however , numerous studies have reported new-onset psoriasis and lichen planus , or worsening of existing psoriasis , following TNFα antagonist therapy in adult patients [5]–[10] . Despite these clinical data pointing to an ambiguous function of TNFα in psoriasis and lichen planus , the role of TNFα , and in particular the contribution of each TNFR , in the regulation of skin inflammation has been scarcely studied . An earlier study using gene-targeted mutant mice lacking either TNFR1 or TNFR2 showed that skin inflammation induced indirectly by irritant chemicals or directly by intradermal administration of TNFα was greatly attenuated in TNFR1-deficient mice , whereas TNFR2-deficient siblings responded normally [11] . In addition , mice with an arrested canonical NF-κB activation pathway in the keratinocytes develop a severe inflammatory skin disease shortly after birth , which is caused by TNFα- and macrophage-mediated , but T-cell–independent , mechanisms [12]–[16] . The characteristics of this complex disorder are strikingly similar to those associated with the human X-linked genodermatosis incontinentia pigmenti ( IP ) [17] . To the best of our knowledge , however , the role played by TNFα in the homeostasis of healthy skin has never been studied . TNFα and TNFRs are conserved in all vertebrates . Recent studies have shown that in the zebrafish ( Danio rerio ) Tnfa functions as a pro-inflammatory cytokine [18] and Tnfr signaling plays an important role in the homeostasis of endothelial cells [19] . In the present study , we have taken advantage of the strengths of the zebrafish embryo model to study the impact of Tnfa , Tnfr1 , and Tnfr2 deficiencies in a whole vertebrate organism . We found that Tnfa and Tnfr2 are both crucial , whereas Tnfr1 is dispensable , for the homeostasis of the skin . Genetic inhibition of Tnfa and Tnfr2 promotes H2O2-mediated skin infiltration by neutrophils , increased keratinocyte proliferation , and the local activation of the master inflammation transcription factor NF-κB , which then promotes the induction of genes encoding pro-inflammatory molecules . In addition , DUOX1 was strongly induced in keratinocytes of human psoriasis and lichen planus patients .
In wild-type larvae , most neutrophils ( approximately 90% ) were located in the caudal hematopoietic tissue ( CHT ) [20] by 3 d postfertilization ( dpf ) ( Figure 1A–C ) , which is consistent with neutrophil localization patterns described previously [21] , [22] . However , in Tnfa- and Tnfr2-deficient larvae , approximately 40% of neutrophils were located outside the CHT ( Figure 1A–C ) . In addition , Tnfr1-deficient animals showed a normal neutrophil distribution , whereas their double deficient siblings for both Tnfr1 and Tnfr2 showed also a distribution pattern more similar to single Tnfr2-deficient fish ( Figure 1A–C ) . The specificity of this phenotype was confirmed with a dominant negative ( DN ) Tnfr2 , which is lacking the entire intracellular signaling domain , but is identical to full-length Tnfr2 in its transmembrane and extracellular domains , and therefore , its trimerization with endogenous Tnfr2 extinguishes Tnfr2 signaling [19] . The results showed that the altered neutrophil distribution of Tnfr2 morphants was phenocopied by overexpression of DN-Tnfr2 ( Figure S1A ) . In addition , the scattered distribution of Tnfa- and Tnfr2-deficient larvae was partially rescued by overexpression of wild-type Tnfa and Tnfr2 RNAs , respectively ( Figure S1B ) . These results prompted us to examine the distribution of macrophages in TNFα- and TNFR2-deficient fish , and surprisingly , macrophage distribution was apparently normal in all cases ( Figure S2 ) . To ascertain the precise localization of neutrophils in Tnfa/Tnfr2-deficient larvae , we knocked down Tnfr2 in transgenic mpx:eGFP animals followed by whole mount immunohistochemistry ( WIHC ) against p63 ( basal keratinocyte marker ) to visualize neutrophils ( GFP+ ) and skin keratinocytes ( p63+ ) at the same time in whole larvae . The results revealed that although neutrophils from wild-type animals were mainly located in the CHT , a high proportion of neutrophils were seen in close contact with keratinocytes in Tnfr2-deficient animals ( Figure 1D and Videos S1 and S2 ) . Collectively , these results indicate that deficiency of either Tnfa or Tnfr2 specifically promotes neutrophil infiltration into the skin of zebrafish during early development . The phenotype of Tnfa- and Tnfr2-deficient fish is reminiscent of that of spint1a and clint1 mutant fish , which show chronic skin inflammation characterized by increased interleukin-1β ( IL-1β ) production and neutrophil infiltration [23]–[25] . This led us to examine the expression of three genes encoding major pro-inflammatory molecules , namely Tnfa itself , IL-1β , and prostaglandin-endoperoxide synthase 2b ( PTGS2b , also known as COX2b ) , in whole wild-type and Tnfr2-deficient larvae at 3 dpf as well as in sorted mpx:eGFP+ cells—that is , neutrophils . It was found that Tnfr2 deficiency triggered the expression of tnfa , il1b , and ptgs2b genes ( Figure 2A ) . Although neutrophils highly expressed the genes encoding Tnfa and Il1b as well as both Tnfrs , they did not mediate the induction of il1b observed in Tnfr2-deficient fish ( Figures 2B , S3A , and S4A ) . Nevertheless , the transcript levels of tnfa were higher in neutrophils from Tnfr2-deficient fish than in neutrophils from their wild-type siblings ( Figure 2B ) , but this might reflect a positive feedback loop in response to Tnfr2 deficiency [19] . In addition , Tnfr2-deficient embryos showed higher transcript levels of il1b at 24 hpf ( Figure S5 ) , soon after the development of the first neutrophils in the zebrafish embryo [21] , [22] , [26] and before hatching . We then sorted krt18+ cells from Tnfa- and Tnfr2-deficient animals at 3 dpf and found that they show much higher transcript levels of il1b and ptgs2b than krt18+ cells from wild-type animals ( Figures 2C and S3B ) . Notably , krt18+ cells expressed both Tnf receptors ( Figure S4B ) and the specific marker of basal keratinocytes p63 ( Figure S3B ) . We next wondered whether knockdown of Il1b using a specific morpholino ( MO ) [27] might rescue the neutrophil dispersion of Tnfr2-deficient animals . As shown in Figure 2D , genetic inhibition of Il1b failed to rescue the neutrophil dispersion observed in Tnfr2 morphants . These results taken together indicate that the Tnfa/Tnfr2 axis is required for skin homeostasis in zebrafish and that the deficiency of either ligand or receptor triggers an inflammatory response characterized by the induction of pro-inflammatory mediators and neutrophil infiltration . The master regulator of inflammation NF-κB plays an essential role in the homeostasis of skin . Thus , genetic inhibition of the NF-κB pathway in keratinocytes triggers a severe inflammatory skin disease in newborn mice , which is completely rescued by Tnfa and Tnfr1 depletion [12]–[16] . We therefore use a NF-κB reporter line [28] to visualize the dynamics of NF-κB in whole Tnfr2-deficient larvae . Injection of bacterial DNA , which activated TLR9 , resulted in a drastic activation of NF-κB in the whole larvae ( Figure 3A–B ) , as expected from previous results [29] , [30] . Interestingly , Tnfr2 deficiency promoted a restricted activation of NF-κB in the skin ( Figure 3A–E and Videos S3 and S4 ) . Furthermore , although skin integrity was unaffected up to 5 dpf in Tnfr2-deficient larvae , as assayed by histology ( Figure S6 ) , they showed increased keratinocyte proliferation , as assayed in double transgenic NF-κB:eGFP; krt18:RFP animals and double WIHC with anti-RFP and anti-phosphorylated H3 ( Figure 4 ) . Hydrogen peroxide gradients were recently shown to contribute to the early influx of neutrophils in wound [31] and tumor [32] . Interestingly , however , H2O2 is not required for neutrophil detection of localized infection [33] . These gradients are created by the dual oxidase 1 ( Duox1 ) [31] and sensed by neutrophils through the tyrosine kinase Lyn [34] . Although identified and best studied in the zebrafish , H2O2 is likely to play the same function in human neutrophils [34] . We first analyzed the expression of the gene encoding Duox1 and found that Tnfr2-deficient keratinocytes showed higher transcript levels of duox1 than wild-type animals ( Figure 5A ) . Next , using an H2O2-detecting fluorescence probe , we observed that Tnfr2-deficient larvae also produced H2O2 in the skin ( Figure 5B , C ) . We observed similar levels of labeling with the H2O2 probe in Tnfr2-deficient keratinocytes and in local keratinocytes after wounding ( Figure S7 ) . Notably , H2O2 production by Tnfr2-deficient keratinocytes preceded the activation of NF-κB ( Figure S8 ) . Consistent with these observations , genetic inhibition of Duox1 with a specific MO [31] was able to partially prevent the infiltration of neutrophils into the skin of Tnfr2-deficient larvae ( Figure 5D , E ) . To further confirm this result , we designed a DN form of Duox1 [35] , and notably , overexpression of DN-Duox1 was also able to partially prevent neutrophil infiltration in Tnfr2-deficient larvae ( Figure S9A , B ) . Furthermore , we knocked down the H2O2 sensor of neutrophils , Lyn [34] , and found full prevention of neutrophil infiltration in both Tnfr2- and Tnfa-deficient animals ( Figure 5F , G ) . The above results prompted us to evaluate whether pharmacological inhibition of Duox1 using the NADPH oxidase inhibitor dibenziodolium chloride ( DPI ) , which has been shown to inhibit Duox1 and H2O2 gradient formation in zebrafish [31] , [34] , may restore skin homeostasis in Tnfa- and Tnfr2-deficient larvae . The results showed that DPI treatment completely inhibited the generation of H2O2 in the skin ( Figure 5B , C ) , the infiltration of neutrophils ( Figure 6A–C ) into this tissue , and more importantly , skin NF-κB activation ( Figure 6D–F ) in both Tnfa- and Tnfr2-deficient animals . Collectively , these results demonstrate that the Tnfa/Tnfr2 axis is indispensable for skin homeostasis and its inhibition results in the release of Duox1-derived H2O2 , local activation of NF-κB , induction of genes encoding Duox1 and pro-inflammatory mediators , and neutrophil infiltration . The crucial role of Duox1-generated H2O2 in the infiltration of neutrophils into the skin and the induction of NF-κB prompted us to investigate if this inflammatory signal may also play a role in human psoriasis and lichen planus . We analyzed by immunohistochemistry 10 healthy skins and 8 lichen planus and 15 psoriasis lesions using an antibody to human DUOX1 ( Figure 7 ) . The results showed that although DUOX1 was expressed at low levels in healthy epidermis , mainly in the granular layer , a drastic induction of this enzyme was obvious in the keratinocytes of the spinous layer of the epidermis from both psoriasis and lichen planus lesions . In some patients , the induction was obvious in all keratinocytes of the spinous layer , whereas in others it was observed only in the upper layers of this stratum . It was noticeable the localization of DUOX1 in the plasma membrane of psoriasis and lichen planus keratinocytes and also in their cytoplasm , where it was accumulated in the upper side of these cells—that is , facing the cornified layer . Although this particular distribution deserves further investigation , these results strongly suggest a role for DUOX1 in psoriasis and lichen planus .
Increased production of TNFα is associated with the development of autoimmune/chronic inflammatory diseases , including psoriasis , lichen planus , rheumatoid arthritis , and IBD . We have used the unique advantages of the zebrafish embryo for in vivo imaging and cell tracking to demonstrate that the genetic depletion of Tnfa or Tnfr2 , but not Tnfr1 , caused the infiltration of neutrophils into the skin and hyperproliferation of keratinocytes through the activation of an H2O2/NF-κB/Duox1 positive feedback inflammatory loop ( Figure 8 ) . Strikingly , neutrophils , but not macrophages , are rapidly attracted to the skin . However , the activation of NF-κB and the induction of the gene encoding Il1b in the skin occurred before the appearance of the first neutrophils in the developing embryo . More importantly , DUOX1 was also strongly induced in the skin lesions of psoriasis and lichen planus patients . Collectively , these results ( i ) indicate a critical role of TNFα/TNFR2 signaling in the protection of the skin against oxidative stress , ( ii ) might explain the appearance of psoriasis and lichen planus in patients treated with anti-TNFα therapies [5]–[10] , and ( iii ) support the idea that specific inhibition of the TNFα/TNFR1 signaling axis while leaving TNFα/TNFR2 signaling unaffected would inhibit the pathological effects of TNFα and reduce the side effects associated with this therapy [19] , [36] . This apparent discrepancy with TNFα-deficient mice , which do not show skin inflammation , may be due to developmental and/or physiological compensations , which probably do not exist in humans [37]–[39] . One of the most intriguing observations from this study is that impaired Tnfr2 signaling led to the induction of duox1 and the production of H2O2 by keratinocytes . H2O2 gradient was recently shown to contribute to the early influx of neutrophils in wound [31] and tumor [32] , although it seems to be dispensable for neutrophil detection of localized infection [33] . To the best of our knowledge , this is the first study showing a role for Duox1-derived H2O2 in the induction of NF-κB in the skin in vivo , suggesting that H2O2 might play a critical role in the initiation and maintenance of chronic inflammatory diseases in both zebrafish and human . These observations suggest that antioxidants or inhibition of Duox1 might be therapeutic for the treatment of patients suffering from psoriasis , lichen planus , and other inflammatory diseases . Supporting this notion , several studies using psoriasis and IBD mouse models have shown that transgenic overexpression of endogenous antioxidant genes promotes protection , while antioxidant gene knockout promotes sensitization ( reviewed by [40] , [41] ) . Even more importantly , the antioxidant levels and the oxidative stress biomarkers are usually correlated with the disease severity and the extent of inflammation in the psoriasis and IBD patients [40]–[42] . Therefore , all these results taken together suggest that antioxidants should be considered as part of a more specific and effective therapy for the treatment of inflammatory skin diseases , including psoriasis and lichen planus . The ability of Duox1 inhibition by pharmacological approaches , but not of IL-1β , to restore skin homeostasis in Tnfa- and Tnfr2-deficient zebrafish embryos further supports this conclusion . It is known that different reactive oxygen species ( ROS ) act as second messengers , influencing various cellular signal transduction pathways , including NF-κB . However , there are still many inconsistencies concerning the influence of oxidative stress on NF-κB activity [43] , and unfortunately , most studies have been performed in vitro using H2O2 and cultured cells [44] , [45] . Such studies have shown that H2O2 can act as an activator of IκB kinases ( IKKs ) [46] or can inactivate these proteins [47] , probably depending on the cell type . More recently , it has been found that the same prolyl hydroxylases that confer oxygen sensitivity to the hypoxia-inducible factor ( HIF ) pathway , namely PHD1 and PHD2 , seem to act as repressors of the canonical NF-κB pathway through mechanisms that could include direct hydroxylation of IKKβ [48] . Our epistasis study in zebrafish demonstrates for the first time that the absence of Tnfa/Tnfr2 signaling led to the production of H2O2 by keratinocytes , which , in turn , resulted in NF-κB activation and the induction of genes encoding pro-inflammatory mediators . This self-perpetuating cycle may be of clinical importance in view of the presumably key role played by oxidative stress [40]–[42] , HIF [49] , [50] , and NF-κB in psoriasis and IBD . It is tempting to speculate that the Tnfa/Tnfr2 axis would be required to prevent skin oxidative stress through the regulation of ROS-detoxifying enzymes , as it has been reported for oligodendrocyte progenitor cells in vitro [51] . The model reported here might contribute to clarify the mechanisms involved in the regulation of oxidative stress by TNFα , the regulation of NF-κB activity by ROS , and the crosstalk between oxidative stress and inflammation in vivo . The essential role played by NF-κB in the homeostasis of the skin is evidenced by the human X-linked genodermatosis IP , which affects the regulatory subunit of IKK ( IKKγ , NEMO ) [17] . Humans suffering from this genetic disease exhibit severe skin inflammation , paradoxically due to impaired NF-κB activation and reduced resistance to TNFα/TNFR1-mediated apoptosis [52] , [53] . Similarly , although NF-κB actively participates in the excessive inflammatory response observed in IBD patients [54] , [55] , recent studies with mice defective in NF-κB activation have revealed that epithelial NF-κB activation is essential to preserve intestinal homeostasis [56] , [57] . Therefore , a critical NF-κB signaling balance is required for skin and gut homeostasis , as both excessive and defective epithelial NF-κB activation can result in inflammation . Similarly , although the TNFα/TNFR1 axis was earlier appreciated to be involved in the apoptosis of both keratinocytes and enterocytes in the absence of NF-κB signaling [52] , [53] , [56] , [57] , our results show that TNFα signaling through TNFR2 is also critically required for skin homeostasis . Whether the TNFα/TNFR2 axis is also required for gut homeostasis will require further investigation using germ-free and gnotobiotic zebrafish larvae , as host–microbe interactions have a profound impact in gut physiology and are usually involved in IBD . In conclusion , we have found that Tnfa signaling through Tnfr2 is indispensably required for the protection of the skin against oxidative stress-induced inflammation in the zebrafish . Thus , the absence of this signal triggers the local production of H2O2 by Duox1 , which , in turn , activates NF-κB and results in the up-regulation of genes encoding pro-inflammatory mediators and neutrophil infiltration . These results , together with the induction of DUOX1 in the skin lesions of psoriasis and lichen planus patients , reveal a crucial role of H2O2 and DUOX1 in skin inflammation and suggest that pharmacologic and genetic therapies that target these two key factors could provide innovative approaches to the management of psoriasis , lichen planus , and other chronic inflammatory diseases .
The experiments performed comply with the Guidelines of the European Union Council ( 86/609/EU ) . Experiments and procedures were performed as approved by the Bioethical Committee of the University of Murcia ( approval no . 537/2011 ) and the Ethical Clinical Research Committee of the University Hospital Virgen de la Arrixaca ( approval no . 8/13 ) . Zebrafish ( Danio rerio H . ) were obtained from the Zebrafish International Resource Center and mated , staged , raised , and processed as described [58] . The lines Tg ( mpx:eGFP ) i114 [59] , Tg ( lyz:dsRED ) nz50 [60] , Tg ( mpeg1:eGFP ) gl22 [61] , and Tg ( krt18:RFP ) [62] were previously described . The Tg ( NFκB-RE:eGFP ) ( NF-κB:eGFP for simplicity ) line was generated with the method and constructs previously described [28] . Specific MOs ( Gene Tools ) were resuspended in nuclease-free water to 1 mM ( Table S1 ) . In vitro–transcribed RNA was obtained following the manufacturer's instructions ( mMESSAGE mMACHINE Kit , Ambion ) . MOs and RNA ( 200 pg/egg ) were mixed in microinjection buffer ( 0 . 5× Tango buffer and 0 . 05% phenol red solution ) and microinjected into the yolk sac of one- to eight-cell-stage embryos using a microinjector ( Narishige ) ( 0 . 5–1 nl per embryo ) . The same amounts of MOs and/or RNA were used in all experimental groups . The efficiency of the MOS was checked by RT-PCR as described previously [19] , [27] , [31] , [34] . In some experiments , 1 dpf embryos were manually dechorionated and/or treated for 24 h at 28°C by bath immersion with the NADPH oxidase inhibitor dibenziodolium chloride ( DPI , Sigma-Aldrich ) at a final concentration of 100 µM diluted in egg water supplemented with 1% DMSO . At 72 hpf , larvae were anesthetized in tricaine and mounted in 1% ( wt/vol ) low-melting-point agarose ( Sigma-Aldrich ) dissolved in egg water [63] . Images were captured with an epifluorescence Lumar V12 stereomicroscope equipped with green and red fluorescent filters while animals were kept in their agar matrixes at 28 . 5°C . All images were acquired with the integrated camera on the stereomicroscope and were used for subsequently counting the number of neutrophils ( mpx:eGFP ) and examining their distribution . The activation of NF-κB was visualized and quantified using the line NF-κB::eGFP . Stacked images were captured using 1 µm ( neutrophil infiltration into the skin ) or 25 µm ( neutrophil distribution , NF-κB activation , and H2O2 formation ) increments and deconvolved using Huygens Essential Confocal software ( v 4 . 1 0p6b ) by Scientific Volume Imaging . Stacks were processed using the free source software ImageJ ( http://rsbweb . nih . gov/ij ) to obtain a maximum intensity projection of the xy axis of the stack . For the quantification of neutrophil distribution and NF-κB activation , the maximum projection for each larva was then converted to a fluorescence value matrix , where the value obtained for each pixel transversally was the mean ± S . E . M . for all the pixels for each row ( 15 larvae per treatment from 3 different experiments ) . In parallel , the activation of NF-κB in the skin was also quantified by the skin NF-κB activation index , which was defined as the fluorescence in the skin ( a+b ) relative to the total fluorescence of the larvae ( c ) . H2O2 imaging using a live cell fluorogenic substrate was performed essentially as previously described [32] . Briefly , 3-dpf tnfα and Tnfr2 morphants and their control siblings were loaded for 30 min with 50 µM acetyl-pentafluorobenzene sulphonyl fluorescein ( Cayman Chemical ) in 1% DMSO in egg water and imaged as above . As a positive control , complete transfection of the tail of anesthetized 72 hpf larvae was performed with a disposable sterile scalpel [63] . At 3 dpf , approximately 300 to 500 Tg ( mpx:eGFP ) and Tg ( krt18:RFP ) larvae were anesthetized in tricaine , minced with a razor blade , incubated at 28°C for 30 min with 0 . 077 mg/ml Liberase ( Roche ) , and the resulting cell suspension passed through a 40 µm cell strainer . Sytox ( Life Technologies ) was used as a vital dye to exclude dead cells . Flow cytometric acquisitions were performed on a FACSCALIBUR ( BD ) , and cell sorting was performed on a Coulter ( Epics Altra ) . Analyses were performed using FlowJo software ( Treestar ) . Total RNA was extracted from whole embryos/larvae or sorted cell suspensions with TRIzol reagent ( Invitrogen ) following the manufacturer's instructions and treated with DNase I , amplification grade ( 1 U/µg RNA; Invitrogen ) . SuperScript III RNase H− Reverse Transcriptase ( Invitrogen ) was used to synthesize first-strand cDNA with oligo ( dT ) 18 primer from 1 µg of total RNA at 50°C for 50 min . Real-time PCR was performed with an ABI PRISM 7500 instrument ( Applied Biosystems ) using SYBR Green PCR Core Reagents ( Applied Biosystems ) . Reaction mixtures were incubated for 10 min at 95°C , followed by 40 cycles of 15 s at 95°C , 1 min at 60°C , and finally 15 s at 95°C , 1 min 60°C , and 15 s at 95°C . For each mRNA , gene expression was normalized to the ribosomal protein S11 ( rps11 ) content in each sample using the Pfaffl method [64] . The primers used are shown in Table S2 . In all cases , each PCR was performed with triplicate samples and repeated at least with two independent samples . Larvae were fixed overnight in 4% paraformaldehyde solution ( PFA ) , embedded in Paraplast Plus ( Sherwood Medical ) , and sectioned at a thickness of 5 µm . After being dewaxed and rehydrated , they were stained with haematoxylin and eosin ( H&E ) . Tg ( mpx:eGFP ) or Tg ( NF-κB:eGFP ) ; Tg ( krt18:RFP ) 3 dpf larvae were fixed overnight at 4°C in 4% PFA at room temperature , dehydrated in methanol/PBS solutions ( 25 , 50 , 75 , and 100% , 5 min each ) , and stored in 100% methanol at −20°C . For staining , larvae were rehydrated in 75 , 50 , and 25% methanol/PBT ( PBS and 0 . 1% Tween-20 ) solutions for 5 min each , washed three times for 5 min in PBT , incubated for 5 min RT with 150 mM Tris-HCl pH 9 , followed by heating at 70°C for 15 min [65] . After the heating treatment , larvae were directly washed twice in PBT for 10 min and twice in dH2O for 5 min . Subsequently , to enhance tissue permeabilization , larvae were incubated with cold acetone for 20 min at −20°C , washed twice in dH2O and twice in PBT ( 5 min each ) , followed by blocking with blocking solution ( PDT = PBT+1% DMSO ) supplemented with 5% FBS and 2 mg/ml BSA ) for 2 h at 22°C . After blocking , embryos were incubated overnight at 4°C with primary antibodies diluted ( 1∶200 ) in blocking buffer , washed three times in PDT ( 15 min each ) , and blocked again for 2 h at 22°C . Secondary antibody staining was done for 2 h RT at 1∶500 dilution in blocking buffer , and larvae were then washed five times in PBT ( 5 min each ) and stored in Vectashield ( Vector Labs ) until image acquisition . The following primary antibodies were used: rabbit anti-phosphorylated-Histone H3 ( Ser 10 ) -R ( #SC8656-R , Santa Cruz Biotechnology ) and rabbit anti-human p63 ( #SC8343 , Santa Cruz Biotechnology ) . Mouse anti-RFP ( #MA5-15257 , Thermo Scientific ) and Alexa Fluor 594 ( #A11032 ) and Alexa Fluor 532 ( #A11002 ) Goat Anti-Mouse IgG ( H+L ) ( Life Technologies ) were used as secondary antibodies . Confocal immunofluorescence images were acquired with a confocal microscope ( LEICA TCS-SP2 , Leica ) using an NA 0 . 70/20× dry objective . Z-series were acquired using a 210–300 µm pinhole . The 2D and 3D maximum intensity projections and corresponding animation videos were made using ImageJ ( http://rsb . info . nih . gov/ij/ ) . Skin biopsies from healthy donors ( n = 10 ) and lichen planus ( n = 8 ) and psoriasis patients ( n = 15 ) were fixed in 4% PFA , embedded in Paraplast Plus , and sectioned at a thickness of 5 µm . After being dewaxed and rehydrated , the sections were incubated in 50 mM glycine-HCl buffer ( pH 3 . 5 ) containing 0 . 01% ethylenediaminetetraacetic acid ( EDTA ) at 95°C for 5 min and then at room temperature for 20 min to retrieve the antigen . Afterwards , they were immunostained with a 1/50 dilution of a goat polyclonal antibody to human DUOX1 ( sc-48858 , Santa Cruz Biotechnology ) followed by ImmunoCruz goat ABC Staining System ( sc-2023 , Santa Cruz Biotechnology ) following the manufacturer's recommendations . The specificity of the staining was confirmed by pre-incubating a 10-fold excess ( in molarity ) of a commercial blocking peptide ( sc-48858 P , Santa Cruz Biotechnology ) with the DUOX1 antibody overnight at 4°C . No staining was observed in these conditions . Sections were finally examined under a Leica microscope equipped with a digital camera Leica DFC 280 , and the photographs were processed with Leica QWin Pro software . Data were analyzed by analysis of variance ( ANOVA ) and a Tukey multiple range test to determine differences between groups . The differences between two samples were analyzed by the Student t test . The contingency graphs were analyzed by the Chi-square ( and Fisher's exact ) test .
|
Psoriasis and lichen planus are chronic , debilitating skin diseases that affect millions of people worldwide . TNFα is a multifunctional cytokine that mediates acute and chronic inflammation . While TNFα antagonist therapy is used for autoimmune or chronic inflammatory diseases , such as inflammatory bowel disease ( IBD ) , numerous studies have reported new-onset psoriasis and lichen planus following such therapy . We have used the unique advantages of the zebrafish embryo to identify a novel phenotype that mirrors this unexplained and paradoxical onset of psoriasis and lichen planus . We found that depletion of Tnfa or its receptor Tnfr2 caused skin inflammation and hyperproliferation of keratinocytes through the activation of a Duox1/H2O2/NF-κB positive feedback inflammatory loop . Strikingly , DUOX1 was drastically induced in the skin lesions of psoriasis and lichen planus patients , and pharmacological inhibition of Duox1 abrogated skin inflammation , placing Duox1-derived H2O2 upstream of this inflammatory loop . Our results suggest that therapies targeting DUOX1 and H2O2 could provide innovative approaches to the management of skin inflammatory disorders .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
] |
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2014
|
Tnfa Signaling Through Tnfr2 Protects Skin Against Oxidative Stress–Induced Inflammation
|
Rift Valley fever ( RVF ) , a mosquito-borne disease affecting ruminants and humans , is one of the most important viral zoonoses in Africa . The objective of the present study was to develop a geographic knowledge-based method to map the areas suitable for RVF amplification and RVF spread in four East African countries , namely , Kenya , Tanzania , Uganda and Ethiopia , and to assess the predictive accuracy of the model using livestock outbreak data from Kenya and Tanzania . Risk factors and their relative importance regarding RVF amplification and spread were identified from a literature review . A numerical weight was calculated for each risk factor using an analytical hierarchy process . The corresponding geographic data were collected , standardized and combined based on a weighted linear combination to produce maps of the suitability for RVF transmission . The accuracy of the resulting maps was assessed using RVF outbreak locations in livestock reported in Kenya and Tanzania between 1998 and 2012 and the ROC curve analysis . Our results confirmed the capacity of the geographic information system-based multi-criteria evaluation method to synthesize available scientific knowledge and to accurately map ( AUC = 0 . 786; 95% CI [0 . 730–0 . 842] ) the spatial heterogeneity of RVF suitability in East Africa . This approach provides users with a straightforward and easy update of the maps according to data availability or the further development of scientific knowledge .
Caused by a Phlebovirus ( Bunyaviridae ) that affects both humans and livestock , Rift Valley fever ( RVF ) is considered to be one of the most important viral zoonoses in Africa . The RVF virus ( RVFV ) is transmitted from ruminant to ruminant by mosquitoes [1] . Although never demonstrated , there is field , serological and virological evidence of transmission without any use of vectors [2] , suggesting an alternative transmission of the RVFV between ruminants through direct contact . Humans become infected mainly through direct contact with ruminant viremic fluids , such as blood or abortion products , but also through mosquito bites . Although in the majority of human cases RVF infection is asymptomatic or causes mild illness , severe forms are characterized by retinitis , encephalitis or hemorrhagic fever . In ruminants , RVF infection causes abortion storms in groups or flocks of pregnant females and acute deaths in newborns [3] . Both health and economic impacts can be greatly reduced when control measures , such as vaccination , insecticide spraying and dissemination of information , are quickly implemented . The delay between case detection and control measure implementation depends on , among other factors , the efficiency of surveillance networks; therefore , an accurate definition of at-risk areas needs to be monitored along with other factors . RVF virus circulation has been reported in several eco-climatic areas: arid in western Africa and the Arabic Peninsula [4 , 5]; sub-humid in East Africa [6 , 7]; wet forests in central Africa [8]; dam and irrigated agricultural land under hot climatic conditions in Egypt , Mauritania and Sudan [9–11]; and humid highlands in Madagascar [2 , 12] . Depending on the areas of concern , different risk factors have been identified , either for transmission , spread or human and/or livestock occurrence . Potential mosquito vectors of the RVFV belong to the genera Aedes , Anopheles , Culex , Eretmapodites and Mansonia . The majority of the factors driving mosquito vector presence and abundance , thus driving the risk of RVF transmission , are related to climate , water and landscape . The Aedes genus is mostly associated with temporary water bodies such as flooded area , temporary pond , puddles , and rice fields . Culex and Anopheles mosquito breeding areas are diverse and could be temporary ( rice fields , swamps ) or permanent ( lakes , ponds ) bodies of water . Stagnant and permanent water bodies are the habitats of Eretmapodites and Mansonia , respectively [13] . In fact , the presence of temporary water bodies and floodplains are outbreak risk factors for RVF in semi-arid areas in eastern Africa , the Arabian Peninsula and western Africa [4] . In eastern and southern Africa , the risk of RVFV infection has been shown to vary as a function of rainfall , temperature , and a remotely sensed vegetation index ( NDVI: normalized difference vegetation index ) [14 , 15] . Artificial water bodies , such as dams and irrigated rice fields , are also known to be associated with the high abundance of RVFV vectors in western Africa [4] . In addition to eco-climatic factors , cattle density has been identified as a risk factor for transmission of the RVFV [6 , 16] . Habitat , gender , profession , and contact with ruminants and ruminant products have also been identified as risk factors of RVF occurrence in humans [17] . The Horn of Africa has been historically affected by RVF [18] . However , the occurrence of RVF has never been reported in Ethiopia , which shares borders with infected countries , namely , Kenya [6] , northern Somalia [19] , and Sudan [20] . In Uganda , although no outbreak in humans or animals were reported until 2016 , a recent serological survey revealed that RVFV was endemic in goats in four districts [21] . In Kenya and Tanzania , where RVF is endemic , historical knowledge indicates that ‘dambos’ are areas at risk of RVF [22] . Moreover , recent eco-epidemiological studies identified the main environmental risk factors for RVF , which has allowed for health targeted surveillance by health authorities [6 , 14 , 23] . However , the application of these models to regions outside of the study area of interest may lead to incorrect inferences . Moreover , information related to the suitability of both Ethiopia and Uganda ecosystems for the transmission of the RVFV are scarce . Given this lack of information , pragmatic approaches must be developed to provide risk maps that could be used for early warning detection and implementation of control measures . Spatial multi-criteria evaluation ( MCE ) is a rapid and pragmatic knowledge-based method adapted for mapping disease suitability in the absence of large epidemiological datasets . Defined as ‘a process that transforms and combines geographical data and value judgments ( expert and bibliographic knowledge , including uncertainties , subjective and qualitative information ) to obtain appropriate and useful information for decision making’ [24] , this method has been used to map suitable areas for RVF transmission in Africa , on a continental scale [25] and in Senegal [26] , and in the European countries of Spain [27] and Italy [28] . However , the predicted maps could not be validated for European countries which are disease-free regions , while in Senegal the validation could only be performed in a qualitative way [26] . Moreover , in these studies , only the ‘amplification step’ , defined as the local transmission of the RVFV to its hosts by mosquito vectors , was considered and did not account for the possible transportation of the virus from a primary outbreak to secondary foci in a ‘spread step’ . This process may involve different risk factors than those of the amplification step , such as animal trade [12 , 29] . The goal of the present study was to adapt a geographic knowledge-based method [25] to identify suitable areas for RVF amplification and spread in four Eastern African countries , namely , Kenya , Tanzania , Uganda and Ethiopia , and to assess the predictive accuracy of the model using livestock outbreak data from Kenya and Tanzania .
Under suitable conditions and after the introduction or low-level transmission of a given pathogen , the pathogen can be locally transmitted to a ‘primary’ host through direct or vectorial transmission and then transferred from the primary infectious host to several secondary hosts; this is the “amplification” process [30] . Therefore , ‘spread’ is defined as the transportation of the pathogen from the primary outbreak to secondary foci . In this study , ‘suitability’ is defined as the ability of a habitat to support either the amplification or the spread of RVF . Amplification is a necessary condition for primary RVF occurrence . Both amplification and spread are needed for secondary outbreaks . Following the spatial multi-criteria evaluation ( MCE ) methodology that has been detailed elsewhere [25 , 31] , we first identified the amplification and spread risk factors of RVF through a literature review . PubMed and ISI Web of Knowledge were searched for articles published from 1980 to 2014 using the search terms ‘‘rift valley fever” AND ( separately ) ‘‘model” or ‘‘spatial” , or “risk factors” or “analysis” using the ‘‘all fields” option to allow for the retrieval of articles in which the search terms appeared in the titles , abstracts , or keywords . Inclusion criteria were reviews and/or articles using expert knowledge , and/or statistical and mathematical modelling approaches to model RVF risk to ruminants or humans . A total of 62 references were thus included . In Table 1 , we listed the factors associated with the risks of amplification and spread of RVF according to the published literature review . Only risk factors that could be mapped were selected for the risk mapping process . The following risk factors were thus included: A search was conducted to obtain digital geographical data for each identified risk factor ( Table 1 ) . The different sources of the data that were used and their main characteristics are provided in S1 File and S1 Table . The data were imported into a geographic information system ( GIS ) and processed to produce standardized spatial risk factor layers , namely a mosquito index ( reflecting the suitability for RVF mosquito vectors ) , sheep density , goat density , cattle density , proximity to markets , road density , railways density , proximity to water bodies , proximity to wildlife national parks ( software: ESRI ArcMap and ArcMap Spatial Analyst Extension , Redlands , CA , USA ) . At the end of the process , each image layer was raster-based , with pixel dimensions of 300 m x 300 m . The scale of all spatial risk factor layers was continuous , ranging from 0 ( completely unsuitable ) to 1 ( completely suitable ) . The different sources of the data used and the calculation method of the standardized geographical layers are provided in S1 File . The resulting maps for the risk factor layers are presented in S1 Fig . We assumed that the weight of each risk factor in the amplification and spread processes were not equivalent . For example , small ruminants are known to be more susceptible than cattle for the transmission of the RVFV [42] and , therefore , more prone to play a more important role during the amplification phase than the latter . Markets are aggregation points where ruminant herds meet and have potential contact with each other before returning back to their living area; markets are , therefore , more important for spread than roads that may be used by herders for travelling . Based on the literature review and our own expertise , we ranked RVF risk factors for virus amplification and spread according to their putative relative importance in both processes [78] . Factors were compared two at a time: 1 ) We first specified whether risk factor A was more or less important than risk factor B; and 2 ) We specified the degree of importance of factor A regarding factor B on a nine-point scale using the Saaty scale ( factor A can be extremely more important , very strongly more important , strongly more important , moderately more important , equally important , moderately less important , strongly less important , very strongly less important or extremely less important than factor B ) , resulting in a pair-wise comparison matrix . A numerical weight was then derived for each risk factor from the pair-wise comparison matrix [79] . We calculated pair-wise comparison matrices separately for amplification and spread , considering the vector distribution being much more important in the amplification process than in the virus spread phase . Then , three different maps were generated , considering two distinct groups of risk factors and their associated weights for the amplification and spread steps . Assuming that vector presence is a necessary condition for RVF amplification , the suitability of areas for RVF amplification was calculated for each raster cell as the product of the mosquito index map ( computed as described in S1 File ) and a weighted linear combination ( WLC ) of each of the standardized geographical risk factor layers associated with RVF amplification using its corresponding weight . Regarding the spread process , the raster maps for RVF risk factors associated with RVF spread were combined by computing a WLC with their corresponding weights . The suitability maps for amplification and spread were then combined to create two different suitability maps for RVF occurrence . First , the values of suitability indices for amplification and spread were recoded in three classes ( low/medium/high suitability ) using a quantile discretization . These two recoded maps were then merged into a primary synthetic RVF suitability map with nine classes corresponding to all possible combination of amplification suitability ( low/medium/high ) and spread suitability ( low/medium/high ) . Second , the areas with the highest risk ( suitability estimates greater than 0 . 1 , i . e . , in the 90th percentile ) of RVF amplification were selected . The Euclidean distance between these areas was calculated and transformed into a ‘proximity to RVF amplification areas’ index , which assumed a sigmoid-shaped decreasing relationship between 0 and 100 km and negligible risk thereafter . Finally , suitability estimates for RVF occurrence , combining the spread and amplification processes , were computed as the product of the suitability estimates for RVF spread and the proximity to RVF amplification areas index , resulting in a second synthetic RVF suitability map expressed as a continuous suitability index . A sensitivity analysis ( SA ) was conducted to assess the sensitivity of the method to the expert choices . To determine the effect of a change in the weights applied to each risk factor , a range of weight values to explore was defined by adding and subtracting 25% from the original weights . Ten weight values within this range were tested ( +/-5% , +/- 10% , +/-15% , +/-20% and +/-25% ) . Each of the newly calculated weights was incorporated into the modelling process , while other factor weights were proportionally decreased or increased such that the sum of the weights equaled 1 . For each combination of weights obtained , maps of suitability indices for RVF amplification , spread and occurrence were calculated , and a total of 169 suitability maps for RVF occurrence were generated . Based on these different realizations , the contribution of the different risk factor weights to model output variance was calculated for each country ( see S2 File for details ) . Finally , an uncertainty surface was produced . It represented the standard deviation of the different suitability maps resulting from the change in weights [80] . RVF outbreaks in livestock reported in Kenya and Tanzania between 1998 and 2012 were collected to assess the consistency of RVF suitability map ( Source: FAO EMPRES-i database: http://empres-i . fao . org ) . A total of 145 outbreaks were located using geographic coordinates ( Fig 1 ) . Then , 150 locations of disease ‘pseudoabsence’ data were randomly generated in these two countries , under the condition of being 25 km from another ‘pseudoabsence’ or outbreak location . The value of the quantitative suitability estimates for RVF occurrence was extracted for each ‘presence’ or ‘pseudoabsence’ location and the AUC ( area under curve ) of the ROC curve [81] , and the sensitivity and specificity were calculated to evaluate the quality of the suitability map .
The resulting weights of risk factors for RVFV amplification and spread are presented in Table 2 ( see S1 and S2 Tables for the details of the pair-wise comparison matrices ) . Regarding amplification , we assumed that the mosquito index was the most important factor , and a necessary condition for RVFV amplification . Then , small ruminant densities were identified as important factors , followed by ( in descending order ) cattle density , proximity to markets that are aggregation points for animals , proximity to roads , water bodies and railways , and proximity to wildlife parks . Regarding spread , we considered that viremic hosts were the most important means of virus dissemination and that markets were , again , an aggregation point for people and their herds . The proximity to roads and water bodies were also important factors because they allow for trade movements . Finally , proximity to wildlife national parks and the mosquito index were factors of low influence in the spread process . Fig 2 presents the different maps produced from the MCE process: a map of suitability for RVF virus amplification ( Fig 2a ) , a map of suitability for RVF spread ( Fig 2b ) and a final map of suitability for RVF occurrence in domestic ruminants ( Fig 2c ) ( maps of all standardized risk factors are provided in S1 Fig ) . According to the results of the MCE , areas suitable for RVF amplification were located in the low elevation areas of Kenya ( the eastern coast , the northeastern portion that borders Ethiopia and Somaliland , and , to a lesser extent , the northwest region bordering Uganda ) , Tanzania ( northeastern portion ) and Ethiopia ( in the Northwest and Southwest ) . Uganda presented a very low suitability for RVF amplification in domestic ruminants . In addition , the suitability map for RVF spread in domestic ruminants ( Fig 2b ) showed a different pattern , identifying the highlands of Ethiopia , Kenya , and Uganda as areas favorable to RVF spread . In Tanzania , areas suitable for RVF spread were located in the northern part of the country . The combination of the ‘amplification’ and ‘spread’ maps resulted in two final synthetic maps of the areas suitable for RVF occurrence in domestic ruminants that were complementary ( Fig 2c and 2d ) . According to the map that highlights the different categories of amplification/spread combinations ( Fig 2c ) , the majority of eastern Kenya was identified as highly suitable for RVF occurrence , with a medium-to-high suitability for RVF amplification combined with medium-to-high suitability for RVF spread . This pattern was also observed in northeastern Tanzania and southwestern Ethiopia . Areas with medium suitability for both RVF amplification and spread , such as northwestern Tanzania , or areas with low amplification suitability but high spread suitability , such as western Kenya , the majority of Uganda and the Ethiopian highlands , were identified as suitable for RVF occurrence . This first map also highlighted areas with low suitability for RVF occurrence ( low suitability for amplification and spread ) , such as eastern Ethiopia and central Tanzania . Taking into account the proximity of the areas with the highest suitability for RVF amplification , the second synthetic RVF suitability map ( Fig 2d ) highlighted the different patterns of the areas suitable for RVF occurrence: the majority of Kenya was identified as suitable; however , in the three other countries , the areas suitable for RVF occurrence were smaller than those shown in Fig 2c . The uncertainty of the surface-based model produced by the data for the four countries showed that the predictions of the location of suitable areas for RVF occurrence in livestock were robust , meaning that they remained stable when varying the risk factor weights in the ‘amplification’ and ‘spread’ steps . Indeed , the maximum standard deviation ( STD ) of the suitability maps for RVF occurrence was less than 0 . 1 . The results highlighted a spatial heterogeneity in uncertainty , with higher uncertainty in the western parts of Ethiopia and Kenya ( S2 Fig ) . The sensitivity analysis showed that the variation in the suitability index was explained by four factors for the amplification step and by seven factors for spread ( Fig 3 ) . The most sensitive parameters for the amplification step were the sheep density and the proximity to markets , wildlife national parks , and water bodies . Regarding the spread step , the most sensitive parameters were the cattle and goat densities , the density of roads and railways , and the proximity to national parks and water bodies . The importance of these sensitive parameters varied among the four countries , particularly the importance of the livestock densities ( cattle , sheep and goats ) ( Fig 3 ) . The ROC AUC associated with the suitability map for RVF occurrence in Kenya and Tanzania showed a good fitting ( AUC = 0 . 786; 95% CI [0 . 730–0 . 842] ) ( Fig 4a ) , demonstrating the capacity of the model to distinguish ‘presence’ from ‘absence’ locations with good predictive accuracy ( Fig 4b ) . With a cut-off point of 0 . 3 maximizing both sensitivity and specificity , the sensitivity was 0 . 74 , and the specificity was 0 . 75 . A total of 74% ( 107 out of 145 ) of the RVF outbreak locations were mapped in at-risk areas , which were defined as the areas with a suitability index for RVF occurrence greater than 0 . 3 , the cut-off point value maximizing sensitivity and specificity ( Fig 5b ) .
Many regions from Kenya and Tanzania were previously and heavily affected by RVF outbreaks [6 , 82] . However , some areas may be at-risk without having experienced outbreaks in past years . The identification of these areas is essential for implementing risk-based surveillance and reducing the impact of RVF human and animal outbreaks in the coming years . Until 2016 , Uganda and Ethiopia remained free from outbreaks , but their geographical locations as well as the livestock exchanges they have with their neighbors make these two countries highly vulnerable to the disease . In this context , the implementation of the GIS-based MCE method for RVF risk mapping appeared to be a very efficient method to map suitability areas for the amplification and spread of the virus based on freely available geographic data . To our knowledge , this is the first study aiming to produce regional suitability maps for RVF using MCE methodology combined with outbreak dataset validation . Validation of the suitability map using disease presence and background data randomly generated produced good results according to the ROC AUC method ( AUC = 0 . 786 ) . However , the use of randomly generated ‘pseudoabsence locations’ may be controversial; indeed , the absence of reported outbreaks is not an evidence of absence of pathogen transmission . The results of regional serological surveys may give a more precise evaluation of the RVF suitability map . Nevertheless , 74% of the reported RVF outbreaks in livestock were located in areas with the highest predicted suitability for RVF occurrence ( Fig 5b ) . Interestingly our MCE-based model performed better than other predictive models based purely on climatic anomalies and previously validated with human outbreaks [14] . These models , which showed the highest accuracy in the Eastern African region , included 65% of the human case locations in predicted at-risk areas . Two human cases of RVF have been reported in early March 2016 in the Kabale District , southwestern Uganda [83] . The outbreak occurred in an area that was identified by our model as poorly suitable for RVF amplification but highly suitable for RVF spread ( Fig 5a ) . This result is highly consistent with the socio-economic and ecological environment of Kabale district . Indeed , Kabale is an important commercial center with six animal markets , a situation associated with a higher risk for RVF spread according to our assumptions . Being outside of the ‘potential epizootic area mask’ [14] , this area is not predicted by the climate-based model [83] . Despite the strong 2015–2016 El Niño phenomenon and the associated abnormal rainy season in East Africa , no substantial climatic anomalies were observed in the Kabale area during the 2016 epidemics . Differently from the southeastern and central districts in Uganda and neighboring countries , such as Kenya and Tanzania , both cumulative precipitation as well as NDVI values were lower than or equal to average in Kabale area during the period September 2015 to February 2016 , except for short periods in October and December 2015 . We therefore hypothesize a little role of vector-borne transmission in the Kabale outbreak . Thus , our results highlighted the importance of taking into account livestock data and the factor of animal trade in addition to environmental factors to develop predictive maps of RVF occurrence . Moreover , these maps increase the confidence level for the approach applied to RVF free-areas [27 , 28] . Indeed , the MCE approach we applied to four countries of eastern Africa was very similar to previous modelling studies that used the same approach in different geographic contexts [25–28] . All of these studies considered two main categories of risk factors: on the one hand , those related to domestic ruminant densities , and on the other hand , those related to vector presence ( i . e . , vector distributions or proxies of vector distributions , such as temperature , elevation , rainfall , and proximity to aquatic areas ) . One of the distinctive features of our study was the ability to distinguish RVF amplification and spread steps in the modelling process , thus considering risk factors related to animal trade and movements ( markets , roads and railways ) . Moreover , the hypothesized role of wildlife reservoirs in the amplification and spread of RVF was considered . Identifying areas of low amplification with high spread suitability and vice versa ( Fig 5a ) was expected because these two epidemiological phenomena imply different mechanisms: vector and host densities favor local amplification , whereas animal movements favor the long-range spread of the disease . From a control perspective , surveillance strategies should be adapted; active surveillance in sentinel herds would be relevant in amplification areas that act as virus sources for areas that are not at risk of amplification , and analysis of the trade network and the existing links between amplification areas and other regions could be used as an early warning tool to protect spread areas from viremic ruminant introduction in case of primary foci . However , the limits of our method must be noted . First , in the absence of homogeneous information on RVFV vector abundance and distribution in our study area , we used environmental variables to map a vector index reflecting the suitability of locations for the presence of RVF vectors . These variables were identified through a study performed in Kenya; this study may not be perfectly relevant for the three other countries because this vector suitability map needs to be validated by landscape-targeted mosquito trappings in each country . Among mosquito species vectors recorded in the countries of concern , several were demonstrated to be competent in the lab [42] . However , even if competency measures were to provide elements to infer the potential role of a given mosquito species in RVF outbreaks , these measures are not sufficient to definitely incriminate the species . Indeed , mosquito abundance and foraging behavior are major elements that also shape the epidemiology of arboviruses . Better knowledge of these entomological characteristics should be considered to improve the vector index map . Second , the spatial scale chosen for mapping the suitable areas for disease transmission has a great impact on the produced maps due to the spatial resolution of the data used to calculate the risk factors and the choice of the risk factors included to map suitability areas for pathogen transmission . Moreover , the weight attributed to each risk factor may differ between regional and national scales . In this study , we provided regional maps of suitability for RVF; however , maps produced on a national scale with higher spatial resolution , derived from risk factors and weights discussed with experts of each country ( with particular attention paid to the most sensitive weights identified by the SA ) , would be more accurate and useful for surveillance and control purposes . Moreover , the threshold used to define the areas highly suitable for virus amplification ( 90th percentile threshold considering the whole study area ) may introduce a bias in suitability predictions . This threshold value should be adapted for each country to provide better predictions at the national level . Lastly , limitations of the produced map are related to the availability of data used as risk factors and their quality . For instance , due to a lack of geographic data on the locations of veterinary services and surveillance networks within each country , this information was not taken into account in our model , although both are key factors for the control of animal diseases—the spread intensity and magnitude from primary foci depending on the detection delay and , thus , on the efficiencies of veterinary and surveillance services . Moreover , the immunological status of ruminant populations induced by previous virus circulation episodes or vaccination campaigns were not taken into account although they are important factors , as demonstrated in South Africa in 2010 [84] . Additional information on how landscape features and socio-economic factors impact domestic and wild ruminant movements may also be important to refine the cost distance calculations of markets , water and wildlife park proximity indices . In this study , we focused on mapping the areas suitable for RVF amplification and spread; thus , we focused on the spatial dimension of RVF risk . Although transmitted by mosquitoes and probably by direct contact , RVF is a seasonal disease , occurring during or at the end of the rainy season when mosquito abundance is at its highest . Future work should take into account this temporality to provide seasonal suitability maps for RVF transmission in livestock . Coupling climate-based models [14] with the RVF suitability map , which includes livestock and commercial variables , would allow for the development of seasonal suitability maps for RVF transmission in livestock . However , it must be stressed that this requires a good understanding of the drivers of RVF emergence . Indeed , with the exception of Kenya , where a strong association was demonstrated between heavy rainfall events and outbreak occurrence [22] , rainfall may not be the only key factor for RVF emergence . Host density , associated with suitable climatic conditions and the introduction of the virus by ruminant trade , probably led to the 2000 outbreak in Yemen [32] . In Madagascar in 2008 , no abnormal rainfall was noticed before the outbreak [85] . In Senegal in 2003 , an intense transmission was described without any abnormal rainfall [44] . Soti et al . ( 2012 ) observed that in this region , the rainfall pattern rather than rainfall abundance could be responsible for triggering outbreaks [86] . Therefore , the seasonality of outbreaks should be incorporated in models with caution , depending on the area considered .
The present study confirmed the capacity of GIS-based MCE method to synthesize available scientific knowledge and map with accuracy the spatial heterogeneity of RVF suitability in four countries of East Africa . Moreover , such an approach enables users a straightforward and easy updating of the maps according to data availability or scientific knowledge development to include more precise geographic data or additional risk factors and to modify the weights of each factor .
|
Rift Valley fever ( RVF ) is a zoonotic disease affecting ruminants and humans . It occurs mostly in Africa , causing human deaths and important economic losses in the livestock sector . The RVF virus ( RVFV ) is transmitted from ruminant to ruminant by mosquitoes . Different climatic , environmental , and socio-economic factors may impact the transmission of the virus . Our work uses all current available knowledge on the epidemiology of the disease and geographic data to map areas suitable for RVFV . The study area includes four East African countries: Kenya , Tanzania , Uganda , three countries which have been historically affected by RVF , and Ethiopia , where the disease has never been reported but which shares borders with infected countries . The resulting maps are compared with the locations of outbreaks reported in livestock . Our results demonstrate the capacity of the spatial multi-criteria evaluation method to map with accuracy the areas suitable for RVF occurrence . Thus , the method we developed provides users with risk maps that could be used for early warning detection and implementation of control measures .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"Conclusion"
] |
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"livestock",
"medicine",
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"health",
"sciences",
"rift",
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"virus",
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"laboratory",
"medicine",
"ruminants",
"pathogens",
"tropical",
"diseases",
"microbiology",
"vertebrates",
"geographical",
"locations",
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"viruses",
"tanzania",
"neglected",
"tropical",
"diseases",
"bunyaviruses",
"africa",
"infectious",
"diseases",
"zoonoses",
"medical",
"microbiology",
"epidemiology",
"microbial",
"pathogens",
"agriculture",
"people",
"and",
"places",
"kenya",
"viral",
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] |
2016
|
Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable Areas for Rift Valley Fever Transmission in Eastern Africa
|
F-box proteins share the F-box domain to connect substrates of E3 SCF ubiquitin RING ligases through the adaptor Skp1/A to Cul1/A scaffolds . F-box protein Fbx15 is part of the general stress response of the human pathogenic mold Aspergillus fumigatus . Oxidative stress induces a transient peak of fbx15 expression , resulting in 3x elevated Fbx15 protein levels . During non-stress conditions Fbx15 is phosphorylated and F-box mediated interaction with SkpA preferentially happens in smaller subpopulations in the cytoplasm . The F-box of Fbx15 is required for an appropriate oxidative stress response , which results in rapid dephosphorylation of Fbx15 and a shift of the cellular interaction with SkpA to the nucleus . Fbx15 binds SsnF/Ssn6 as part of the RcoA/Tup1-SsnF/Ssn6 co-repressor and is required for its correct nuclear localization . Dephosphorylated Fbx15 prevents SsnF/Ssn6 nuclear localization and results in the derepression of gliotoxin gene expression . fbx15 deletion mutants are unable to infect immunocompromised mice in a model for invasive aspergillosis . Fbx15 has a novel dual molecular function by controlling transcriptional repression and being part of SCF E3 ubiquitin ligases , which is essential for stress response , gliotoxin production and virulence in the opportunistic human pathogen A . fumigatus .
The ubiquitin 26S proteasome system ( UPS ) controls the life span of specific regulatory proteins , which are required for coordinated development , signal transduction and DNA maintenance . Target proteins are linked to ubiquitin by the sequential action of E1 , E2 and E3 enzymes . A crucial step during this enzymatic cascade is carried out by E3 ubiquitin ligases , which recognize their specific substrate and catalyze the transfer of ubiquitin . SCF-complexes are multi-subunit E3 enzymes consisting of three major subunits ( Cul1 , Skp1 and Rbx1 ) , which form the core enzyme and an exchangeable set of substrate-specific adaptors called F-box proteins [1 , 2] . The F-box domain of these adaptors is an N-terminal binding site of approximately 45 amino acids . It binds to the Skp1 linker to connect to Cul1 . The human genome encodes 69 F-box proteins and defects in F-box mediated ubiquitination are associated with various diseases like diabetes , Parkinson or cancer [3–5] . Only little is known about the role of F-box proteins in virulence of fungal pathogens , though fungal F-box proteins play important roles for cellular development , transcription , signal transduction and nutrient sensing [6–8] . Aspergillus fumigatus is a soil borne , ubiquitously distributed filamentous fungus , growing on organic matter [3 , 9] . Besides its saprophytic lifestyle A . fumigatus also acts as an opportunistic human pathogen and causes life-threatening invasive pulmonary aspergillosis ( IPA ) in immunocompromised hosts . High mortality rates of up to 90% among infected patients are linked to azole resistance , the lack of new antifungals and increasing numbers of immunosuppressive therapies [9–12] . Great efforts have been conducted to identify virulence factors , which discriminate A . fumigatus from its closely related but significantly less pathogenic relative Aspergillus nidulans [13–19] . Virulence of A . fumigatus is presumably the result of a complex multifactorial network , rather than unique and sophisticated virulence factors . A . fumigatus pathogenicity is based on small infectious conidia and its ability to rapidly adapt to constantly changing conditions including high temperature , nutritional changes , hypoxia or high pH [4 , 20] . This is further supported by the production of secondary metabolites ( SM ) such as melanins , which protect from UV radiation or the immunosuppressive mycotoxin gliotoxin [21–23] . The rapid responses of A . fumigatus to environmental stressors are linked to distinct evolutionary conserved molecular mechanisms , which are often part of development regulating processes [1 , 2] . A recently identified important developmental regulator in A . nidulans is Fbx15 , which is required for sexual and asexual development . Furthermore , Fbx15 accumulates in SCFFbx15 complexes in csn-deficient mutants of A . nidulans [3–5] . The COP9 signalosome ( CSN ) multi-subunit complex plays a crucial role in fruiting body formation , oxidative stress tolerance and SM production in A . nidulans [6 , 7 , 24 , 25] . CSN acts as deneddylase by removing the isopeptide bond of the ubiquitin-like protein Nedd8 from a lysine residue of cullin scaffolds of those E3 ligases , which are not interacting with substrate molecules for ubqituitination [3 , 9] . Repetitive cycles of cullin neddylation/deneddylation are especially important for development because they promote the exchange of F-box adaptors from SCF E3 ligases [9–12] . The genomes of A . nidulans or its pathogenic counterpart A . fumigatus comprise approximately 70 F-box protein encoding genes , of which three ( fbx15 , fbx23 , grrA ) have been reported to influence developmental steps in A . nidulans [4 , 13–18 , 26] . In this study we characterized the molecular function of the Fbx15 counterpart of the pathogen A . fumigatus . We were originally interested whether F-box proteins , which play a crucial role for fungal development in A . nidulans are connected to A . fumigatus pathogenicity . We found a novel dual function for F-box proteins because Fbx15 is not only part of nuclear SCF complexes but also controls the nuclear localization of the SsnF/Ssn6 component of the highly conserved eukaryotic transcriptional co-repressor complex Ssn6/Tup1 . Cellular Fbx15 function is controlled by posttranslational phosphorylation and dephosphorylation during stress . Fbx15 including the F-box domain is required for cellular stress responses , the control of gliotoxin production and for virulence in a mouse model . Fbx15 is fungal-specific and might therefore be an interesting novel target for new drugs to treat invasive aspergillosis .
A . fumigatus fbx15 ( Afu3g14150 ) corresponds to the gene of A . nidulans encoding F-box protein 15 , which is required for development [4 , 20] . Several cDNAs of this gene locus were sequenced and revealed that the A . fumigatus fbx15 gene structure consists of two exons and one intron resulting in a deduced open reading frame of 655 codons for a protein with a predicted molecular mass of 75 kDa ( Fig 1A ) . Alignments of the A . fumigatus Fbx15 primary sequence revealed that high similarities are restricted to the Aspergilli counterparts with similarities between 72 . 8% and 59 . 8% , whereas Fbx15-like proteins of other filamentous fungi like Penicillium chrysogenum or Neurospora crassa share significant lower similarities of 43 . 6% and 24 . 7% respectively ( Fig 1B , S1 Table ) . Two signature motifs are located almost in the middle of Fbx15 and include motif 1 , which is specific for the genus Aspergillus and motif 2 that is also present in other fungi as P . chrysogenum or N . crassa ( Fig 1B , S2 Table ) . Bioinformatics analysis predicts two nuclear localization signals ( NLS ) , which suggest that Fbx15 might exhibit its function in the nucleus ( Fig 1B ) . The function of fbx15 in A . fumigatus was genetically addressed by creating an fbx15 deletion . The mutant strain formed normal colonies on minimal medium ( MM ) , but oxidative stress caused by 3 mM H2O2 abolished growth , whereas the wild type could still grow ( Fig 1C ) . 1 mM H2O2 , which had no impact on wild type , resulted in hyperbranched , swollen hyphae in the Δfbx15 strain ( Fig 1D ) . On media containing superoxides-producing menadione or thiol-oxidizing diamide the Δfbx15 mutant displayed defects in growth and sporulation , which implies a general Fbx15 mediated resistance mechanism against oxidative stress , upstream of distinct stress response pathways ( S1B Fig ) . Severe growth and sporulation defects were also observed when the Δfbx15 mutant was exposed to other stress conditions including elevated temperature , amino acid starvation , microtubule stress , osmotic stress or mutagenic stresses ( S1C Fig ) . The importance of Fbx15 during stress was compared to other F-box proteins , which have been previously shown to be involved in fungal development . Fbx23 is important to initiate A . nidulans asexual development [4 , 21–23] , whereas GrrA is an F-box protein that is required for the maturation of sexual spores [26–29] . These two F-box proteins are conserved from fungi to higher eukaryotes ( S1 Table ) . Characterization of both deletion mutants in A . fumigatus showed more distinct and less prominent phenotypes compared to the Δfbx15 mutant strain . In contrast to the wild type , deletion of fbx23 led to a decreased colony size , whereas the colony size of the ΔgrrA mutant was not affected under normal growth conditions ( Fig 1C ) . Growth defects of Δfbx23 and ΔgrrA in comparison to wild type were observed under amino acid starvation or microtubule stress . Both deletion strains were sensitive to topoisomerase I inhibitor CPT , whereas DNA-methylating MMS had a similar impact on wild type and mutant growth ( S1C Fig ) . These results indicated that Fbx23 as well as GrrA are involved in the DNA replication process , whereas Fbx15 is additionally required for the repair of DNA-damage caused by methylation . Growth under increased temperature or oxidative stress conditions are important virulence factors of A . fumigatus [30] . The deletion of fbx23 led to a reduced colony size under high temperature ( 42°C ) or oxidative stress , when compared to the wild type , whereas the ΔgrrA strain was only slightly affected by H2O2 ( Fig 1C , S1C Fig ) . These data suggest that the three F-box proteins Fbx23 , GrrA and Fbx15 are part of a general stress response in A . fumigatus caused by multiple environmental stressors . The impact of the strain lacking Fbx15 suggests that this protein plays a key role in the fungal stress response . Oxidative stress caused drastic effects on the Δfbx15 mutant , whereas fbx15 overexpression resulted in a wild type like phenotype ( S1A Fig ) . Oxidative stress , in particular by peroxides like H2O2 , leads to an elevated expression of catalases , which function as potent ROS scavenger [31 , 32] . A . fumigatus produces one conidial and two mycelial catalases . The expression of cat1 , which encodes one of the mycelial catalases , was regulated in an Fbx15 dependent manner . The lack of fbx15 led to an approximately 2 . 5x derepressed basal cat1 expression , compared to wild type or a complemented strain during non-stress conditions ( Fig 1E ) . After exposure to H2O2 , the expression of cat1 in the Δfbx15 mutant was further increased , whereas wild type or complemented strain displayed only slight upregulation . Increased cat1 levels were not sufficient to protect the Δfbx15 mutant from oxidative stress . Fbx15 presumably is involved in additional molecular mechanisms , which protect against oxidative stress . The exposure to oxidative stress was analyzed as a possible external signal , which triggers changes in fbx15 expression in A . fumigatus . Fungal cultures were exposed to H2O2 and harvested at different time points within a 120 min period . fbx15 transcript levels were determined with real-time PCR ( RT-PCR ) . A rapid increase of fbx15 expression was observed in the first 20 min reaching its maximum peak at 40 min with a 14-fold increased gene expression . Afterwards the expression decreased to a basal level , which is approximately 4-fold increased compared to the non-induced expression . Proteins from the same samples were extracted to test whether the changes in fbx15 transcript levels are also reflected on protein level . Fbx15 was visualized after immunoblotting by incubation with a polyclonal Fbx15 specific antibody . The general abundance of Fbx15 was very low . A three-fold increase of Fbx15 protein amounts was measured , starting after 40 min of H2O2 exposure , which was similar in the complemented strain and absent in the Δfbx15 mutant , mirroring the increased gene expression on the protein level with a delay of 20 min ( Fig 1F , S1D Fig ) . These data suggest that the Fbx15 protein levels are increased as part of a stress adaption response towards H2O2 mediated ROS . Two putative nuclear localization sites of Fbx15 suggest a nuclear function . A similar situation is found for the conserved A . fumigatus F-box protein SconB , which also possesses two nuclear localization signals , and was used as control ( S1 Table ) . Similar to sconB of A . nidulans , sconB is essential for A . fumigatus as we could show with heterokaryon rescue assay ( S2A and S2B Fig ) . Constitutively expressed GFP fusions of Fbx15 and SconB were compared for their subcellular localizations . Both F-box GFP fusions were functional during normal and oxidative stress growth conditions ( S2C Fig ) . Fbx15 and SconB are primarily co-localized in fluorescence microscopy with DAPI stained nuclei , though small subpopulations of both proteins remain in the cytoplasm ( S2D Fig ) . These data suggest a predominant nuclear function for both A . fumigatus F-box proteins . Fbx15 is primarily nuclear but has a significant cytoplasmic subpopulation . Different localizations of Fbx15 could be a result of controlled posttranslational phosphorylation . A bioinformatic analysis of the deduced amino acid sequence of Fbx15 with NetPhos 2 . 0 ( http://www . cbs . dtu . dk/services/NetPhos ) [33–35] predicts in total 15 serine , 11 threonine and 4 tyrosine residues as putative phosphorylation sites ( score value between 0 and 1; cutoff value >0 . 5: S3A Fig ) . An Fbx15 phosphopeptide with a single phosphorylation was identified with mass-spectrometry of purified functional Fbx15-TAP fusions from non-stressed cultures . Analysis of the MS2-spectra of this phosphopeptide with the phosphoRS software [36] revealed serine residues 468 and 469 as potential phosphorylation sites with probabilities of 45 . 5% each , whereas Ser473 only showed a low probability of 8 . 9% ( Fig 2A ) . Theoretical analysis for Fbx15 phosphosites showed a high score value of 0 . 988 for Ser469 , whereas Ser468 had a low score value of 0 . 029 and therefore is unlikely to be phosphorylated ( S3A Fig ) . In contrast no phosphopeptides were identified for purified Fbx15-GFP fusions when the cells were grown under oxidative stress . Fbx15 is presumably phosphorylated during vegetative growth under non-stress conditions at Ser468 or Ser469 ( Ser468/469 ) , whereas it is unphosphorylated when H2O2-mediated oxidative stress is applied . Phosphorylation of Fbx15 under normal growth conditions and dephosphorylation during H2O2-mediated oxidative stress suggests the presence of a phosphatase that might be specifically activated . GFP-traps with Fbx15-GFP recruited GlcA as the only phosphatase that interacts in cultures with or without stress ( S3B Fig ) . A direct interaction of Fbx15 with GlcA could be observed using bimolecular fluorescence complementation ( BiFC ) , which was primarily visible after induction with H2O2 ( S3C Fig ) . The A . nidulans GlcA homolog BimG has been characterized as essential major protein phosphatase 1 , which is associated with thermo tolerance and hyphal morphology , features that were impaired in the Δfbx15 mutant [37 , 38] . Heterokaryon rescue experiments with a glcA deletion cassette and accompanying Southern-hybridizations verified that the situation is similar and glcA is essential for A . fumigatus as well ( S3C and S3D Fig ) . The dephosphorylation rate of Fbx15 in response to H2O2 was quantified . Fbx15-GFP expressing strains were grown in liquid cultures and subjected to oxidative stress by adding 3 mM H2O2 . Fbx15-GFP from these cultures was purified and treated with an antibody against phosphorylated Ser/Thr residues . The rate of dephosphorylation was quantified against the overall amount of Fbx15-GFP , determined by an anti-GFP antibody ( Fig 2B ) . 40% dephosphorylation upon H2O2-treatment indicated that Fbx15 becomes dephosphorylated in an oxidative stress-dependent manner . The Fbx15 dephosphorylation sites were localized by TMT isobaric mass tag labeling and LC-MS/MS [39] . Fbx15-GFP was enrichment from cultures before and after H2O2 treatment and separated on a coomassie-stained SDS-PAGE . After tryptic digestion , peptides from the untreated culture were labeled with TMT127 ( heavy ) whereas TMT126 ( light ) was used for all time points of the H2O2 treated cultures . The sample of time point zero was individually mixed with all other time points and analyzed by LC-MS/MS . The parameters for fragmentation during mass spectrometry were set to identify only the phosphorylated peptide of Fbx15 . Specific ratios of the heavy labeled phosphopeptide were obtained from time point zero against the light labeled phosphopeptides from the other conditions . These values were quantified against the ratios of two unmodified reference peptides of Fbx15 , which represented the overall amount of purified Fbx15 . The reciprocal values of these ratios are decreasing , thus reflecting a specific dephosphorylation on Ser468/469 ( Fig 2C ) . Fbx15 , which becomes phosphorylated at Ser468/469 under non-stress conditions , is presumably dephosphorylated when cells encounter H2O2-mediated oxidative stress by the Fbx15 interacting phosphatase GlcA/BimG . The canonical function of F-box proteins is their ability to form ubiquitinating SCF ligase complexes by binding to the SkpA adaptor . A . fumigatus Fbx15 and SconB interactions to the SkpA SCF adaptor were compared by using BiFC . Both Fbx15 and SconB protein fusions produced an YFP-signal , indicating F-box protein-SkpA interactions . SconB interacted with SkpA almost exclusively in the nucleus ( >90% ) , whereas 74% of the Fbx15-SkpA interaction was cytoplasmic ( Fig 2D and S4A Fig ) . The Fbx15 serine codons of wild type positions S468 and S469 were replaced to alanine residues to mimic a constantly dephosphorylated Fbx15[S468|9A] to analyze whether Fbx15 phosphorylation is relevant for the location of the Fbx15-SkpA interaction . Interaction of Fbx15[S468|9A] variant with SkpA resulted in a nuclear signal ( Fig 2D ) . Dephosphorylated Fbx15 primarily interacts with SkpA in the nucleus to form SCF-complexes . Growth without stress rather results in phosphorylated Fbx15 with presumably only limited amounts of dephosphorylated Fbx15 in the nucleus . Dephosphorylation of Fbx15 is triggered by H2O2-mediated ROS . The impact of the phosphorylation state of Fbx15 on its ability to interact with SCF-complexes was analyzed by replacing the serine residue S469 in Fbx15 with aspartate to mimic a constant phosphorylation Fbx15[S469D] or S468 and S469 with alanine to mimic unphosphorylated Fbx15[S468|9A] . Both constructs and the wild type gene were expressed under the native promoter as Fbx15-RFP fusions . Immunoblotting confirmed that all Fbx15 versions were more abundant after H2O2 exposure ( Fig 3A ) . RFP-trap co-purifications followed by LC-MS/MS with wild type Fbx15 or phosphomutant variants after oxidative stress induction with H2O2 were performed to identify which subunits of the SCF-ligase machinery interact with Fbx15 . Further analysis included MaxQuant quantitative proteomic software in conjunction with Perseus software for statistical analysis , with a focus on the subunits of the SCF-ligase machinery . SCFs are activated by the RING protein RbxA-mediated interaction with E2 ubiquitin-conjugating enzyme and covalent cullin modification by ubiquitin like NeddH ( Fig 3B ) . SCF core components SkpA or CulA interacted with Fbx15 , independently of the phosphorylation-state . Native Fbx15 , which is presumably dephosphorylated at S468/S469 after oxidative stress or unphosphorylated Fbx15[S468|9A] could co-purify NeddH , but not RbxA or an E2 enzyme . In contrast the Fbx15 , mimicking a constant phosphorylation , co-purified all subunits for an active SCF complex . NeddH was more abundant in co-purifications of the constantly phosphorylated Fbx15[S469D] than of unphosphorylated versions of Fbx15 . RbxA and the E2 enzyme UbcM were only purified with negatively charged Fbx15[S469D] , indicating an improved assembly of functional SCF-ligase complexes when Fbx15 is phosphorylated at Ser469 ( Fig 3C ) . Active SCFFbx15 complexes contain therefore more likely phosphorylated than unphosphorylated Fbx15 . Since Fbx15 interacted within SCF complexes the cellular ubiquitination pattern of the Δfbx15 mutant , wild type and the fbx15 overexpression strain were compared before and after induction with H2O2 . Neither the ubiquitination-pattern nor the general protein composition of the Δfbx15-strain was significantly altered in comparison to the wild type or the fbx15 overexpression strain , suggesting that the ubiquitination targets of SCFFbx15 complexes are limited ( S4B and S4C Fig ) . Additional potential interacting proteins for Fbx15 were identified by tandem-affinity-purification ( TAP ) and compared to co-purifications of SconB-TAP fusions . Variants of fbx15 and sconB were created where the conserved proline of the F-box domain was exchanged to a serine . This should weaken the F-box-SkpA binding and enable recruitment of SCF-independent interaction partners . The exchanged proline in SconB led to significantly increased protein stability , whereas stability of Fbx15 was not altered ( S5 Fig ) . This reflects a possible autocatalytic mechanism for SCFSconB , where SconB is ubiquitinated within its own SCF ligase and eventually degraded [40 , 41] , whereas Fbx15 stability seems to be independent of SCFFbx15 . SconB-TAP recruited less proteins ( 22 ) than Fbx15 ( 38 ) . SconB interactors include transcriptional activator MetR as known SCFSconB target [1] and 11 proteins that were identified for both F-box proteins including SCF subunits CulA and SkpA . Only Fbx15 was able to co-purify three subunits of the CSN deneddylase , which acts on neddylated cullin complexes , which do not interact with substrates [3] . This might reflect a highly dynamic assembly/disassembly of SCFFbx15 complexes ( S3 Table ) . The predominant nuclear localization of Fbx15 and SconB is consistent with nuclear interaction partners , which were identified during TAP co-purifications . These included two transcriptional regulators ( RcoA/Tup1 and a putative APSES transcription factor ) , a DNA repair enzyme ( AFUA_2G06140 ) and a single-stranded DNA binding protein ( AFUA_5G07890/Rim1p ) ( Fig 4A ) . The fact that Fbx15 and SconB recruited these proteins might reflect a tight stability control by more than one F-box protein . In addition Fbx15 recruited specifically three transcriptional regulators ( OefC , SrbB and SsnF/Ssn6 ) , a nuclear GTPase ( AFUA_4G8930/Nog2p ) and the nuclear pore protein Nic96 . ( Fig 4A ) . A potential candidate , responsible for Fbx15 phosphorylation is the interacting cyclin-dependent serine/threonine kinase NimX/Cdc28p , which is required for cell cycle control and conidiophore morphology in A . nidulans [6] . A direct interaction of Fbx15 and NimX could be verified by BiFC assay , where the interaction signal from the reconstituted YFP was observed predominantly in the cytoplasm ( S6A Fig ) . However , the NimX homolog in A . fumigatus is presumably encoded by an essential gene as we could show by heterokaryon assay and Southern hybridization ( S6B and S6C Fig ) . Fbx15 and SconB interaction partners include possible targets for SCF mediated ubiquitination . Three Fbx15 interacting CSN subunits suggest a more dynamic assembly/disassembly of SCFFbx15 in comparison to SCFSconB . An interesting finding is that both F-box proteins co-purified RcoA/Tup1 as part of the conserved transcriptional co-repressor complex RcoA/Tup1-SsnF/Ssn6 , but only Fbx15 recruited the SsnF/Ssn6 subunit of this complex . The yeast Ssn6-Tup1 co-repressor complex affects the expression of 7% of all genes with emphasis on stress responses [9] . A homo-tetramer of RcoA/Tup1 repressor subunits is connected to one SsnF/Ssn6 adaptor protein , which binds to a DNA-binding protein , escorting the repressor complex to the target genes . The corresponding SsnF encoding gene of the model A . nidulans is essential for growth , whereas the yeast counterpart Ssn6 is dispensable [9 , 42] . With heterokaryon rescue and subsequent Southern analysis we could show that ssnF is not only essential for A . nidulans but also for A . fumigatus ( S6D and S6E Fig ) . A BiFC signal verified the direct interaction of Fbx15 and SsnF in A . fumigatus under non-stress conditions in the cytoplasm , often located close to nuclei . Unphosphorylated Fbx15[S468|9A] which reflected H2O2-mediated oxidative stress conditions interacted with SsnF predominantly in the nucleus , similar to the Fbx15-SkpA interaction ( Fig 4B ) . Fbx15 levels in different strain backgrounds did not influence SsnF stability in cycloheximide ( CHX ) protein-stability assays ( Fig 4C ) . The amount of SsnF-GFP in wild type or fbx15 deletion mutant did not change in the absence or presence of 3 mM H2O2 nor did SsnF exhibit an Fbx15 dependent ubiquitination pattern ( S6F and S6G Fig ) . This suggests that SsnF is not a significant substrate of an active SCFFbx15 E3 complex but acts as additional physical Fbx15 interaction partner . SsnF-GFP is localized in the nucleus in the presence of Fbx15 , but accumulates at the nuclear envelope presumably enriched at nuclear pore complexes in the absence of Fbx15 . This suggests that SsnF import is impaired without Fbx15 ( Fig 5A ) . It was analyzed whether dephosphorylation of Fbx15 is involved in SsnF nuclear localization . In fbx15 wild type as well as in the fbx15 variant strain ( Fbx15[S469D] ) , that mimics constant phosphorylation , SsnF-GFP was localized in the nucleus . In the fbx15 codon replacement mutant expressing the Fbx15[S468|9A] variant , which cannot be phosphorylated , SsnF accumulated at the nuclear envelope similar to the Δfbx15 strain . Correct nuclear SsnF-GFP localization could be either abolished after Fbx15 wild type dephosphorylation due to H2O2 or in the presence of the Fbx15[S468|9A] variant , which cannot be phosphorylated . In contrast , oxidative stress did not interfere with SsnF-GFP nuclear localization , when only the negatively charged Fbx15[S469D] was present , which mimics constant phosphorylation ( Fig 5B ) . These data support that phosphorylation of Fbx15 under non-stress conditions favors nuclear localization of SsnF , whereas Fbx15 dephosphorylation during H2O2-mediated oxidative stress leads to an accumulation of SsnF at the nuclear envelope . The contribution of the Fbx15[S468|9A] variant , which cannot be phosphorylated and interferes with nuclear SsnF , to the fungal oxidative stress response was analyzed . Growth tests on different oxidative stress providing media showed that mutant strains expressing the non-phosphorylable Fbx15[S468|9A] as well as the phosphate mimicking Fbx15[S469D] showed mild colony growth reductions in comparison to the wild type version of Fbx15 ( Fig 5C ) . Similar effects where observed on medium containing superoxide-producing menadione , where the phosphomutant strains showed a slight growth reduction compared to the RFP-tagged wild type Fbx15 . Growth on thiol oxidizing media was not affected ( S7 Fig ) . These data support that Fbx15 phosphorylation and dephosphorylation and the control of cellular SsnF localization contribute to an appropriate oxidative stress response . The function for oxidative stress resistance of the F-box domain of Fbx15 as link to E3 ubiquitin ligases was examined . A gene for an RFP-tagged Fbx15 variant , that lacks the N-terminal F-box domain , and the two additional combinations with the alternative phosphovariants were constructed and the resistance against oxidative stress was tested . Loss of the F-box in all three strains completely abolished growth on media containing H2O2 , similar to the Δfbx15 mutant ( Fig 5C , S7 Fig ) . These results indicate that the F-box , which is required to assemble Fbx15 into SCF ubiquitin ligases , is essential for the fungal oxidative stress response . The function of the oxidative stress controlled phosphorylation status of Fbx15 , which channels SsnF nuclear localization , is presumably part of an additional fine-tuning of the appropriate cellular oxidative stress response . A group of genes , which are usually repressed during normal fungal growth , are secondary metabolite genes . Defects in the CSN-regulated ubiquitination machinery result in a drastic misregulation of secondary metabolite formation , such as mycotoxins [13] . A potent immunosuppressive mycotoxin in A . fumigatus is gliotoxin , which is considered as one of multiple virulence factors [20] . It was analyzed , whether Fbx15 is part of the transcriptional repression of gliotoxin synthesis genes , because SsnF , which represents a part of a general conserved transcriptional repression mechanism , is accumulated at the nuclear periphery in the Δfbx15 mutant . We analyzed the expression of gliZ , which encodes a transcriptional activator that has been shown to drive the expression of gli-genes encoded in the gliotoxin gene cluster and gliP , encoding the non-ribosomal peptide synthetase GliP with a key role in gliotoxin biosynthesis [21 , 23] . The expression of gliZ and gliP in the Δfbx15 mutant increased by almost thirteen and five times respectively in comparison to wild type or complemented strain . Gliotoxin is a toxic metabolite for A . fumigatus itself . Expression patterns of gli genes , which are important for detoxification mechanisms , were examined . These include gliK , which is required for gliotoxin biosynthesis and secretion or gliT encoding a oxidoreductase with the ability to reversibly form the toxic disulphide bond of gliotoxin [27–29] . Compared to wild type the gliK and gliT mRNA levels increased significantly in the Δfbx15 mutant by three and twelve times , respectively ( Fig 6A ) . Gliotoxin production of the Δfbx15 mutant was determined to analyze whether increased transcription of genes involved in gliotoxin production and detoxification correlates to a change in secondary metabolism . High-performance liquid chromatography ( HPLC ) revealed that the gliotoxin production of Δfbx15 was increased by 3-fold compared to wild type ( Fig 6B ) . The phosphomutant versions of Fbx15 , which mediate different localization of SsnF in or outside the nucleus , were included into the analysis of the transcription of gliotoxin biosynthetic and protecting genes . The fbx15 variant that produces the non-phosphorylatable Fbx15[S468|9A] , which leads to cytoplasmic accumulation of SsnF , showed similarly increased gli gene transcript levels as the Δfbx15 deletion strain for gliZ and gliP and a moderately increased expression of gliK and gliT . In contrast , the Fbx15[S469D] variant mimicking constantly phosphorylation of Fbx15 and supporting nuclear localization of SsnF , resulted in a significantly lower expression of gli gene transcript levels as the non-phosphorylatable Fbx15[S468|9A] ( Fig 6C ) . Gliotoxin production levels of strains expressing either variant of unphosphorylated Fbx15[S468|9A] or phosphorylation mimicking Fbx15[S469D] were in a similar range as wild type and significantly lower than the Δfbx15 deletion strain ( Fig 6D ) . These results suggest that Fbx15 is required for repression of gli gene expression . The transcription of gli genes is derepressed and in addition the gliotoxin production is increased in the absence of Fbx15 , when SsnF accumulates at the nuclear periphery . The transcription of gli genes is also increased in the presence of an unphosphorylatable Fbx15 , which excludes SsnF from the nucleus , but gliotoxin production is not increased . This suggests an additional function of Fbx15 at a posttranscriptional layer of control for gliotoxin synthesis , which is independent of its phosphorylation status . An established murine model of invasive pulmonary aspergillosis ( IPA ) was used to analyze whether Fbx15 mediated stress response and gliotoxin production control affect fungal virulence in comparison to wild type , Δfbx23 and ΔgrrA strains . Immunosuppressed mice infected with wild type , Δfbx23 , ΔgrrA or complemented strains displayed normal mortality rates within 14 days , although the Δfbx23 strain displayed a slightly increased virulence ( p = 0 . 03 ) in direct comparison to the wild type . In contrast , the Δfbx15 mutant completely lost its virulence ( Fig 6E ) . The Δfbx15-infected mice did not show any symptoms and had the same clinical appearance as the mock infected control group , treated with phosphate buffered saline ( PBS ) . Histopathology analyses of infected lung tissue were consistent with survival rates . Mice infected with either wild type or complemented strains showed fungal hyphae surrounded by tissue necrosis and extensive immune cell infiltration ( Fig 6F ) . Moderate immune cell infiltrates were found in the lungs of mice infected with the Δfbx15 mutant , but no fungal hyphae could be detected . The fungus was cleared at an early stage of infection , probably by innate immune responses and increased temperature and elevated oxidative stress . Our data suggests that Fbx15 , which is not required for growth without stress , plays a crucial role during infection because it enables A . fumigatus to adapt to innate immune response conditions of the host including limiting nutrition , fever or oxidative stress .
Aspergillus fumigatus is the most prevalent cause for pulmonary infections in immunocompromised patients . High thermo- and oxidative stress tolerance , toxic metabolites and a versatile metabolism allow A . fumigatus to colonize host tissue [30] . We identified the fungal-specific F-box protein Fbx15 , which is not required for vegetative growth in the absence of stress , as key determinant for stress response , controlled gliotoxin production and virulence . A novel dual molecular function was discovered for Fbx15 . Fbx15 can be part of an SCF E3 ubiquitin ligase complex and in addition controls nuclear localization of SsnF as transcriptional repressor subunit . Fbx15 levels are transcriptionally regulated and Fbx15 location in either the nucleus or the cytoplasm is determined by phosphorylation and dephosphorylation , respectively . Fbx15 is a potential target for antifungal drugs , because it is essential for A . fumigatus virulence . Our data demonstrate that Fbx15 plays a crucial role for adaptive responses to environmental changes and general stress response mechanisms in A . fumigatus whereas during non-stress conditions fbx15 is dispensable for normal growth . This correlates with the expression patterns of fbx15 gene transcription and translation , which demonstrated that Fbx15 becomes only abundant during oxidative stress induction . Whether this behavior is similar for different stressors remains to be shown . The low expression levels of fbx15 during non-stress conditions are is required for the nuclear localization of the transcriptional co-repressor subunit SsnF ( Fig 7 ) . The best known example of this repressor complex , conserved in eukaryotes , is yeast Ssn6 ( Cyc8 ) -Tup1 . It affects expression of at least 334 genes during normal growth conditions , which include developmental , metabolic or stress response pathways [31] . Ssn6 acts as an adaptor between a tetramer of Tup1 and additional DNA-binding proteins which mediate sequence specificity [33 , 35] . Tup1 alone is able to promote transcriptional repression in yeast , which might explain the ability of the A . fumigatus Δfbx15 mutant to grow under normal conditions , where nuclear SsnF is compromised . The oxidative stress response gene cat1 , which encodes a mycelial catalase , represents a target gene , which is repressed in a Fbx15-dependent way [32 , 43] . Expression of cat1 is increased in the absence Fbx15 , but this derepression is not sufficient for an appropriate oxidative stress response [5 , 44 , 45] . Oxidative stress results in Aspergillus nidulans in genome-wide transcriptional changes , rather than a specific response of distinct gene groups [7 , 8 , 46] . Derepression of cat1 as well as of several gliotoxin biosynthetic genes correlates with the mislocalization of the nuclear repressor subunit SsnF in the cytoplasm in the absence of Fbx15 . The control of nuclear SsnF localization by the phosphorylation status of Fbx15 contributes but is not the only cause of an appropriate oxidative stress response . The F-box domain of Fbx15 is essential for an appropriate oxidative stress response . This domain serves as binding site for the assembly into SCF ubiquitin ligases . This indicates additional SCF-dependent functions for Fbx15 during oxidative stress . Disassembly of those E3 SCF ubiquitin ligases , which are not binding target substrates for ubiquitination , requires their inactivation by the COP9 signalosome CSN [3 , 47] . The csn-deficient mutant strains of A . nidulans are impaired in this control of cellular ubiquitin ligase activities and show a severe growth phenotype in the presence of oxidative stress [11 , 25 , 48 , 49] . Impaired CSN activity in csn-deficient mutant strains resulted in the enrichment , isolation and identification of SCF-Fbx15 complexes in A . nidulans [17] . This suggests that the F-box mediated assembly of SCF-Fbx15 as well as the disassembly by CSN are crucial for the function of Fbx15 during oxidative stress . Yeast Tup1 corresponds to Aspergillus RcoA and to Groucho/TLE of higher eukaryotes . Multiple repression mechanisms are associated to these complexes such as histone deacetylation , chromatin rearrangements , modification of RNA polymerase II activity and competition with transcriptional activators [14–19 , 50–52] . Repression can even be changed to activation as shown for the transcriptional repressor Sko1 , which inhibits hyperosmotic stress response genes in conjunction with Ssn6-Tup1 . Osmotic stress leads to the phosphorylation of Sko1 , which turns it into a transcriptional activator that in conjunction with Tup1 recruits chromatin-remodeling complexes such as SAGA and SWI/SNF to the respective promoter sites . ATP-driven chromatin remodeling mediated by the SWI/SNF complex is further required for cell cycle progression and activation of DNA damage repair pathways [53–55] . The lack of appropriate molecular derepression or activation of stress response genes during stress contributes to the observed serious defects in fungal growth in the A . fumigatus Δfbx15 mutant . There seems to be an additional posttranscriptional layer of control , which does not depend on the phosphorylation status of Fbx15 . A block of nuclear SsnF in the presence of unphosphorylated Fbx15[S468|9A] results in increased transcription of gli genes but not increased gliotoxin production and has only a partial contribution for the resistance towards oxidative stress . Rapid Fbx15 dephosphorylation during oxidative stress could be triggered by the essential protein phosphatase 2A catalytic subunit GlcA/BimG ( Fig 7 ) . GlcA belongs to the serine/threonine phosphatases and shares homology with the yeast protein phosphatase 1 ( PP1 ) catalytic subunit Glc7 . GLC7 is essential for yeast as well , but conditional mutant alleles of GLC7 could be connected to defects in adaptive functions like temperature tolerance , glucose repression , amino acid starvation , cell morphology and DNA damage repair , which are reminiscent to the growth defects of the Δfbx15 mutant on the respective conditions [4 , 22 , 56–59] . Dephosphorylation of Fbx15 results in nuclear clearance of SsnF . This could be due to nuclear export combined with reduced import of SsnF and/or ubiquitin dependent or even independent degradation of SsnF . Nuclear trafficking control is supported by the observation that SsnF is blocked at NPCs in an Fbx15-dependent manner upon stress . In this context a potential ubiquitinating function of Fbx15 towards the nuclear pore complex ( NPC ) subunit Nic96 , which was co-purified during our TAP-tag pull-downs , was a reasonable function for SCFFbx15-ligase complexes to promote nuclear transport control . And although the localization of SsnF-GFP in Δfbx15 background and the localization of Nic96-GFP shared some similarities , we could not observe an Fbx15-specific or oxidative stress-dependent ubiquitination pattern for Nic96 ( Fig 5A , S8 Fig ) . However , the nuclear pore complex is a massive multi-protein complex composed of 30 different NPC-proteins , which are arranged in multiples and finally reach a molecular mass between 66 and 125 MDa [2 , 60] . In 2012 Hayakawa et al . showed that approximately half of the NPC-proteins in yeasts are ubiquitinated , but not necessarily targeted for proteasomal degradation [4 , 61] . It might be possible that Fbx15 plays a role in NPC-protein ubiquitination , which targets NPC-proteins in close proximity to Npc96 and thereby promotes a more general nuclear transport control . Nuclear clearance of SsnF may also be triggered by selective ubiquitin-independent degradation of nuclear SsnF populations . Previous studies have shown that some E3-ubiquitin ligases directly interact with the regulatory particle of the proteasome and thus are able to transfer target proteins to the proteasome for degradation [24 , 25 , 62] . A similar scenario could be responsible for selective nuclear degradation of SsnF , where dephosphorylated Fbx15 incorporates into inactive SCF-core complexes , which have the potential to carry specific target substrates such as SsnF directly to the proteasome . Phosphorylated or dephosphorylated Fbx15 interacts with SsnF at different cellular localizations . However , Fbx15 and SsnF are not present in stoichiometric amounts , which argues against stable Fbx15-SsnF complexes . Phosphorylated Fbx15 interacts with SsnF under non-stress conditions predominantly in the cytoplasm , suggesting a cargo function for Fbx15 , which facilitates the nuclear import of Fbx15-SsnF heterodimers . Fbx15 protein levels are increased during stress and Fbx15 is dephosphorylated . Unmodified Fbx15 interacts with SsnF primarily in the nucleus and might compete with Tup1 interaction . In addition , Fbx15 possibly exhibits a nuclear export function by acting as a cargo receptor for SsnF export . Similar to the interaction with SsnF , Fbx15 interaction with the adaptor protein SkpA , which bridges Fbx15 into SCF complexes was not disturbed due to the dephosphorylation of Fbx15 . However , the interaction was shifted from cytoplasm to the nucleus . The Fbx15 phosphorylation site Ser468/469 is located between two NLSs . Therefore phosphorylation/dephosphorylation events on Fbx15 Ser468/469 might determine nuclear Fbx15 import or export by rearranging the NLS availability . Fbx15 phosphorylation affects SsnF location . Disturbed localization patterns of SsnF in constantly unphosphorylated fbx15 mutants resulted in moderate phenotypes , whereas the additional deletion of the F-box domain of Fbx15 led to impaired oxidative stress resistance similar to the complete fbx15 deletion mutant . This suggests that Fbx15 as subunit of the SCFFbx15 complex is an important player in the stress tolerance mechanism of A . fumigatus . This additional SCF-dependent function for Fbx15 , apart from SsnF localization control , is also supported by the fact that only complete fbx15 deletions led to increased gliotoxin levels in the mutant , whereas constantly unphosphorylated fbx15 mutants showed increased gli-gene expression , which did not lead to increased gliotoxin levels . The formation of active SCFFbx15 complexes was especially promoted in fbx15 mutants , which mimic a constant phosphorylation , indicating an ubiquitinating function of phosphorylated Fbx15-carrying SCF ligases during non-stress conditions . Fbx15 abundance under non-stress conditions is very low and overall ubiquitin-patterns of the cellular pool of proteins were not significantly changed between wild type and Δfbx15 mutants . This suggests that putative target ( s ) of SCFFbx15 are highly specific . The role of F-box proteins in virulence might vary in different pathogenic fungi . A . nidulans ΔgrrA mutants are unable to produce mature ascospores due to a block in meiosis [3 , 26] . We showed here that the deletion of grrA in A . fumigatus did not affect virulence . GrrA shares structural similarity to Fbp1 of the opportunistic human pathogen Cryptococcus neoformans and deletion of fbp1 led to a loss of virulence [10 , 12 , 63] . In contrast to Fbx15 , Fbp1 is not involved in a broad range of stress responses but plays a more specific role in cell membrane integrity where the loss of fbp1 results in a block in meiosis , which finally leads to an impaired sexual sporulation , similar as GrrA in A . nidulans . Fbx15 is a developmental regulator in the model organism A . nidulans , where the deletion of fbx15 results in a complete block in sexual and asexual development [4 , 26] . The deletion of the Tup1 homolog rcoA in A . nidulans leads to a phenotype very similar to A . nidulans Δfbx15 mutant strains , which are blocked in developmental pathways and secondary metabolism [4 , 64] . Thus Fbx15 mediated localization control of SsnF might be a conserved mechanism in filamentous fungi . However , fbx15 deletion mutants in the opportunistic pathogen A . fumigatus did not display a developmental defect , but Fbx15 emerges as key regulator for stress response and virulence . Several virulence factors of A . fumigatus , like mycotoxin production , oxidative stress resistance and nutritional versatility are linked to developmental control mechanisms , which were identified in the apathogenic model organism A . nidulans . This example of a connection between developmental regulators of non-pathogenic fungi and their role for virulence in fungal pathogens might be an interesting paradigm for future approaches to identify novel so far unknown virulence determinants . A . fumigatus Fbx15 is specific to filamentous fungi , which provide the opportunity for drug design . The treatment of invasive aspergillosis is still primarily based on aggressive and toxic antifungal drugs . This disadvantage is aggravated by increasing numbers of A . fumigatus species , with resistances against commonly used medical triazoles [4 , 65–67] . Fbx15 might be a potential drug target , excluding the risk of cross-reactions with human proteins . In contrast to novel drugs , which target the general ubiquitin proteasomal machinery by inhibiting their core components , such as Nedd8-activating enzymes , the SCF-adaptor Skp1 or the proteasome , and thus providing therapeutic chances for cancer , neurodegenerative diseases and immune deficiencies , a drug against Fbx15 would not affect the ubiquitin-proteasome system itself , but instead offer a highly specific inhibitor for fungal dissemination during life threatening aspergillosis [5 , 26 , 68 , 69] . So far promising drugs , targeting specific F-box proteins have been identified for human F-box proteins , which are connected to a diverse set of cancers [70] . The possibility to treat fungal diseases with F-box specific inhibitors is supported by Lobo et al . , who discovered the plant defensin Psd1 from Pisum sativum that interacts with the nuclear F-box protein cyclin F of N . crassa and exhibits antifungal activity against several Aspergillus species [71] . A putative drug against Fbx15 might have a highly specific fungal target spectrum , because the function of Fbx15 varies between a stress response regulator in A . fumigatus and a developmental regulator in A . nidulans . Fbx15 might bear the potential to identify new virulence determining factors , which can be used for advanced drug design . Our identification of further Fbx15 interaction partners provides a promising base for the characterization of other novel virulence traits in A . fumigatus . Taken together , Fbx15 is a crucial regulator for stress response and virulence in A . fumigatus , which provides the known function of an F-box protein , interacting with the ubiquitin SCF E3 ligase machinery . As a second function it controls the nuclear localization of the transcriptional co-repressor SsnF , which is part of a broad transcriptional network , including histone modifications . The broad impact of Fbx15 on stress responses and the fact that it is specific for the fungal kingdom makes this protein an interesting target for drug development .
The generation of deletion- , complementation- and tagged strains was carried out in the ΔakuA-strain AfS35 a derivative of WT-strain D141 , which provides high levels of homologous recombination [30 , 72 , 73] . BiFC was done in pyrG1-strain Af293 . 1 ( FGSC#1137 ) , obtained from the Fungal Genetics Stock Center [32 , 74] . A . fumigatus strains were cultivated in minimal media ( MM ) with appropriate supplements . For cloning techniques E . coli strains DH5α and MACH-1 ( Invitrogen ) were applied . Fungal and bacterial transformations were carried out as described [34 , 75] . Plasmids and A . fumigatus strains are given in Tables S4/S6 and S1 Text . Total RNAs were extracted with “RNeasy plant mini kit” ( Qiagen ) . 0 . 8 μg RNA was transcribed into cDNA using “QuantiTect reverse transcription kit” ( Qiagen ) . Gene expressions were measured with quantitative real-time PCR using either a Light Cycler 2 . 0 System ( Roche ) with “RealMasterMix SYBR ROX 2 . 5x” ( 5Prime ) or a CFX Connect Real-Time System ( Bio-Rad ) with “MESA GREEN qPCR MasterMix Plus for SYBR Assay” ( Eurogentec ) . Histone ( h2A ) and Glyceraldehyde-3-phosphate dehydrogenase ( gpdA ) expression were used as reference for relative quantification . Details about used cDNA concentrations and primer pairs are given in S5 Table and S1 Text . For Immunoblotting experiments 100–150 μg crude protein extract was separated by SDS-PAGE and transferred to a nitrocellulose membrane by electro blotting as described previously [36 , 76] . Antibodies used for detection of fusion proteins are described in S1 Text . Signals were detected by enhanced chemiluminescence technique with either an Amersham Hyperfilm-P ( GE Healthcare Limited ) or with the Fusion SL7 system ( Peqlab ) . For signal quantification Bio 1D imaging software ( Peqlab ) was used . Protein stability was measured by the addition of 25 μg/ml cycloheximide prior to protein extraction . Co-purification with TAP-tagged Fbx15 , Fbx15 ( P12S ) , SconB and SconB ( P200S ) was performed with a modified version of the Tandem Affinity Purification protocol as described in S1 Text . Immunoprecipitation of GFP- or RFP-tagged proteins were performed with “GFP-trap_A and “RFP-trap_A ( chromotek ) . Proteins were extracted from 5 ml frozen pulverized mycelium . 5 ml of protein crude extracts were incubated with 40 μl of GFP-Trap_A or RFP-Trap_A agarose , which was previously equilibrated to the B300-buffer . After two hours of incubation agarose was washed twice with B300 buffer . The agarose was boiled in 100 μl 3x SDS-sample buffer to elute the bound proteins . The extracted proteins were used directly for SDS-PAGE followed by immunoblotting or coomassie-staining and tryptic digestion for LC-MS/MS analysis . Fbx15-GFP was purified from cultures before and after treatment with 3 mM H2O2 and run on an SDS-PAGE . After coomassie-staining the proteins were in-gel digested with trypsin . The purified peptides were labeled with an isobaric mass tag using the “TMTduplex Isobaric Mass Tagging Kit” ( Thermo Scientific ) , where Fbx15-GFP before H2O2 treatment was labeled with heavy TMT-127 and all time points after H2O2 induction were labeled with TMT-126 ( for details see S1 Text ) . Proteins were digested with “Sequencing Grade Modified Trypsin” ( Promega ) . Digested peptides were extracted from polyacrylamide gel and separated using reversed-phase liquid chromatography with an RSLCnano Ultimate 3000 system ( Thermo Scientific ) followed by mass identification with a Orbitrap Velos Pro mass spectrometer ( Thermo Scientific ) . Details about mass spectrometry and data analysis are given in S1 Text . The virulence of A . fumigatus Δfbx mutants and the corresponding complemented strains was tested in an established murine model for IPA [37 , 38 , 77] . In brief , female CD-1 mice were immunosuppressed with cortisone acetate ( 25 mg/mouse intraperitoneally; Sigma-Aldrich ) on days -3 and 0 . Mice were anesthetized and intranasally infected with 20 μl of a fresh suspension containing 106 conidia . A control group was mock infected with PBS to monitor the influence of the immunosuppression . The health status was monitored at least twice daily for 14 days and moribund animals ( defined by severe dyspnoea and/or severe lethargy ) were sacrificed . Infections were performed with a group of 10 mice for each tested strain . Lungs from euthanized animals were removed , and fixed in formalin and paraffin-embedded for histopathological analyses according to standard protocols [39 , 78] . Mice were cared for in accordance with the principles outlined by the European Convention for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes ( European Treaty Series , no . 123; http://conventions . coe . int/Treaty/en/Treaties/Html/123 ) . All animal experiments were in compliance with the German animal protection law and were approved by the responsible Federal State authority “Thüringer Landesamt für Verbraucherschutz” and ethics committee “Beratende Komission nach § 15 Abs . 1 Tierschutzgesetz” with the permit Reg . -Nr . 03-001/12 .
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The opportunistic human fungal pathogen Aspergillus fumigatus is the most prevalent cause for severe fungal infections in immunocompromised hosts . A major virulence factor of A . fumigatus is its ability to rapidly adapt to host conditions during infection . The rapid response to environmental changes underlies a well-balanced system of production and degradation of proteins . The degradation of specific target proteins is mediated by ubiquitin-protein ligases ( E3 ) , which mark their target proteins with ubiquitin for proteasomal degradation . Multisubunit SCF Cullin1 Ring ligases ( CRL ) are E3 ligases where the F-box subunit functions as a substrate-specificity determining adaptor . A comprehensive control of protein production includes global co-repressors as the conserved Ssn6 ( SsnF ) -Tup1 ( RcoA ) complex , which reduces transcription on multiple levels . We have identified a novel connection between protein degradation and synthesis through an F-box protein . Fbx15 can be incorporated into SCF E3 ubiquitin ligases and controls upon stress the nuclear localization of the SsnF . Fbx15 plays a critical role for A . fumigatus adaptation and is essential for virulence in a murine infection model . Fbx15 is a fungal-specific protein and therefore a potential target for future drug development .
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2016
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SCF Ubiquitin Ligase F-box Protein Fbx15 Controls Nuclear Co-repressor Localization, Stress Response and Virulence of the Human Pathogen Aspergillus fumigatus
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Covalent linkage to members of the small ubiquitin-like ( SUMO ) family of proteins is an important mechanism by which the functions of many cellular proteins are regulated . Sumoylation has roles in the control of protein stability , activity and localization , and is involved in the regulation of transcription , gene expression , chromatin structure , nuclear transport and RNA metabolism . Sumoylation is also linked , both positively and negatively , with the replication of many different viruses both in terms of modification of viral proteins and modulation of sumoylated cellular proteins that influence the efficiency of infection . One prominent example of the latter is the widespread reduction in the levels of cellular sumoylated species induced by herpes simplex virus type 1 ( HSV-1 ) ubiquitin ligase ICP0 . This activity correlates with relief from intrinsic immunity antiviral defence mechanisms . Previous work has shown that ICP0 is selective in substrate choice , with some sumoylated proteins such the promyelocytic leukemia protein PML being extremely sensitive , while RanGAP is completely resistant . Here we present a comprehensive proteomic analysis of changes in the cellular SUMO2 proteome during HSV-1 infection . Amongst the 877 potentially sumoylated species detected , we identified 124 whose abundance was decreased by a factor of 3 or more by the virus , several of which were validated by western blot and expression analysis . We found many previously undescribed substrates of ICP0 whose degradation occurs by a range of mechanisms , influenced or not by sumoylation and/or the SUMO2 interaction motif within ICP0 . Many of these proteins are known or are predicted to be involved in the regulation of transcription , chromatin assembly or modification . These results present novel insights into mechanisms and host cell proteins that might influence the efficiency of HSV-1 infection .
Herpes simplex virus type-1 ( HSV-1 ) is an alphaherpesvirus which causes vesicular oral and genital lesions , and has the capacity to cause more severe diseases such as meningitis and encephalitis , particularly in immunocompromised individuals and neonates ( see [1 , 2] for general reviews ) . Characteristic of an alphaherpesvirus , HSV-1 establishes latency within sensory neurons , from which reactivation occurs periodically . The lytic , latent and reactivation states are governed by the innate , intrinsic and adaptive immune responses and the mechanisms by which HSV-1 has evolved to counteract these immune responses . The attachment and entry of HSV-1 into a cell causes the activation of the innate and intrinsic immune responses . The former involves production of interferons ( IFNs ) which activate signal transduction pathways , resulting in the expression of IFN stimulated genes ( ISGs ) ( reviewed in [3] ) . Intrinsic antiviral resistance , on the other hand , is mediated by constitutively expressed proteins . Amongst the various factors that have been identified as contributing to intrinsic resistance are certain components of promyelocytic leukaemia ( PML ) nuclear bodies ( PML NBs , also known as ND10 ) , including the PML protein itself and other major components such as Sp100 , hDaxx and ATRX [4] . Both PML and Sp100 are heavily modified by the SUMO family of ubiquitin-like proteins [5] , and both sumoylation and interaction with sumoylated proteins are key factors in the assembly of PML NBs [6 , 7] . These proteins are recruited to sites of incoming HSV-1 genomes very early in infection [8] and they have the potential to restrict HSV-1 replication as soon as the cell becomes infected [9–12] . The mechanism of this recruitment is incompletely understood , but it is clear that both sumoylation and SUMO-mediated interactions play important roles [13] . The HSV-1 regulatory protein ICP0 reduces the sensitivity of HSV-1 to IFN [14–16] and also counteracts the restrictive effects of PML NBs through its ubiquitin E3 ligase activity [17] . ICP0 induces the degradation of the sumoylated forms of PML through an activity that has similarities to those of SUMO-targeted ubiquitin ligases ( STUbLs ) [18 , 19] , and it also degrades the unmodified forms of the most abundant isoform of PML in a SUMO-independent manner [20] . In addition , at later times of HSV-1 infection a widespread loss of high molecular weight cellular SUMO conjugates occurs in an ICP0-dependent manner [18 , 19] . These activities combine to cause complete disruption of PML NBs , dispersal of those components such as hDaxx and ATRX that are not degraded , and inhibition of the recruitment of PML NB components and other as yet uncharacterized sumoylated proteins to the sites of HSV-1 genomes . Given that depletion of the only known SUMO E2 conjugating enzyme Ubc9 also diminishes intrinsic resistance to HSV-1 infection ( and hence augments the replication of ICP0-null mutant HSV-1 ) [18] , there is accumulating evidence that mechanisms that are regulated by sumoylation play an important part in intrinsic resistance to HSV-1 infection . The SUMO family of proteins includes three members ( for general reviews of the SUMO pathway , see [21 , 22] ) . By sequence , SUMO1 is 18% related to ubiquitin and is conjugated to specific lysine residues in substrate proteins through an isopeptide bond between its C-terminal glycine carboxyl group and the lysine side chain in the substrate . SUMO2 and SUMO3 are closely related to each other ( and share about 50% identity with SUMO1 ) and they include an internal lysine residue to which other SUMO moieties can be conjugated , and hence they can form poly-SUMO chains . The SUMO conjugation pathway involves a sequence of events that are analogous to those of ubiquitin conjugation , with SUMO first forming a thioester bond with the SAE1/2 SUMO E1 activation enzyme , followed by the activities of Ubc9 and in some cases SUMO E3 ligases that catalyze conjugation to specific substrate proteins . Several previous studies have analyzed the diversity of cellular proteins that can be sumoylated under a variety of conditions [23–28] . In this study we set out to characterize changes to the cellular SUMO2 proteome in response to infection with wild type ( wt ) HSV-1 using Mass Spectrometry ( MS ) -based quantitative proteomics . Our experimental design allowed the identification of sumoylated proteins that are preferentially degraded in the presence of ICP0 , and it also revealed a number of cellular proteins that were not candidate sumoylated species that were also degraded . We investigated a number of the identified proteins , including known controls such as PML and Sp100 , to determine their sumoylation status and whether or not their degradation was ICP0 dependent . The data revealed widespread changes in the SUMO2 proteome during HSV-1 infection , revealing many proteins that are potentially involved in the regulation of gene expression that have hitherto not been previously identified or examined in the context of a viral infection . Of particular note , we identified several members of the ZBTB family of proteins , and other proteins with related BTB domains , that are susceptible to degradation by ICP0 . We present examples of these proteins that are degraded by mechanisms that are influenced by modification by SUMO2/3 , or on the presence of a SUMO2/3 interaction motif within ICP0 . Our results provide detailed insight into the numerous and complex changes in protein stability that occur during HSV-1 infection .
Cells expressing poly-histidine tagged SUMO1 , -2 or -3 were isolated after transduction with lentivirus vectors . HepaRG hepatocytes were used because they are readily infected by HSV-1 and , unlike human diploid fibroblasts ( HFs ) , their growth was not compromised in isotopically labeled SILAC medium . We concentrated on poly-histidine tagged SUMO2 ( His-SUMO2 ) -expressing cells ( herein named HA-HisSUMO2 ) because of the potential for poly-SUMO2 chains and because of the existing depth of knowledge of the SUMO2 proteome [23–28] . Infection of HA-HisSUMO2 cells with wt HSV-1 caused a reduction in the overall abundance of His-SUMO2 conjugated proteins ( Fig 1A ) , which was more marked at later times of infection ( S1A Fig ) . This was not as pronounced as in previous studies using HFs [18] , perhaps because of over-expression of His-SUMO2 ( S1B Fig ) , or because reductions in overall sumoylation levels are less pronounced in HepaRG cells compared to HFs [18] . Despite the increased expression of SUMO2 , the sumoylated forms of PML were not substantially more abundant in HA-HisSUMO2 cells ( S1C Fig ) and they were readily degraded by HSV-1 ( Fig 1A ) . Wt HSV-1 gene expression ( Fig 1B ) and plaque formation efficiency ( Fig 1C ) were as efficient in HA-HisSUMO2 cells as in parental HepaRG cells and control transduced cells expressing the His-tag only ( HA-His only cells ) . Surprisingly , the plaque formation efficiency of ICP0-null mutant HSV-1 was increased in HA-HisSUMO2 cells ( S1D Fig ) . Given previous results on the role of Ubc9 in restricting this virus [18] , it might be expected that over-expression of SUMO2 could be inhibitory . On reflection however , this result does not necessarily challenge the hypothesis that sumoylation of repressive proteins , and/or their interactions with sumoylated proteins , contributes to the regulation of HSV-1 infection . Over-expression of a protein can affect the proper functioning of the pathway in which it is involved , and in this case over-expression of SUMO2 might affect the balance of interactions between sumoylated proteins and those containing SUMO interaction motifs ( SIMs ) . Support for the role of sumoylation in intrinsic resistance to HSV-1 infection comes from a study of HepaRG cells highly depleted of SUMO2/3 , in which PML NBs are disrupted and ICP0 null mutant HSV-1 replicates with increased efficiency ( M . Glass , manuscript submitted for publication ) . These issues are clearly open for further experimentation , but they are beyond the scope of this particular paper . A method involving nickel affinity purification of His-tagged sumoylated proteins under denaturing conditions [27] gave efficient recovery of both overall sumoylated proteins ( Fig 1D ) and the specific example of sumoylated PML ( Fig 1E ) . HA-HisSUMO2 cells were grown in isotopically normal ( light; L ) or heavy ( H ) SILAC media ( in which lysine and arginine have the heavy isotopes of nitrogen ( 15N ) and carbon ( 13C ) ) . Uninfected HA-His only control cells were grown in isotopically intermediate ( M ) medium , as defined in the methods section . The L cells were infected with wt HSV-1 at MOI 10 , then all but one plate of cells from each condition were harvested directly into guanidinium denaturing buffer 12 h later . The H , L and M lysates were mixed in equal protein amounts then used for nickel affinity purification of SUMO2-modified proteins . To allow analysis of total protein abundance changes and confirm efficient virus infection and degradation of previously characterized cellular proteins , cells from one plate of each set were harvested directly into SDS-PAGE loading buffer . Samples of the crude and affinity purified mixtures were subjected to SDS-PAGE , then the gels were stained and cut into slices for in gel tryptic peptide production . The peptides were analyzed by LC-MS/MS and the data analyzed using MaxQuant software ( See Materials and Methods for details ) . Fig 2A shows a flow diagram of the experimentation and images of the resulting SDS-PAGE stained gels . Data derived from the crude and purified samples gave information on the relative levels of total protein and putative sumoylated protein species respectively . Analysis of the H/M ratios in the purified sample allowed the separation of likely sumoylated species from non-specifically purified proteins , while the H/L ratios of these protein IDs in the same sample can be used to assess changes in relative abundance during HSV-1 infection . A total of 6128 cellular proteins were identified , with 5508 and 2842 from the crude and purified fractions respectively , of which 2222 were in common ( S1 Table , sheet 1 and Fig 2B ) . A number of proteins were detected only in the affinity purified sumoylated fraction , probably because their intrinsic abundance is too low for detection in the crude ( Fig 2B ) . Because the majority of proteins would not be expected to change in abundance during infection , and also the presence of non-sumoylated proteins in the purified fraction , MaxQuant internally calculated normalised ratios could be used to correct for any errors in the mixing of the various samples prior to gel electrophoresis . Frequency plots for log2 HA-HisSUMO2 to HA-His only ratios ( log2 H/M ) showed little variation in abundance of proteins in crude extracts ( Fig 2C , blue line ) , with the frequency plot forming a tight normal distribution around the 1:1 ratio region . Two distinct sub-populations can be seen by the same analysis of data derived from purified samples ( Fig 2C , red line ) . While there is a large peak of proteins with ratio 1:1 ( log2 = 0 ) , consistent with these being non-specific purification contaminants , there is also a smaller , broader peak in the region of log2 ratio of 1 to 6 . These proteins are much more abundant in nickel purifications from HA-HisSUMO2 expressing cells compared to those from HA-His only cells , and so are likely to be SUMO2 conjugates . Ratio cut-offs for H/M and M/L were defined such that an estimated false discovery rate of less than 1% for SUMO2 substrates was applied ( see Methods for further details ) , giving 877 putative SUMO2 conjugates ( including multiple isoforms of some proteins such as PML ) , as listed ( S1 Table , sheet 2 ) . This method of SUMO2 substrate identification was validated by assessing the difference between the apparent MW of proteins based upon gel retention , and their predicted MW by sequence alone ( Fig 2D ) ( see [29] for details of the method ) . The substrates and non-substrates clearly form two independent distributions in frequency plots , with substrates running in gels on average 20 kDa heavier than expected . Furthermore , comparison with a previous SUMO2 proteome analysis at the level of identified modification site [26] indicated 324 proteins in common with this set , while comparison with several major SUMO2 proteome studies revealed 521 proteins in common [23–28] ( S1 Table , sheet 2 , column P ) . In summary we can be confident that this list of 877 proteins represents true cellular SUMO2 substrates under these experimental conditions . A complete listing of all the data on the cellular proteins identified is presented in S1 Table . In addition to these cellular proteins , 71 viral proteins ( i . e . all but one of the major viral polypeptides , the exception being US5 ) were identified in the crude sample ( S2 Table ) . H/L ratios can be used to study changes to either the total proteome ( via ‘crude’ data ) , or the SUMO proteome ( via ‘pure’ data ) upon HSV-1 infection . Frequency distribution charts comparing ‘crude’ ratios from infected cells and uninfected cells showed few changes in total protein levels ( Fig 2E , blue line ) . However , while most proteins in purified preparations also showed no change in abundance , the distribution is skewed towards larger ratios ( Fig 2E , red line ) , with putative SUMO2 substrates being mostly responsible for this high ratio tail ( Fig 2E , insert ) . This shows that the non-substrates are largely unchanged during HSV-1 infection , while SUMO2 conjugates have a tendency toward high ratios , indicative of a widespread loss of SUMO2 conjugation during infection . This is consistent with western blot data ( Fig 1A ) . To test the reproducibility of these data a similar quantitative proteomic experiment was undertaken , this time only including Light infected and Heavy uninfected samples ( S2A Fig ) . Although the total number of proteins was lower in this compared to the triple labeled experiment ( S2B Fig ) , there was considerable overlap between the proteins in the purified fractions ( S2C Fig ) , and the details of their ratio changes correlated substantially ( S2D Fig; S3 Table ) . Because the triple labeled experiment was both larger and included the His-only control , subsequent sections mostly refer in detail only to this dataset . To determine which SUMO2 substrates changed significantly during HSV-1 infection , Significance B ( SigB ) values were calculated using Perseus from the MaxQuant suite of software [30] . SigB is calculated using both signal intensity and SILAC ratio , and is indicative that a protein abundance change significantly deviates from the bulk of the quantified proteins . Of the 877 putative sumoylated proteins , 260 changed in abundance ( either up or down ) in the purified fraction of the triple SILAC experiment with SigB values of less than 0 . 1 ( S1 Table , sheet 3 ) . This number includes duplicate entries for proteins , such as PML , for which more than one isoform was detected . Removal of these duplicates and restricting the list to entries with H/L ratio increases of 2-fold or more results in a list of 185 proteins , shaded according to degree of change , and listed in order of decreased abundance ( Fig 3 ) . An additional 18 proteins on the putative sumoylated substrates list were detected with H/L ratios of 2 or greater and SigB values of less than 0 . 1 in the purified fraction of the replicate experiment , but which were not recorded as significantly regulated substrates in the primary experiment ( S4 Table ) . As shown below , at least one of these is an authentic sumoylated substrate whose abundance decreases during HSV-1 infection . Overall therefore , up to around 200 cellular proteins identified in the purified fractions of the experiments decreased significantly in abundance during infection . The degree of change in abundance of the putative sumoylated forms of these proteins varies considerably , up to a maximum of greater than 20-fold . In broad view , these data are consistent with previous observations on the decreased stability of sumoylated cellular proteins during HSV-1 infection . They also support the idea that there is considerable specificity or selectivity to the extent of sensitivity to desumoylation , as two-thirds of sumoylated proteins remain largely unaltered during infection while only 14% and 1% decrease in abundance by over 3-fold and 7-fold respectively . We also identified a further 72 proteins that were not defined as potentially sumoylated on the basis of H/M ratios , which nonetheless had H/L ratios of greater than 2 and SigB of less than 0 . 1 in the purified fraction ( S5 Table ) , although only 32 of these also complied with these criteria in the double labeled experiment . These may represent proteins that are non sumoylated , but which have an affinity for the Ni-agarose beads and whose abundance decreases during HSV-1 infection . A small number of putative sumoylated proteins listed in S1 Table sheet 3 also exhibited H/L ratios of greater than 2 in the crude fraction ( S6 Table ) , indicating candidate SUMO2 substrates whose total protein levels also reduced in infected cells . In the cases of IFI16 , CENPB and PML , this has been reported previously [19 , 31–33] . Another protein on this list ( NACC1 ) will be considered below , and further data presented below suggests that this list does not include all proteins that behave in this manner . The levels of a number of proteins were changed significantly in the crude samples ( S1 Table , sheet 4 ) . Of these , and excluding those already noted above as regulated SUMO2 substrates , 128 proteins gave H/L ratios of greater than 2 in the crude samples ( S7 Table ) . Given that HSV-1 infection causes the shut-off of host protein synthesis through induction of mRNA instability [34] , this list is perhaps shorter than might be expected . It is possible that low abundance proteins with short half-lives ( and thus those most susceptible to decreased host transcription ) have not been detected efficiently in the ‘crude’ preparations by this approach , which will naturally favor the most abundant cellular proteins . An unexpected finding concerns a small group of cellular proteins whose degree of sumoylation appears to contradict the general trend of deconjugation , and actually increases during infection . S8 Table shows proteins with H/L ratios in the purified fraction of less than 0 . 5 and SigB values of less than 0 . 1 . Several of these proteins are components of the basic transcriptional apparatus ( MED9 , TAFs 1 , 9 and 12 ) or transcription factors ( MAFA , MAFB ) . This may indicate overall changes in transcription complexes as infection progresses . Validation of an example of a protein in this category ( ZBTB7A ) will be presented below . Similarly , in the crude fraction a small number of cellular proteins increased in overall abundance by a factor of 2-fold or more and with SigB values of less than 0 . 1 ( S9 Table ) . Perhaps surprisingly , no induction of interferon-stimulated genes was detected under these infection conditions . By applying the same method for monitoring apparent in gel molecular weights of cellular proteins ( see Fig 2D ) we were able to investigate the difference between apparent and predicted molecular weights of viral proteins . Most viral proteins were found in gel slices that were consistent with their predicted molecular weights , although the UL26 capsid maturation protease exhibited higher gel mobility than expected by sequence alone , consistent with its known cleavage during capsid assembly and also the production of its C-terminal half ( protein VP22a ) as a separate protein from an independent transcription unit [35 , 36] . Several proteins , however , had lower gel mobilities than predicted . In the case of glycoproteins gC , gL , gK and gM this is likely due to glycosylation ( S2 Table ) . A small group of viral proteins exhibited decreased gel mobility over that predicted in the purified but not the crude sample , and in most of these cases the size difference could be consistent with sumoylation ( Fig 4A ) . Note that the predicted molecular weight of some of these proteins differs considerably from their established gel mobilities , and in the case of ICP0 , for example , this has been attributed to the nature of the primary sequence rather than post-translational modification . Therefore we investigated whether putative sumoylated species could be detected by western blotting of purified extracts from infected HA-HisSUMO2 cells . Analysis of UL42 , the processivity factor for the viral DNA polymerase , in the crude and purified samples of infected HA-HisSUMO2 cells revealed clear evidence of slower migrating species whose mobility is consistent with sumoylation ( Fig 4B ) . This is of interest because the analogous protein ( UL44 ) of HCMV is sumoylated [37] . However , these putative sumoylated UL42 bands were not clearly detectable in the crude fraction of HA-HisSUMO2 cells , nor were they evident during a normal wt HSV-1 infection of control HepaRG cells ( Fig 4C ) . It is possible that over-expression of SUMO2 in the HA-HisSUMO2 cells forces a sumoylation event , or shifts the sumoylation equilibrium so that such species become more detectable . Therefore , while the evidence indicates that sumoylation of UL42 can occur , the likely sumoylated species seem to be in very low abundance during the course of a normal infection . We also detected likely sumoylated forms of UL6 and ICP0 in the purified fraction ( albeit for the latter only on very long exposures of the blot ) ( Fig 4D ) , and extended exposure of the UL12 samples also revealed a possible sumoylated form . For the other proteins on the list of Fig 4A , we were either unable to detect sumoylated species by western blot of purified fractions ( Fig 4D ) , or we lacked the reagents required to perform the analysis . Considering the scale of the proteomic method , it is conceivable that the sensitivity of the mass spectrometric approach is higher than the western blots shown here , and the possibility that all these proteins have a sumoylated component cannot be excluded . Analysis of the sequences of these proteins for potential sumoylation sites revealed consensus modification sites in US3 , UL12 and UL42 , but not the others . As an initial step towards functional analyses of the proteins showing the greatest degree of change in sumoylation during HSV-1 infection , we grouped the 124 proteins of Fig 3 and S3 and S4 Tables with greater than 3-fold increases in H/L ratios in the purified fractions ( pooling the data from experiments 1 and 2 ) , then grouped them into broad , sometimes overlapping , categories ( Fig 5 ) . The proteins in each category are listed in order of degree of H/L ratio change and shaded as in Fig 3 . The largest group of proteins include those with zinc finger domains , followed by transcription factors and chromatin-related proteins . BTB proteins , many of which have an additional zinc finger domain ( the ZBTB proteins ) , form another marked group . There is a group of nuclear structure components such as lamins , and the PML NB components PML , Sp100 and MORC3 , and also three centromere proteins . Several centromere proteins are already known to be degraded during HSV-1 infection in an ICP0-dependent manner [33 , 38 , 39] . Other groups of proteins have functions in RNA metabolism , interferon related pathways , and general metabolism ( such as kinases ) . It is striking that so many ZNF and ZBTB proteins were identified as regulated substrates , opening the question whether the zinc finger or the BTB domain of itself is contributing to the sensitivity of the sumoylated forms of these proteins to HSV-1 mediated degradation . We identified approximately 200 proteins with ZNF in the gene name ( this will not include all proteins that include a zinc finger ) of which 59 were sumoylated candidates and 24 of these were reduced in abundance by 3-fold or more . For ZBTB proteins , 19 in total were detected , 18 of which were sumoylation candidates and 11 were reduced in abundance by 3-fold or more . Thus the presence of a zinc finger or ZBTB domain itself does not generally confer sensitivity to HSV-1 , but rather it seems that the proportion of these classes of proteins that are subject to sumoylation is increased compared to the bulk of cellular proteins . Analysis of the functional consequences of these changes in abundance of these proteins during HSV-1 infection is obviously beyond the scope of this study , but the results certainly provide many novel avenues to pursue . Especially for those proteins undergoing the most dramatic changes in sumoylation , and in some cases overall abundance , there will inevitably be substantial disruption of the pathways in which they are involved during HSV-1 infection . Known examples of this include disruption of PML NBs and centromeres . But it is also reasonable to expect that the effects on chromatin related proteins and transcription factors will have consequences to chromatin structure or modification and transcriptional activity . Given that previous SUMO proteomic studies have highlighted that sumoylation of the proteins that are involved in these pathways is common [23–28] , it is not surprising that these pathways feature prominently amongst those that are potentially disrupted by HSV-1 infection . While many such proteins may be innocent victims that are affected simply because of their sumoylation status , it is likely that this analysis includes previously unrecognized proteins which impact on the efficiency of HSV-1 infection . The proteomic data for several previously studied proteins were consistent with their established behaviour during HSV-1 infection . For example , the H/L ratios in both crude and purified fractions were increased for PML ( for which both sumoylated and unmodified forms are known to be degraded [18 , 19] ) , while the Sp100 H/L ratio increased only in the purified fraction ( consistent with the loss of only the sumoylated forms [40 , 41] ) , and there was no change in H/L ratio for RanGAP1 ( which is neither degraded nor regulated at the level of sumoylation during HSV-1 infection [19] ) ( S1 Table , sheet 2; and S3 Fig ) . We analyzed a number of proteins listed in Fig 3 that had not been previously investigated , selected on the basis of being amongst those with the greatest changes in H/L ratios , or being representatives of groups of related proteins , and on antibody availability . ZBTB4 , ZBTB10 , ZBTB38 , NACC1 and MORC3 all exhibited high H/L ratios in the purified sample , while NACC1 and to a lesser extent MORC3 and ZBTB4 also showed high H/L ratios in the crude samples , indicative of reduced total protein amounts ( peptides for ZBTB10 and ZBTB38 were not detected in the crude sample ) . Total protein and His-purified extracts of uninfected and infected HA-HisSUMO2 cells and uninfected HA-His only cells were blotted for the above proteins , using PML and RanGAP1 as controls ( Fig 6 ) . The sumoylated forms of PML were readily identified in the purified sample of uninfected HA-HisSUMO2 cells , and both these and the major unmodified form were degraded during infection . In contrast , sumoylated RanGAP1 was stable ( Fig 6 , upper left panels ) . Similarly , a sumoylated form of NACC1 was detected , and both this and the non-sumoylated form were degraded . Analysis of the other proteins was complicated by likely non-specific bands , but in all cases bands consistent with sumoylated forms were detected in the purified fraction , and in all cases except ZBTB7A ( see below ) these were lost during infection . For ZBTB10 and MORC3 ( and ZBTB4 to a lesser extent ) , likely unmodified forms ( marked by asterisks ) were also diminished during infection . The anti-ZBTB38 antibody was particularly prone to detection of potentially spurious bands , but likely sumoylated forms were clearly detected in the uninfected sample and lost during HSV-1 infection ( Fig 6 , upper right panel , see also below ) . Therefore , where reagents of sufficient quality are available , these results validate the SILAC data with a high degree of success . They also reveal a number of proteins whose apparently unsumoylated forms are also degraded during HSV-1 infection , and may therefore constitute previously unrecognised substrates of ICP0 . ZBTB7A was selected as a representative protein with a low H/L ratio in the purified fraction , potentially indicating an increase in abundance of sumoylated forms following infection . Potential sumoylated species of ZBTB7A were detected , albeit weakly , in the purified sample , and these were of increased abundance in the infected sample ( Fig 6 ) , again consistent with the proteomic data . By analyzing total protein extracts over time following HSV-1 infection of normal human fibroblasts , we found that the major forms of NACC1 , ZBTB10 , ZBTB38 and MORC3 were all degraded within 3 or 6 h ( Fig 7 ) . In these cells , the slower migrating sumoylated forms were not generally detected in the total protein extracts , although in the case of ZBTB4 the major form of the protein appeared stable while a potential sumoylated species was rapidly lost . The difference between the fate of the major form of ZBTB4 in this Fig compared to that in Fig 6 may be due to cell type , as the latter was performed in HA-HisSUMO2 cells . CITED2 was included in this analysis as an example of a protein with a high H/L ratio in the purified fraction , yet for which there was no evidence of authentic sumoylation ( S1 and S3 Tables ) . This protein was also rapidly degraded during HSV-1 infection ( Fig 7 ) . While confidence in the reliability of the proteomic data is strengthened by the above results , the analysis is limited by the quality of available antibodies . Therefore we selected further candidate proteins for study using an inducible expression system [42] . This allows addition of an epitope tag and expression in a high proportion of transduced cells at levels that could be controlled by the length of time of induction . The proteins selected included some analyzed in Figs 5 and 6 ( ZBTB4 and ZBTB10 ) and several more for which antibodies either gave ambiguous results or were unavailable ( BEND3 , ETV6 , MBD1 , ZBTB12 and ZBTB20 , all of which are amongst those with extreme H/L ratios; Fig 3 ) . ARID3A was included in this set because preliminary analysis of the data of the experiment of S2 Fig identified it as a protein in the purified fraction that was sensitive to HSV-1 infection , consistent with published studies [43] . Although the H/L ratio of ARID3A was not significantly reduced in the experiment of Fig 2 , it was identified as a likely sumoylated substrate ( S1 Table , sheet 2 ) and a related protein ( ARID4A ) was reduced during infection ( Fig 3 ) . NACC2 was included in the analysis because it was the highest scoring protein ID of the experiment of Fig 2 that did not achieve the cut-off values of H/L ratio change in the triple labeled experiment . All the proteins were expressed in the inducible system ( most by 2 h after induction ) , and most gave a major band close to the predicted molecular weight plus minor slower migrating species that are likely sumoylated products ( S4 Fig ) . ZBTB4 was the least efficiently expressed of these proteins , and any sumoylated forms were below the level of detection in the presented exposure ( but see Fig 8 , in which putative sumoylated forms are visible ) . The various cell lines were then treated with doxycycline for the appropriate length of time , the doxycycline was then washed out and the cells infected at moi 10 for 8 h with either wt or ICP0 null mutant HSV-1 ( lanes marked dl ) . The extracts were also analyzed for the efficiency of viral gene expression in each instance ( Fig 8 ) . Where visible on the blot exposures presented , the slower migrating probable sumoylated forms were invariably lost during wt but not ICP0 null mutant infection , indicating that their loss is , directly or indirectly , dependent on ICP0 . The major likely unmodified forms of ZBTB4 , ZBTB10 , ZBTB12 , MBD1 , BEND3 and NACC2 were also reduced to a greater or lesser extent in the wt virus infected cells ( Fig 8 ) . In contrast , potential sumoylated forms of ZBTB4 , ZBTB10 and ETV6 appeared to increase in abundance in the ICP0-null mutant infected samples , which may be related to the overall accumulation of sumoylated species that occurs during the mutant virus infection [18 , 19] . The reduction in the major form for ZBTB4 observed here is consistent with the data of Fig 6 , with both experiments being conducted in HepaRG-based cells , and in contrast to the infection time course of Fig 7 ( performed in HF cells ) , supporting the possibility of cell type differentials in the behaviour of certain proteins during HSV-1 infection . Taking into account all the proteins analyzed in Figs 5 , 6 and 8 , of the 124 proteins listed in Fig 5 , five had been defined previously as decreasing in abundance during HSV-1 infection ( although not all have well characterized sumoylated forms ) . We have also analyzed 11 further proteins that had not been studied in HSV-1 infection , finding that all showed evidence of sumoylated forms which were sensitive to HSV-1 infection , and in some cases their likely unmodified forms also . Thus of the 13% of the proteins in Fig 5 investigated , 100% were confirmed as behaving as predicted from the proteomic analysis . This very high confirmation rate gives much confidence about the overall validity of the proteomic analysis . There are several mechanisms that could reduce the amounts of the candidate proteins identified in this study . The prime aim of the project was to identify sumoylated forms of proteins that are degraded in an ICP0-dependent manner , and it is likely that these constitute a major grouping . However , proteins that alter in abundance during HSV-1 infection that are either unsumoylated or modified to only a very minor degree may also be detected by this methodology , allowing the possible identification of substrates of ICP0 that are degraded in a SUMO-independent manner . Host cell proteins may also become less abundant during HSV-1 infection in an ICP0-independent manner , either through induced degradation as a consequence of virus infection in general or more passively because of reduced rates of host transcription . Distinguishing between substrates that are degraded by ICP0-dependent and-independent mechanisms during HSV-1 infection is not always straightforward due to the low infectivity of ICP0-null mutant HSV-1 . For example , initial studies indicated that IFI16 was degraded by ICP0 during HSV-1 infection [32] , but it later emerged that the degradation could occur in the absence of ICP0 in conditions in which the mutant infection was progressing as rapidly as the wt [31] . Therefore we have investigated the degradation of selected proteins of interest in cells induced to express ICP0 in the absence of infection ( as described in [42] ) . Control HA-TetR and ICP0 inducible cells were treated or not with doxycycline then the whole cell extracts were analyzed by western blotting . As reported previously [42] , PML is efficiently degraded in this system ( Fig 9 , top left ) . The stability of the endogenous forms of proteins MORC3 , ZBTB10 , ZBTB38 , NACC1 , ZBTB4 and CITED2 after induction of ICP0 expression was analyzed because of the availability of antibodies that detect endogenous levels of the proteins . It is clear that the first four of these proteins can be degraded by ICP0 in the absence of infection , thereby identifying a number of previously undescribed ICP0 substrates . CITED2 , on the other hand , appears only be degraded in HSV-1 infected cells , while the major form of ZBTB4 ( but not a more slowly migrating , potential sumoylated form of the protein; see also Fig 7 ) appears to be relatively stable in the presence of ICP0 . The relative loss of the major form of ZBTB4 during infection seen in Figs 5 and 8 does not seem to occur in the presence of ICP0 but the absence of infection , even though the protein was stable during infection with an ICP0-null mutant virus . To expand the repertoire of proteins for which we could test the effect of ICP0 specifically we constructed vectors that expressed blasticidin resistance and myc tagged versions of BEND3 and MBD1 constitutively . ICP0 inducible cells were transduced with these vectors , then induction of ICP0 expression revealed that both proteins could be degraded by ICP0 alone ( Fig 9B ) . We investigated whether the degradation of selected proteins was influenced by the presence of SUMO2/3 by analyzing cells transduced by a lentivirus expressing multiple shRNAs that target both SUMO2 and SUMO3 . These cells were highly depleted of SUMO2/3 ( Fig 10A ) and they exhibited highly reduced levels of sumoylated PML , disrupted PML NBs and increased replication of ICP0-null mutant HSV-1 ( M . Glass , submitted for publication ) . In parallel , cells depleted of SUMO1 were also examined ( Fig 10B ) . Depletion of the SUMO isoforms did not affect the efficiency of wt HSV-1 gene expression during high multiplicity infections ( Fig 10C ) and the remaining SUMO1 and SUMO2/3 proteins in these cells were sensitive to HSV-1 mediated reductions ( Fig 10A and 10B ) . We analyzed examples of proteins for which antibodies that detect the endogenous proteins were available , finding that degradation of NACC1 was unaffected by depletion of SUMO2/3 ( Fig 10D ) , while ZBTB10 was more stable in the SUMO2/3 depleted cells ( but not the SUMO1-depleted cells ) ( Fig 10E ) . These results indicate that degradation of ZBTB10 is ICP0-dependent and is influenced by the abundance of SUMO2/3 . ZBTB38 gave a marked interesting result , in that the bands detected by the antibody were clearly shifted in mobility in the SUMO2/3 depleted cells , but not in the SUMO1 depleted cells , and the novel ZBTB38 bands were lost in the infected cells ( Fig 10F ) . These data indicate that ZBTB38 may be preferentially modified by SUMO2/3 compared to SUMO1 , and its HSV-1 induced degradation is dependent on ICP0 but not on modification by SUMO2/3 . ICP0 includes several candidate SUMO interactions motifs ( SIMs ) , one of which was shown by yeast 2 hybrid assay to bind to SUMO2 , and was hence termed SIM-like sequence ( SLS ) -4 [18] . A recombinant HSV-1 mutant ( mSLS4 ) was constructed with mutations in SLS4 , which , although causing only a slight defect in itself , resulted in a highly defective phenotype when present in conjunction with mutations in other SLS motifs [44] . Mutant SLS4 was also found to be defective in degrading sumoylated forms of PML isoforms other than PML . I ( when these were expressed in isolation ) , although it retained the ability to degrade PML . I in a sumoylation-independent manner [44] . As PML . I is the most abundant PML isoform and it interacts with all the others , mSLS4 retains the ability to degrade endogenous PML isoforms . We therefore investigated whether ICP0 lacking functional SLS4 could degrade a selection of the proteins of interest during infection either of normal cells or of transduced cell lines expressing a myc-tagged version of selected proteins , depending on antibody availability . The results indicated that the degree of degradation of a given protein could be influenced by the SLS4 mutation in ICP0 when expressed in the context of infection . For example , endogenous NACC1 appeared more stable during mSLS4 than wt virus infection whereas MORC3 was equally sensitive to the two viruses ( Fig 11 ) . We present three examples from the myc tagged protein data , including ZBTB20 ( for which the sumoylated form is more resistant to loss in mSLS4 than wt virus infection , while the major band is relatively unchanged ) , MBD1 ( for which all forms are reduced to a lesser extent in the mSLS4 compared to wt infection ) , and ZBTB10 ( for which the major band is equally sensitive in the two infections while the abundance of likely sumoylated species increases ) ( Fig 11 ) . The reasons for this last observation will be considered in the Discussion , but note that the sumoylated forms of tagged ZBTB10 also increased in abundance in cells infected with ICP0-null mutant HSV-1 ( Fig 9 ) . Taken together , the results of Figs 9 to 11 indicate a range of factors which influence the reduction in abundance of these proteins during HSV-1 infection . These include via mechanisms for which ICP0 is insufficient ( such as CITED2 ) , or ICP0-dependent mechanisms which can be influenced or not by the abundance of SUMO2/3 ( ZBTB10 or NACC1 respectively ) and/or by the presence of the SUMO2 interaction motif of ICP0 . This analysis illustrates the complexities that regulate cellular protein stability during HSV-1 infection , and each example requires careful analysis to determine all the factors involved; there is no one single simple mechanism at play .
This study is the first to report a comprehensive analysis of the cellular proteins whose abundance is affected by HSV-1 infection , with specific focus on proteins modified by SUMO2 . The loss of SUMO-modified PML and Sp100 in an ICP0-dependent manner during HSV-1 infection has been reported previously [18 , 19 , 40 , 41 , 45] , and these proteins play important roles in an intrinsic immune response to HSV-1 infection [9 , 10] . The bulk of SUMO-conjugated proteins are also reduced following infection with wt HSV-1 but increased during ICP0 null mutant infection [18 , 19] . The biological relevance of these observations was supported by the finding that disruption of the SUMO pathway through knockdown of Ubc9 enhanced the replication of an ICP0-null mutant HSV-1 [18] . These results prompted the question as to the identity of the affected SUMO-modified proteins and their role in the context of HSV-1 infection . To address these questions we used Mass Spectrometry ( MS ) -based quantitative proteomics analysis of HepaRG cells expressing His-tagged SUMO2 ( HA-HisSUMO2 cells ) with and without HSV-1 infection . The use of SILAC Light , Heavy and Medium media for HSV-1 infected and uninfected HA-HisSUMO2 cells and uninfected control HA-His only cells , respectively , allowed for the relative fold changes to be calculated for His-SUMO2 purified proteins and unmodified proteins following infection . This analysis identified with high confidence 877 cellular sumoylated proteins under these experimental conditions , of which 521 ( 59% ) were in common with a compilation of the largest previous SUMO substrate identification proteomics studies [23–28] . Following HSV-1 infection 260 of these proteins changed in abundance with SigB values of less than 0 . 1 , indicating some specificity in the targeted group of proteins; relative loss of sumoylated species is not simply a consequence of sumoylation per se , but the identity of the sumoylated species is also important . This is further illustrated by the fact that only 14% of these proteins decreased in abundance by over 3-fold and only 1% by 7-fold or more . Our analysis of specific proteins in this group has therefore been restricted to a subset of those most highly affected by HSV-1 infection . Identification in our MS dataset of the sumoylated forms of PML and Sp100 as proteins whose abundance is significantly reduced during infection supported the validity of the experiment , as did the lack of change in sumoylated RanGAP1 , which is not affected by HSV-1 [19] . Of the other proteins whose sumoylated forms reduced by a factor of at least 3-fold , a significant proportion may be linked to transcriptional regulation or chromatin related pathways ( Fig 5 ) . This supports the view that one of the most recognized functions of sumoylation is to regulate transcription [46–49] . SUMO-modification of transcriptional regulators is often described as having an inhibitory effect on transcription [47 , 50–52] , however , there have been instances where SUMO-modification enhances transcription factor activity [53] . SUMO-modification of proteins in the context of transcriptional regulation may include transcription factors themselves , transcriptional co-regulators , and chromatin-remodeling proteins [46] . Sumoylation of these proteins may regulate their DNA binding activity , subcellular localization , assembly of multi-component complexes , interaction between transcription factors and co-regulators , and also DNA repair pathways and chromatin structure [46 , 54 , 55] . The reduction in the levels of the sumoylated forms of the number of proteins linked to these functions , and in some cases also their total abundance , would likely in normal circumstances have profound effects on the cell . In the samples analyzed here , the cells are subject to an extremely active and soon to be fatal infection , so even drastic changes to cellular gene expression may be inconsequential . The more interesting questions concern the consequences these changes may have on viral gene expression , and the potential identification of previously unrecognized preferential substrates for ICP0-mediated degradation . Therefore our analysis was driven first by authentication of a number of potentially interesting proteins , then by investigation of the role of ICP0 in their desumoylation or degradation . We verified several example proteins on the list of sumoylation substrates that change during HSV-1 infection through a combination of western blotting of total protein extracts and His-SUMO2 purified proteins from mock and infected HA-HisSUMO2 cells ( Fig 6 ) , time course of degradation of endogenous proteins in whole infected cell extracts ( Fig 7 ) , and analysis of epitope tagged protein expression in stable cell lines ( Fig 8 ) . Evidence in favor of the sumoylation of these proteins , and the loss of these sumoylated forms ( and in some cases their unmodified species ) was obtained in one or more of these approaches . Overall , 13% of the proteins listed in Fig 5 were subjected to verification , with entirely positive results . The loss of bulk sumoylated species following HSV-1 infection is more pronounced in human fibroblasts than HepaRG cells [18] , and therefore we also analyzed the fate of several designated sumoylated proteins ( ZBTB4 , ZBTB10 , ZBTB38 , NACC1 , and MORC3 ) over a time course of HSV-1 infection of HFs ( Fig 7 ) . All of these proteins decreased in abundance following wt HSV-1 infection , some as early as 3 h p . i . This suggests many proteins may be targeted much earlier than the 12 h time point used for the MS experiments ( chosen to ensure complete infection and maximal effects ) . The available antibodies to these endogenous proteins do not always recognize a clearly sumoylated form , but it is clear that in most cases even the major unmodified form is sensitive to HSV-1 infection . Similar results were obtained in HepaRG cells , and these proteins remained stable during infection with ICP0-null mutant HSV-1 . In some cases , problems with lack of availability , specificity , or affinity of available antibodies were overcome using an inducible lentiviral expression system , in which the level of protein expression can be regulated by time of induction . In all cases , likely sumoylated bands were detected ( S4 Fig ) and these , together in some cases with the unmodified forms , were sensitive to HSV-1 infection ( Fig 8 ) . These proteins were however stable during infection with an ICP0 null mutant virus used at a multiplicity allowing an equivalent level of infection between wt and ICP0 null virus infected cells ( Fig 8 ) . To investigate whether ICP0 alone in the absence of infection is sufficient to decrease putative SUMO-modified protein bands , we utilized cells that can be induced to express ICP0 at levels equivalent to those during wt infection [42] . These experiments illustrated that ZBTB4 , ZBTB10 , ZBTB38 , NACC1 , MORC3 , BEND3 and MBD1 all suffered a loss of total protein or likely sumoylated bands in the presence of ICP0 , illustrating that ICP0 can cause this phenotype in the absence of other viral proteins . It is likely that the reduced abundance of many other proteins identified in this study is also ICP0-dependent . Whether ICP0 induces desumoylation or degradation of the sumoylated forms of these proteins requires further investigation , but in those instances where there is a loss of total protein suggests that the sumoylated species are being degraded rather than desumoylated . We investigated the role of the sumoylation itself in substrate targeting in a limited number of instances through two approaches , firstly using SUMO depleted cells , and secondly analysis of the effects of a mutant form of ICP0 with a SUMO2 interaction motif inactivated . A complete analysis of these issues is beyond the scope of this paper , but the results demonstrate that sumoylation can play a role in the response of a protein to HSV-1 infection , but that the details may differ between proteins . It is intriguing that infection with the mSLS4 virus causes an increase in overall SUMO conjugate levels [44] , and this is reflected in either reduced loss of presumed sumoylated forms of certain proteins during mSLS4 infection , or indeed an increase in their abundance ( as in the case of ZBTB10; Fig 11 ) . Presumed sumoylated forms of ZBTB10 and ZBTB4 also increase during ICP0 null mutant HSV-1 infection ( Fig 8 ) . These results confirm the role of sumoylation and SUMO-SIM interactions in some of the effects we observe . It is more difficult to determine whether these functions also impact on the degradation of the unmodified forms of selected proteins , as ICP0 can also target proteins in a sumoylation-independent manner [44] , and it also includes other potential SIMs . We did not analyse the activity of a form of ICP0 that lacks multiple potential SIMs in this study because this mutant is highly defective in degrading all substrates previously analyzed [44] . Proteins containing a zinc finger ( ZF ) and/or BTB ( broad-complex , tramtrack and bric-à-brac ) domain were prominent on the list of those showing a greater than 3-fold reduction in the purified fraction following wt HSV-1 infection ( Fig 5 ) . These ZF and BTB domains are likely to bind DNA and mediate protein:protein interactions , respectively . Apart from being predicted to play roles in transcriptional regulation , the precise functions for most ZBTB proteins , the proteins they interact with and the genes they regulate are yet to be discovered [56] . Thus loss of sumoylation of ZBTB proteins may control transcription of cellular genes required for a successful immune response to the infection , or prevent repression of transcription of viral genes . In addition to potential roles in chromatin and transcription-related pathways , many BTB proteins are now known to act as substrate-specific adaptors for Cullin3-based E3 ligases [57] and hence play a role in modulating protein stability . Some of the known functions of the BTB and ZBTB and other proteins validated in this study are summarized in Table 1 . In addition to the ZBTB and BTB proteins listed in Table 1 , we also validated some other proteins implicated in chromatin-related pathways and transcriptional repression , such as BEND3 , MORC3 , MBD1 and ETV6 . It is intriguing that BEND3 promotes heterochromatinization and that sumoylation is important for its repressive activities [58] , while MORC3 is a PML NB component that interacts with PML in a SUMO-dependent manner [59 , 60] . MBD1 is the largest of the methyl-CpG binding domain ( MBD ) family of proteins [61] that can also bind to unmethlyated DNA to mediate transcriptional repression [62 , 63] via its interaction with HDACs [63 , 64] . ETV6 is also a transcriptional repressor that can be sumoylated [65 , 66] and which interacts with transcriptional co-repressors and histone deacetylases [67–71] . While the biological significance of the changes in abundance and/or sumoylation status of these and the other proteins listed in Figs 3 and 5 remains to be determined , we note that of the 124 proteins whose sumoylation status changes by 3-fold or more during HSV-1 infection ( Fig 5 ) , four ( PML , Sp100 , ATRX and IFI16 ) have already been reported to affect the efficiency of ICP0 null mutant HSV-1 infection [9 , 10 , 12 , 31 , 72] . Given the number of proteins listed in Fig 5 that have potential roles in gene expression and chromatin-related pathways , it seems likely that future studies will reveal more such examples . In addition to cellular proteins , we also identified a number of viral proteins that had higher than predicted molecular weights in the His-SUMO2 purified fraction . Excluding glycoproteins , these included nuclear protein UL3 , capsid portal protein UL6 , protein kinase US3 , deoxyribonuclease UL12 , tegument protein US11 , DNA polymerase processivity subunit UL42 , transcriptional regulator ICP4 , ICP0 , and regulatory protein ICP22 ( Fig 4 ) . There is no previous report of HSV-1 proteins being modified by SUMO , although the UL42 homologues expressed by HCMV ( UL44 ) [37] , and possibly that of EBV ( BMRF1 ) [73] can be sumoylated . Thus the potential to be sumoylated may be a feature of the DNA polymerase accessory subunits expressed by herpesviruses . Of the HSV-1 proteins listed above , US3 , UL12 and UL42 contain potential SUMO conjugation sites and analysis of His-SUMO2 purified proteins with available antibodies revealed likely sumoylated species , albeit in low amounts , for UL6 , UL12 , ICP0 and UL42 . Further analysis will be required to investigate the extent and consequences of this potential sumoylation . In general , there are several reports of viral proteins subject to sumoylation , and in some cases this is an important aspect of their activity ( reviewed in [74 , 75] ) . In summary , post-translational modification of proteins with SUMO has important implications for protein function , and has roles in many pathways of importance for viral infections , including transcriptional regulation , and innate and intrinsic immune responses ( reviewed in [74 , 75] ) . This proteomics study has provided a large data set opening many avenues of research for not only herpes virology , but also other areas where the sumoylation of proteins is of heightened interest . The validation using independent methods of the changes in abundance of many proteins identified by the MS approach lends considerable confidence to the overall utility of this study , and we have documented several previously unrecognized examples of proteins that are subject to ICP0 mediated degradation . In that a number of known biologically relevant substrates of ICP0 were identified in our study , it is likely that future studies on the basis of this analysis will reveal important novel aspects of the regulation of herpesvirus infection .
Human diploid foreskin fibroblasts ( HFs , obtained from Dr Thomas Stamminger , University of Erlangen ) , HEK-293T human embryo kidney cells ( American Type Culture Collection CRL-11268 ) and human osteosarcoma cells ( U2OS , American Type Culture Collection HTB96 ) cells were grown in Dulbecco’s Modified Eagles’ Medium ( DMEM ) supplemented with 10% fetal calf serum ( FCS ) . Baby hamster kidney cells ( BHK-21 , obtained from original Glasgow MRC Virology Unit stocks ) were grown in Glasgow Modified Eagles’ Medium ( GMEM ) supplemented with 10% new born calf serum and 10% tryptose phosphate broth . HepaRG cells [76] were grown in William’s Medium E supplemented with 10% fetal bovine serum Gold ( PAA Laboratories Ltd ) , 2 mM glutamine , 5 μg/ml insulin and 0 . 5 μM hydrocortisone . Derivatives of HepaRG cells expressing the tetracycline repressor ( HA-TetR cells ) and wt ICP0 in a doxycycline inducible manner ( HA-cICP0 cells ) have been described previously [18 , 42] . HepaRG cells transduced with lentiviruses expressing multiple anti-SUMO1 or a combination of anti-SUMO2 and antiSUMO3 shRNAs have been described elsewhere ( M . Glass , manuscript submitted ) , as has the control lentivirus expressing an shRNA that does not target any human gene ( shNeg ) [11] . All cell growth media were supplemented with 100 units/ml penicillin and 100 μg/ml streptomycin . Lentivirus transduced cells were maintained with continuous antibiotic selection , as appropriate . HSV-1 wild type ( wt ) strain 17 and mutant dl1403 [77] were the wt and ICP0-null mutant strains used . Virus mSLS4 , which expresses a form of ICP0 with an inactivated SIM-like sequence SLS4 , has been described previously [44] . These viruses were grown in BHK cells and titrated in U2OS cells , using 1% human serum in the overlay . ICP0 is not required for HSV-1 plaque formation in U2OS cells [78] , therefore allowing a true comparison of the titres of wt and mutant virus stocks . Estimation of plaque formation efficiencies in cell lines expressing SUMO family members was performed using wt and ICP0 null mutant HSV-1 isolates ( viruses in1863 and dl1403CMVlacZ respectively ) that express a β-galactosidase marker gene linked to the HCMV promoter , as described previously [42] . Briefly , cells were seeded into 24-well dishes then infected with 3-fold serial dilutions of the viruses the following day . After 24 h incubation in the presence of additional 1% human serum , the cells were stained for β-galactosidase activity as described [42] . Relative plaque forming efficiencies were calculated by determining the number of plaques in each cell line at a given dilution of virus , then calculating fold changes in plaque number compared to controls cells at the same dilution . Averages and standard deviations were calculated from at least three independent determinations . Lentivirus vector plasmid pLVX-6His-SUMO2 , in which the human SUMO2 cDNA with a polyhistidine tag was inserted between the XhoI and XbaI sites of pLVX-IRES-Puro ( Clontech ) , and pLVX-6His ( a control with only the tag sequence ) were kindly provided by Ben Hale . Plasmids with cDNAs of selected cellular proteins were either purchased from Source Bioscience or were gifts from Pierre-Antoine Defossez ( MBD1 transcript variant 3 , ZBTB4 ) . The cDNAs were amplified by PCR using primers containing suitable restriction sites and encoding an in frame N-terminal myc tag , then the products were inserted in place of the ICP0 cDNA in doxycycline inducible lentiviral vector pLKO . DCMV . TetO-cICP0 ( pLDT-cICP0 ) [42] . Lentiviral vectors expressing myc tagged versions of BEND3 and MBD1 in a constitutive manner were constructed by inserting the relevant cDNAs in place of the EYFP-PML cDNA in plasmid pLKOneo . gD . EYFP-PML . I [79] which had been modified by inserting the blasticidin resistance coding region in place of that for neomycin . Lentivirus transductions of were performed as described [9] , with stable cell lines selected using puromycin ( 1 μg/ml , reduced to 0 . 5μg/ml for subsequent passage ) , G418 ( 0 . 5 mg/ml ) or blasticidin ( 1 μg/ml ) , or combinations thereof , as relevant . HA-HisSUMO2 and HA-His Only cells were cultured in SILAC DMEM lacking L-lysine and L-arginine , which were replaced with normal ( light; L ) , heavy ( H ) or medium ( M ) stable isotopically labeled forms of these amino acids ( all SILAC medium reagents were sourced from Cambridge Isotope Laboratories ) . These media were supplemented with 10% dialyzed fetal bovine serum ( FBS ) , 100 units/ml penicillin and 100 μg/ml streptomycin and 0 . 5 μg/ml puromycin . Mock infected HA-HisSUMO2 cells were cultured in H medium ( 13C6 15N2-lysine , Lys8 , and 13C6 15N4-arginine , Arg10 ) , wt HSV-1 infected HA-HisSUMO2 cells were cultured in L medium ( isotopically normal; Lys0 , Arg0 ) , and mock infected HA-His Only cells were cultured in M medium ( 4 , 4 , 5 , 5-D4-lysine , Lys4 , and 13C6-arginine , Arg6 ) . Cells were cultured for six population doublings in their respective SILAC media before being expanded into 12 x 150 mm dishes for each condition . HA-HisSUMO2 cells cultured in SILAC Light medium were infected at an MOI of 10 plaque forming units per cells for 12 h . The method used was essentially as described [27] . Cells were washed twice with phosphate buffered saline ( PBS ) , then lyzed in denaturing nickel sample buffer [6 M guanidinium hydrochloride ( Merck ) , 94 . 7 mM Na2HPO4 ( VWR Prolabo ) , 5 . 3 mM NaH2PO4 ( VWR Prolabo ) , 10 mM Tris/HCl ( Roche ) pH 8 . 0 , 20 mM imidazole ( Sigma ) , 5 mM β-mercaptoethanol ( Sigma ) , complete EDTA free protease inhibitor cocktail ( Roche ) ] , and stored at -70°C . Once thawed , equal amounts of protein from each Light , Medium and Heavy SILAC treatment ( determined by BCA assay ( Pierce ) and confirmed by Silver staining after SDS-PAGE ) were mixed and sonicated . Sonicated lysates were then centrifuged at 1 , 000 x g at 4°C for 10 min followed by passage through a 0 . 45 μm filter . Lysates were then added to 50 μl Ni2+ NTA agarose beads ( Qiagen ) , pre-equilibrated with denaturing nickel sample buffer , and incubated with rotation at 4°C for 24 h . Beads were centrifuged out of suspension 1 , 000 x g at 4°C for 10 min . Beads were washed by centrifugation at 720 x g for 2 min in 1 ml buffer in a Lo-Bind Eppendorf tube as follows: once in denaturing nickel sample buffer , twice with wash buffer pH 8 . 0 [8 M urea ( Sigma ) , 94 . 7 mM Na2HPO4 , 5 . 3 mM NaH2PO4 , 10 mM Tris/HCl pH 8 . 0 , 20 mM imidazole , 5 mM β-mercaptoethanol , complete EDTA free protease inhibitor cocktail] , twice in wash buffer with the pH reduced to 6 . 3 , and once with a final wash in a new Lo-Bind Eppendorf tube in wash buffer pH 8 . 0 . Bound proteins were then eluted from beads in 40 μl nickel resin elution buffer [2x LDS ( Invitrogen ) , 1x reducing reagent ( Invitrogen ) , 200 mM imidazole] at room temperature with agitation for 10 min , followed by boiling for 2 min . Samples were stored at -20°C . For both quantitative proteomic experiments a ‘crude’ sample was prepared by TCA precipitation of proteins from a sample of the mixed lysates prior to nickel affinity purification; 35 μl of the protein mixture in the 6 M guanidine hydrochloride buffer ( see above ) , containing about 70 μg of protein , was mixed with 400 μl 10% trichloroacetic acid ( TCA ) , incubated in ice for 20 min and centrifuged at 19000 x g for 15 min at 4°C . The pellet was washed with 1 ml 100% ethanol at 4°C and re-centrifuged at 19000 x g for 15 min at 4°C . Supernatants were aspirated and the pellets dried in a gyrovap before resuspension in 80 μl 1 . 5 x LDS sample buffer containing reducing agent ( Invitrogen ) . Then 35 μl of this and the nickel affinity chromatography elutions ( representing between 20 and 40 μg of total protein ) were fractionated by polyacrylamide gel electrophoresis containing SDS ( NuPage 10% polyacrylamide , Bis-Tris with MOPS buffer—Invitrogen ) and stained with Coomassie blue . For each experiment both crude and pure lanes were excised into identical slices according to apparent MW of markers , as indicated in Figs 2 and 3 . Peptides were extracted from each slice by tryptic digestion [80] , including alkylation with chloroacetamide . Peptide samples were analyzed by LC-MS/MS on a Q Exactive mass spectrometer ( Thermo Scientific ) coupled to an EASY-nLC 1000 liquid chromatography system via an EASY-Spray ion source ( Thermo Scientific ) running at 75 μm x 500 mm EASY-Spray column . Elution gradient durations of 150 min and 240 min were used . Data were acquired in the data-dependent mode . Full scan spectra ( m/z 300–1800 ) were acquired with resolution R = 70 , 000 at m/z 400 ( after accumulation to a target value of 1 , 000 , 000 with maximum injection time of 20 ms ) . The 10 most intense ions were fragmented by HCD and measured with a target value of 500 , 000 , maximum injection time of 60 ms and intensity threshold of 1 . 7e3 . A 40 second dynamic exclusion list was applied . Raw MS data files were processed together with the quantitative MS processing software MaxQuant ( version 1 . 3 . 0 . 5 ) [30 , 81] Enzyme specificity was set to trypsin-P as required . Cysteine carbamidomethylation was selected as a fixed modification and methionine oxidation , protein N-acetylation and gly-gly adducts to lysine were chosen as variable modifications . The data were searched against a target/decoy human database in addition to the HSV-1 database ( GenBank accession number JN555585 . 1 ) . Initial maximum allowed mass deviation was set to 20 parts per million ( ppm ) for peptide masses and 0 . 5 Da for MS/MS peaks . The minimum peptide length was set to 7 amino acids and a maximum of four missed cleavages . 1% false discovery rate ( FDR ) was required at both the protein and peptide level . The ‘match between runs’ option was selected with a time window of two minutes . Data were output twice; firstly separated by ‘crude’ and ‘pure’ conditions , and secondly such that each digestion of each gel slice was considered a single ‘experiment’ . The former is used for overall protein ratio changes , and the latter for apparent MW analysis [29] . In the triple SILAC labeled experiment one condition represented non-infected cells expressing the 6His sequence only , ( HA-His Only cells ) ( See Fig 2 ) . This was in the isotopically ‘medium’ condition ( M ) , and so any ratio comparing this condition with the ‘light’ ( L ) or ‘heavy’ ( H ) conditions where 6His-SUMO-2 was expressed , i . e . M/L and H/M can be used as comparison between HA-HisSUMO2 and HA-His Only purifications . As SUMO2 substrates by definition should be more abundant in H or L conditions than M , substrates will be characterized by large H/M and small M/L ratios . For log2 M/L and log2 H/M ratios , two cutoffs of <-1 . 790 and >0 . 822 respectively were used . If applied to pure ratios this defined 877 putative SUMO2 substrates , representing 30 . 8% of all identifications . The same criteria applied to the crude ratios shortlisted 36 proteins , representing 0 . 65% of all identifications . By this method the false discovery rate for SUMO2 substrate definition is estimated to be below 1% . For analysis of whole cell extracts , cells were seeded into 24-well dishes at 1 x 105 cells per well , then infected or treated with doxycycline the following day , as described in the figure legends . Cell monolayers were washed twice with PBS before harvesting in SDS-PAGE loading buffer . Proteins were resolved on 7 . 5% SDS-polyacrylamide gels , then transferred to nitrocellulose membranes by western blotting . Antibodies directed against the following proteins were used: 6xHis monoclonal antibody ( mAb ) ( ab18184 , Abcam ) , SUMO2/3 rabbit polyclonal antibody ( rAb ab3742 , Abcam ) , RanGAP1 ( mAb 33–0800 , Invitrogen ) , PML 5E10 mAb [82] , Sp100 rAb SpGH [5] , actin ( mAb AC-40 , Sigma ) , myc tag ( mAb 9E10 , Santa Cruz ) , β-tubulin ( mAb T4026 , Sigma ) , NACC1 ( rAb ab29047 , Abcam ) , ZBTB10 ( rAb A303-257A , Bethyl ) , ZBTB38 ( affinity purified rAb prepared by PRIMM ) , ZBTB4 ( rAb #120/4 , a gift from Pierre-Antoine Defossez ) , MORC3 ( rAb NBPI-83036 , Novus Biologicals rAb ) , CITED2 ( rAb EPR3416 ( 2 ) Abcam ) , ZBTB7A ( rAb ab123075 , Abcam ) . The sources of antibodies to HSV-1 proteins ICP0 ( mAb 11060 ) , ICP4 ( mAb 58S ) and UL42 ( mAb Z1F11 ) have been described previously [42] . Monoclonal antibody 175 to detect UL6 and rabbit antibody BWp12 for UL12 were kindly provided by Frazer Rixon and Nigel Stow , respectively . Secondary antibodies include horse radish peroxidase conjugated goat anti-rabbit IgG ( whole molecule ) ( Sigma A0545 ) and goat anti-mouse IgG ( whole molecule ) ( Sigma A4416 ) .
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Proteins are subject to many types of modification that regulate their functions and which are applied after their initial synthesis in the cell . One such modification is known as sumoylation , the covalent linkage of a small ubiquitin-like protein to a wide variety of substrate proteins . Sumoylation is involved in the regulation of many cellular pathways , including transcription , DNA repair , chromatin modification and defence to viral infections . Several viruses have connections with sumoylation , either through modification of their own proteins or in changing the sumoylation status of cellular proteins in ways that may be beneficial for infection . Herpes simplex virus type 1 ( HSV-1 ) causes a widespread reduction in uncharacterized sumoylated cellular protein species , an effect that is caused by one of its key regulatory proteins ( ICP0 ) , which also induces the degradation of a number of repressive cellular proteins and thereby stimulates efficient infection . This study describes a comprehensive analysis of cellular proteins whose sumoylation status is altered by HSV-1 infection . Of 877 putative cellular sumoylation substrates , we found 124 whose sumoylation status reduces at least three-fold during infection . We validated the behavior of several such proteins and identified amongst them several novel targets of ICP0 activity with predicted repressive properties .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Analysis of the SUMO2 Proteome during HSV-1 Infection
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Among other effects , post-translational modifications ( PTMs ) have been shown to exert their function via the modulation of protein-protein interactions . For twelve different main PTM-types and associated subtypes and across 9 diverse species , we investigated whether particular PTM-types are associated with proteins with specific and possibly “strategic” placements in the network of all protein interactions by determining informative network-theoretic properties . Proteins undergoing a PTM were observed to engage in more interactions and positioned in more central locations than non-PTM proteins . Among the twelve considered PTM-types , phosphorylated proteins were identified most consistently as being situated in central network locations and with the broadest interaction spectrum to proteins carrying other PTM-types , while glycosylated proteins are preferentially located at the network periphery . For the human interactome , proteins undergoing sumoylation or proteolytic cleavage were found with the most characteristic network properties . PTM-type-specific protein interaction network ( PIN ) properties can be rationalized with regard to the function of the respective PTM-carrying proteins . For example , glycosylation sites were found enriched in proteins with plasma membrane localizations and transporter or receptor activity , which generally have fewer interacting partners . The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic PIN properties . By integrating global protein interaction networks and specific PTMs , our study offers a novel approach to unraveling the role of PTMs in cellular processes .
As chief actors within living cells , proteins serve diverse functions such as catalysis , transport , structural building material and many others [1] . While the human gene set was estimated at about 25 , 000 genes [2] , the human proteome size is expected to be far larger and estimated at over 1 million proteins [3] . Beyond alternative splicing of mRNA as a source of protein diversity , post-translational modifications ( PTMs ) of proteins further modulate and extend the range of possible protein functions by covalently attaching small chemical moieties to selected amino acid residues . More than 200 different types of PTMs have been identified that affect many aspects of cellular functionalities , such as metabolism , signal transduction , and protein stability [4 , 5] . These modifications include phosphorylation , glycosylation , methylation , acetylation , amidation and many other types , see http://www . uniprot . org/docs/ptmlist for a more detailed controlled vocabulary of PTMs curated by UniProt [6] . With technological advances , PTMs can be detected at an ever increasing breadth , precision , and quantity e . g . by using mass spectrometry ( MS ) based methods [7] . Several databases have been established to store the obtained information , such as UniProt [8] , dbPTM [9] , PTMCuration [10] , PTMcode [11] and many others . Among them , a number species-specific databases have been developed [12–14] offering the opportunity to investigate PTMs in an evolutionary context as well . Many studies on PTMs have focused on specific types and their relevance for protein function with phosphorylation representing the most actively researched PTM-type [15–19] . More recently , the interplay between different PTM-types has moved into the focus of attention [20–23] . For example , evidence of an interdependence of phosphorylation and acetylation was reported for a genome-reduced bacterium Mycoplasma pneumoniae [24] . Furthermore , so-called integrative PTM spots ( PTMi ) have been identified as site in proteins at which different PTMs operated in a combinatorial manner to modulate protein function [25] . A more global view of the interplay between PTM-types was presented in a study on the co-evolution between 13 frequent PTM types in 8 eukaryotic species [26] . Carboxylation was identified as evolutionarily most conserved , whereas phosphorylation was found among those PTM-types playing a central role in the modulation of the dynamics of protein function . For a recent review on the evolution and functional cross-talk between PTMs , see [27] . In addition to PTMs , protein function is also regulated and mediated by non-covalent protein-protein interactions [28–33] . As many PTMs modulate the binding affinities between proteins by changing the electrostatic or structural properties of the involved interaction sites [29] , PTMs and protein-protein interactions are frequently functionally connected . Based on data from the dbPTM database of protein post-translational modifications [9] , more than 60% of PTM sites are related to those protein functional domains that were shown to preferentially engage in direct protein-protein interactions suggesting a central regulatory role of PTMs in the modulation of protein interactions , and thus , function . Therefore , it appears plausible that proteins carrying a particular PTM-type may possess specific interaction characteristics . Indeed , for the important and intensively investigated PTM-type phosphorylation , it was found that in yeast , phospho-proteins engage in many more protein-protein interaction than proteins without phosphorylation sites [34] , found similarly in Arabidopsis thaliana [35] . Thus , phosphorylation of a single protein potentially leads to a modulation of many different interactions , and thus , molecular processes , simultaneously . As the different PTMs modify proteins in specific ways , it appears furthermore likely that their consequences on protein-protein interactions may be different as well . This hypothesis formed the starting point for the present study . Specifically , we asked whether different PTM-types are associated with characteristic protein-protein network properties , such as interaction degree , clustering coefficient , and closeness centrality , for those proteins carrying them . We selected these three properties as they each reflect on potential functional role such as scope of impact ( degree ) , diversity of responses ( clustering coefficient ) , and placement within a possible signaling cascade ( closeness centrality ) . Furthermore , we investigated whether those characteristics are conserved across different species , and if the particular functions the different PTMs fulfil may become apparent when inspected from the viewpoint of protein-protein interactions .
Frequently , proteins are modified not only by a single PTM event , but by several and of different PTM-types . Thus , if we wish to understand the role of individual PTM-types in the context of protein-protein interactions , we first need to understand their co-occurrence on the same protein as well as their functional profile as it seems plausible that PTM-types associated with similar functional involvement will also exhibit similar characteristics with regard to their protein interactions . The functional significance of specific PTM-types and the respective proteins carrying them has been amply investigated [26 , 32 , 36 , 37] . To provide a comparative overview of the selected PTM-types studied here , we integrated all species-specific gene ontology ( GO ) annotations into a merged set and determined preferred biological process involvements , functional roles , and subcellular locations based on GO-enrichment statistics computed for this artificial “super species” . Consistently across all GO-term domains ( process , function , and location ) , the 12 PTM-types are grouped into two major groups with sumoylation , nitrosylation , methylation , acetylation , phosphorylation , ubiquitination in one group ( group-I ) , and disulfide bond , carboxylation , hydroxylation , proteolytic cleavage , glycosylation and amidation in the other ( group-II ) ( Fig . 1 , S1 Fig . ) . While group-I PTM-types were found preferentially in proteins located in the cytosol and nucleus and involved in regulatory processes ( most noteworthy , transcriptional regulation ) , group-II PTM-types appear associated with membrane- , subcellular compartment localizations ( carboxylation ) , extracellular locations and secretory processes . In line with several reported observations on their concerted action [38] , phosphorylation and ubiquitination were found with similar GO process and location profiles . Acetylation appears to be involved in similar processes as well . Indeed , the combined action of these three PTM-types has been described in selected cases as , for example , for the protein p53 [20] . Furthermore , glycosylation and proteolytic cleavage exhibit similar GO-term characteristics , which may reflect the involvement of and even interplay between both PTMs in the modification of secreted and/or membrane-embedded proteins [39] . Thus , different PTM-types have similar functional involvement and location profiles suggesting that their characteristic protein-interaction network properties may also be similar , which appears implied in particular based on common localizations influencing the scope of potential protein interaction partners . The co-occurrence patterns of different PTM-types on the same protein are critical confounding factors for the analysis of the individual PTM-types in the context of protein interactions . Evidently , frequent PTM-types will have a greater chance of co-occurring with other PTM-types on the same protein . Indeed , as judged by the Jaccard-distance , protein sets in human associated with phosphorylation , acetylation , and ubiquitination—the three PTM-types with the most observed instances ( Table 1 ) —exhibit large overlaps ( Fig . 2 ) . However , as we tested for deviations from the expected chance overlap as well , this co-occurrence on the same protein also seems significant . In general , the overlap amongst all PTM-types studied here is extensive . The reduced overlap for amidation , disulfide bond , hydroxylation carboxylation , and methylation with other PTM-types appears largely caused by their low frequency . Similarly , when expanding the overlap analysis to all species considered here , a large overlap between phosphorylation and acetylation is evident ( Fig . 3 ) . Interestingly , when viewed across several species , methylation emerges as a PTM-type with significant co-occurrence with both acetylation and phosphorylation possibly reflecting their joint association with histones [40] . Furthermore , glycosylation and phosphorylation appear to frequently co-exist on the same protein . In conclusion , the overlap of different PTM-types on the same proteins is extensive and greater than expected by chance . Even though suggested by the separate clustering of PTM-types based on their functional and location annotations ( Fig . 1 ) , with regard to their co-occurrence pattern , no equivalent segregation is apparent . Therefore , all PTM-types will—when analyzed jointly—likely exhibit similar protein-interaction characteristics . While primarily reporting results on the global protein sets ( including overlaps ) , we also performed analyses on the one-PTM-type-only protein sets . Evidently , one cannot be certain that those unique sets are truly unique in reality as not all PTM-types and their instances have been identified yet . Furthermore , rendering the data set PTM-type specific , i . e . reducing the protein sets to sets conforming to one PTM-type only must inevitably lead to a massive reduction of statistical power . We inspected the protein sets associated with specific PTM-types in the context of known protein-protein interactions . We mapped all proteins with annotated PTMs onto the respective protein interaction networks ( PINs ) of the nine selected species ( Table 2 ) . By computing three network properties , the degree , the clustering coefficient and the closeness centrality , we wished to investigate whether proteins associated with particular PTM-types exhibit distinct interaction characteristics that may be indicative of a PTM-specific function . The degree quantifies the average number of connections a protein engages in . Thus , it reflects on how many interaction partners may be affected by a PTM of a given protein . The clustering coefficient allows estimating whether the proteins connected to a central reference protein are in turn connected amongst themselves . High clustering coefficients would indicate a closely knit network of local interactions , whereas low clustering coefficients would suggest that separate molecular processes with little communication between them are modulated , when a central protein undergoes a PTM . Finally , the closeness centrality allows assessing how centrally a particular protein resides relative to the overall network . Proteins with high closeness centrality are situated in central network positions such that they may serve central information relay functions . By contrast , low closeness centrality corresponds to peripheral locations as typical of initial receptor molecules . ( For a formal definition of the three network properties , please see Methods . ) Therefore , all three chosen network properties allow a direct interpretation of the specific function of PTMs with regard to impact ( degree ) and role as a potential information relay hub ( clustering coefficient and closeness centrality ) in the network of all interacting proteins in the cell . Fig . 4 shows the frequency distributions of the PTM-type-specific network properties exemplified in Homo sapiens . Overall , all PTM-types exhibit a tendency to have higher degrees , lower clustering coefficients , and higher closeness centralities than protein sets not carrying the respective PTM-type , which includes a set of human proteins ( 1 , 864 or 20 . 8% of all human proteins ) currently not known to undergo any of the 12 PTM-types considered here . The latter set ( no PTM ) , was observed with lower degree , higher clustering coefficient , and lower closeness centrality than proteins undergoing a PTM ( S2 Fig . ) . With regard to degree , the largest increases relative to the respective reference sets were observed for sumoylation , proteolytic cleavage , and amidation , albeit for the latter the count of observed instances is low . By contrast , glycosylation shows almost no change of degree relative to its reference set . With regard to the clustering coefficient , sumoylation , proteolytic cleavage , and carboxylation were found with the largest decreases relative to their respective control sets . Finally , sumoylation , proteolytic cleavage , and amidation were the top-three PTM-types associated with the largest relative increase in their median closeness centrality compared to their proteins sets devoid of the respective PTM-type . Again , glycosylation was found with the smallest relative change with regard to closeness centrality . Thus , excluding the PTM-types with very low counts ( amidation and carboxylation ) , sumoylation and proteolytic change were identified as the two PTM-types associated with the largest relative differences across all three network properties examined . In short , both are characterized by high degree , low clustering coefficient , and high closeness centrality . Glycosylation is found at the other end of the spectrum with no change with regard to degree and closeness centrality , but a drop in clustering coefficient . The three most abundant PTM-types in human—based on available data—acetylation , phosphorylation , and ubiquitination , all show significant and comparable degree and closeness centrality increases . With regard to closeness centrality , phosphorylation is signified by the largest drop among the three PTM-types relative to its control protein set , while the other two show a smaller ( ubiquitination ) or no change ( acetylation ) . Next , we expanded our analyses to the remaining eight species considered here ( Fig . 5 ) . To avoid database specific effects , we considered two sources of PIN information , STRING and IntAct [41 , 42] . While STRING contains integrated interactions from different sources , IntAct contains experimentally verified interactions extracted from literature and based on direct user submissions . Except for a few cases ( 13 out of 86 with data available for both PIN-resources ) , consistent results across the two PIN-data resources were obtained . Furthermore , for some of the IntAct derived PIN-properties , we detected a difference in their mean value compared to their median , reflecting the lower counts of IntAct events resulting in non-Gaussian/asymmetric distribution . Furthermore , truly comparing the different PTM-types across several species is possible only for the PTM-types acetylation , glycosylation , and phosphorylation , as for the others sufficient PIN-information is lacking . Also , we restricted the analyses to the addition-type PTM-types with sufficient data acetylation , glycosylation , methylation , phosphorylation , nitrosylation , sumoylation , and ubiquitination . Despite these data limitations , a similar overall picture emerges as observed in human . All examined PTM-types appear to be associated with proteins with increased degree and closeness centrality , but decreased clustering coefficient relative to protein sets not carrying the respective PTM-type . Glycosylation is a notable exception and seems associated with slightly decreased , rather than increased , degree centrality , while exhibiting similarly decreased clustering coefficients across all species as the other PTM-types . With regard to closeness centrality , the results for glycosylation are mixed with a few species ( the four mammalian species and the plant Arabidopsis thaliana ) showing slightly increased values for STRING-based PINs and decreased values in the remaining species . However , clearly more table cells are colored blue signifying lowered values than for the other PTM types further supported by negative fold-change values for IntAct PINs . Thus , overall , glycosylation appears to generally be associated with no significantly changed , or slightly lowered closeness centrality relative to control protein sets . Several of the PTM-types inspected here can be subdivided further into separate sets based on the identity of the targeted group on the protein ( Lysine vs . N-terminal acetylation , N- or O-linked glycosylation , arginine-/lysine-methylation , and serine/threonine and tyrosine phosphorylation . Especially in the case of S/T vs . Y-phosphorylation differences in the associated PIN-properties would be of interested given the importance of phosphorylation in general , and in particular , as the two different types are catalyzed by different kinases [43] . However , when subdividing the protein sets into the target-specific PTM types , consistent results were obtained as reported for the merged sets ( S3 Fig . ) . As the only notable exception , the difference in degree observed for O-linked glycosylation ( tendency for increased degree relative to reference set ) compared to N-linked glycosylation ( trending towards decreased degree ) is worth mentioning . In an attempt to address a possible confounding influence of co-occurring different PTM-types on the same protein , we repeated the analysis of PTM-type-specific PIN properties shown in Fig . 5 for sets of proteins that are annotated to undergo one PTM-type only ( S4 Fig . ) . Inevitably , this dramatically reduced the number of proteins that can be used ( S1 Table ) . Hence , a meaningful analysis was possible for four PTM-types ( acetylation , glycosylation , phosphorylation , and ubiquitination ) only . Again , some conflicts between STRING and IntAct derived results render drawing clear conclusions difficult . However , phosphorylation again comes out as being associated with high-degree , low clustering coefficient , and high closeness centrality proteins compared to reference unphosphorylated protein sets . Glycosylation , on the other hand , is found again with low degree , low clustering coefficient , and low closeness centrality compared to control sets of proteins that are not glycosylated . Acetylation and ubiquitination both appear less consistent with the results reported for the whole protein set ( Fig . 5 ) . Ubiquitination was found with low degree , high clustering coefficient , and low closeness centrality; i . e . opposite the trend reported in Fig . 5 . For acetylation , no clear trends are evident also because of many conflicts between STRING and IntAct based results . Thus , PIN-properties for phosphorylation and glycosylation are confirmed in the unique protein sets , whereas acetylation and ubiquitination either behave differently when protein sets are properly reduced to unique sets , or clear conclusions cannot be drawn as of yet because of data limitations . Above , we examined the co-occurrence of different PTM-types detected on the same protein ( Fig . 3 ) . We extended the co-occurrence analysis to pairwise physically interacting proteins carrying different PTMs based on protein sets characterized by one PTM-type only ( Fig . 6 ) . For all PTM-types but disulfide-bond proteins , there is a tendency to self-interact , i . e . two separate proteins carrying the same PTM-type interact more often than randomly expected . Phosphorylated proteins display the broadest interaction range with significantly more interactions than expected to 8 other PTM-type proteins , followed by glycosylated proteins ( 6 distinct PTM-type partners ) , and acetylation ( 5 distinct PTM-type partners ) . By contrast , proteins associated with methylation , disulfide-bond formation or amidation exhibit a reduced interaction spectrum with likely interactions to only three of fewer other PTM-type proteins including interactions between two proteins carrying the same PTM-type . Acetylation , glycosylation , and phosphorylation form a clique of more than expected interactions among these three PTM-types . Especially phosphorylated proteins were found to interact more often with acetylated proteins than expected by chance . Please note that the analysis displayed in Fig . 6 is controlled for abundance; i . e . the reported interactions are above the expected chance-encounters . Interaction statistics including proteins including those with multiple different PTM-types is provided as S5 Fig . As frequently , proteins carry multiple different PTM-types , conclusions with regard to preferred cross-protein PTM-type interactions is less meaningful . However , the trends described above are apparent as well . We tested whether proteins are more likely to be implicated in human disease when their associated PIN property values were at the high or low end of the spectrum . Most significantly for phosphorylation , but also evident for ubiquitination , acetylation , and glycosylation , we detected a larger than expected overlaps with known human disease proteins for high-degree proteins only , but not for proteins undergoing the same PTM-type but with low interaction degree proteins . Similarly , glycosylated and phosphorylated proteins are more likely disease associated when they have high closeness centrality . By contrast , low clustering coefficient appears correlated with disease association for proteins undergoing phosphorylation , acetylation , and ubiquitination ( Table 3 ) .
The modulation of protein function via different types of post-translational modifications ( PTMs ) and their combinatorial interplay has attracted considerable attention in recent years [15–27] . In this study , we added the interaction layer to the study of PTMs by performing a systematic investigation of the network properties of the different PTM-types in the context of the physical interactions of PTM-carrying proteins . For twelve different PTM-types and across nine diverse species , we determined characteristic and informative network parameters with the goal to investigate whether particular PTM-types are associated with specific and possibly “strategic” placements in the context of all protein interactions such that their individual role in the orchestration of the combined action of all proteins becomes apparent . Generalized across all PTM-types and species investigated here , PTM-carrying proteins appear engage in more physical contacts , with a reduced clustering coefficient among those proteins they are interacting with , and elevated closeness centrality than their respective protein sets devoid of the particular PTM-type ( Fig . 5 ) or that , as far as we currently know , do not harbor any PTM of any type ( S2 Fig . ) . Differences between the twelve studied PTM-types proved less pronounced with essentially all—except for glycosylation ( see below ) —following the same trend of high degree , low clustering coefficient , and high closeness centrality with only subtle differences in magnitude between them . However , given the present data coverage , it is not yet possible to conclusively decide whether these differences are statistically significant and biologically relevant . When further subsetted into special types of PTMs ( e . g . S/T/Y phosphorylation ) , no significant sub-type differences were evident ( S3 Fig . ) . As motivated above , the three selected network properties were selected specifically to allow conclusions as for the “strategic” roles of PTM in the context of interactions . According to this logic , proteins with PTMs engage in more and different process than non-PTM proteins and play central information relay functions . Focusing on human PIN and PTM data , sumoylation and proteolytic cleavage stand out as being associated with the largest relative increase of degree and closeness centrality relative to reference sets . Proteolytic cleavage has been associated with activation processes and protein targeting events ( cleavage of targeting N-terminal peptide ) and constitute a “dramatic” modification as the relative change of molecular composition of a protein can be significant . Furthermore , transporting proteins to different compartments will inevitably influence the possible interaction scope . The significance of sumoylation in a range of regulatory processes has been increasingly recognized [44] . Our results underscore the importance of this PTM-type . Phosphorylation , the PTM-type with the largest data support , was identified as the PTM-type with the consistently central and with the largest potential influence scope ( Fig . 5 ) . Phosphorylated proteins reside in central network positions ( high closeness centrality ) and interact with many other proteins ( high degree ) including specifically pairwise interactions with proteins carrying any of the other four PTM-types as well as other phosphorylated proteins ( Fig . 6 — pairwise interaction figure ) . Examples from human of phospho-proteins interacting with proteins carrying other PTM-types include the kinases: mitogen-activated protein kinase 1 ( MAPK1 ) , interleukin-1 receptor-associated kinase 2 ( IRAK2 ) , and spleen tyrosine kinase ( SYK ) . Those proteins each interact with other proteins representing four different PTM-types . These findings underscore once again the central importance of phosphorylation as perhaps the most important and central PTM-type identified so far . Similar characteristics were found for acetylation , albeit the detected magnitude and statistical support is lower . By contrast , glycosylation was found associated with proteins of low degree , low clustering coefficient , and low closeness centrality ( Fig . 5 ) . In particular the low degree and low closeness centrality of glycosylated proteins may be interpreted as consistent with their preferred location in cytosolic membranes and to act as receptors and cell-cell communication mediators ( Fig . 1 , GO-term clustering ) [45] . Unlike the other four PTM-types , the transferred glycosyl-groups can be large leading to impeded protein-protein interactions of glycosylated proteins . In addition , because of their frequent embedding in membranes , they operate in two dimensions , not three as for soluble cytosolic proteins , effectively cutting down the interaction potential . As shown in Fig . 6 , all PTM-types are found on proteins that exhibit a tendency to interact with other proteins carrying the same PTM-type . In the case of phosphorylation , such interactions are interpretable as the well known as phosphorylation/kinase cascades [46 , 47] . It is also possible that the detected tendency of PTM-types to self-interact originates from protein complexes , in which all partners undergo the PTMs of the same type . For example , in histone complexes , lysine residues on different proteins are acetylated modifying the binding affinity of histones to DNA [48 , 49] . Similar consideration apply to methylation events in histone [50] and other protein complexes [51] . By including nine species from different kingdoms and lineages , we aimed to extract both general and species/lineage-specific trends . However , currently available datasets proved comprehensive enough for a few species only ( human , mouse , rat ) . In the case of phosphorylation , sufficient data were available across all nine species and provided a consistent result of increased degree and closeness centrality and a decreased clustering coefficient ( Fig . 5 ) . The increased likelihood of a functional association of proteins with high interaction degree and their involvement in human disease has been reported before [52 , 53] . In selected cases , proteins carrying PTMs have also been reported to be more likely related to disease processes than non-PTM proteins [54–56] . Our dataset allowed us to expand this analysis to testing specific PTM-types combined with their PIN-characteristics . Our results suggest that not only does a PTM render proteins more likely disease associated , but that this association may depend on what PIN context it is embedded in . High degree , low clustering coefficient , and high closeness centrality proteins are more likely to be disease associated ( Table 3 ) than their respective counterpart sets at the respective other end of the property PIN-property spectrum , especially for the PTM-types phosphorylation and glycosylation , albeit it for the latter , no significant clustering coefficient trend was detected . Examples of disease-associated phosphorylated or glycosylated proteins detected with high degree and closeness centrality or low clustering coefficient are provided in Table 4 . It may be speculated that proteins with the properties identified as more likely disease associated based on their PIN properties may constitute promising candidates for intensified research . Evidently , the relevance of the protein p53 in human cancer development has long been recognized [57] . In our study , it was identified as one with characteristic network properties typical of disease associated proteins in general . Evidently , this study hinges on the completeness and accuracy of the available PTM and PIN data as well . Any bias towards a specific detection of particular protein classes and their associated PTM may further skew our results . By imposing a high significance cutoff for the PIN-data ( confidence score > 0 . 9 ) , and furthermore exploiting two data sources ( STRING and IntAct ) , we believe to have taken proper precautionary steps even though some discrepancies were detected ( Fig . 5 ) . However , at this point it cannot be decided whether the size of the dataset ( relatively small IntAct data set ) or the type of PINs that are recorded cause these differences . With regard to PTMs , we used experimentally verified PTMs only . Future investigations of the PIN characteristics of PTMs will benefit from the expected significant increase of experimentally verified sites . In addition , a larger set of different PTMs with sufficient numbers will likely become available , allowing also to further specify the PTM-types used in this study . A possible selection bias may also come from preferentially profiling those proteins for PTMs that possess “interesting” properties such has high degree . However , as PTMs are increasingly identified in massive , “shotgun” style omics studies , such selection bias may not be that critical . Rather , abundance may be a concern then . However , for phosphorylation it was reported that protein abundance is not correlated with network properties [34 , 35] . Furthermore , we also found that network properties are largely independent of the number of PTMs on a given protein ( S6 Fig . ) . While significant due to the large number of observations , no relevant correlation was found neither for degree and nor clustering coefficient with the number of phosphorylation sites taken as the PTM-type with the largest available dataset . However , for closeness centrality , a more sizable positive correlation ( r = 0 . 164 ) was detected suggesting that more heavily phosphorylated proteins occupy more central positions in the network of protein-protein interactions . In conclusion , proteins carrying different types of PTMs differ from average non-PTM-proteins and differ between each other with regard to their protein interaction characteristics . Thus , their location within the web of physical protein-protein interactions is not only non-random , but very likely indicates their specific functional roles in the orchestration of molecular processes mediated by the physical interactions between proteins .
Post-translational modifications . Post-translational modifications annotated as “experimentally verified” and the associated proteins were extracted from UniProt [58] , PhosphoSitePlus [59] , dbPTM [9] , Phospho . ELM [12] , PhosphoGRID [60] , PHOSIDA [13] , HPRD [61] , OglycBase [62] , PhosPhAt [14] , P3DB [63] , PTMcode [11] as of 2014 April . Subsequently , the sets obtained from the different data sources were consolidated to create a single set based on protein sequence position information . Initially , only those PTM-types were considered further , for which more than 1000 sites were reported across the various data resources , regardless of species . Subsequently , only those species were retained for which the count of PTM-sites across all PTM-types was 1000 or more . The intersection of both sets yielded the primary PTM-type-species dataset for analysis . To remove outliers , extremely long or short proteins as determined by falling below the 1-percentile or above the 99-percentile of observed protein sequence length distribution were removed . Furthermore , proteins with an extremely high number of PTM sites greater than or equal to . average ( number of PTM sites ) +3*sd ( number of PTM sites ) ) were discarded as well . Twelve PTM types met those criteria in at least one species including 10 PTM-types in which a chemical moiety is attached: acetylation , amidation , carboxylation , glycosylation , hydroxylation , methylation , nitrosylation , phosphorylation , sumoylation , ubiquitination , and two PTM-types that modify the protein via forming or breaking bonds within the protein: disulfide bond and proteolytic cleavage . Nine species met those criteria for at least one PTM-type including representatives from the animal ( mammals: Mus musculus , Rattus norvegicus , Bos taurus , Homo sapiens; insects: Drosophila melanogaster , invertebrates: Caenorhabditis elegans ) , plant ( Arabidopsis thaliana ) , and fungal ( Saccharomyces cerevisiae , Schizosaccharomyces pombe ) kingdom , respectively ( Table 1 ) . Many proteins carry more than one PTM-type . Some analyses were meaningful only if the considered proteins carry one PTM-type only ( e . g . interaction between proteins carrying different PTM-types ) . Then , protein sets were filtered further and referred to as “one-PTM-type-only” ( S1 Table ) . For statistics of the associated sub-sets of the dataset , see S2 Table . Protein-protein interaction networks . For the nine species selected based on available PTM information , high confidence ( confidence score>=0 . 9 ) protein-protein interactions ( PINs ) were extracted from STRING ( version 9 . 05 ) [41] . In order to avoid database biases , each species’ protein-protein interactions were also extracted from the IntAct database ( downloaded on 9 . 26 . 2013 ) [42] , which stores interactions derived from literature curation or direct user submissions . To remove isolated interactions significantly affecting some of the network properties ( e . g . closeness centrality ) , the network components with the size less than 100 were excluded ( S3 Table ) . Only one component ( the “giant” ) component was left for each selected species . The sizes of the different PINs ( species and data source ) are summarized in Table 2 . As the overlap between the STRING and IntAct interaction set is relatively small , the two datasets can be seen as largely disjoint and independent . Network properties . The PINs associated with the selected species correspond to undirected networks . The degree of a node n is the number of edges linked to n [64] . The clustering coefficient [64] of a node n is defined as . Cn = 2en/ ( kn ( kn - 1 ) ) , where kn is the number of neighbors of n and . en is the number of connected pairs between all neighbors of n . It reflects the connectivity of adjacent nodes . The closeness centrality Cc ( n ) of a node n is defined as the reciprocal of the sum of shortest path length originating from n to all other nodes m [64]: C c ( n ) = 1 Σ m L ( n , m ) , where L ( n , m ) is the length of the shortest path between two nodes n and m . It ranges between 0 and 1 . It corresponds to the inverse of the number of steps needed to traverse from all other nodes in the network to a selected node . The R package “igraph” ( http://cran . r-project . org/web/packages/igraph/index . html ) was used to compute the above mentioned network properties . For each PTM-type and across the selected species , the three selected network properties ( degree , clustering coefficient , and closeness centrality ) were computed for all proteins carrying the respective PTM-type and compared to those proteins not carrying this particular PTM-type . Significant differences between the respective two distributions were detected based on a Mann–Whitney test . The p-values were corrected for multiple testing considering as the total number of tests all tests across species , all PTM-types for each network property . In all cases of multiple testing correction , the FDR method was used [65] . Significance testing was applied to only those PTM-types and species with 30 proteins instances or more . For all species selected in this study , the available genome gene ontology ( GO ) process , function and cellular compartment annotations were extracted from UniProtKB-GOA [66] as the reference set . All the selected species were combined into one ‘species’ in the enrichment analysis . The method “elim” and the Fisher’s exact test with FDR were used for enrichment analysis using the “topGO” R package . The cutoff P-value was set to 0 . 01 . For each pair of PTMs ( A and B ) in one species , The Jaccard index for the co-existence of A and B is defined as |Intersection ( SA , SB ) |/|Union ( SA , SB ) | , where the set of proteins associated with A and B are denoted as SA and SB . Fisher’s exact test was used to test the significance of co-existence . For each pair of PTM-types and across the selected species , Fisher’s exact test was designed to test the over and under protein interaction frequency . The p-values were corrected for multiple testing considering as the total number of tests across all pair of PTM-types for each species . The cutoff p-value was set to 0 . 01 . Significance testing was applied to only those pairs of PTM-types in each species with at least 10 proteins instances or more separately . Human disease proteins were downloaded from OMIM ( http://www . ncbi . nlm . nih . gov/omim ) as of May , 2014 . In order to test the overlap between human diseases proteins with proteins associated with high ( top 25% ) or low ( bottom 25% ) network property values , Fisher’s exact test was used for all PTM-types . The p-values were corrected for multiple testing ( FDR ) across all PTM-types and network properties .
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The function of proteins is frequently modulated by chemical modifications introduced after translation from RNA . These post-translational modifications ( PTMs ) have been shown to also influence the interaction between proteins carrying them . We tested whether specific PTM-types characterized by attaching different chemical groups are associated with proteins with characteristic and possibly strategic positions within the network of all protein interactions in cellular systems . Based on network-theoretic analyses of PTMs in the context of protein interaction networks of nine selected species , we indeed observed distinctive properties of twelve PTM-types tested . Phosphorylation was found associated with proteins in central locations with the broadest interaction scope , while glycosylation was more prominent in proteins at the periphery of the web of all protein interactions . The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic protein interaction network properties . Our study highlights common and specific roles of the various PTM types in the orchestration of molecular interactions in cells .
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"and",
"Methods"
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2015
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The Roles of Post-translational Modifications in the Context of Protein Interaction Networks
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Perception of microbe-associated molecular patterns ( MAMPs ) elicits transcriptional reprogramming in hosts and activates defense to pathogen attacks . The molecular mechanisms underlying plant pattern-triggered immunity remain elusive . A genetic screen identified Arabidopsis poly ( ADP-ribose ) glycohydrolase 1 ( atparg1 ) mutant with elevated immune gene expression upon multiple MAMP and pathogen treatments . Poly ( ADP-ribose ) glycohydrolase ( PARG ) is predicted to remove poly ( ADP-ribose ) polymers on acceptor proteins modified by poly ( ADP-ribose ) polymerases ( PARPs ) with three PARPs and two PARGs in Arabidopsis genome . AtPARP1 and AtPARP2 possess poly ( ADP-ribose ) polymerase activity , and the activity of AtPARP2 was enhanced by MAMP treatment . AtPARG1 , but not AtPARG2 , carries glycohydrolase activity in vivo and in vitro . Importantly , mutation ( G450R ) in atparg1 blocks its activity and the corresponding residue is highly conserved and essential for human HsPARG activity . Consistently , mutant atparp1atparp2 plants exhibited compromised immune gene activation and enhanced susceptibility to pathogen infections . Our study indicates that protein poly ( ADP-ribosyl ) ation plays critical roles in plant immune gene expression and defense to pathogen attacks .
Plants sense the presence of pathogens by the cell surface-localized pattern recognition receptors ( PRRs ) , which perceive evolutionarily conserved pathogen- or microbe-associated molecular patterns ( PAMPs or MAMPs ) , including bacterial flagellin , lipopolysaccharide ( LPS ) , peptidoglycan ( PGN ) , elongation factor Tu ( EF-Tu ) , and fungal chitin [1]–[3] . A 22-amino-acid peptide corresponding to a region near the amino-terminus of flagellin ( flg22 ) is recognized by the Arabidopsis PRR Flagellin-Sensing 2 ( FLS2 ) , a leucine-rich repeat receptor-like kinase ( LRR-RLK ) [4] , [5] . Perception of flg22 by FLS2 induces instantaneous association with another LRR-RLK , Brassinosteroid Insensitive 1 ( BRI1 ) -Associated Kinase 1 ( BAK1 ) , mainly through ectodomain heterodimerization of flg22-activated FLS2/BAK1 complex [6]–[9] . The receptor-like cytoplasmic kinases ( RLCKs ) , BIK1 and its homolog PBL1 , constitutively associate with FLS2 and BAK1 , and are released from the receptor complex upon flg22 perception [10]–[12] . BAK1 directly interacts and phosphorylates BIK1 at both serine , threonine and tyrosine residues , thereby activating downstream signaling [12] , [13] . In addition , both BAK1 and BIK1 complex with PRR EFR ( receptor for EF-Tu ) [11] , [14] , AtPEPR1 ( receptor for endogenous danger signal Pep1 ) [12] , [15] , [16] , and plant brassinosteroid hormone receptor BRI1 [17]–[19] . Activation of PRR complex by the corresponding MAMP triggers a series of defense responses , including rapid activation of MAP kinases ( MAPKs ) and calcium-dependent protein kinases , transient reactive oxygen species ( ROS ) production and calcium influx , stomatal closure , callose deposition and massive transcriptional reprogramming [1]–[3] . It has been shown recently that BIK1 is able to phosphorylate plasma membrane-resident NADPH oxidase family member respiratory burst oxidase homolog D ( RBOHD ) , thereby contributing to ROS production [20] , [21] . However , it remains largely unknown how PRR complex activation leads to profound immune gene transcriptional reprograming . Protein poly ( ADP-ribosyl ) ation ( PARylation ) , an important post-translational modification process , plays a crucial role in a broad array of cellular responses including DNA damage detection and repair , cell division and death , chromatin modification and gene transcriptional regulation [22]–[24] ( S1 Fig . ) . PARylation is primarily mediated by members of poly ( ADP-ribose ) polymerases ( PARPs ) , which transfer ADP-ribose moieties from nicotinamide adenine dinucleotide ( NAD+ ) to different acceptor proteins at glutamate ( Glu ) , aspartate ( Asp ) or lysine ( Lys ) residues resulting in the formation of linear or branched poly ( ADP-ribose ) ( PAR ) polymers on acceptor proteins ( S1 Fig . ) . PAR activities and PARPs have been found in a wide variety of organisms from archaebacteria to mammals and plants , but they are apparently absent in yeast [25] . Human PARP-1 ( HsPARP-1 ) is the most abundant and ubiquitous PARP among a family of 17 members , and it catalyzes the covalent attachment of PAR polymers on itself ( auto-PARylation ) and other target proteins , including histones , DNA repair proteins , transcription factors , and chromatin modulators [22] . HsPARP-1 possesses three functional domains with a DNA binding domain at N-terminus , auto-modification domain in the middle and a catalytic domain at C-terminus ( S2A Fig . ) . PARylation is a reversible reaction and the covalently attached PAR on the target proteins can be hydrolyzed to free PAR or mono- ( ADP-ribose ) by poly ( ADP-ribose ) glycohydrolase ( PARG ) [22] , [23] ( S1 Fig . ) . PARG contains both endo- and exo-glycohydrolase activities that promote rapid catabolic destruction of PAR of target proteins [26] . There is only one PARG gene in humans with three different isoforms: PARG99 and PARG102 in the cytoplasm and PARG110 in the nucleus [26] . Mammalian PARG possesses a regulatory and targeting domain ( A-domain ) at the N-terminus , a mitochondrial targeting sequence ( MTS ) in the middle and a conserved catalytic domain at the C-terminus [27] ( S2B Fig . ) . The catalytic core containing “GGG-X6-8-QEE” PARG signature motif interacts with PAR and executes hydrolysis activity [28] . Despite of their apparently opposing activities , members of PARPs and PARGs coordinately regulate protein PARylation and play essential roles in a wide range of cellular processes and contribute to the pathogenicity of various diseases , including cancer , cardiovascular diseases , stroke , metabolic disorders , diabetes and autoimmunity [25] . The Arabidopsis genome encodes three members of PARPs , AtPARP1 ( At2g31320 ) , AtPARP2 ( At4g02390 ) and AtPARP3 ( At5g22470 ) and two members of PARGs , AtPARG1 ( At2g31870 ) and AtPARG2 ( At2g31865 ) [23] , [29] ( S2 Fig . ) . AtPARP1 ( it was originally named as AtPARP2 ) shares the conserved domain structure with HsPARP-1 , whereas AtPARP2 ( it was originally named as AtPARP1 ) and AtPARP3 more closely resemble HsPARP-2 and HsPARP-3 [29] ( S2A Fig . ) . As their mammalian counterparts , plant PARPs are implicated in DNA repair , cell cycle and genotoxic stress [29]-[32] . Importantly , plant PARPs play an essential role in response to abiotic stresses . Transgenic Arabidopsis or oilseed rape ( Brassica napus ) plants with reduced PARP gene expression were more resistant to various abiotic stresses , including drought , high light and heat , partially attributed to a maintained energy homeostasis of reduced NAD+ and ATP consumption and alternation in plant hormone abscisic acid ( ABA ) levels in the transgenic plants [33] , [34] . The two Arabidopsis PARG genes , AtPARG1 and AtPARG2 , which were likely derived from a tandem duplication event , locates next to each other on the same chromosome [23] . AtPARG1 ( TEJ ) was originally identified as a regulator of circadian rhythm and flowering in Arabidopsis [35] . Interestingly , the AtPARG2 gene was robustly induced by the treatments of MAMPs and various pathogens [36] . The plants carrying mutation in AtPARG1 , but not AtPARG2 , showed the elevated elf18 ( a 18-amino-acid peptide of EF-Tu ) -mediated seedling growth inhibition and phenylpropanoid pigment accumulation , suggesting a negative role of Arabidopsis PARG in certain plant immune responses [37] . Similar to AtPARP1 , AtPARG1 also plays a role in plant drought , osmotic and oxidative stress tolerance [38] . In contrast to the extensive research efforts on PARPs/PARGs in animal systems , the biochemical activities and molecular actions of plant PARPs/PARGs remain poorly characterized . To elucidate the signaling networks regulating immune gene activation , we developed a sensitive genetic screen with an ethyl methanesulfonate ( EMS ) -mutagenized population of Arabidopsis transgenic plants carrying a luciferase reporter gene under the control of the FRK1 promoter ( pFRK1::LUC ) . The FRK1 ( flg22-induced receptor-like kinase 1 ) gene is a specific and early immune responsive gene activated by multiple MAMPs [39] , [40] . A series of mutants with altered pFRK1::LUC activity upon flg22 treatment were identified and named as Arabidopsis genes governing immune gene expression ( aggie ) . In this study , we isolated and characterized the aggie2 mutant , which exhibited elevated immune gene expression upon multiple MAMP treatments . Map-based cloning coupled with next generation sequencing revealed that Aggie2 encodes AtPARG1 . Extensive biochemical analysis demonstrates that both AtPARP1 and AtPARP2 carry poly ( ADP-ribose ) polymerase activity , whereas AtPARG1 , but not AtPARG2 , possesses poly ( ADP-ribose ) glycohydrolase activity in vivo and in vitro . Significantly , the enzymatic activity of AtPARP2 is enhanced upon flg22 perception , suggesting the potential involvement of protein PARylation in MAMP-triggered immunity . The aggie2 mutation ( G450R ) occurs at a highly conserved PARG residue which is essential for both Arabidopsis AtPARG1 and human HsPARG enzymatic activity . Consistent with the negative role of AtPARG1 in plant innate immunity , AtPARP1 and AtPARP2 positively regulate immune gene activation and plant resistance to virulent bacterial pathogen infection . Our results indicate that the reversible posttranslational PARylation process mediated by AtPARPs and AtPARGs plays a crucial role in mounting successful innate immune responses upon MAMP perception in Arabidopsis .
The aggie2 mutant isolated from a genetic screen of the EMS-mutagenized pFRK1::LUC transgenic plants exhibits elevated FRK1 promoter activity upon flg22 treatment compared to its parental line , pFRK1::LUC ( WT ) ( Fig . 1A ) . The elevated luciferase activity in the aggie2 mutant was observed over a 48-hr time course period upon flg22 treatment ( Fig . 1B ) . Notably , the aggie2 mutant did not display detectable enhanced FRK1 promoter activity in the absence of flg22 treatment , suggesting its specific regulation in plant defense . In addition to flg22 , other MAMPs , including elf18 , LPS , PGN and fungal chitin , also elicited the enhanced FRK1 promoter activity in the aggie2 mutant ( Fig . 1C ) , indicating that Aggie2 functions as a convergent component downstream of multiple MAMP receptors . Consistently , the aggie2 mutant displayed the enhanced FRK1 promoter activity in response to the non-pathogenic bacterium Pseudomonas syringae pv . tomato ( Pst ) DC3000 hrcC defective in type III secretion of effectors , and a non-adaptive bacterium P . syringae pv . phaseolicola NPS3121 ( Fig . 1D ) . The pathogenic bacterium Pst DC3000 failed to activate pFRK1::LUC , likely due to the suppression function of multiple effectors secreted from virulent bacterium [40] . Pathogen infection or purified MAMPs could induce callose deposits in leaves or cotyledons of Arabidopsis , which has emerged as an indicator of plant immune responses [41] . We compared callose deposits by aniline blue staining in WT and aggie2 mutant plants upon flg22 treatment . The aggie2 mutant deposited more callose than WT plants 12 hr after flg22 treatment , and the size of each callose deposit appeared bigger in the aggie2 mutant than that in WT plants ( Fig . 1E ) . We also detected MAPK activation and ROS production , two early events triggered by multiple MAMPs , in WT and aggie2 mutant . The flg22-induced MAPK activation detected by an α-pERK antibody did not show significant and reproducible difference in WT and aggie2 seedlings ( Fig . 1F ) , suggesting that Aggie2 acts either independently or downstream of MAPK cascade . The flg22-induced ROS burst appeared to be similar in the aggie2 mutant compared to that in WT plants ( Fig . 1G ) . We did not observe reproducible disease alternation in the aggie2 mutant compared to WT plants in response to Pst DC3000 infection either by hand-infiltration or spray-inoculation with various inoculums and conditions ( S3A Fig . ) . Among 7 times of disease assays with Pst DC3000 hand-infiltration , we observed that aggie2 was slightly more resistant than WT plants for 4 times , whereas we did not see the significant difference between aggie2 and WT for other 3 times ( S3A Fig ) . By contrast , the aggie2 mutant showed enhanced susceptibility to a necrotrophic fungus Botrytis cinerea compared to WT plants as evidenced by symptom development and lesion progression after infection ( S3B Fig ) . We further detected endogenous FRK1 expression in flg22-treated seedlings of WT and aggie2 mutant with quantitative reverse transcription-polymerase chain reaction ( qRT-PCR ) analysis . The FRK1 expression was significantly elevated in the aggie2 mutant compared to that of WT pFRK1::LUC transgenic plants at both 30 min and 90 min after flg22 treatment ( Fig . 1H ) . Similarly , the expression of several other early MAMP marker genes , including MYB15 and At2g17740 was also enhanced in the aggie2 mutant ( Fig . 1H ) . Taken together , the results indicate that Aggie2 negatively regulates the expression of certain flg22-induced genes . To isolate the causative mutation in aggie2 , we crossed aggie2 ( in the Col-0 accession background ) with the Ler accession and mapped aggie2 to an 88 kilobase pair ( kb ) region between markers F20M17 and F22D22 on Chromosome 2 ( Fig . 2A ) . We then performed Illumina whole genome sequencing of aggie2 and WT pFRK1::LUC transgenic plants . The comparative sequence analysis identified a G to A mutation at the position 1348 bp of At2g31870 within this 88 kb region . The mutation was further confirmed by Sanger sequencing of the genomic DNA of At2g31870 . At2g31870 encodes AtPARG1 and the mutation in the aggie2 mutant causes an amino acid change of Glycine ( G ) at 450 to Arginine ( R ) ( G450R ) ( Fig . 2B ) . The G450 in AtPARG1 resides in a highly conserved region at the C-terminus with unknown function . Notably , this residue is invariable in different species of plants and animals , including Arabidopsis , poplar , tomato , maize , sorghum , rice , moss , rat , mouse , human and fruit fly , suggesting the essential role of this residue in PARG functions ( Fig . 2B ) . To confirm that the G450R lesion in AtPARG1 is the causative mutation in aggie2 , we complemented the aggie2 mutant with a construct carrying AtPARG1 cDNA fused with a FLAG epitope tag under the control of its native promoter ( pAtPARG1::AtPARG1-FLAG ) . Two homozygous T3 transgenic lines , one line with relatively low ( C2-3 ) and another line with moderate ( C4-1 ) expression of AtPARG1-FLAG , were chosen for complementation assays . Both lines restored WT level of pFRK1::LUC activity upon flg22 treatment either imaged with an EMCCD camera ( Fig . 2C ) or quantified by a luminometer ( Fig . 2D ) , confirming that the enhanced FRK1 promoter activity in aggie2 is caused by the mutation in AtPARG1 . We also isolated T-DNA insertion line of AtPARG1 , parg1-1 ( SALK_147805 ) and parg1-2 ( SALK_116088 ) , and examined flg22-induced immune gene activation . Similar to the aggie2 mutant , parg1-1 and parg1-2 displayed the elevated activation of FRK1 , MYB15 and At2g17740 after flg22 treatment compared to WT Col-0 plants ( Fig . 2E ) . PARP inhibitor disrupted MAMP-induced cell wall lignification [37] . We found that both parg1-1 and aggie2 mutants showed the enhanced accumulation of lignin biosynthesis precursors , O-4-linked-coniferyl and sinapyl aldehydes , upon flg22 treatment by Wiesner staining ( Fig . 2F ) . The complementation line C2-3 restored accumulation of these lignin biosynthesis precursors to the WT level ( Fig . 2F ) . Consistent with a previous report [36] , the transcript of AtPARG2 , but not AtPARG1 , was induced by flg22 treatment ( S4A Fig . ) . AtPARG1 encodes a putative poly ( ADP-ribose ) glycohydrolase with a predicated activity to remove poly ( ADP-ribose ) polymers on the acceptor proteins catalyzed by poly ( ADP-ribose ) polymerases ( PARPs ) . To elucidate the biochemical activity and function of AtPARGs , we first characterized the function of AtPARPs and established in vivo and in vitro protein PARylation assays . The Arabidopsis genome encodes three PARPs , AtPARP1 , AtPARP2 and AtPARP3 , with each consisting of a conserved PARP catalytic domain and a variable DNA binding domain ( S2 Fig . ) . AtPARP1 and AtPARP3 carry zinc-finger domains for DNA binding , which is similar with human HsPARP-1 , whereas AtPARP2 contains two SAP domains with putative DNA binding activity . The SAP domain was named after scaffold attachment factor A/B ( SAF-A/B ) , apoptotic chromatin condensation inducer in the nucleus ( Acinus ) and protein inhibitors of activated STAT ( PIAS ) , which all have DNA and chromatin binding ability and regulate chromatin structure and/or transcription [42] . Analysis of their tissue expression pattern suggests that AtPARP1 and AtPARP2 are expressed in leaves , whereas AtPARP3 is primarily expressed in developing seeds ( S4B-S4C Fig . ) . Thus , we focused on AtPARP1 and AtPARP2 for the functional studies . We first tested whether AtPARP1 and AtPARP2 carry poly ( ADP-ribose ) polymerase activity with recombinant proteins of AtPARP1 and AtPARP2 fused with Maltose Binding Protein ( MBP ) . In the presence of activated DNA , both AtPARP1 and AtPARP2 could catalyze PARylation reaction by repeatedly transferring ADP-ribose groups from NAD+ to itself ( auto-PARylation ) as appeared a ladder-like smear with high-molecular-weight proteins in a Western blot using an α-PAR antibody which detects the PAR polymers of PARylated proteins ( Fig . 3A ) . Apparently , AtPARP2 exhibited stronger in vitro enzymatic activity than AtPARP1 when detected by α-PAR antibody . The enzymatic activity of AtPARP1 and AtPARP2 was blocked by 3-AB , a competitive inhibitor of PARP ( Fig . 3A ) . The activity of AtPARP2 is comparable with that of human HsPARP-1 ( S5A Fig . ) . In addition , both AtPARP1 and AtPARP2 were able to transfer ADP-ribose from Biotin-NAD+ to itself and a relatively discrete band could be detected by horseradish peroxidase ( HRP ) conjugated streptavidin ( Fig . 3B ) . The specificity of PARP activity was confirmed with 3-AB treatment , which dramatically reduced auto-PARylation . Similar with the observation using α-PAR antibody , AtPARP2 exhibited stronger in vitro enzymatic activity than AtPARP1 when detected by streptavidin-HRP for biotinylated NAD+ . We further developed a PARylation assay with radiolabeled 32P-NAD+ as the ADP-ribose donor ( Fig . 3C ) . Clearly , both AtPARP1 and AtPARP2 were able to transfer ADP-ribose from 32P-NAD+ to itself as shown with SDS-PAGE autoradiograph ( Fig . 3C ) . The formation of relatively discrete band was likely caused by these assay conditions , which favor synthesis of short polymers due to limited amount of NAD+ [43] . Together , the data support that both AtPARP1 and AtPARP2 are active poly ( ADP-ribose ) polymerases in vitro . It has been shown that human HsPARP-1 could modify linker histone H1 proteins and thereby create a chromatin structure more accessible to RNA polymerase II ( RNAPII ) to regulate transcription [44] . We further examined whether AtPARP2 was also able to PARylate Arabidopsis histone proteins . As detected with radiolabeled 32P-NAD+ , AtPARP2 could PARylate two Arabidopsis histone proteins H1 . 1 and H1 . 3 ( Fig . 3D ) . It is possible that Arabidopsis PARPs may use a similar mechanism for transcriptional regulation . We further developed an in vivo PARylation assay with transiently expressed AtPARP2 tagged with an HA epitope at the C-terminus in Arabidopsis protoplasts . After feeding the cells with 32P-NAD+ , the AtPARP2 proteins were immunoprecipitated with an α-HA antibody and separated in SDS-PAGE . A band corresponding to the predicated molecular weight of AtPARP2 was observed with autoradiograph , indicating in vivo AtPARP2 activity ( Fig . 3E ) . This band is specific to AtPARP2 since it was absent in the vector control transfected cells . Strikingly , the flg22 treatment enhanced AtPARP2 in vivo PARylation activity as detected by increased band intensity with autoradiograph . Apparently , the flg22-mediated enhancement of AtPARP2 activity was not due to the increase of protein expression after treatment ( Fig . 3E ) . The data demonstrate that AtPARP2 possesses poly ( ADP-ribose ) polymerase activity in vivo and AtPARP2-mediated protein PARylation is regulated by flg22 signaling . We further examined AtPARP2-GFP localization with Agrobacterium-mediated Nicotiana benthamiana transient assay . A strong fluorescence signal from AtPARP2-GFP was exclusively detected in the nucleus ( Fig . 3F ) , which is consistent with its potential role in DNA repair , chromatin modulation and transcriptional regulation . We next tested whether AtPARG1 and AtPARG2 possess poly ( ADP-ribose ) glycohydrolase activity ( Fig . 4A ) . We isolated and purified AtPARG1 and AtPARG2 proteins fused with glutathione S-transferase ( GST ) expressed from E . coli , and established an in vitro PARG assays to examine whether AtPARGs could remove PAR from auto-PARylated AtPARP2 in vitro . As shown in Fig . 4B , AtPARG1 diminished the formation of the ladder-like smear of auto-PARylated AtPARP2 detected in a Western blot with an α-PAR antibody , suggesting the PARG activity of AtPARG1 towards AtPARP2 . However , AtPARG2 appeared to be inactive towards auto-PARylated AtPARP2 in this assay ( Fig . 4B ) . Similarly , AtPARG1 , but not AtPARG2 , could remove PAR polymers from auto-ADP-ribosylated AtPARP2 as detected with 32P-NAD+ autoradiograph ( Fig . 4C ) . We further examined whether AtPARG2 may possess PARG activity specifically towards AtPARP1 but not AtPARP2 . As shown in Fig . 4C , AtPARG2 did not remove PAR polymers from auto-ADP-ribosylated AtPARP1 . The 6xHistidine ( His6 ) -tagged AtPARG2 also did not display in vitro enzymatic activity ( S5B Fig . ) . Similar to the above assays using in vitro expressed AtPARG1 proteins ( Fig . 4B & 4C ) , the immunoprecipitated AtPARG1 expressed in Arabidopsis protoplasts almost completely removed PAR polymers from in vitro PARylated AtPARP2 ( Fig . 4D ) . Furthermore , AtPARG1 was able to remove PAR polymers from auto-PARylated human HsPARP-1 ( S5C Fig . ) . Similarly , human HsPARG was also able to remove PAR polymers from AtPARP2 ( S5D Fig . ) , suggesting the functional conservation of human and Arabidopsis PARPs/PARGs . To test whether AtPARGs carry enzymatic activity in vivo , HA-tagged AtPARG1 or AtPARG2 was co-expressed with FLAG-tagged AtPARP2 transiently expressed in Arabidopsis protoplasts . After feeding the protoplasts with 32P-NAD+ , AtPARP2 activity was detected with autoradiograph after immunoprecipitation with an α-FLAG antibody . Significantly , co-expression of AtPARG1 , but not AtPARG2 , substantially removed PAR polymers from in vivo PARylated AtPARP2 ( Fig . 4E ) . The expression level of AtPARG1 and AtPARG2 was similar in protoplasts as detected by an α-HA Western blot ( Fig . 4E ) . Taken together , our data indicate that AtPARG1 has in vivo and in vitro poly ( ADP-ribose ) glycohydrolase activity and AtPARG2 activity was not detected . Consistently , the parg1 mutant , but not parg2 mutant , accumulated higher PAR polymers than Col-0 with dot blotting of nuclear proteins by α-PAR antibody ( S5E Fig . ) . Subcellular localization study indicates that AtPARG1-GFP and AtPARG2-GFP reside mainly in nucleus , but also in plasma membrane and cytoplasm when transiently expressed in Arabidopsis protoplasts ( Fig . 4F ) . Notably , AtPARG1 protein possesses a PARG signature motif with the conserved sequence of “GGG-X7-QEE” . Mutation of E273 ( the last E in the signature motif ) in AtPARG1 to glycine ( E273G ) blocked its enzymatic activity , implicating the importance of this signature motif in PARG enzymatic activity ( Fig . 5A ) . Examination of AtPARG2 sequence revealed that AtPARG2 has a polymorphism in the PARG signature motif . Instead of the conserved sequence “GGG-X7-QEE” , AtPARG2 possesses “GGL-X7-QEE” ( Fig . 4A and 5A ) . The importance of this residue was shown by that the mutation of G264 ( third G in the signature motif ) in AtPARG1 to leucine ( G264L ) blocked its PARG enzymatic activity ( Fig . 5A ) . We further determined whether lack of enzymatic activity of AtPARG2 ( Fig . 4B , 4C & 4E ) is due to this polymorphism in the PARG signature motif . We mutated leucine ( L275 ) in AtPARG2 to glycine and generated the conserved “GGG-X7-QEE” motif . However , AtPARG2L275G mutant with a perfectly conserved PARG signature motif still did not show any detectable poly ( ADP-ribose ) glycohydrolase activity ( Fig . 5A ) . The data suggest that the polymorphism of the PARG signature motif in AtPARG2 is not the sole determinant of its lack of detectable enzymatic activity and additional polymorphisms/deletions also account for its loss of PARG functions . There are only about 52% amino acid identity and 66% similarity between AtPARG1 and AtPARG2 ( S6 Fig . ) . We further addressed whether the aggie2 ( G450R ) mutation affected its PARG activity . Significantly , the aggie2 ( G450R ) mutant of AtPARG1 completely abolished its enzymatic activity detected by either α-PAR antibody ( Fig . 4B ) or 32P-NAD+ autoradiograph-based assay ( Fig . 4C ) . Notably , the G450 in AtPARG1 is highly conserved among PARGs of different species ( Fig . 2B ) . Interestingly , the corresponding mutation in human HsPARG ( G867R ) also abolished its activity towards HsPARP-1 and AtPARP2 , suggesting the essential role of this highly conserved residue in different PARGs ( Fig . 5B and S5D Fig . ) . The phenylalanine ( F ) at position 227 in bacterium Thermomonospora curvata PARG is implicated in positioning the terminal ribose and the mutation of which rendered the enzyme inactive [28] . Surprisingly , mutation of the corresponding residue F457 to glycine ( F457G ) in AtPARG1 did not affect its enzymatic activity ( Fig . 5A ) , suggesting a possible distinct function mediated by this residue in different PARGs and potentially divergent evolution . We tested the involvement of AtPARPs in plant innate immunity and immune gene activation . Because of the potential functional redundancy of AtPARP1 and AtPARP2 [30] , [31] , we performed disease assay and analyzed defense gene expression in atparp1atparp2 ( atparp1/2 ) double mutant . The atparp1/2 mutant plants were more susceptible to virulent P . syringae pv . maculicola ES4326 ( Psm ) infection compared to WT plants as indicated by more than 10 fold increase of bacterial growth in the atparp1/2 mutant ( Fig . 6A ) . The disease symptom development was more pronounced in the atparp1/2 mutant than WT plants ( Fig . 6A ) . Similarly , the atparp1/2 mutant plants showed the enhanced susceptibility with bacterial growth and symptom development to the infections by Pst DC3000 and a less virulent bacterium Pst DC3000ΔavrPtoavrPtoB ( Fig . 6B & S7 Fig . ) . In addition , the atparp1/2 mutant plants showed the reduced induction of MAMP marker genes , including FRK1 and At2g17740 , compared to WT plants at 90 min after flg22 treatment ( Fig . 6C ) . Together , these data indicate that AtPARP1 and AtPARP2 are positive regulators in plant immunity and defense gene activation to bacterial infections .
Protein PARylation mediated by PARPs and PARGs is an important , but less understood posttranslational modification process implicated in the regulation of diverse cellular processes and physiological responses [26] . In this study , an unbiased genetic screen revealed that Arabidopsis AtPARG1 plays an important role in regulating immune gene expression upon pathogen infection . We established and performed extensive in vitro and in vivo biochemical assays of PARP and PARG enzymatic activities . We have shown for the first time that Arabidopsis AtPARP1 and AtPARP2 are able to transfer ADP-ribose moieties from NAD+ to itself and acceptor proteins in vitro and in vivo . Thus , they are bona fide poly ( ADP-ribose ) polymerases . Interestingly , in contrast to their mammalian counterparts , AtPARP2 is more enzymatically active than AtPARP1 . Significantly , MAMP perception promotes substantial enhancement of AtPARP2 enzymatic activity in vivo , reconciling the biological importance of PARPs/PARGs in regulating immune gene expression . AtPARG1 , but not AtPARG2 , is able to remove PAR polymers from PARylated proteins in vivo and in vitro and it is a bona fide poly ( ADP-ribose ) glycohydrolase . The Arabidopsis parg1 ( aggie2 ) mutant plants exhibited elevated expression of several MAMP-induced genes and callose deposition . Conversely , the Arabidopsis atparp1/2 mutant showed reduced expression of MAMP-induced genes and enhanced susceptibility to virulent Pseudomonas infections . Thus , the data suggest that protein PARylation positively regulates certain aspects of plant immune responses . Notably , the viability and normal growth of Arabidopsis parp and parg null mutants represent a unique opportunity to study protein PARylation regulatory mechanisms in diverse biological processes at the whole organismal level . Our results lend support to a previous study that treatment of pharmacological inhibitor of PARPs , 3-AB , disrupted elf18- and/or flg22-induced callose and lignin deposition , pigment accumulation and phenylalanine ammonia lyase activity [37] . However , the flg22-induced defense genes ( FRK1 and WRKY29 ) were not affected by 3-AB treatment [37] . Our study with Arabidopsis parg and parp genetic mutants revealed a previously unrecognized function of protein PARylation in regulating immune gene expression upon pathogen infection . This is consistent with the general role of human PARPs and PARG in transcriptional regulation and chromatin modification [43] , [45] and further substantiates the hypothesis that plant PARPs could ameliorate the cellular stresses caused by antimicrobial defenses ( e . g . the effects of elevated ROS levels ) [23] . Interestingly , ADP-ribosylation has also been exploited by pathogens as a means to quell plant immunity . Two Pseudomonas syringae effectors , HopU1 and HopF2 , mono-ADP-ribosylate RNA-binding protein GRP7 and MAPK kinase MKK5 respectively , and interfere with their activities in plant defense transcription regulation and signaling [46] , [47] . Unlike mammals and most other animals that encode a single PARG gene , the Arabidopsis genome encodes two adjacent PARG genes , AtPARG1 and AtPARG2 , as well as a pseudogene At2g31860 . Surprisingly , only AtPARG1 , but not AtPARG2 , possesses detectable poly ( ADP-ribose ) glycohydrolase activity in vitro and in vivo with our extensive biochemical assays . Sequence analysis identified a polymorphism in the conserved PARG signature motif “GGG-X7-QEE” , where the third G is replaced with an L in AtPARG2 . The PARG signature motif is absolutely required for its enzymatic activity as mutations at this motif in AtPARG1 completely abolished its activity . However , creation of the conserved signature motif in AtPARG2 was unable to gain its PARG activity suggesting that other polymorphisms in AtPARG2 are also responsible for its lack of enzymatic activity . Consistent with our biochemical assays , the PAR polymer concentration was much higher in atparg1 mutant than that in WT plants . A similar conclusion was reached on tej mutant , which carries a G262E mutation in the invariable signature motif of AtPARG1 [35] . The atparg1 , but not atparg2 mutant , affected elf18-induced seedling growth inhibition and pigment formation , and sensitivity to DNA-damaging agent [37] . Interestingly , AtPARG2 is substantially induced in multiple plant-pathogen interactions [36] and it is required for plant resistance to B . cinerea infections [37] . Thus , despite of lacking detectable enzymatic activity , AtPARG2 may still play certain role in plant immunity . It is possible that AtPARG2 may regulate AtPARG1 activity . It is also possible that AtPARG2 has evolved novel functions in plant immune responses . Several other plant species , including rice , poplar , tomato and maize , are also predicted to encode multiple PARGs [23] ( S8 Fig . ) . Unlike Arabidopsis PARGs , different PARG members in other species have invariant signature motif . For example , all three PARGs in poplar contain GGG-X7-QEE signature motif ( S8 Fig . ) . However , a few other species such as Eutrema salsugineum , Capsella rubella , Phaseolus vulgaris , Oikopleura dioica , and Xenopus laevis , contain PARGs with an AtPARG2-like signature GGL-X7-QEE . It remains unknown how many PARGs are enzymatic active in the species with multiple PARGs . Although there are 17 PARPs in mammals , the parp-1parp-2 double mutant mice are not viable and die at the onset of gastrulation , suggesting the essential role of protein PARylation during early embryogenesis [48] . The lethality of parp-1parp-2 double mutant mice might be due to genomic instability . However , Arabidopsis atparp1/2 double mutant is largely morphologically similar with WT plants and does not display any obvious growth defects . Although Arabidopsis atparp1/2 double mutant was hypersensitive to genotoxic stress , they did not have significant changes in telomere length nor end-to-end chromosome fusions [30] . Albeit mainly expressed in developing seeds , AtPARP3 may have redundant functions with AtPARP1 and AtPARP2 in maintaining genome stability . It remains interesting whether atparp1/2/3 triple mutant will exert abnormal plant growth and development . Consistent with the essential function of PARylation during embryogenesis , PARG-deficient mice and Drosophila are embryonic lethal which is probably due to the accumulation of PAR polymers and uncontrolled PAR-dependent signaling [49] , [50] . The normal plant growth phenotype of atparg1 mutant might be due to the redundant function of AtPARG2 . However , our extensive biochemical analysis indicates that AtPARG1 , but not AtPARG2 , accounts for most of PARG enzymatic activity . As AtPARG1 and AtPARG2 reside next to each other on the same chromosome , it is challenging to generate the double mutant . It remains possible that other PAR-degrading enzymes with distinct sequences exist in Arabidopsis . In vertebrate , ADP-ribosyl hydrolase 3 ( ARH3 ) , a structurally distinct enzyme from PARG , could also degrade PAR polymers associated with the mitochondrial matrix [26] . We observed that AtPARP2 activity was rapidly and substantially stimulated by flg22 treatment . In line with this observation , it has been shown that bacterial infections induced the increase of PAR polymers in Arabidopsis [37] . It is well established that damaged DNA stimulates PARP activity . Recent studies have shown that pathogen treatments induce DNA damage [51] , [52] , which could potentially serve as a trigger to activate PARP . Treatments with virulent or avirulent Pst strains for hours could induce DNA damage in Arabidopsis as detected by abundance of histone γ-H2AX , a sensitive indicator of DNA double-strand breaks or by DNA comet assays [51] . Prolonged pathogen treatment is often accompanied with the elevated accumulation of plant defense hormone salicylic acid ( SA ) . It has also been shown that SA can also trigger DNA damage in the absence of a genotoxic agent [53] . However , treatments of flg22 or elf18 did not induce detectable DNA damage [51] . In addition , flg22-mediated stimulation of AtPARP2 activity occurs rather rapidly and within 30 min after treatment . Apparently , flg22 signaling could directly activate AtPARP2 . It is well known that human HsPARP-1 is regulated by different posttranslational modification processes , such as phosphorylation , ubiquitination , SUMOylation and cleavage [22] . HsPARP-1 could be activated by phosphorylated MAPK ERK2 in a broken DNA-independent manner , thereby enhancing ERK-induced Elk1 phosphorylation , core histone acetylation , and transcription of the Elk1-target genes [54] . MAPK cascade plays a central role functioning downstream of multiple MAMP receptors . It will be interesting to test whether flg22-activated MAPKs directly modulate PARP and/or PARG activities . Our genetic and biochemical analyses revealed that PARP/PARG-mediated PAR dynamics regulates immune gene expression in Arabidopsis . Mammalian PARPs/PARG regulate gene expression through a variety of mechanisms including modulating chromatin , functioning as transcriptional co-regulators and mediating DNA methylation [55] . PARylation of histone lysine demethylase KDM5B maintains histone H3 lysine 4 trimethyl ( H3K4me3 ) , a histone mark associated with active promoters , by inhibiting KDM5B demethylase activity and interactions with chromatin . In addition , HsPARP-1 is able to promote exclusion of H1 and opening of promoter chromatin , which collectively lead to a permissive chromatin environment that allows loading of the RNAPII machinery [45] . HsPARG is also able to promote the formation of a chromatin environment suitable for retinoic acid receptor ( RAR ) -mediated transcription by removing PAR polymer from PARylated H3K9 demethylase KDM4D/JMJD2D thereby activating KDM4D/JMJD2D to inhibit H3K9me2 , a histone mark associated with transcriptional repression [43] . Arabidopsis PARPs and PARGs are localized in the nucleus , and AtPARP2 could PARylate Histone H1 . It is plausible to speculate that similar modes of action of protein PARylation-mediated transcriptional regulation exist in plants . Future identification of PARP/PARG targets ( promoters and proteins ) and PAR-associated proteins , especially during plant immune responses , will elucidate how protein PARylation modulates plant immune gene expression .
Arabidopsis accession Col-0 , pFRK1::LUC transgenic plants , aggie2 mutant , atparg1-1 ( SALK_147805 ) , atparg1-2 ( SALK_16088 ) , atparg2 ( GABI-Kat 072B04 ) , atparp1/atparp2 ( GABI-Kat 692A05/SALK_640400 ) , pPARG1::PARG1-FLAG transgenic plants were grown in soil ( Metro Mix 366 ) at 23°C , 60% humidity and 75 µE m−2s−1 light with a 12-hr light/12-hr dark photoperiod . Four-week-old plants were used for protoplast isolation and transient expression assays according to the standard procedure [56] . Seedlings were germinated on ½ Murashige and Skoog ( MS ) plate containing 1% sucrose , 0 . 8% Agar and grown at 23°C and 75 µE m-2s−1 light with a 12-hr light/12-hr dark photoperiod for 12 days , transferred to a 6-well tissue culture plate with 2 ml H2O for overnight , and then treated with 100 nM flg22 or H2O for indicated time . Pseudomonas syringae pv . tomato ( Pst ) DC3000 , hrcC , ΔavrPtoavrPtoB , P . syringae pv . maculicola ES4326 ( Psm ) , or P . syringae pv . phaseolicola NPS3121 strains were cultured overnight at 28°C in the KB medium with 50 µg/ml rifampicin or streptomycin . Bacteria were harvested by centrifugation , washed , and adjusted to the desired density with 10 mM MgCl2 . Leaves of 4-week-old plants were hand-infiltrated with bacterial suspension using a 1-ml needleless syringe and collected at the indicated time for luciferase activity or bacterial growth assays . To measure bacterial growth , two leaf discs were ground in 100 µl H2O and serial dilutions were plated on TSA medium ( 1% Bacto tryptone , 1% sucrose , 0 . 1% glutamic acid , 1 . 5% agar ) with appropriate antibiotics . Bacterial colony forming units ( cfu ) were counted 2 days after incubation at 28°C . Each data point is shown as triplicates . Botrytis cinerea strain BO5 was cultured on Potato Dextrose Agar ( Difco ) and incubated at room temperature . Conidia were re-suspended in distilled water and spore concentration was adjusted to 2 . 5 × 105 spores/ml . Gelatin ( 0 . 5% ) was added to conidial suspension before inoculation . Leaves of six-week-old plants were drop-inoculated with B . cinerea at the concentration of 2 . 5 × 105 spores/ml . Lesion size was measured 2 days post-inoculation . The pFRK1::LUC construct in a binary vector was transformed into Arabidopsis Col-0 plants . The homozygous transgenic plants with flg22-inducible pFRK1::LUC were selected for mutagenesis . The seeds were mutagenized with 0 . 4% ethane methyl sulfonate ( EMS ) . Approximately 6 , 000 M2 seedlings were screened for their responsiveness to flg22 treatment . The seedlings were germinated in liquid ½ MS medium for 14 days , and then transferred to water for overnight and treated with 10 nM flg22 . After 12 hr flg22 treatment , the individual seedlings were transferred to a 96-well plate , sprayed with 0 . 2 mM luciferin and kept in dark for 20 min . The bioluminescence from induced pFRK1::LUC expression was recorded by a luminometer ( Perkin Elmer , 2030 Multilabel Reader , Victor X3 ) . The candidate mutants with altered flg22 responsiveness were recovered on ½ MS plate for 10 days , and then transferred to soil for seeds . The aggie2 mutant was crossed with Arabidopsis Ler accession , and an F2 population was used for map-based cloning . Mapping with 270 F2 plants with aggie2 mutant phenotype placed the causal mutation in an 88 kb region between marker F20F17 and F22D22 on chromosome 2 . The aggie2 genomic DNA was sequenced with the 100 nt paired-end sequencing on an Illumina HiSeq 2000 platform at Texas AgriLife Genomics and Bioinformatics Service ( TAGS ) ( College Station , TX , USA ) . Ten-fold genome coverage was obtained with 11M reads . The Illumina reads were analyzed using CLC Genomics Workbench 6 . 0 . 1 software . By mapping to Col-0 genomic sequence ( TAIR10 release ) , SNPs were identified as candidates of aggie2 mutation . In the aforementioned 88 kb region , a G to A mutation at the position of 1348 nt of At2g31870 was identified with 100% frequency . The mutation was confirmed by Sanger sequencing of aggie2 genomic DNA . The AtPARP1 , AtPARP2 , AtPARG1 , AtPARG2 and Histone H1 . 1 ( At1g06760 ) genes were amplified from Arabidopsis Col-0 cDNA and cloned into a plant transient expression vector ( pHBT vector ) with an HA , FLAG or GFP epitope tag at the C-terminus via restriction sites NcoI or BamHI and StuI respectively . The oligos used to amplify aforementioned cDNAs are listed in S1 Table . The target genes were confirmed by Sanger sequencing . The cloned genes in plant expression vector were then sub-cloned into protein fusion vectors , pGEX-4T ( Pharmacia , USA ) , pMAL-c ( NEB , USA ) or pET28a ( EMD Millipore , USA ) , for protein expression in bacteria . For Histone H1 . 3 ( At2g18050 ) , we ordered cDNA from ABRC ( G13366 ) and cloned it into a modified pMAL-c via SfiI site . Point mutations were introduced by site-directed mutagenesis PCR . The AtPARG1 promoter ( 1163 bp upstream of start codon ATG ) was amplified from the genomic DNA of Col-0 and digested with KpnI and NcoI . The AtPARG1-FLAG-NOS terminator fragment was released from pHBT-AtPARG1-FLAG via NcoI and EcoRI digestion . The two fragments were ligated and sub-cloned into a binary vector , pCAMBIA2300 via KpnI and EcoRI sites to yield expression construct ( pAtPARG1::AtPARG1-FLAG ) . The resulting binary vector was transformed into aggie2 via Agrobacterium-mediated transformation . The primers for cloning and point mutations were listed in the S1 Table . Expression and purification of GST , His6 and MBP fusion proteins were performed according to the manufacturer's manuals . For in vitro auto-PARylation reaction , 1 . 2 µg of MBP-AtPARP2 or MBP-AtPARP1 proteins were incubated in a 20 µl reaction with 1 × PAR reaction buffer ( 50 mM Tris-HCl , pH8 . 0 , 50 mM NaCl ) with 0 . 2 mM NAD+ , and 1 × activated DNA ( Trevigen , USA ) . To inhibit PAR reaction , 2 . 5 mM PARP inhibitor , 3-Aminobenzamide ( 3-AB , Sigma , USA ) , was added to the reaction . The reactions were kept at room temperature for 30 min and stopped by adding SDS loading buffer . To detect PARG activity , about 1 . 0 µg of purified GST , GST-AtPARG1 or GST-AtPARG2 proteins together with 2 . 5 mM 3-AB were added to auto-PARylated AtPARP2 proteins derived from the above PAR reactions and incubated at room temperature for another 30 min . PARylated proteins were separated in 7 . 5% SDS-PAGE and detected with an α-PAR polyclonal antibody ( Trevigen , USA ) . For Biotin NAD+ PAR assay , 25 µM Biotin-NAD+ ( Trevigen , USA ) was added to replace NAD+ in the reaction described above . The PAR polymer formation was detected by Streptavidin-HRP ( Pierce , USA ) . For in vitro 32P-NAD+-mediated PAR assays , 1 . 0 µg of MBP-AtPARP2 or MBP-AtPARP1 proteins were incubated in a 20 µl reaction in the buffer containing 50 mM Tris-HCl , pH8 . 0 , 4 mM MgCl2 , 300 mM NaCl , 1 mM DTT , 0 . 1 µg/ml BSA , 1 × activated DNA , 1 µCi 32P-NAD+ ( Perkin Elmer , USA ) and 100 nM cold NAD+ for 30 min at room temperature . For Histone PARylation assays , 2 . 0 µg of MBP-H1 . 1 or MBP-H1 . 3 proteins were added in the above reactions . The radiolabeled proteins were separated in SDS-PAGE and visualized by autoradiography . For in vivo PAR assays , 500 µl Arabidopsis protoplasts at the concentration of 2 × 105/ml were transfected with 100 µg of plasmid DNA of pHBT-AtPARP2-HA . After 12 hr incubation , the protoplasts were treated with 100 nM flg22 for 30 min and fed with 1 µCi 32P-NAD+ for 1 hr . The protoplasts were then lysed in IP buffer ( 50 mM Tris-HCl , pH7 . 5 , 150 mM NaCl , 5 mM EDTA , 1% Triton , 1 × protease inhibitor , 1 mM DTT , 2 mM NaF and 2 mM Na3VO4 ) and the AtPARP2-HA proteins were immunoprecipitated with α-HA antibody ( Roche , USA ) and protein-G-agarose ( Roche , USA ) in a shaker for 3 hr at 4°C . In vivo PARylated proteins enriched on the beads were then separated in 10% SDS-PAGE and visualized by autoradiography . For in vivo PARG assay , AtPARG1-HA or AtPARG2-HA plasmid DNA was co-transfected with AtPARP2-FLAG plasmid DNA into protoplasts , and expressed for 12 hr . The protoplasts were fed with 32P-NAD+ and subjected to immunoprecipitation as described above . The AtPARP2-FLAG proteins were immunoprecipitated with α-FLAG agarose gel ( Sigma , USA ) , separated in 10% SDS-PAGE and visualized by autoradiography . The expression of AtPARPs and AtPARGs was detected with Western blot ( WB ) using the corresponding antibodies . The 12-day old seedlings grown on ½ MS plates were harvested and ground into fine powder in liquid nitrogen . Isolation of nuclei with Honda buffer was performed according to published procedure [57] . Nuclear proteins were released in 1xPBS buffer with 1% SDS and spotted on nitrocellulose membrane . The protein loaded on the membrane was normalized by using α-Histone H3 antibody ( Abcam , USA ) , and the PAR polymers were detected by α-PAR antibody . The relative PAR level was determined by calculating the ratio of PAR signal to Histone H3 signal after quantification of hybridization intensity with ImageJ software . For RNA isolation , 12-day-old seedlings grown on ½ MS plate were transferred to 2 ml H2O in a 6-well plate to recover for 1 day , and then treated with 100 nm flg22 for 30 or 90 min . RNA was extracted using TRIzol reagent ( Life Technologies , USA ) and quantified with NanoDrop . The RNA was treated with RQ1 RNase-free DNase I ( Promega , USA ) for 30 min at 37°C , and then reverse transcribed with M-MuLV Reverse Transcriptase ( NEB , USA ) . Real-time RT-PCR was carried out using iTaq Universal SYBR Green Supermix ( Bio-Rad , USA ) on 7900HT Fast Real-Time PCR System ( Applied Biosystems , USA ) . The primers used to detect specific transcript by real-time RT-PCR are listed in S2 Table . Leaves of six-week-old plants grown in soil were hand-inoculated with 0 . 5 µM flg22 or H2O for 12 hr . The leaves were then transferred into FAA solution ( 10% formaldehyde , 5% acetic acid and 50% ethanol ) for 12 hr , de-stained in 95% ethanol for 6 hr , washed twice with ddH2O , and incubated in 0 . 01% aniline blue solution ( 150 mM KH2PO4 , pH 9 . 5 ) for 1 hr . The callose deposits were visualized with a fluorescence microscope . Callose deposits were counted using ImageJ 1 . 43U software ( http://rsb . info . nih . gov/ij/ ) . Leaves of six-week-old plants grown in soil were surface-sterilized by 70% ethanol , rinsed with H2O and incubated with 100 nM flg22 or H2O for 12 hr . The leaves were then de-stained in 95% ethanol with 2% chloroform for 12 hr and 95% ethanol for 6 hr , washed twice with 95% ethanol , and incubated in 2% phloroglucinol solution ( 20% ethanol , 20% HCl ) for 5 min . The images were scanned by HP officejet Pro 8600 Premium . Ten-day-old seedlings germinated on ½MS plate were transferred to 2ml H2O in a 6-well plate to recover for 1 day , and then treated with 100 nM flg22 for 5 , 15 or 45 min . The seedlings were grinded in IP buffer . The cleared lysate was mixed with SDS sample buffer and loaded onto 12 . 5% SDS-PAGE . Activated MAPKs were detected with α-pErk1/2 antibody ( Cell Signaling , USA ) . ROS burst was determined by a luminol-based assay . At least 10 leaves of four-week-old Arabidopsis plants for each genotype were excised into leaf discs of 0 . 25 cm2 , followed by an overnight incubation in 96-well plate with 100 µl of H2O to eliminate the wounding effect . H2O was replaced by 100 µl of reaction solution containing 50 µM luminol and 10 µg/ml horseradish peroxidase ( Sigma , USA ) supplemented with or without 100 nM flg22 . The measurement was conducted immediately after adding the solution with a luminometer ( Perkin Elmer , 2030 Multilabel Reader , Victor X3 ) , with a 1 . 5 min interval reading time for a period of 30 min . The measurement values for ROS production from 20 leaf discs per treatment were indicated as means of RLU ( Relative Light Units ) . Arabidopsis protoplasts were transfected with various GFP-tagged pHBT constructs as indicated in the figures . Fluorescence signals in the protoplasts were visualized under a confocal microscope 12 hr after transfection . To construct 35S::AtPARP2-GFP binary plasmid for Agrobacterium-mediated transient assay , the NcoI-PstI fragment containing AtPARP2-GFP was released from pHBT-35S::AtPARP2-GFP and ligated into pCB302 binary vector . For tobacco transient expression , Agrobacterium tumefaciens strain GV3101 containing pCB302-35S::AtPARP2-GFP was cultured at 28°C for 18 hr . Bacteria were harvested by centrifugation at a speed of 3500 rpm and re-suspended with infiltration buffer ( 10 mM MES pH = 5 . 7 , 10 mM MgCl2 , 200 µM acetosyringone ) . Cell solution at OD600 = 0 . 75 was used to infiltrate 3-week-old Nicotiana benthamiana leaves . Fluorescence signals were detected 2 days post-infiltration . Fluorescence images were taken with Nikon-A1 confocal laser microscope systems and images were processed using NIS-Elements Microscope Imaging Software . The excitation lines for imaging GFP , RFP and chloroplast were 488 , 561 and 640 nm , respectively .
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Fine-tuning of gene expression is a key feature of successful immune responses . However , the underlying mechanisms are not fully understood . Through a genetic screen in model plant Arabidopsis , we reveal that protein poly ( ADP-ribosyl ) ation ( PARylation ) post-translational modification plays a pivotal role in controlling plant immune gene expression and defense to pathogen attacks . PARylation is primarily mediated by poly ( ADP-ribose ) polymerase ( PARP ) , which transfers ADP-ribose moieties from NAD+ to acceptor proteins . The covalently attached poly ( ADP-ribose ) polymers on the accept proteins could be hydrolyzed by poly ( ADP-ribose ) glycohydrolase ( PARG ) . We further show that members of Arabidopsis PARPs and PARGs possess differential in vivo and in vitro enzymatic activities . Importantly , the Arabidopsis parp mutant displayed reduced , whereas parg mutant displayed enhanced , immune gene activation and immunity to pathogen infection . Moreover , Arabidopsis PARP2 activity is elevated upon pathogen signal perception . Compared to the lethality of their mammalian counterparts , the viability and normal growth of Arabidopsis parp and parg null mutants provide a unique genetic system to understand protein PARylation in diverse biological processes at the whole organism level .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"plant",
"science",
"genetics",
"biology",
"and",
"life",
"sciences",
"immunology",
"molecular",
"biology"
] |
2015
|
Protein Poly(ADP-ribosyl)ation Regulates Arabidopsis Immune Gene Expression and Defense Responses
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The virulence of intracellular pathogens such as Leishmania major ( L . major ) relies largely on their ability to undergo cycles of replication within phagocytes , release , and uptake into new host cells . While all these steps are critical for successful establishment of infection , neither the cellular niche of efficient proliferation , nor the spread to new host cells have been characterized in vivo . Here , using a biosensor for measuring pathogen proliferation in the living tissue , we found that monocyte-derived Ly6C+CCR2+ phagocytes expressing CD11c constituted the main cell type harboring rapidly proliferating L . major in the ongoing infection . Synchronization of host cell recruitment and intravital 2-photon imaging showed that these high proliferating parasites preferentially underwent cell-to-cell spread . However , newly recruited host cells were infected irrespectively of their cell type or maturation state . We propose that among these cells , CD11c-expressing monocytes are most permissive for pathogen proliferation , and thus mainly fuel the cycle of intracellular proliferation and cell-to-cell transfer during the acute infection . Thus , besides the well-described function for priming and activating T cell effector functions against L . major , CD11c-expressing monocyte-derived cells provide a reservoir for rapidly proliferating parasites that disseminate at the site of infection .
Many pathogens have developed strategies to hijack host phagocytes and withstand their intracellular defense mechanisms . The ability to undergo cycles of replication within these phagocytes , release , and uptake into new host cells is central to the intracellular lifestyle , but has hardly been studied in the ongoing infection [1] . The parasite Leishmania major ( L . major ) represents such a well-adapted pathogen which can survive and replicate under the harsh microenvironmental conditions of endocytic compartments of professional phagocytes [2–4] . Although adaptive T cell responses increase the capability of phagocytes to control pathogens and limit further infection [5–7] , L . major can prevail at the site of infection for extended periods of time . This can result in chronic infections of several months duration , often accompanied with disfiguring and disabling pathologies [8] . A complex host cell tropism is critical for efficient establishment of L . major infection . Flagellated promastigote forms of the parasite are rapidly taken up by neutrophils after inoculation of the skin [9] . Few days after infection , the parasites persist mainly within monocytes , macrophages and dendritic cells in the form of short , aflagellated amastigotes [9–12] . Activation of these phagocytes seems to differentially contribute to the control of L . major [11–17] , and dampening of microbial proliferation is a crucial containment mechanism of the immune system for controlling the intracellular pathogens [18 , 19] . Specifically , nitric oxide produced by the inducible nitric oxide synthase iNOS can non-lethally slow down L . major replication rates [18] , a mechanism that has been shown to be enhanced during secondary infections [12] . While activated dendritic cell-like populations have been shown to harbor a large fraction of the parasite in the infected skin [10 , 11] , more recent studies focused on the characterization of monocyte subsets , showing an important role of inflammatory monocytes as a niche for the parasite during primary infection , and for efficient containment of L . major during secondary infections [12] . Moreover , monocytes cells have been shown to promote parasite survival and suppress clearance of Leishmanial donovani during visceral leishmaniasis [20 , 21] . Despite these findings , it has remained very difficult to dissect the different host cell types infected by L . major in the infected skin regarding their permissiveness for rapid parasite proliferation in vivo . This is of particular interest since recent studies suggest that different pools of high and low proliferating L . major coexist at the site of infection [18 , 22] . However , a side-by-side characterization of the phenotypes of the cell types harboring parasites of rapid versus slow proliferation rates has been lacking so far . Furthermore , pathogen burden increases a thousand fold between inoculation and the peak of the infection , and can remain high over weeks even after the onset of a protective gamma interferon ( IFN-γ ) -dominated T cell response [23] . Thus , pathogen proliferation must fuel the spread of the amastigotes to new host cells at the site of infection . However , while the uptake mechanisms for both promastigotes and amastigotes into host phagocytes have been intensely studied in vitro [24–26] , knowledge on the dynamics of the spread at the site of infection is scarce . Also , any link between distinct pathogen proliferation rates and in vivo cell-to-cell transmission has remained unexplored . Yet , this would be crucial to understand mechanisms which could drive , or impede , the cycle of L . major replication , release , and uptake into new host cells . Here , we employed a genetically encoded proliferation biosensor to investigate the cellular niches of high versus low L . major proliferation in the ongoing cutaneous infection , and to characterize pathogen proliferation rates in the context of transmission to new host cells . We found that parasite proliferation rates were homogenous within individual host cells , however varied substantially between different infected phagocytes . Specifically , monocyte-derived dendritic cell-like , Ly6C+CCR2+ monocytes with high CD11c expression were harboring parasites with the highest proliferation rates at the site of infection , and were highly overrepresented among infected cells in the acute infection . In contrast , newly recruited phagocytes were , irrespective of their cell type , preferentially infected by high proliferating parasites . This suggests that CD11c-expressing monocytes infected by high proliferating L . major serve as a reservoir for pathogen spread to all new host phagocytes , while other phagocyte populations are less efficient in fueling L . major replication and dissemination at the site of infection . By revealing specific niches and host cell tropisms for high proliferating L . major , our findings delineate the impact of a specific physiological parameter of a pathogen on its interaction with the host immune system . This provides a critical contribution to our understanding of how intracellular pathogens establish infection and counteract the containment by the immune response .
Many pathogens , including Leishmania spp . , do not proliferate uniformly at the same rate during an infection , but occur as heterogenic populations of high and low proliferating microbes [22 , 27–29] . However , analyzing pathogen proliferation on a cellular level , i . e . in combination with immunofluorescence , had remained challenging [22 , 30] . To overcome this limitation , we set out to characterize L . major proliferation using a photoconversion-based in vivo proliferation biosensor [18 , 31] . The system relies on the photoconvertible mKikume protein constitutively expressed in L . major ( LmSWITCH ) . In its native form , mKikume exhibits green fluorescence ( excitation/emission: 488nm/515nm ) , but can be photoconverted to red fluorescence ( excitation/emission: 561nm/590nm ) using a light pulse of 405 nm[32] . The dilution rate of the red , photoconverted protein strictly and quantitatively correlates with cell division , whereas high proliferation is associated with more production of green , non-photoconverted protein [18] . Consequently , the ratio between diluted , photoconverted red fluorescence and newly expressed green fluorescence can be used to measure LmSWITCH proliferation 48h after a photoconversion applied to the parasite ( Fig 1A and 1B ) . We employed this system to determine L . major proliferation rates in vivo three weeks post infection of the ear dermis ( Fig 1C ) [18] . For this , we infected C57BL/6 mice intradermally in the ear with LmSWITCH . After three weeks , the sites of infection were photoconverted , and ears were harvested 48h after photoconversion , fixed and embedded for cryosectioning . Using confocal immunofluorescence microscopy , we observed that L . major proliferation rates were very similar among the parasites within individual infected cells . However , the pathogen proliferation rates between different host cells varied dramatically ( Fig 1D ) . In order to quantify this cell-specifically distinct proliferation , we determined in 50 infected cells the red and green mKikume fluorescence of each individual parasite . We could observe that the infected cells predominantly contained parasites either above or below the mean red and green fluorescence ratio over all parasites analyzed ( Fig 1E , upper graph ) . Of note , this was not the case in a scrambled analysis where the parasites were arbitrarily assigned to the cells ( Fig 1E , lower graph ) . In line , we observed a strong negative correlation between the number of red and green parasites within individual cells ( Fig 1F ) . Intravital 2-photon imaging of mouse ear tissue infected and photoconverted using the same conditions also clearly revealed clusters of high proliferating parasites separated from clusters of low proliferating parasites ( Fig 1G–1I ) . Thus , we concluded that distinct pathogen proliferation rates are linked to specific cellular niches . Recruited monocytes , monocyte-derived macrophages and dendritic cells , as well as neutrophils have been shown to constitute the main infected cell types in an ongoing L . major infection [10–12 , 33] . However , it had been impossible to show in vivo whether any of these cell types preferentially harbors parasites of a distinct proliferation rate , which would be critical to dissect niches permissive for pathogen proliferation from phagocytes that restrict the growth and dissemination of L . major at the site of infection [22] . In order to characterize L . major infected cell types in situ , we analyzed ear tissue sections infected for three weeks with LmSWITCH by Multi-Epitope Ligand Cartography ( MELC ) , a multiparameter microscopy approach based on consecutive immunofluorescence staining/bleaching cycles [34] . In brief , cryosections fixed and embedded 48h after photoconversion were imaged to detect both red and green mKikume in L . major , photobleached , and , in subsequent automated immunofluorescence staining/photobleaching cycles , probed for expression of a series of surface markers at the same tissue site . With each cycle , a transmitted light image was recorded to which the fluorescence images were aligned . Antibodies against CD45 , CD54 , CD11b , CD11c , F4/80 , CD86 , MHCII , CD45R , as well as propidium iodide were found to be compatible with the fixation conditions needed for the preservation of the mKikume protein . ( S1 Fig Panel A ) . Cell outlines were automatically defined from the CD45 , CD11b and CD54 images , and mean cell body and outline fluorescence values were normalized and converted into cytometry data [35 , 36] ( Fig 2A , S1 Fig Panels B-C ) . To detect cells infected by L . major , combined red and green mKikume fluorescence ( total mKikume ) in infected and non-infected cells manually selected from the propidium iodide DNA staining were analyzed ( Fig 2B and 2C ) . This allowed for determination of a total mKikume threshold applicable to images acquired in different experiments and different tissue depths ( Fig 2D , S1 Fig Panel D ) . Likewise , manually selected cells from three different experiments served to define a threshold for CD11c , F4/80 , CD86 , MHCII as markers for characterization of infected phagocytes [11] , and CD45R as a marker unrelated to the different monocyte subsets ( S1 Fig Panel E ) . Thus , we could reliably and automatically determine the localization of LmSWITCH in the infected tissue in conjunction with multiplex analysis of the parasite’s host cell . We then gated for each marker on a positive and a negative population of infected cells , and compared L . major proliferation rates depending on each individual marker ( S1 Fig Panel F ) . We observed that infected cells positive for CD11c were enriched in high proliferating pathogens , whereas all other markers tested were not per se specific for a distinct pathogen proliferation rate ( S1 Fig Panel G ) . In order to find differences in pathogen proliferation within subsets of CD11c-positive and negative phagocyte subsets , we performed combined marker analysis , in which L . major proliferation was assessed in subpopulations of cells differentially expressing CD11c or F4/80 in combination with class II MHC or CD86 ( Fig 2E ) . Importantly , also when analyzed in combination with CD11c expression , F4/80 , class II MHC and CD86 expression were not characteristic of a distinct pathogen proliferation rate ( Fig 2E and 2F , i-iii ) . Likewise , analysis of F4/80-positive and negative subpopulations regarding class II MHC and CD86 expression revealed no significant change of pathogen proliferation in any subset analyzed ( Fig 2E and 2F , iv , v ) . In order to analyze the cellular niche of proliferating L . major via flow cytometry , we infected C57BL/6 mice intradermally in the ear with LmSWITCH . After three weeks , the sites of infection were photoconverted , and ears were harvested 48h after photoconversion for tissue homogenization and flow cytometry analysis of LmSWITCH proliferation in infected cells . To enable an undisturbed ratiometric analysis of mKikume recovery after photoconversion by flow cytometry , we chose a limited set fluorophores with both excitation ( ex ) and emission ( em ) spectra different from the two forms of mKikume ( green , ex/em 488nm/515nm and red , ex/em 561nm/590nm ) . This strategy limited the number of antibodies for staining to two to three , but efficiently excluded spectral overlap with the reporter system . The isolated cells were thus stained anti-CD45 to identify leukocytes , as well as anti-CD11c and anti-F4/80 to analyze subpopulations comparable to MELC analysis ( Fig 3A and 3B ) . In perfect accordance with the MELC data , L . major proliferation was significantly higher in CD11c+F4/80+ and CD11c+F4/80- cells compared to CD11c-negative cell populations , whereas F4/80 expression did not correlate with pathogen proliferation ( Fig 3C and 3D ) . To exclude any residual spectral overlap of the antibody staining or cell-type-specific autofluorescence as an explanation for the different proliferation rates observed in the distinct cell populations , a scrambled control with arbitrary photoconversion right before proliferation measurement was analyzed . To this end , LmSWITCH in the ear were photoconverted at random by using a grid ( S2 Fig Panel A ) . In these controls , host cell CD11c expression and pathogen proliferation index were no longer correlated ( S2 Fig Panels B-C ) . Thus , a possible spectral overlap between marker staining and mKikume red and green fluorescence readout was not responsible for the enrichment in parasites exhibiting the high proliferation observed in CD11c+ cells . When we compared the distribution of CD11c and F4/80-expression among infected and non-infected host cells , we observed that the CD11c+F4/80+ population was significantly overrepresented among the infected cells ( Fig 3E ) . Thus , in the acute infection , L . major exhibits high proliferation rates preferentially in CD11c+ cells , and is overrepresented in CD11c+F4/80+ phagocytes . In order to test the effect of a short-term absence of CD11c+ cells on pathogen burden , we employed the CD11c-DTR-GFPtg model [37] , in which cell-specific expression of the human Diphtheria toxin receptor ( DTR ) mediates susceptibility to Diphtheria toxin ( DTX ) in CD11c+ cells ( Fig 3F ) . This enabled us to specifically deplete CD11c-expressing cells , which are also identifiable by GFP-expression in this mouse model , for 48h in an on-going infection ( Fig 3G ) . Strikingly , the depletion of the CD11c+ cells resulted in a significant reduction in pathogen burden , underlining that the presence of these cells might represent a niche in which L . major can efficiently proliferate in order to maintain parasite numbers at the site of infection ( Fig 3H ) . Taken together , these results suggest that CD11c+ cells represent a niche in which L . major can efficiently proliferate . The different subsets of phagocytes derived from monocytes after recruited to the skin exhibit a high degree of functional specialization and have been assigned divergent roles in the control of L . major infection[11 , 12 , 15 , 38] . In order to better characterize the infected subpopulations , we sought to analyze the populations defined in MELC by CD11c and F4/80 using the monocyte markers Ly6C , CCR2 and CD11b , as well as MHC class II . For this , we used a monofluorescent DsRed-expressing L . major compatible with multicolor flow cytometry [39] . As expected , CD11b-positive cells were the main infected cell population [10–12] , which were subdivided according to CD11c and F4/80 expression for the analysis of the remaining markers ( Fig 4A and 4B ) . Strikingly , the CD11c+F4/80+ population exhibited the highest expression of Ly6C and CCR2 , suggesting these cells were mainly inflammatory monocytes [12] ( Fig 4C and 4D ) . Interestingly , this population also exhibited high levels of MHC class II , indicating that the CD11c+F4/80+ phagocytes might include monocyte-derived dendritic cells [11 , 17] . In contrast , the CD11c+F4/80- and CD11c-F4/80+ populations exhibited intermediate Ly6C and low CCR2 , or Low Ly6C and high CCR2 expression , respectively , indicating that these included recently recruited inflammatory monocytes , and , for the CD11c-F4/80+ , monocyte-derived macrophages [11 , 12 , 38] . Interestingly , although their mean Ly6C expression level was lower , we found that a part of the cells in the CD11c+F4/80- , the CD11c-F4/80+ , and the double-negative population expressed high levels of Ly6C . This prompted us to analyze L . major proliferation in the context of Ly6C and CD11c ( Fig 4E and 4F ) . As expected , we found a very high proliferation in the infected CD11c+Ly6C+ population , but interestingly , also within CD11c-Ly6C+ cells , indicating that among the monocyte-derived phagocytes , not only the CD11c+ subpopulation exhibited high L . major proliferation ( Fig 4G and 4H ) . However , when we compared the distribution of CD11c versus Ly6C of infected versus non-infected cells , we observed that CD11c+Ly6C+ cells were substantially overrepresented , whereas CD11c-Ly6C+ cells , despite the high L . major proliferation within them , were severely underrepresented among infected phagocytes ( Fig 4I ) . This suggests that although high proliferating L . major are observed also within other cell types , the CD11c+Ly6C+ cells represent the main niche for high proliferating pathogens . Also , as indicated by the higher absolute CD11c expression , the CD11c+Ly6C- population is likely to include the dermal dendritic cells [38] ( Fig 4J ) . Therefore , we concluded that monocyte-derived dendritic cell-like , CD11c-expressing Ly6C+CCR2+ phagocytes represent the main niche for high proliferating pathogens . Next , we analyzed whether cells infected with high proliferating L . major would , as a result , be populated with more parasites per cell . However surprisingly , we found a slight but significant negative correlation between L . major proliferation rates within individual cells and the respective cellular parasite burden determined from confocal microscopy . This suggested that high proliferating parasites were present at lower numbers per infected cell ( Fig 5A and 5B ) . One explanation for this could be that high proliferating L . major are more efficiently released from infected cells , and thus might represent the main parasite population infecting new host cells . In order to test whether newly infected cells preferentially harbor high proliferating pathogens , we synchronized the arrival of newly recruited cells by adoptive bone marrow transfer [11 , 33] . For this , C57BL/6 mice ( CD45 . 2+ ) infected for 16 days with LmSWITCH were injected with 108 CD45 . 1+ bone marrow cells . The LmSWITCH-infected ears were photoconverted 19 days post infection ( p . I . ) , and analyzed at day 21 p . I . . Thus , pathogen proliferation in infected newly recruited cells ( CD45 . 1+ ) could be compared to pathogen proliferation in CD45 . 2+ recipient cells , which , on average , are expected to be present at the site of infection earlier than the transferred CD45 . 1+ . ( Fig 5C and 5D ) . Of note , a shorter time frame for CD45 . 1+ cell recruitment did not yield enough infected cells for proliferation analysis ( S3 Fig Panels A-B ) . Besides a higher content of CD11c+F4/80- cells ( Fig 5E ) and slightly elevated Ly6C expression of the CD11c+F4/80+ population ( Fig 5F and 5G ) , the newly recruited cells had very similar phenotype compared with the recipient cell population , suggesting the newly recruited cells had differentiated in all different monocyte-derived subsets present at the site of infection . However strikingly , we observed that pathogen proliferation in newly recruited CD45 . 1+ cells was significantly higher than in CD45 . 2+ recipient cells ( Fig 5H and 5I ) . Switched color experiments were performed to exclude an influence of the antibody staining on proliferation measurements , and yielded the same results ( S3 Fig Panels C-D ) . This suggests that high pathogen proliferation occurs in the context of the infection of new host cells . L . major has been shown to replicate once every 15 to 60 hours within host cells [18 , 22 , 30] . Thus , the high parasite proliferation observed in newly recruited host cells could be either due to the transmission of already rapidly proliferating parasites , or due to an increase of proliferation upon infection of a new host cell . In order to determine L . major proliferation shortly after transmission to new host cells , we employed in vitro cell culture infections . First , to visualize the spread of L . major to new host cells , we performed time-lapse microscopy of parasite uptake events into new host cells in macrophages cultured infected for 24 h . Strikingly , in all de novo infection events observed in this setting , fluorescent parasites were taken up directly from infected host cells into a new cell ( Fig 6A , S1 Movie ) . Specifically , in most cases , the original host cells exhibited signs of cell death ( membrane blebbing ) shortly before the parasites associated with them were taken up by a new phagocyte ( Fig 6B ) . Very often , the original host cell was eventually phagocytosed as well by the new host cell , however , transfer of the parasite preceded phagocytosis of the whole original host cell by several hours ( Fig 6C ) . We therefore concluded that the infection of new host cells mainly occurs via cell-to-cell transmission from dying phagocytes . In order to quantitatively analyze pathogen proliferation shortly after infection of cells , we adapted our system for analyzing de novo infection ( see Fig 5C ) for in vitro cell culture infections using in vitro-differentiated bone marrow-derived dendritic cells and macrophages ( S4 Fig Panel A ) . For this , we infected in vitro differentiated CD45 . 2+ bone marrow-derived macrophages ( BMMP ) , dendritic cells ( BMDC ) , or mixtures ( BMMDC ) with serum-opsonized LmSWITCH . 24h after infection , the parasites were photoconverted and 24h later , CD45 . 1+ BMDC , BMMC , or BMMDC were added for 5 hours and the infection of newly added CD45 . 1+ cells was analyzed by flow cytometry ( Fig 6D , S4 Fig Panels B-D ) . While the transition of parasites between preinfected non-mixed BMMP and BMDC varied dramatically in efficiency depending on the cell type used , we found that infection rates in BMMDC mixtures were much more homogenous ( S4 Fig Panels C-E ) . Thus these mixtures were used for the analysis of newly infected phagocytes . Confocal microscopy of FACS-sorted newly infected CD45 . 1+ cells revealed that the parasites were intracellular and not extracellularly adhering to the cells ( Fig 6E ) . Importantly , even at this very early time point after uptake into a new host cell , high proliferating parasites were significantly overrepresented in the newly infected CD45 . 1+ phagocytes as compared to the initially infected CD45 . 2+ cells ( Fig 6F and 6G , S4 Fig Panel F-G ) . Control measurements with photoconversion applied just before co-culture ensured that the recovery from photoconversion during the 5h coculture phase was negligible ( Fig 6G , right ) . Furthermore , both F4/80+ , macrophage-like and CD11c+ , dendritic cell-like BM-derived cells were infected equally by high proliferating parasites , irrespective of iNOS production in the culture , probably due to the short infection times ( S4 Fig Panel H-I ) . Thus , the differences in L . major proliferation observed in the in vitro system are not attributable to the host cell type in this system , but rather to the dissemination among phagocytes . In order to visualize the transfer of parasites from infected CD11c+ cells to newly recruited phagocytes in vivo , we infected CD11c-EYFP reporter mice with monofluorescent DsRed-expressing L . major for 16 days , adoptively transferred bone marrow cells from constitutively ECFP-expressing Actin-ECFP mice , and subjected the mice to intravital 2-photon imaging after five days ( Fig 7A ) . We could observe the transit of DsRed-expressing parasites fully engulfed by ( Fig 7B and 7C , S2 Movie ) or juxtapositioned to ( S5 Fig ) CD11c-EYFP-expressing cells into newly recruited ECFP-expressing cells . This indicates cell-to-cell transfer of L . major from infected CD11c+ cells of the host to newly recruited monocytes in vivo . Of note , the transition occurred within about 1-2h , which corresponded very well with the observations from the in vitro system ( see S1 Movie ) . Taken together , our data suggest that CD11c+ cells can harbor L . major which infect new host cells in vivo , and that high proliferating L . major preferentially undergo cell-to-cell transmission . In order to investigate whether distinct subpopulations of newly recruited host phagocytes were preferentially infected upon arrival at the site of infection , we compared CD11c and F4/80 expression on infected and non-infected newly recruited cells ( CD45 . 1+ ) and recipient cells ( CD45 . 2+ ) expected to be , on average , present at the infection site for a longer time period before analysis ( Fig 8A–8C ) . As expected from our previous work [33] , the composition of the newly recruited phagocyte subsets changed substantially between day 2 and 5 after adoptive transfer . Specifically , CD11c+F4/80+ double positive as well as CD11c+F4/80- cells were increased significantly by day 5 as compared to day 2 ( Fig 8B ) . However strikingly , among newly recruited cells , no differences in the composition of infected versus non-infected cells were observed ( Fig 8B , compare infected with non-infected ) . This suggests that the parasite has no preference for a specific cell type when infecting new host cells . In contrast , we found that in the recipient cell population , more CD11c+F4/80+ double positive and less CD11c-F4/80+ single positive phagocytes were present in the infected cell population as compared to the non-infected cells ( Fig 8C ) . Thus , in line with our proliferation biosensor data ( see Figs 2 and 3 ) , CD11c+F4/80+ phagocytes seem to represent a more suitable niche for L . major in the long term perspective . The late occurrence of CD11c+F4/80+ and CD11c+F4/80- cells ( Fig 8B ) prompted us to determine how long the recruited monocyte-derived cells would need to mature to these populations at the site of infection [11] . In order to address this question , we infected C57BL/6 mice with non-fluorescent wild type L . major for 3 weeks and adoptively transferred mKikume-expressing bone marrow cells . 3 days after transfer , we marked by photoconversion all mKikume-expressing transferred cells that had already been recruited to the ear . Analysis of infected tissue at 5 days after transfer would also yield non-photoconverted mKikume-expressing cells , which consequently must have been recruited between day 3 and 5 ( Fig 8D and 8E ) . This enabled us to compare the surface marker expression of cells present at the site of infection for longer ( photoconverted ) , or shorter ( not photoconverted ) than two days . Control experiments showed that cell metabolism-related recovery from photoconversion in leukocytes is slow enough to use mKikume to mark cells over several days ( S6 Fig ) . Importantly , we observed that the large majority of cells expressing CD11c and F4/80 were photoconverted , thus had been present in the lesion at least for 2 days . We concluded that these cells had matured at the site of infection from cells recruited between day 1 and 3 after transfer . In contrast , cells present at the site of infection for less than 3 days are in a substantially less mature state ( Fig 8E and 8F ) . Therefore , these data underline that newly recruited monocyte-derived phagocytes are infected by L . major irrespective of their differentiation state .
To understand the interaction between the immune system and the pathogen , it is indispensable to extract data on the proliferation states of infectious microorganisms as well as to define the niches in which differentially proliferating populations are located . This information is especially critical for intracellular pathogens persisting for long periods of time at an infection site , such as L . major . Several recent approaches have addressed this question in the ongoing infection [22 , 30] . These elegant experiments have defined slow overall proliferation rates and postulated high proliferating subpopulations of parasites in the established infection , but neither approach was compatible with intravital or multiparameter host cell-resolved analysis of pathogen proliferation . This has severely hampered the unambiguous assignment of surface marker expression levels to individual host cells harboring high versus low proliferating parasites . However , this information would be indispensable for elucidating the link between L . major proliferation and host cell tropism . We achieved exactly this side-by-side analysis of pathogen proliferation and cellular surface markers by using the mKikume reporter system . Specifically , our experiments show that fluorescence recovery after photoconversion-based measurement of LmSWITCH infected cells and tissues is compatible not only with intravital 2-photon microscopy , but also with immunofluorescence-based analysis approaches such as flow cytometry as well as confocal and multiparameter microscopy ( MELC ) . In line with previous BrdU-based proliferation measurements [22] , we show that L . major parasites grow at a broad distribution of different proliferation rates . The harsh fixation conditions in bioorthogonal labeling experiments preclude a concomitant analysis of the cellular niche of the pathogens . In contrast , we show that individual host cells contain parasites with a proliferation rate which is similar among all L . major within the same cell . Thus , we conclude that L . major proliferation rate is linked to the cellular niche in which the parasite resides . In the established infection , this niche is constituted mainly of monocyte-derived phagocytes , many of them have been shown to express CD11c . Specifically , monocyte-derived dendritic cells have been proposed to mature in the skin after their recruitment , and then serve as important initiators for protective adaptive immune responses [11] . Furthermore , monocyte-derived iNOS and TNF-producing dendritic cells have been proposed to constitute a major fraction of L . major-infected cells and represent the main producers of iNOS in the skin [10] , a defence mechanism critical for L . major containment . On the other hand , using cell type-specific gene ablation , it was shown that Interleukin-10 receptor signaling in CD11c+ cells is involved in dampening the immune response against L . major burden at the peak of the infection [16] . Furthermore , a recent study showed that maturation to a dendritic cell phenotype is not required for iNOS-production . Instead , Ly6C+CCR2+CX3CR1+ inflammatory monocytes were identified as important effector cells producing iNOS during secondary infections [12] . Whether any of these cell types would be particularly permissive for higher pathogen proliferation in vivo had remained uncharacterized . Our multiparameter microscopy analysis approach suggested that CD11c+ expression by infected phagocytes correlates with high pathogen proliferation , irrespective of the level of F4/80 , class II MHC and CD86 . However , CD11c+F4/80+ cells seemed to be overrepresented among infected cells , thus representing an important niche for the parasite . We furthermore show that these cells also express high levels of CCR2 and Ly6C , thus suggesting that they have characteristics of inflammatory monocytes [12] , but also of dendritic cells , as suggested by the high level of MHC class II . Based on the observation that CD11c+Ly6C+ , but not CD11c+Ly6C- cells harbor high proliferating L . major , we conclude that the cell population most permissive for high parasite proliferation has important phenotypic similarities with monocyte-derived dendritic cells [17 , 38] . Dendritic cells have been shown to harbor phagocytosed antigen at near-neutral pH , which has been proposed to ensure efficient antigen presentation [40] . In contrast , the intracellular amastigote form of Leishmania seems to preferentially proliferate at low pH for differentiation and proliferation [41] . Interestingly , the parasite can evade antigen presentation by decreasing the intraphagosomal pH within dendritic cells [42] . With regard to our finding that the niche of high-poliferating L . major has characteristics of monocyte derived dendritic cells , a parasite-induced pH decrease might , besides interference with antigen presentation , serve the generation of a parasitophorous vacuole which has a pH optimal for rapid proliferation . Alternatively , as different maturation stages towards monocyte-derived dendritic cells are present at the site of infection [11] , it is possible that within these cells , the pH is lower than in mature dendritic cells after migration to the lymph node . Our previous work had shown that several layers of cell-extrinsic L . major containment are in place . First , a gradient of IFN-γ mediates induction of iNOS , the main cellular defense mechanism against the parasite , also in cells that are not directly engaged by effector T cells [39] . Second , diffusible nitric oxide produced by iNOS seems to provide another layer of cooperative control of the pathogen on the tissue level [33] . In contrast to these findings , our data showing that proliferation rates are linked with a specific cell type suggest that additional cell-intrinsic control mechanisms against L . major proliferation exist . This could be achieved by differential production of reactive oxygen , which is generated through induction of the NADPH oxidase machinery at the phagosomal membrane in macrophages [43 , 44] . Nitric oxide , together with reactive oxygen , can form highly toxic peroxynitrite , which has a diffusion range of less than 5 μm from its site of production [45 , 46] . Thus , while nitric oxide seems to diffuse to neighboring cells , peroxynitrite formation at sites of high NADPH oxidase activity might represent a cell-intrinsic component of L . major containment . A further possible explanation for distinct proliferation rates within different cells might be the capacity of the parasites to counteract cellular defense mechanisms by deactivating NADPH oxidase assembly , detoxifying enzymes or interference with host phagocyte signaling pathways linked with antimicrobial activity , which might not be equally efficient in all cell types [47 , 48] . Finally , while a tissue-wide mode of L . major control could be mainly shown for CD4+ T cell-dependent effector functions , CD8+ cytotoxic T cells and NK cells might mediate target cell-intrinsic containment mechanisms [49–51] . Strategies of intracellularly proliferating pathogens to exit infected cells in order to be transmitted into a new cellular niche are critical for survival of pathogens especially in long-lasting infections [1] . This process has profound implications for the cell tropism of the pathogens , as well as immune activation , but has not been understood . In vitro evidence from L . amazoniensis suggests direct cell-to-cell transfer via LAMP1-rich extrusions [52] . Furthermore , intravital 2-photon imaging had shown that the L . major is taken up by neutrophils immediately after inoculation of the skin , and is then phagocytosed by both macrophage and dendritic cell-like phagocytes , a process that involves the apoptosis of the neutrophils [9 , 53] . While in the established infection later on , monocytes and monocyte-derived macrophages and dendritic cells have been shown to represent the main infected cell type [10–12] , nothing is known about how L . major disseminates to new host cells during this phase . Our in vitro time-lapse microscopy suggests that apoptosis of the original host cell might also be involved during the dissemination from host cells different from neutrophils . Furthermore , synchronization of the arrival of newly recruited phagocytes revealed that high proliferating parasites are more efficiently transmitted to newly recruited host cells . In line with this , not only CD11c+Ly6C+ cells , but also some Ly6C+CD11c-negative phagocytes were observed to harbor high proliferating pathogen . We speculate that within this population , newly recruited monocytes are overrepresented , which we could show to harbor mainly high proliferating L . major . It is conceivable that eventually , the newly infected CD11c-negative monocyte-derived populations are able to dampen pathogen proliferation , resulting in the observed overall distribution of high proliferating parasites in CD11c-expressing monocytes and low proliferating parasites in most other host cell types . The monocytes infiltrating the site of infection have been shown to coexist in a variety of maturation states , and L . major uptake can dampen the maturation of infected host cells [11 , 12] . By marking newly arrived monocytes at the site of infection , we could show that these cells require more than two days for differentiation at the site of infection . The potential of L . major to rather non-specifically infect different cell types at different maturation states , might therefore support the parasite’s ability to prevent efficient maturation of the recruited cells . Of note , our in vitro data suggest that the high pathogen proliferation index detected in newly recruited cells is due to the successful transmission of L . major which were already exhibiting a higher proliferation rate , and not due to an increase in proliferation upon infection of a new host cell . We therefore propose that the CD11c-expressing monocytes not only represent a cellular niche of high L . major proliferation , but also the main source of parasites disseminating to new cells . In contrast , it is likely that CD11c-negative monocyte-derived cells , although initially infected as efficiently as the CD11c+ cells , fuel the cycle of intracellular proliferation , infection of new host cells , and thus the dissemination of the parasite in the infected skin , much less efficiently . Our study focuses on the acute phase of the infection , with high pathogen burden and increasing pathology [39] , in which we assume that efficient establishment of the parasite at the infection site is ensured by massive proliferation . Also , we have shown in an earlier study that non-lethal dampening of parasite proliferation with very little overt killing can efficiently contain the parasite in this phase [18] . Related to this , our demonstration of short-term depletion of CD11c+ cells resulting in a decreased parasite burden supports the hypothesis that CD11c-expressing monocytes can influence the pathogenesis of L . major also by serving as a niche for efficient establishment of an infection . However , as parasite replication represents a source of non-self antigen and pathogen-associated molecular pattern molecules , decreased proliferation is probably not purely detrimental for L . major . For example , it is possible that the establishment of a balance of low pathogen burden and low pathology , observed at very late phases of L . major infection [54] , is achieved by residence within host cells that permit only low proliferation rates . Taken together , besides their role in the maintenance of an adaptive immune response in a variety of infections [11 , 55 , 56] , our findings establish that CD11c-expressing monocytes can represent a reservoir for rapidly proliferating L . major that disseminate at the site of infection . This quantification a pathogen physiology in the ongoing infection can critically contribute to our understanding of interactions between infectious organisms with the host immune system .
All animal experiments were reviewed and approved by the Ethics Committee of the Office for Veterinary Affairs of the State of Saxony-Anhalt , Germany ( permit license numbers 42502-2-1253 Uni MD , and 42502-2-1314 Uni MD ) in accordance with legislation of both the European Union ( Council Directive 499 2010/63/EU ) and the Federal Republic of Germany ( according to § 8 , Section 1 TierSchG , and TierSchVersV ) . L . major LRC-L137 V121 wild-type , DsRed or mKikume expressing LmSWITCH parasites were previously described [18 , 57 , 58] . Parasites were grown in M119 medium completed with 10% heat-inactivated fetal calf serum , 0 . 1 mM adenine , 1 mg/ml biotin , 5 mg/ml hemin , and 2 mg/ml biopterin ( all from Sigma ) for maximally 6 passages . Wild-type CD45 . 1 ( B6 . SJL-PtprcaPepcb/BoyJ ) , Actin-ECFP ( B6 . 129 ( ICR ) -Tg ( CAG-ECFP ) CK6Nagy/J ) , CD11c-EYFP ( B6 . Cg-Tg ( Itgax-Venus ) 1Mnz/J ) , CD11c-DTR-GFPtg ( B6 . FVB-Tg ( Itgax-DTR/EGFP ) 57Lan/J ) and mKikume expressing ( Tg ( CAG-KikGR ) 33Hadj/J ) mice were purchased from Jackson Laboratories ( Bar Harbor , MA ) , wild-type C57BL/6J and B6N-Tyrc BrdCrCrl ( B6 albino wild-type ) mice were obtained from Charles River ( Sulzfeld , Germany ) . All mice were bred under specific pathogen-free conditions at Otto-von Guericke University , Magdeburg . For the infection of mice , stationary phase parasites were centrifuged ( 3500 g , 5 min , RT ) and resuspended in PBS . 2x106 parasites were subsequently injected in 10 μl into the ear dermis . Analysis was performed 3 weeks post infection . Mice were anaesthetized and prepared for intravital microscopy as described previously [39] . Two-photon imaging was performed with a W Plan-Apochromat 20x/1 , 0 DIC VIS-IR objective ( Zeiss ) on a LSM 700 confocal laser scanning microscope ( Zeiss ) and a Mai Tai DeepSee laser ( Spectra-Physics ) tuned at 920 nm . For analysis of parasite proliferation in vivo , the emitted mKikume signal and second harmonics were split with 625 nm long pass , 495 nm long pass , and 555 nm long pass dichroic mirrors and filtered with 470/20 ( second harmonics ) , 525/50 ( mKikume green ) and 600/40 ( mKikume red ) nm bandpass filters before collection with nondescanned detectors . For intravital analysis of cell-to-cell transmission , ECFP , EYFP and DsRed fluorescence as well as harmonics were split with 560 nm long pass , 470 nm long pass , and 520 nm long pass dichroic mirrors and filtered with 600/40 ( DsRed ) , 470/20 ( second harmonics ) , 506/20 ( ECFP ) and 543/20 ( EYFP ) nm bandpass filters . Typically , imaging volumes of 0 . 8 mm3 for automated analysis were obtained by collecting 3–4 μm spaced z stacks using the ZEN acquisition software ( Zeiss ) . Images were color corrected using the channel arithmetics function , superimposed and analyzed using the Imaris software ( Bitplane ) , 3D projections and slices were extracted using the Fiji software ( NIH , http://rsb . info . nih . gov/ij/ ) . LmSWITCH parasites in the mouse ear were photoconverted with violet light at 405 nm wavelength by assembling 2x2 LED diodes ( Strato , half-viewing angle: 15°; Radiant Power: 10 mW ) spaced 5 mm apart . Ears of anaesthetized mice were fixed and illuminated from each side for 1 minute in a distance of 1 . 3 cm . The photoconverted parasites were analyzed after 48h by flow cytometry , multi-epitope ligand cartography , confocal microscopy or intravital microscopy . For in vitro analysis of de novo infection , parasites were photoconverted in 24-well plates via illumination with 405 nm wavelength by assembling 3x3 diode ( see above ) array for 1 minute and analyzed after 24h via flow cytometry . Ears were harvested and incubated for 2 h at 4°C in 4% paraformaldehyde in phosphate-buffered saline before they were stored in 20% sucrose in phosphate-buffered saline at 4°C overnight . Samples were frozen in Tissue-Tek O . C . T . Compound ( Sakura ) by liquid nitrogen and stored at -80°C . 10 μm cryosections were transferred on a 0 . 1% Poly-L-Lysin ( Sigma-Aldrich ) in H2O coated Superfrost slides ( Thermo Scientific ) and air-dried . Multi-Epitope Ligand Cartography was performed as previously described [34] . In brief , directly labelled antibodies ( S2 Table ) were incubated consecutively and 3D images of the fluorescence signal were acquired by a DMI6000B microscope ( Leica ) equipped with a 40x/NA1 . 25 lens and a KX4 CCD camera ( Apogee Instruments ) resulting in 3D image stacks of 2048 × 2048 × 16 or 8 voxels ( voxel size 225 × 225 × 500 or 1000 nm3 ) . The fluorescence signals were removed by bleaching of the directly coupled fluorophores . Using the corresponding phase contrast images acquired with every staining cycle , the fluorescence images were automatically aligned voxel-wise with accuracy of 1/10 pixel in XY direction and ½ pixel in Z direction . Illumination faults of the fluorescence images were eliminated using flat-field correction before the resolution of the wide field fluorescent image stacks were improved by applying a deconvolution/deblurring algorithm ( XCOSM software package ) , an interface to Computational Optical Sectioning Microscopy algorithms for removing out-of-focus light in 3D image volumes ( Washington University St . Louis , MO ) . Ears of mice were separated in two sheets ( ventral and dorsal ) using forceps and enzymatically digested in RPMI 1640 medium containing 1 mg/ml collagenase ( Sigma ) and 50 ng/ml DNase ( Sigma-Aldrich ) for 60 min at 600 rpm and 37°C , and passed through a 70 μm cell strainer . Surface staining of cells was done by using APC or APC-Cy7 conjugated anti-CD45 . 2 ( clone 104 ) , APC , PerCP-Cy5 . 5 or APC-Cy7 conjugated anti-CD45 . 1 ( clone A20 ) , BV421 conjugated anti-F4/80 ( clone BM8 ) , Pe-Cy7 , APC or APC-Fire conjugated anti-CD11c ( clone N418 ) , PE-Cy7 or APC-Cy7 conjugated anti-Ly6C ( clone HK1 . 4 ) , FITC conjugated anti-CCR2 ( clone SA203G11 ) , BV510 conjugated anti-MHC class II ( IA/IE , clone M5/114 . 15 . 2 ) , APC or APC-Cy7 conjugated anti-CD11b ( clone M1/70 ) , and PerCP-Cy5 . 5 conjugated anti CD45 ( clone 30-F11 ) , which were all purchased from BioLegend . Samples were Fc-blocked using anti-CD16/32 antibody ( clone 93 ) ( BioLegend ) before antibody staining . Analysis was performed with a Fortessa or FACS ARIA III ( BD Biosciences ) using 405 , 488 , 561 , and 633 nm lasers: . Photoconverted or non-photoconverted mKikume fluorescence was read out at 561 nm excitation and 610/20 nm emission , or 488 nm excitation and 530/30 nm emission , respectively . An autofluorescence signal was recorded at 488 excitation and 695/40 nm emission . Data were analyzed by using the FlowJo X software ( FlowJo , LLC ) . Ears were harvested and incubated for 2 h at 4°C in 4% paraformaldehyde in phosphate-buffered saline before incubation in 20% sucrose in phosphate-buffered saline at 4°C overnight . Samples were frozen in Tissue-Tek O . C . T . Compound ( Sakura ) in liquid nitrogen and stored at -80°C . 10 μm cryosections were prepared , transferred onto Poly-L-Lysin ( 0 . 1% in H2O for coating ) coated Superfrost slides ( Thermo Scientific ) , air-dried and stained with Armenian hamster anti-CD54 ( clone 3E2 , from BD Biosciences ) and DyLight649-conjugated goat anti-Armenian hamster Ig ( Jackson ImmunoResearch ) . Analysis was performed by confocal laser scanning microscopy ( TCS SP8 Confocal , Leica ) . 488 nm excitation and 491–526 nm emission was used for non-photoconverted mKikume , 561 nm excitation and 571–620 nm emission for photoconverted mKikume , and 633 nm excitation and 640–720 nm for detection of the CD54 staining . Image analysis was done with the Fiji software ( NIH , http://rsb . info . nih . gov/ij/ ) . Deconvolved MELC image stacks of CD45 , CD54 , and CD11b stainings were segmented using the RACE tool developed by Stegmaier et al . [35] . The Propidium Iodide staining was used as nuclei seed dataset . In brief , the segmented images were converted into ImageJ regions of interest ( ROIs ) for three Z planes spaced 3 μm apart and centered around the middle of the image stack , and combined for the three markers . RACE parameters were optimized in order to detect the largest number of cells and highest percentage of infected cells ( see S1 Text and S1 Table for a detailed description of the optimization and segmentation procedure ) . Mean fluorescence values of the cell area as well as a 0 . 4 μm rim zone were extracted , normalized between the 20th and 80th percentile of the corresponding fluorescence values of each image , and converted into . fcs FlowJo files using the DiscIT software [36] . Thresholds for individual markers were set according to at least 30 marker-positive and 30 marker-negative cells manually selected in three different images ( S2 Fig Panel E ) . The relative proliferation index of L . major within the different cell populations for both flow cytometry and MELC was defined as 1− ( mKikumeRedmKikumeGreen ) cell ( mKikumeRedmKikumeGreen ) mean ( allinfectedcells ) and represented as percent deviation from the total infected cell population within one sample or imaged infection site . For visualizing qualitative comparisons within the same sample using the FlowJo software , values were plotted as C−100* ( mKikumeRedmKikumeGreen ) cell ( mKikumeRedmKikumeGreen ) mean ( allinfectedcells ) with chosen C between 100 and 250 and kept constant within the same sample for which the comparison was made , and the factor 100 introduced in order to analyze integer fluorescence values in FlowJo . For in vitro determination and inter-experiment standardization of proliferation indices , a non-photoconverted ( green control ) and fully photoconverted ( 0h recovery from photoconversion , red control ) sample were measured with each experiment , and for each infected cell , the proliferation index was defined as 10−100* ( mKikumeRedmKikumeGreen ) cell−100* ( mKikumeRedmKikumeGreen ) mean ( allinfectedcells , greencontrol ) 100* ( mKikumeRedmKikumeGreen ) mean ( allinfectedcells , redcontrol ) −100* ( mKikumeRedmKikumeGreen ) mean ( allinfectedcells , greencontrol ) with the factor 100 introduced in order to analyze integer fluorescence values in FlowJo , and the constant 10 in order to obtain positive values . Bone marrow cells were isolated from tibia and femur of mice and passed through a 70 μm cell strainer . 8–10 x 107 cells were resuspended in 300 μl PBS and intravenously injected into the recipient mice 2 or 5 days before the analysis . Bone marrow cells from either C57BL/6 or CD45 . 1 wildtype mice were filtered through a 70 μm cell strainer in PBS before they were differentiated in vitro into macrophages and dendritic cells . For differentiation of macrophages , cells were plated in RPMI 1640 ( Merck ) supplemented with 10% FCS and 20% 3T3 cell culture supernatant and incubated at 37°C and 5% CO2 . Three days later , the medium was exchanged for fresh medium and after another four days of incubation macrophages were used for infection experiments . Dendritic cells were differentiated by culturing isolated bone marrow cells in BM-DC medium ( 1x NEAA ( Gibco ) , 5% FCS ( PAA ) , 2 mM L-Gltamin ( Gibco ) , 50 μM b-Mercaptoethanol ( Gibco ) , 50 μg/ml Genatmycin ( Gibco ) , 100 U/ml IL-4 , 255 U/ml GM-CSF at 37°C and 5% CO2 for three days . Afterwards , the medium was exchanged for fresh medium and after another four days of incubation the dendritic cells were used for infection experiments . For infection , differentiated macrophages and dendritic cells from C57BL/6 mice ( CD45 . 2 ) were pooled in a ratio of 1:1 and LmSWITCH stationary phase promastigotes ( opsonized with 5% mouse immune serum for 30 min at 26°C ) were added with a MOI of 5 . 24h later , parasites were photoconverted . Cells were induced with IFN-gamma ( 0 . 01 ng/μl , R&D Systems ) and LPS ( 1μg/ml , E . coli O26:B6 , Sigma-Aldrich ) and optionally , the nitric oxide synthase iNOS was inhibited by addition of N6- ( 1-iminoethyl ) -L-lysine hydrochloride ( L-NIL ) ( 0 . 023μg/μl , Sigma-Aldrich ) . After another 24h , a 1:1 mixture of CD45 . 1 macrophages and dendritic cells was added to the cell culture . After 5h of coculture , cells were analyzed by flow cytometry . For isolation of peritoneal macrophages mice were sacrificed and subsequently 5 ml of cold PBS were injected intraperitoneally . The cell suspension was aspirated and cells were seeded in RPMI 1640 supplemented with 10% heat-inactivated fetal bovine serum for infection and live cell imaging . Time-lapse microscopy of cell culture infections was performed with the use of a DMI6000B inverted microscope ( Leica Microsystems ) or a CellR imaging workstation ( Olympus ) using an upright microscope stage ( BX61 ) equipped with a 20x dry objectives . Images were automatically acquired every 10 minutes and movies were processed with the Fiji software ( NIH , http://rsb . info . nih . gov/ij/ ) . Spearman correlations and all comparisons between groups were calculated using the Prism 7 software ( GraphPad Inc . ) . Statistical analysis of multiple cell populations was performed with by one-way analysis of variance ( ANOVA ) with a Tukey post-test for multiple cross-comparisons , and a Bonferroni post-test for comparison of selected pairs of conditions or with a control condition , respectively . Comparisons with only two experimental conditions were performed using a Mann Whitney test . P values under 0 . 05 were regarded as significant and marked with an asterisk . P values lower than 0 . 01 or 0 . 001 were hence allocated two or three asterisks , respectively .
|
Infection with Leishmania parasites can result in chronic disease of several months duration , often accompanied with disfiguring and disabling pathologies . Central to Leishmania virulence is the capability to survive and multiply within professional phagocytes . While it is assumed that the parasites at some point have to exit the infected cell and infect new cells , the cycle of intracellular multiplication , release , and uptake into new host cells has never been studied in the ongoing infection . Therefore , it is unclear whether efficient growth of the pathogen takes place in a specific host cell type , or in a specific phase during the residency within , or during transfer to new cells . Here , we used a pathogen-encoded biosensor for measuring Leishmania proliferation in the ongoing infection , and in combination with a detailed analysis of the infected host cells involved . We could show that a monocyte-derived dendritic cell-like phagocyte subset , which is known for its role in inducing adaptive immune responses against Leishmania , represents a reservoir for efficient intracellular multiplication and spread to new host cells . These findings are important for our understanding of how the residency within a specific the cellular niche enables Leishmania parasites to efficiently multiply and persist at the site of infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"cells",
"fluorescence",
"imaging",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"viral",
"transmission",
"and",
"infection",
"pathogens",
"antigen-presenting",
"cells",
"immunology",
"microbiology",
"parasitic",
"diseases",
"otology",
"dendritic",
"cells",
"ear",
"infections",
"phagocytes",
"research",
"and",
"analysis",
"methods",
"white",
"blood",
"cells",
"imaging",
"techniques",
"animal",
"cells",
"otorhinolaryngology",
"host",
"cells",
"cell",
"biology",
"monocytes",
"virology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types"
] |
2018
|
CD11c-expressing Ly6C+CCR2+ monocytes constitute a reservoir for efficient Leishmania proliferation and cell-to-cell transmission
|
Snake bite is a neglected public health problem in communities in rural areas of several countries . Bothrops jararaca causes many snake bites in Brazil and previous studies have demonstrated that the pharmacological activities displayed by its venom undergo a significant ontogenetic shift . Similarly , the venom proteome of B . jararaca exhibits a considerable variation upon neonate to adult transition , which is associated with changes in diet from ectothermic prey in early life to endothermic prey in adulthood . Moreover , it has been shown that the Brazilian commercial antibothropic antivenom , which is produced by immunization with adult venom , is less effective in neutralizing newborn venom effects . On the other hand , venom gland transcripts of newborn snakes are poorly known since all transcriptomic studies have been carried out using mRNA from adult specimens . Here we analyzed venom gland cDNA libraries of newborn and adult B . jararaca in order to evaluate whether the variability demonstrated for its venom proteome and pharmacological activities was correlated with differences in the structure of toxin transcripts . The analysis revealed that the variability in B . jararaca venom gland transcriptomes is quantitative , as illustrated by the very high content of metalloproteinases in the newborn venom glands . Moreover , the variability is also characterized by the structural diversity of SVMP precursors found in newborn and adult transcriptomes . In the adult transcriptome , however , the content of metalloproteinase precursors considerably diminishes and the number of transcripts of serine proteinases , C-type lectins and bradykinin-potentiating peptides increase . Moreover , the comparison of the content of ESTs encoding toxins in adult male and female venom glands showed some gender-related differences . We demonstrate a substantial shift in toxin transcripts upon snake development and a marked decrease in the metalloproteinase P-III/P-I class ratio which are correlated with changes in the venom proteome complexity and pharmacological activities .
In the last decade high throughput methodologies have been increasingly employed in toxinology , mainly in snake venom studies , and the results obtained by omics analysis have allowed a comprehensive view of the complexity of venom transcriptomes/proteomes [1]–[3] . Since the first publication of a snake venom gland transcriptome [4] , a number of snake species had their venom gland transcriptomes revealed . These results had a tremendous contribution in snake venom protein identification by mass spectrometric analysis since no snake genome is available so far . However , all venom gland transcriptomic studies reported , including those using new generation deep sequencing technologies [5] , [6] , have focused on the diversity of transcripts from adult specimens . Hence , we decided to explore the venom gland transcriptome of a medically important South American snake species [7] , Bothrops jararaca , at two different stages of its ontogeny . Previous studies have demonstrated that the ontogenetic shift in diet , from ectothermic prey in early life to endothermic prey in adulthood , and in animal developmental stages ( newborn and adult ) are associated with changes in the venom proteome of this species [8]–[11] . Some snake venom toxin classes undergo remarkable post-translational modifications , including proteolytic processing , oligomerization and glycosylation , which contribute to the complexity of the venom proteomes [12]–[14] . Ontogenetic variability has also been extensively reported for snake venoms [9] , [11] , [15]–[18] and the modification of prey types has been considered an important factor for the ontogenetic variability of venom composition [9] , [11] , [16] , [17] , [19] . Taking into consideration that snake bite was recently recognized by World Health Organization as a neglected tropical disease , the understanding of snake venom composition have remarkable implications on the improvement of anti-venom therapy and the management of snake bite [20] . Therefore , the analysis of geographical , seasonal and ontogenetic intraspecies venom variability must be taken into account in order to prepare representative venom pools for antivenom production [21] . In order to evaluate whether the variability demonstrated for the B . jararaca venom proteome upon newborn to adult transition was correlated or not with variations in the structure of toxin transcripts we carried out a transcriptomic analysis of venom gland from newborn and adult male and female specimens . To the best of our knowledge this is the first report on age- and gender-related variability of a snake venom gland transcriptome and the results reported here allowed insights into the ontogenetic and sexual variability of snake venom proteomes as well as provided additional information for the development of more appropriate anti-bothropic antivenom for clinical intervention .
B . jararaca specimens were obtained from the Herpetology Laboratory , Instituto Butantan , São Paulo , Brazil . Thirty-two animals were used , among which 20 newborns ( two weeks old; 10 males and 10 females ) and 12 adults ( older than 3 years; 6 males and 6 females ) , from São Paulo State , Brazil . Even though a few specimens are suitable to provide enough material ( mRNA ) for cDNA library construction , this protocol was used because the low amount of mRNA obtained for newborn specimens ( data not shown ) . The venom was milked and 4 days later the animals were subjected to CO2 anesthetization and were sacrificed by decapitation . The venom glands were carefully dissected , frozen in liquid nitrogen and kept at −80°C until use . All animal work has been conducted in agreement with the Ethical Principles in Animal Research , adopted by the Brazilian College of Animal Experimentation and was approved by the Ethical Committee for Animal Research of Butantan Institute ( protocol n° 377/07 ) . The integrity of total RNA was checked by discerning the 28S and 18S bands of ribosomal RNA in a formaldehyde denaturing 1% agarose gel [22] . Messenger RNA ( mRNA ) purification was performed on a column of oligo-dT cellulose ( GE Healthcare ) and the cDNAs were synthesized from 5 µg of mRNA using the Superscript Plasmid System for cDNA synthesis and Cloning ( Invitrogen ) , and selected by size ( 350–600 pb and ≥600 pb ) in agarose gel electrophoresis . The adapter-linked cDNAs were directionally cloned in pSPORT-1 plasmid ( Invitrogen ) and transformed in Escherichia coli DH5α . Plasmid DNA was isolated using alkaline lysis from randomly chosen clones as described [23] . DNA was sequenced on an ABI 3100 sequencer using BigDye2 kit ( Applied Biosystems ) with standard primers . A homemade pipeline of EST analysis software was developed and used to remove poor quality sequences , vector , adaptors and short ESTs ( <150 bp ) , as described elsewhere [23] . All ESTs ( from adult and newborn libraries ) were then assembled into clusters of contiguous sequences using the CAP3 program [24] , set to join only sequences with at least 98% of base identity . The counting of ESTs originated from adult or newborn libraries in each cluster represents its level of expression in one or another group . The clusters were filtered by BLASTN against a dataset of ribosomal RNAs , mitochondrial , E . coli and vector sequences to mask them from statistical analysis . Each cluster was then searched against GenBank databases using BLASTX and BLASTN algorithms using Blast2Go software [25] to identify similar products with an e-value cutoff <10−5 . Unidentified sequences and those with unpredicted function were checked for the presence of signal peptide and for orthologous occurrence in other ESTs ( dbEST –www . ncbi . nml . nih . gov/nucest ) . A final annotation table in Microsoft Excel format was generated containing all relevant information about clusters . Methodologies of other analyses are described within the figure legends . Both single-pass reads ( ESTs ) and contigs are available upon request by e-mail at ijuncaze@butantan . gov . br .
We have generated three cDNA libraries using mRNA isolated from newborn ( male and female ) , adult male and adult female B . jararaca venom glands . A total of 2077 random clones including 998 from the newborn library , 559 clones from the adult male library and 520 clones from the adult female library were sequenced ( Table S1 ) . All ESTs were clustered regardless of the source of cDNA ( newborn or adult ) . After assembling , 924 clusters were formed with lengths ranging from 153 to 2340 bp . Slight differences in terms of abundance of toxin classes , especially Snake Venom Metalloproteinases ( SVMPs ) and Snake Venom Serine Proteinases ( SVSPs ) , were noticed in comparison to a previously reported B . jararaca venom gland transcriptome [26] . These differences may be due to several factors such as individual and/or geographical variability . Moreover , when compared with other Bothrops species , the most common toxin groups ( SVMPs , Bradykinin Potentiating Peptides - BPPs , SVSPs , Phospholipase A2 – PLA2 and C-type lectins- CTLs ) showed a similar pattern [4] , [27] , [28] . SVMPs were the most abundant toxin transcripts and showed considerable content differences between newborn ( 53 . 2% ) and adult ( 29 . 9% ) cDNA libraries ( Figure 1 ) . Interestingly , the amount of transcripts for snake venom Vascular Endothelium Growth Factors ( svVEGF ) and Cysteine-rich Secretory Proteins ( CRISPs ) was about 3-fold higher in the newborn venom glands ( Figure 1 ) . On the other hand , the percentage of transcripts encoding CTLs , BPP precursors , L-amino acid oxidases ( LAAO ) and SVSPs doubled in adult venom glands ( male and female ) compared to newborn ones . In addition , a few singletons corresponding to hyaluronidases , nucleotidases , phosphodiesterases and ohanin-like transcripts were also identified ( Table S2 ) . The comparison of content of ESTs encoding toxins in adult male and female venom glands showed some gender-related differences . Transcripts for svVEGF , CRISP and CTL were more abundant ( twice as much ) in female venom glands while the number of transcripts for PLA2 was about five times higher in the male venom glands ( Figure 1 ) . SVMP transcripts were somewhat more abundant in the female venom gland , however , no significant difference was detected among the P-classes encoded by the clusters from the male and female libraries . Interestingly , snake venom Nerve Growth Factor ( svNGF ) transcripts were detected only in the male venom gland cDNA library ( 2 . 5% of total transcripts ) . As expected , after in silico assembly several clusters had ESTs derived from both newborn and adult cDNA libraries . However , some newborn ESTs did not overlap any EST derived from the adult cDNA library and vice-versa; therefore these clusters were considered as ‘exclusive’ . Even though this assumption is indicative of differentially expressed genes it should be considered that it is not unequivocal and might result from a failure in the clustering of similar transcripts . Figure 2 depicts 89 SVMP clusters assembled from both newborn and adult cDNA libraries . Given the significant differences between newborn and adult SVMP expression , we have selected some clusters for a detailed analysis since SVMPs have a multi-domain architecture in which the presence of distinct domains has an implication on the biological activity of the mature protein . In this context , the only way to assign a given SVMP transcript to a SVMP class is to analyze its translated sequence inspecting for the presence of the ancillary domains ( disintegrin or disintegrin-like and cysteine-rich ) . The analysis of 48 newborn exclusive SVMP clusters , composed by 156 ESTs , revealed 16 clusters assigned to P-III class SVMP ( 84 ESTs ) while the other two thirds of clusters encode P-I or P-II SVMPs ( 72 ESTs ) ( Figure 2 ) . Among these , three clusters were significantly more expressed: BJOTD0209C ( length 2 . 0 kb; 25 ESTs ) , BJOTD0228C ( 2 . 3 kb; 20 ESTs ) and BJOTD0238C ( 1 . 1 kb; 10 ESTs ) . The translated sequences of these three clusters showed structural elements which allowed their allocation into the P-III class of SVMPs ( Figure 3 ) . On the other hand , the analysis of adult SVMP exclusive clusters , composed by 95 ESTs , revealed that P-I and P-II class transcripts ( 66 ESTs ) are clearly more abundant than P-III ones ( 30 ESTs ) ( Figure 2 ) . The cluster BJOTD0087C ( length 1 . 1 kb ) was the most expressed ( 32 ESTs ) and its corresponding translated sequence resulted in a protein with 95% similarity with the P-II class SVMP precursor of bothrostatin ( gi|82219563| ) from B . jararaca , which shows the canonical R-G-D motif typical of true disintegrins ( Figure 3 ) [29] . The sequence analysis of SVSP , BPP precursors , CTL and PLA2 clusters revealed a lower level of complexity among these toxin classes in comparison to the SVMP clusters . Interestingly , no cluster for SVSP , BPP , CTL and PLA2 was found as exclusive in the newborn venom glands ( Figure 4 ) . However , a number of clusters of SVSP , BPP precursors , CTL and PLA2 were found as common for both newborn and adult transcriptomes whereas many clusters were identified as specific for the adult venom gland transcriptome . These data suggest that a process of diversification of transcription of these toxins classes occur upon newborn to adult transition leading to a higher degree of complexity of SVSP , BPPs , CTLs and PLA2 in the adult venom .
The analysis of mRNA transcripts by sequencing ESTs from snake venom glands cDNA libraries provides a good snapshot of the toxin arsenal of a given species . In the case of B . jararaca , the results on the transcriptomic analysis are broadly consistent with our previous analyses indicating a shift in SVMPs from a P-III rich newborn venom to a P-I rich adult venom [9] . The presence of transcripts for svNGF only in the male cDNA library is in accordance to the results reported by Thoenen and Barde [30] who showed that the modulation of Nerve Growth Factor ( NGF ) expression by male hormone testosterone was the main factor responsible for the 10-fold higher levels of this growth factor in male mouse salivary glands . Gender-related variability was already reported in B . jararaca venom composition and activities [31] , [32] . Coagulant and hemorrhagic activities ( due to the action of SVMPs or SVSPs ) were detected as significantly higher in female venoms while phospholipase A2 and myotoxic activities were higher in male venoms [32] which is in accordance with the toxin profile observed in Figure 1 . Significant differences in the BPPs content were also reported for male and female B . jararaca venoms . Four new BPPs were detected only in female venoms and identified by de novo sequencing as cleaved BPPs lacking the C-terminal Q-I-P-P sequence [33] . The differential abundance in toxin transcripts reported here are closely associated with the biochemical and biological properties reported for adult male and female venoms . As an interesting outcome of this study , was the fact that the content of transcripts for SVMPs , which correspond to more than 50% of the total toxin transcripts in the newborn gland , diminishes dramatically in the adult venom gland while transcripts for other important toxins classes ( SVSP , CTL and BPP precursor ) increase , which suggests that the snake has at its disposal a more complex toxin arsenal to deal with different types of prey in the animal adult life . Structural diversity among SVMPs is a well known feature and is a result of the existence of distinct precursors as well as differential post-translational processing events in this toxin class [12] . The abundance of P-III class SVMP transcripts in B . jararaca newborn venom gland might explain the intense pro-coagulant activity that was recently reported for the newborn B . jararaca venom [11] . On the other hand , it was also recently shown a lower metalloproteinase activity in neonate B . jararaca venoms [34] . This apparent discrepancy may be explained by the differences in structural diversity of SVMPs precursors found in neonate and adult venoms . Therefore , our results suggest a diversification of the biological roles displayed by the newborn SVMPs , mainly those belonging to PIII-class , upon neonate to adult transition . The variability in B . jararaca venom gland transcriptome is quantitative , as illustrated by the higher content of SVMPs in the newborn venom glands . Moreover , the variability is also characterized by the structural diversity of SVMP precursors found in newborn and adult transcriptomes . The main biological implications derived from these observations were verified in the analysis of the ontogenetic variation in B . jararaca venom proteome [11] . The venom from newborn specimens is almost 10 times more coagulant upon human plasma than the adult venom . Furthermore , the newborn venom is able to activate Factor II and Factor X of the coagulation cascade at a much faster rate than the adult venom [11] . The coagulant activity of newborn venom is strictly related to SVMPs activity whereas in the adult venoms both SVSPs and SVMPs are involved in the generation of fibrin clots [11] . On the other hand , the hemorrhagic activity , a known feature related to P-III class SVMPs , did not vary significantly between newborn and adult venoms . According to our previous investigations [11] , both newborn and adult specimens have P-III class SVMPs in their venoms , though their biological targets differ significantly . Unspecific proteolysis , a common feature displayed by P-I class SVMPs [12] , [35] , [36] , was higher in adult venoms [11] . In fact , P-I class SVMP transcripts are highly expressed in the adult venom gland ( Figure 2 ) . In this context , the molecular basis for the explanation of distinct SVMP substrate specificities in both venoms could be related to the structural diversity verified among SVMP precursors in newborn and adult venom glands . As the snake matures and grows , the P-III/P-I class SVMP ratio decreases , while no alteration in the venom hemorrhagic activity is detected [11] . Therefore , even among newborn and adult P-III class SVMPs there are differences regarding substrate specificity; most of the newborn P-III class SVMPs tend to display pro-coagulant activity , acting upon FII/FX or both . On the other hand , among hemorrhagic P-III class enzymes there is apparently no clear difference in potency between newborn and adult venoms . Similarly , distinct profiles/contents of SVMPs were detected by the analysis of other venoms indicating a shift from a P-III rich newborn venom to a P-I rich adult venom [18] , [37] , [38] . Whether the P-III/P-I SVMP ratio represents an ‘ontogenetic molecular marker’ for the venoms from Bothrops genus remains to be evaluated in other species . The fact that no SVSP , CTL , BPP precursor and PLA2 cluster exclusive for the newborn transcriptome was detected in our analysis suggests that these toxins may have less significant roles in the pharmacological activities displayed in the newborn venom . Accordingly , the higher complexity and abundance of sequences encoding SVSP , CTL , and BPP precursor in the adult venom , which occurs in parallel to the lower expression of SVMPs , indicates that these toxins are crucial for the animal to deal with the endothermic prey . Envenomation by venomous snakes was only recently recognized by the World Health Organization as a neglected tropical disease with yearly mortality greater than several other presently recognized neglected tropical diseases [20] . Therefore , several attempts have been made in order to improve the antivenom therapy as well as the treatment of snake bite [3] , [20] , [21] . In this context , the knowledge of snake venom composition through high-throughput ( omics ) approaches provides subsidy for the understanding of the molecular targets of snake venom toxins as well as for the improvement of antivenoms . In Brazil , the human accidents caused by Bothrops genus are treated with anti-bothropic antivenom , which is a mixture of horse immunoglobulins obtained by immunization of horses with a pool of venom from 6 species from Bothrops genus ( Instituto Butantan , São Paulo , Brazil ) . Of interesting note is the fact that no newborn venom is used in the composition of the immunogen mixture . Recently Antunes and coworkers [34] showed that the anti-bothropic antivenom was less efficient in neutralizing in vivo and in vitro activities of newborn B . jararaca venom . The knowledge of the transcriptome of B . jararaca newborn venom gland provided insights into the repertoire of toxins of this tissue at an early life stage . Furthermore we verified that there is a significant difference in terms of SVMP precursors . This feature might have an effect in the biological activities observed during the life span of B . jararaca species as revealed by our previous functional and proteomic analysis . In conclusion , our results were robust enough to provide the molecular basis of venom composition differences among newborn and adult B . jararaca as well as gender-related differences .
|
Bothrops jararaca is one of the most abundant venomous snake species in Brazil . It is primarily a nocturnal and generalist animal , however , it exhibits a notable ontogenetic shift in diet , feeding mainly on arthropods , lizards , and amphibians ( ectothermic prey ) through its juvenile phase and on small mammals ( endothermic animals ) during adult life . Due to its broad geographical distribution , this species is responsible for the majority of the accidents by Bothrops genus in Brazil . Studies on envenomation cases with newborn and adult B . jararaca snakes have shown distinct patterns , mainly related to blood coagulation disorders , which seems to be prominent in accidents with newborn specimens . Moreover , it has been demonstrated that the Brazilian commercial antibothropic antivenom , which is produced by immunization with adult venom , is less effective in neutralizing newborn venom effects . In this study we analyzed the venom gland transcriptome of newborn snake specimens and compared the content of toxin transcripts with that of adult specimens . We demonstrate that upon B . jararaca development , its repertoire of mRNAs encoding toxins changes both qualitatively and quantitatively and these alterations are associated with the venom proteome profiles and pharmacological activities displayed by newborn and adult specimens .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biochemistry",
"genetics",
"biology",
"proteomics",
"genetics",
"and",
"genomics"
] |
2012
|
A Transcriptomic View of the Proteome Variability of Newborn and Adult Bothrops jararaca Snake Venoms
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Cyclic paroxysm and high fever are hallmarks of malaria and are associated with high levels of pyrogenic cytokines , including IL-1β . In this report , we describe a signature for the expression of inflammasome-related genes and caspase-1 activation in malaria . Indeed , when we infected mice , Plasmodium infection was sufficient to promote MyD88-mediated caspase-1 activation , dependent on IFN-γ-priming and the expression of inflammasome components ASC , P2X7R , NLRP3 and/or NLRP12 . Pro-IL-1β expression required a second stimulation with LPS and was also dependent on IFN-γ-priming and functional TNFR1 . As a consequence of Plasmodium-induced caspase-1 activation , mice produced extremely high levels of IL-1β upon a second microbial stimulus , and became hypersensitive to septic shock . Therapeutic intervention with IL-1 receptor antagonist prevented bacterial-induced lethality in rodents . Similar to mice , we observed a significantly increased frequency of circulating CD14+CD16−Caspase-1+ and CD14dimCD16+Caspase-1+ monocytes in peripheral blood mononuclear cells from febrile malaria patients . These cells readily produced large amounts of IL-1β after stimulation with LPS . Furthermore , we observed the presence of inflammasome complexes in monocytes from malaria patients containing either NLRP3 or NLRP12 pyroptosomes . We conclude that NLRP12/NLRP3-dependent activation of caspase-1 is likely to be a key event in mediating systemic production of IL-1β and hypersensitivity to secondary bacterial infection during malaria .
Every year , approximately 250 million people are infected with Plasmodium , contributing to significant social and economic instability in the developing countries around the world [1] . One of the main physiological responses to Plasmodium infection is the paroxysm – characterized by cycles of sharp peaks of high fever accompanied by chills and rigors , which coincide with the release of parasites from synchronized infected red blood cells [2] , [3] . Parasite components , such as DNA bound to hemozoin [4] , [5] and glycosylphosphatidylinositol ( GPI ) anchors [6] , trigger the production of proinflammatory cytokines , including interleukin-1 beta ( IL-1β ) , via activation of Toll-Like receptors ( TLRs ) [7] . Furthermore , malaria sepsis [8] leads to an exquisite sensitivity to secondary bacterial infections , in particular non-typhoidal salmonellosis , that often associate with severe disease [9]–[12] . Hence , a better understanding of the mechanisms involved on this inflammatory stage during malaria is critical for the clinical management and prevention of severe disease . TLRs are only one family of the receptors required for the release of active IL-1β , as cleavage of pro-IL-1β by caspase-1 also requires activation of Nod-Like Receptors ( NLRs ) [13] , [14] . Upon stimulation , the respective NLRs oligomerize and recruit pro-caspase-1 directly via a N-terminal caspase recruitment domain ( CARD ) homotypic interaction ( CARD-CARD ) ( e . g . , CARD-containing NLRs such as NLRP1 or NLRC4 ) or indirectly via the adaptor molecule called apoptosis-associated speck-like protein containing a caspase recruitment domain ( ASC ) , as is the case of NLRP3-inflammasome [13] . The inflammasome assembly culminates on activation of caspase-1 , and consequent release of the active form of IL-1β . NLRP3 containing inflammasome is activated in response to a large range of insults , such as pathogens , bacterial RNA , and crystal structures [15]–[17] . The NLRP12 was the first NLR shown to associate with ASC and to form an active IL-1β-maturing inflammasome [18] . This receptor was initially placed as a negative regulator of inflammation [19] , [20] , but it was also shown to be involved in periodic fever of cryopyrinopathies [21] , and to mediate host resistance to Yersinia pestis [22] . Here , we asked what are the molecular steps required for and the physiological role of inflammasome assembly during malaria sepsis . Our results indicate that symptomatic Plasmodium infection triggers inflammasome formation and caspase-1 activation via an intricate process that requires several inflammatory mediators as well as NLRP3 and NLRP12 . Furthermore , we found that the malaria-primed monocytic cells produce deleterious amounts of IL-1β when exposed to a second microbial challenge , being an important component of the overwhelming inflammatory response observed during bacterial superinfection .
The Plasmodium chabaudi AS rodent model was used to evaluate the in vivo activation of inflammasome . The microarray analysis of splenocytes from C57BL/6 at 6 days post-infection demonstrates enhanced expression of various genes from the inflammasome pathway , including Casp1 and Il1b ( Figure 1A ) . Consistently , the FLICA assay , which employs the fluorescent probe FAM-YVAD-FMK and Western blot , indicate that infection with P . chabaudi is sufficient to promote caspase-1 activation ( Figures S1A and 1B ) . Immunoblots evidenced enhanced expression and cleavage of pro-caspase-1 in spleens from P . chabaudi infected mice ( Figures S1B , S1C and 1C ) . The FLICA assay also revealed that macrophages ( CD11b+F4/80+ ) and dendritic cells ( DCs ) ( CD11c+MHC-II+ ) are the main source of active caspase-1 ( Figure 1B ) in the spleens from infected mice . We also observed a high frequency of macrophages and DCs undergoing inflammatory cell death ( pyroptosis ) , as defined by damage of cell membranes evaluated by DNA-7AAD staining and augmented cell size associated with active caspase-1 ( Figure 1B ) . Other splenic cell subsets did not express active caspase-1 or were undergoing pyroptosis during P . chabaudi infection ( Figure S1D ) . Importantly , macrophages and DCs from mice deficient for Asc ( ASC−/− ) and Casp1 ( Casp-1−/− ) were negative for both active caspase-1 and pyroptosis markers during P . chabaudi infection ( Figures 1B ) . Similar results were obtained when we used splenocyte lysates in immunoblots to detect active caspase-1 ( Figures 1C ) . We also evaluated the role of caspase-1 activation in host resistance to P . chabaudi . C57BL/6 , ASC−/− and Casp-1−/− mice injected with 105 P . chabaudi infected erythrocytes displayed similar parasitemia beginning at day 5 , peaking at days 7–8 days , and completely resolved at 25 days post-infection ( Figure S2A ) . No lethality was observed until the end of the experiment . Leuckocytes from mice and humans infected with Plasmodium are hyper-responsive to TLR agonists [23] . This hyperresponsiveness does not occur at all phases of infection . For example , as we show in Figure S2B , mice that are infected with P . chabaudi were hyperresponsive to small doses of TLR ligands , such as lipopolysaccharide ( LPS ) , during the acute phase of the disease ( day 7 ) . This hyperresponsiveness was no longer observed when mice were challenged with low dose LPS 4 weeks after infection . Based on this and other studies [24] , [25] , we hypothesize that bacterial superinfection is a common co-factor for severe malaria . Thus , we used a challenge with low dose of bacterial LPS to mimic secondary bacterial infection and to evaluate the role of inflammasome and IL-1β in this process . C57BL/6 mice infected with P . chabaudi produced low , but significant levels of IL-1β , when compared to uninfected controls ( Figures 1D and S2B ) . Surprisingly , these low levels of circulating IL-1β were produced in an ASC and Caspase-1-independent manner ( Figure 1D ) . In contrast , production of IL-1β , which was extremely high in infected C57BL/6 mice challenged with LPS , was severely impaired in ASC−/− or Casp-1−/− mice ( Figure S2B and Figure 1D ) . In order to check whether the active caspase-1+ cells were the main sources of active IL-1β , highly purified CD11b+ and CD11c+ cells from infected mice were stimulated with LPS and IL-1β measured thereafter . Both CD11b+ as well as CD11c+ cells produced high levels of IL-1β ( Figure 1E ) . The in vivo proinflammatory priming promoted by Plasmodium infection is partially dependent on TLR9 activation by parasite DNA [4] , [23] , [26] . Consistently , expression of various inflammasome genes was no longer enhanced in Myd88-deficient mice ( MyD88−/− ) infected with P . chabaudi ( Figure 1A and Table S1 ) . Accordingly , we did not observe active caspase-1 , and pyroptotic features in macrophages and DCs from MyD88−/− mice infected with P . chabaudi ( Figure 1C and 1F ) . As expected , no systemic IL-1β was detected in sera of infected MyD88−/− mice challenged or not with LPS ( Figure 1G ) . Furthermore , the data presented in Figure S3A show reduced expression of pro-caspase-1 in Tlr9-deficient mice ( TLR9−/− ) . In view of this reduced level of basal expression of the pro enzyme , it is not surprising that expression levels of its active form were decreased in TLR9−/− mice infected with P . chabaudi . Likewise , the levels of circulating IL-1β were partially affected and survival rates increased in infected TLR9−/− mice challenged with the low LPS dose ( Figures S3B and S3C ) . Finally , we found that priming with CpG immunostimulatory oligonucleotides mimics P . chabaudi infection leading to enhanced susceptibility to LPS challenge , but does not induce lethality when used to challenge P . chabaudi infected mice ( Figure S3D ) . The levels of circulating interferon gamma ( IFN-γ ) , tumor necrosis factor-alpha ( TNF-α ) and IL-1β in sera of P . chabaudi infected mice , challenged or not with LPS , are presented in Figure S2B . Very low levels of IFN-γ , TNF-α , and IL-1β were detected in sera from uninfected mice challenged with low dose LPS . The hyperresponsiveness to LPS was observed in the first two weeks , but not at 28 days post-infection ( Figure S2B ) . IFN-γ was shown to be a key mediator of inflammatory priming in febrile malaria patients and in mice infected with P . chabaudi [23] . Furthermore , TNF-α , a critical cytokine in malaria pathogenesis [27] , [28] was shown to mediate expression of inflammasome components and pro-IL-1β [29] , [30] . Indeed , even upon LPS challenge , active IL-1β was not produced in either Ifng or Tnfrsf1a-deficient mice ( IFN-γ−/− ) and ( TNFR1−/− ) respectively , when infected with P . chabaudi ( Figure 2A ) . Normal activation of caspase-1 was observed in macrophages and DCs from TNFR1−/− , but not from IFN-γ−/− mice ( Figures 2B , 2C and S4 ) . In contrast , both IFN-γ and TNF-α were required for expression of pro-IL-1β by spleen cells of infected mice challenged with LPS ( Figures 2D and S4 ) . We next evaluated the requirement of specific NLRs for caspase-1 activation . The P2X7 receptor ( P2X7R ) which senses extracellular ATP , opens a cation-specific channel that alters the ionic environment of the cell [31] culminating on NLRP3-inflammasome assembly under certain conditions [32] , [33] . Here we found that P2X7R is necessary for caspase-1 activation during in vivo infection with P . chabaudi ( Figure 3A ) . Consistently , NLRP3 and NLRP12 were required for activation of caspase-1 , systemic production of IL-1β and pyroptosis ( Figures 3B , 3C and 3D ) . Other cytosolic receptors , i . e . NLRC4 and absent in melanoma 2 ( AIM2 ) were not essential for IL-1β release ( Figure 3D ) . The levels of circulating IL-1β in sera of C57BL6 , NLRP3−/− , NLRP12−/− , ASC−/− as well as Casp-1−/− infected mice , but no challenged with LPS , were not statically different ( Figure 3D ) . The infected Il1r-deficient mice ( IL-1R−/− ) mice are partially resistant to the LPS challenge , despite of the sustained levels of active caspase-1 , IL-1β and TNF-α ( Figures 4A and 4B ) . Importantly , in the different mouse lineages infected with P . chabaudi , we observed a striking correlation of high circulating IL-1β , but not necessarily active caspase-1 , and lethality induced by LPS ( Table 1 and Figure 3D ) . Consistently , treatment with IL-1 receptor antagonist ( IL-1RA ) prevented lethality of P . chabaudi infected mice challenged with LPS ( Figure 4C ) . These results were validated in P . chabaudi infected mice challenged with sub-lethal cecal ligation puncture ( CLP ) ( Figure 4D ) , a classic model for bacterial sepsis , as well as peroral infection with Salmonella typhimurium ( Figure 4E ) . In both cases , bacterial superinfection leaded to rapid lethality that was associated with high circulating levels of IL-1β . Furthermore , mortality of co-infected mice was delayed or prevented by treatment with the IL-1RA . The loads of bacteria translocation were similar in co-infected mice treated or not with IL-1RA ( Figures 4F and 4G ) . We next studied caspase-1 activation and IL-1β release in peripheral blood mononuclear cells ( PBMCs ) from patients undergoing febrile malaria . Two different subsets of monocytes were closely examined: CD14+CD16− or CD14dimCD16+ monocytes ( Figure 5A and S5A ) . Active caspase-1 was constitutively expressed in CD14+CD16− cells from healthy individuals , as previously described [34] . Nevertheless , the frequency of CD14+CD16− cells was augmented on average 3 fold in P . vivax malaria patients . The frequency and intensity of caspase-1 expression , indicated by median fluorescence intensity ( MFI ) , in different monocyte subsets from healthy and P . vivax infected individuals before and after treatment are shown in Figure 5A . Expression of active caspase-1 in association with membrane damage and augmented cell size was also observed in CD14dimCD16+ monocytes ( Figure S5A ) . The representative histograms presented in Figure S5A were obtained from counter plot gates shown in Figure 5A and illustrate the results for active caspase-1 , membrane damage and cell size change from one malaria patient before and after treatment , and a healthy donor . Other cell populations found in PBMCs were negative for active caspase-1 and pyroptosis markers ( Figure S5B ) . We also observed increased cleavage of caspase-1 ( p10 ) in PBMCs from either P . vivax or P . falciparum malaria patients ( Figures 5B , 5C and S5C ) . In addition , LPS-induced release of IL-1β is highly augmented in PBMCs from the same patients undergoing malaria sepsis ( Figure 5B and 5C – bottom panels ) . Furthermore , we observed enhanced expression of inflammasome genes in PBMCs from P . falciparum malaria patients ( Figure S5D and Table S2 ) . As observed in the rodent malaria model ( Figure 2 ) , IFN-γ-priming of primary human monocytes mimics in vivo infection with Plasmodium and augments expression of pro-caspase-1 , pro-IL-1β as well as IL-1β release induced by LPS stimulation ( Figure S5E ) . The nature of malaria-induced inflammasome was further explored by performing a crosslinking assay and confocal analyses . We observed an augmented multimerization of ASC ( Figure 6A ) in PBMCs from P . vivax infected individuals . Additionally , confocal microscopy indicated that inflammasome specks contained either NLRP3 ( green ) or NLRP12 ( red ) , were present in 7 and 10% of monocytes from P . vivax malaria patients , respectively ( Figure 6B and 6C ) . There was no co-localization of NLRP3 and NLRP12 specks . We did not detect monocytes containing NLRC4-inflammasome , and AIM2 specks appeared in a low frequency , ∼0 . 25% of monocytes from infected individuals . No specks were detected in monocytes from uninfected healthy donors ( Figure 6C ) . Figure S6 provides controls of the confocal analysis .
Paroxysm , an acute fever accompanied by chills and rigors , is a hallmark of Plasmodium infection [3] , [35] . While the physiological role of fever is controversial , it can aid in host defense , delaying the growth of pathogens with strict temperature preferences [36] . In fact , prior to the discovery of antibiotics , deliberate infection with P . vivax was used to induce high fever and eliminate infection with Treponema pallidum in neurosyphilis patients [37] . However , fever is also associated with various pathological processes , such as respiratory distress , anemia , and neurological manifestations that cause morbidity and mortality in malaria [3] , [35] . These clinical manifestations are related to the intensity of the systemic inflammatory response , yet the basic details of malaria-induced cytokinemia are not understood . Overall , our results indicate that Plasmodium infection primes innate immune cells leading to the oligomerization of inflammasomes containing , ASC , NLRP3 and NLRP12 , resulting in activation of caspase-1 and the production of copious amounts of IL-1β upon a second TLR activation . An immediate consequence of this inflammatory priming is a drastic reduction in the threshold to septic shock-like syndrome caused by secondary bacterial infection . Although there is a controversy about the role of different TLRs in the pathogenesis of malaria [38]–[41] , various studies indicate that the initial cytokine storm during malaria is driven by TLR activation . Initial studies suggested that GPI anchors were the main P . falciparum molecules responsible for eliciting the production of proinflammatory cytokines during malaria [6] . However , recent studies indicate that parasite derived DNA containing both immunostimulatory CpG and AT-rich motifs , is instead the main force driving the cytokine storm during malaria sepsis [4] , [5] , [42] . Indeed , mice bearing non-functional TLR9 or MyD88 , or treated with a TLR7/TLR9 antagonist display a less pronounced inflammatory response and attenuated pathology during experimental malaria [23] , [26] , [38] , [41] . Moreover , mutations in TLR2 , TLR9 and Mal/TIRAP appear to affect the outcome of human disease [24] , [25] , [43] , [44] . Importantly , infection with Plasmodium in humans leads to a proinflammatory priming and hyperresponsiveness to microbial products [23] , [45]–[47] . This enhanced ability to respond to microbes during immunosurveillance protects the host against infectious insult , but the innate immune response is the classic “double-edged sword” . Thus , the proinflammatory priming means that the innate immune system can overreact to secondary infection , leading to a septic shock syndrome with clinical manifestations . It is noteworthy that malaria is often associated with bacterial infections [12] . Furthermore , a recent study highlights that the chance to develop severe malaria is elevated 8 . 5 fold in children with bacteremia [9] . Similarly , bacterial and viral infections may also act as co-factors for severe P . vivax malaria [10] , [35] , [48] . We propose that inflammasome and IL-1β [35] , [49] , [50] are important components of the proinflammatory priming and the exquisite sensitivity to superinfection during malaria . Here , we demonstrate that both in mouse and human malaria expression of inflammasome genes , caspase-1 activation and pyroptosis are induced in phagocytic cells . As a consequence , during Plasmodium infection the threshold for LPS sensitivity is decreased in at least 100 folds , as compared to non-infected control mice [51] , [52] . Previous studies have shown that 1 . 0 mg of LPS induces the production of high IL-1β levels and lethality [52] , [53] . In our model , challenge with 10 µg of LPS did not promoted or augmented caspase-1 activation in either uninfected or infected mice , respectively . Hence , Plasmodium infection is sufficient to induce inflammasome assembly and caspase-1 activation , but requires a challenge with a low dose of LPS to induce expression of pro-IL-1β and release of high levels of its active form . Our studies in the P . chabaudi mouse model demonstrate that expression of inflammasome genes; pro-caspase-1 as well as pro-IL-1β are all dependent on intact Myd88 function . The MyD88 role on the process is independent of the IL-1 receptor signaling , as macrophages and DCs from P . chabaudi-infected IL-1R−/− mice show normal levels of caspase-1 activation and pyroptosis . In fact , we found that TLR9 explains , in part , the role of MyD88 on caspase-1 activation . As previously reported [54] , treatment with CpG oligonucleotides mimics the P . chabaudi infection making the mice more susceptible to septic shock . Curiously , challenge of infected mice with CpG oligonucleotides did not result in lethality . As CpG DNA and LPS preferentially target DCs and monocyte/macrophages , respectively [55] , our data suggest that the nature of the ligand and differential expression of TLRs are important determinants on priming and cytokine production by Plasmodium infected mice challenged with LPS . We hypothesize that by targeting TLR9 on DCs , Plasmodium infection elicits the IL-12 production , and consequent IFN-γ-dependent inflammatory priming [23] . Priming with IFN-γ mediates caspase-1 activation through the induction of pro-caspase-1 expression , and the lack of either IFN-γ or TNFR1 results in impaired expression of pro- IL-1β . Hence , LPS , but not CpG , stimulates the production of high levels of TNF-α and pro-IL-1β culminating on the release of deleterious amounts of active IL-1β by IFN-γ-primed monocytes/macrophages . An alternative explanation is that by inducing Type I IFN production by DCs , Plasmodium infection inhibits the excessive production of IL-1β [56] , [57] , and prevents lethality induced by TLR9 activation . Another important requirement for caspase-1 activation in rodent malaria was the purinergic P2X7 receptor that under certain conditions mediates NLRP3-inflammasome activation [32] , [58] . Indeed , in vitro experiments reported that synthetic hemozoin induces inflammasome formation , activation of caspase-1 and release of IL-1β by macrophages via NLRP3 [59]–[62] . Here , we demonstrated an in vivo requirement of both NLRP3 and NLRP12 for inflammasome formation and caspase-1 activation during in vivo infection with P . chabaudi . We also report that symptomatic infection with either P . falciparum or P . vivax leads to enhanced expression of inflammasome related genes and caspase-1 activation . Notably , we notice an in vivo assembly of NLRP3 and NLRP12 specks and ASC oligomerization in febrile malaria patients . A diagram detailing the different steps required for inflammasome assembly during malaria sepsis , and release of copious amounts of IL-1β during bacterial superinfection is presented in Figure 7 . It is noteworthy that periodic fever is a main symptom of cryopyrinopathies , a human inflammatory disorder that is associated with mutations in both NLRP3 and NLRP12 genes [21] . These patients have less severe symptoms when treated with IL-1R antagonist [63] . However , the relevance of this process during in vivo Plasmodium infection is controversial . While Dostert and colleagues [59] demonstrated a partial protection to experimental cerebral malaria in NLRP3−/− mice , other studies reported that this pathological process occurs independent of NLRP3 , ASC , Caspase-1 , IL-1R , IL-1β , and IL-18 [62] , [64] . Furthermore , a study in the P . chabaudi model shows that caspase-12 ( but not caspase-1 ) , modulates cytokine responses and development of acquired immunity [65] . Thus , our results indicate that bacterial superinfection overcome the regulatory role of caspase 12 . Our results demonstrate that P . chabaudi infection triggers low levels of IL-1β release in an inflammasome-independent manner . Nevertheless , the parasite-induced NLRP3 and NLRP12 inflammasomes play a key role in the release of high IL-1β levels and hypersensitivity to LPS during malaria sepsis . Importantly , the lethality induced by low dose LPS , peroral infection with S . typhimurium , or sublethal CLP in P . chabaudi infected mice was prevented or delayed by treatment with IL-1R antagonist . Relevant to our findings is the recently published study demonstrating that malaria impairs host resistance to Salmonella infection [11] . They propose that induction of heme oxygenase −1 ( HO-1 ) by Plasmodium infection limits the generation of reactive oxygen species ( ROS ) , an important mechanism of host resistance to Salmonella infection [66] . Another consequence of the decreased ROS generation during malaria would be the uncontrolled activation of caspase-1 and release copious amounts of active IL-1β by phagocytes , as previously reported in patients with chronic granulomatous disease ( CGD ) [67]–[69] . Similarly to murine malaria , extremely high levels of IL-1β are produced by PBMCs from P . vivax or P . falciparum malaria patients exposed to bacterial components [23] , [47] . Importantly , the bacterial load in mice undergoing malaria sepsis was not different from mice treated with IL-1RA , suggesting that the acute lethality caused by bacterial superinfection is due to the deleterious inflammatory response . Overall , our data argue that the IFN-γ , TNF-α and MyD88 role on hypersensitivity to septic shock during malaria is , at least in part , mediated by inflammasome-dependent release of IL-1β . In conclusion , years of research on malaria pathogenesis have funneled into the consensus that the clinical manifestations are often a result of the excessive activation of the innate immune cells . Recent reports have emphasized the important role of bacterial infections as co-factors for severe disease . Here we report that Plasmodium-induced NLRP3 , NLRP12 , ASC containing inflammasomes and caspase-1 activation , which are important events for the overwhelming IL-1β response and morbidity observed in bacterial superinfection during malaria sepsis .
The study with Plasmodium infected patients and healthy controls was approved by the Ethical Committee of Research ( CEP ) from the Research Center of Tropical Medicine ( CEP-CEPEM 096/09 ) ; the Brazilian National Committee of Research ( CONEP/Ministry of Health – 15653 ) ; as well as the Institutional Research Board from the University of Massachusetts Medical School ( UMMS ) ( IRB-ID11116_1 ) . Informed written consent was obtained before enrollment of all subjects ( Plasmodium infected patients and healthy control ) . All experiments involving animals were in accordance with guidelines set forth by the American Association for Laboratory Animal Science ( AALAS ) and with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) and the Federal Law 11 . 794 ( October 8 , 2008 ) . All protocols developed for this work were approved by the Institutional Animal Care and Use Committee ( IACUC ) at the UMMS ( ID - 2371-12 ) , ( ID - 1369-11 ) and also were approved by the Council of Animal Experimentation of Oswaldo Cruz Foundation ( CEUA protocol 38/10-3 ) . LPS O55:B55 from E . coli , nigericin and RIPA buffer were obtained from SIGMA . All mAbs used in flow cytometry , as well as 7AAD and PI were purchased from Ebiosciences . Active caspase-1 detection kit was obtained from ImmunoChemistry Technologies , LLC ( catalog no . 98 ) . Human ( SC-515 ) and mouse ( SC-514 ) anti-caspase-1 ( p10 ) , ASC ( SC-22514-R and SC-271054 ) primary antibodies were obtained from Santa Cruz Biotechnology and anti-actin ( A2066 ) was purchased from SIGMA . Anti-caspase-1 ( p20 ) was donated by Genentech . Anti-NLRP3 ( ab4207 and ab17267 ) , -NLRP12 ( ab64928 and ab57906 ) , -NLRC4 ( ab99860 ) , secondary Anti-rabbit IgG Texas Red ( ab6800 ) , secondary anti-goat IgG Texas Red ( ab6883 ) , secondary anti-goat IgG FITC ( ab6881 ) , and secondary anti-mouse IgG FITC ( ab7057 ) were purchased from Abcam . The secondary antibodies used in western blots were purchased from KPL . Ficoll-Paque from GE Healthcare , and the crosslinker disuccinimidyl suberate [70] was obtained from Thermo Scientific . Protease inhibitor ( EDTA free ) was purchased from Roche and RPMI and DMEM from Gibco . Cytokine ELISA kits and Cytometric Bead Array were obtained from R&D Systems and BD Biosciences , respectively . Flow cytometry mAbs for mouse were from BD Biosciences: CD11c-PE ( cat-557401 ) , CD11b-PE ( cat-557397 ) , CD3-FITC ( cat-553062 ) , B220-APC ( cat-561880 ) , and from Ebioscience: MHC-II-PercpCy5 ( cat-15-5321-82 ) , and F4/80-APC ( cat-17-4801-82 ) , CD4-PE ( cat-12-0041 ) , CD8-PE ( cat-12-008-81 ) , and NKG2d-PE ( cat-12-5872 ) . Flow cytometry mAbs for human were from BD Biosciences: CD16-FITC ( cat-555969 ) , CD14-APC ( cat-561708 ) , CD19-FITC ( cat-555412 ) ; and from Ebioscience: CD3-FITC ( cat-55332 ) , CD4-PE-Cy5 ( cat-555348 ) , CD8-PE-Cy5 ( cat-555368 ) , CD1c-APC ( cat-17-0015 ) , CD123-PercpCy5 . 5 ( cat-45-1239 ) , CD303-FITC ( cat-11-9818-42 ) , CD56-PE ( cat-9012-0567 ) . Patients with acute febrile malaria were seen in the outpatient malaria clinic in the Tropical Medicine Research Center in Porto Velho , Brazil . Patients infected with P . falciparum received a fixed dose of the artemeter ( 20 mg ) and lumefetrine ( 120 mg ) combination four times a day for three days . Patients infected with P . vivax were treated with chloroquine ( 150 mg ) every 8 hours for three days and primaquine ( 15 mg ) in a single dose per day for two weeks . Up to 100 cc of blood was obtained immediately after confirmation and differentiation of Plasmodium infection by a standard peripheral smear; and 30–40 days after therapy with confirmed parasitological cure by PCR . Non-infected subjects living in Porto Velho were also included in the study . The knockout mice , ASC−/− , NLRP3−/− , and NLRP12−/− mice were generated by Millennium Pharmaceuticals and backcrossed 8–11 generations to C57BL/6 background . MyD88−/− and NLRC4−/− mice were provided by S . Akira and R . Flavel . AIM2−/− mice were provided by K . Fitzgerald . C57BL/6 , IL-1R−/− , TNFR1−/− , P2X7R−/− and IFN-γ−/− mice were purchased from Jackson Laboratories . The caspase-1 knockout mice used in this work was originally provided by Dr . Flavell from Yale University School of Medicine . All mice used in experiments were 8–12 weeks of age and bred in isolated conditions in the animal house at CPqRR or at UMMS Animal Facility . The Plasmodium chabaudi chabaudi AS strain was used for experimental infections . This strain was kept in our laboratory as described elsewhere [71] . Briefly , P . chabaudi strain was maintained in C57BL/6 mice by passages once a week . For experimental infection mice were injected i . p . with 105 infected red blood cells and parasitemia followed every three days . Although animals exhibit signs of disease , lethal infection is uncommon . The course of parasitemia in WT mice was similar to that reported in other studies [23] , [72] , . For sub-lethal sepsis induced by CLP , mice were anesthetized , incision made on the anterior abdomen , cecal ligated and punctured two times with a 22-gauge needle . Bacterial load in exudates from the peritoneal cavity and blood 24 hours after CLP was evaluated on Mueller-Hinton agar dishes [74] . For Salmonella infections mice were inoculated intragastrically with Salmonella enterica serovar Typhimurium ( ATCC 14028 ) ( 108 cfu ) . Three days after infection , mice were euthanized , liver aseptically collected , weighed , and homogenized in sterile PBS ( 1∶10 , w/v ) . One hundred µl aliquots of serial decimal dilutions of liver homogenates and blood were plated onto MacConkey agar [75] . PBMCs were isolated from whole blood on Ficoll-paque Plus ( GE Healthcare ) . Cells were then plated into 96-well cell culture plates at a density of 2×105 in DMEM containing 10% FCS and 10 µg/ml ciprofloxacin . Supernatants were collected 24 hours after stimulation and used to determine the levels IL-1β . Monocytes were purified from PBMCs of P . vivax infected patients and healthy donors by using a kit based on immunomagnetic negative selection from StemCell Technologies ( catalog number 19058 ) . PBMCs from acutely infected patients were stained with combinations of the following mAbs: monocytes ( CD14/CD16 ) , T lymphocytes ( CD3+/CD4+ or CD3+/CD8+ ) , B lymphocytes ( CD19 ) , myeloid DCs ( CD1c+/CD19− ) , plasmacytoid DCs ( CD123+/CD303+ ) and NK cells ( CD3−/CD56+ ) . Splenocytes from infected mice were stained with combinations of the following mAbs: macrophages ( CD11b+/F4/80+ ) , DCs ( CD11c+/MHC-II+ ) , T lymphocytes ( CD3+/CD4+ or CD3+/CD8+ ) , B lymphocytes ( B220+ ) and NK cells ( NKG2d+ ) . To each sample , FLICA reagent and PI ( or 7AAD ) were added as indicated . The data were acquired using a LSRII cytometer . Ripa buffer ( 250 µl ) plus protease inhibitor cocktail from Roche were added to a pellet containing 4×107 splenocytes or PBMCs . After 15 minutes on ice , lysates were centrifuged at 13 , 000 g for 20 min at 4°C . The supernatants were separated in a 15%-acrylamide SDS-PAGE , transferred onto nitrocellulose membranes . The membranes were incubated with caspase-1 or pro-IL-1β specific antibodies , and then revealed with HRP-conjugated antibody and the ECL system from Amersham ( Bucks , UK ) . PBMCs were resuspended in a hypotonic solution ( 10 mM Hepes - pH 7 . 9 , 1 . 5 mM MgCl2 , 10 mM KCl , 0 . 2 mM PMSF , 0 . 5 mM DTT , protease inhibitor cocktail Roche ) , incubated on ice for 15 minutes , homogenized ( Kontes 22 mm ) and centrifuged for 8 minutes at 10 , 000 g . The pellets were resuspended in 500 µl of CHAPs buffer ( 20 mM HEPES-KOH - pH 7 . 5 , 5 mM MgCl2 , 0 . 5 mM EGTA , 0 . 1% CHAPs , 0 . 1 mM PMSF , and protease inhibitor cocktail from Roche ) and centrifuged for 8 minutes at 10 , 000 g . Finally , the pellets were resuspended in 200 µl of CHAPs buffer , 4 µl of a 100 mM DSS stock solution to a final concentration of 2 mM , and incubated for 30 min in the dark . The oligomers were resolved on a 12% SDS-PAGE and visualized by immunoblotting with an anti-ASC antibody ( SC-22514-R ) . Cells were fixed with paraformaldehyde 4% , permeabilized using Triton X-100 and stained with anti-NLRP3 ( FITC ) , anti-NLRP12 ( Texas Red ) , NLRC4 ( Texas Red ) and anti-AIM2 ( Texas Red ) . Images were acquired using a Zeiss LSM510 Microscope and analyzed by ImageJ software . Dual color images were acquired by consecutive scanning with only one laser line active per scan to avoid cross-excitation . Measurements of mouse cytokines were performed using commercially available ELISA Duoset kits from R&D Systems . The ranges of detection are 15 . 6–1000 pg/ml for IL-1β; and 31 . 2–2000 pg/ml for TNF-α and IFN-γ . Human IL-1β was detected by ELISA kit from Ebioscience in a range of 4 to 500 pg/ml . For mouse experiments we used splenocytes from 3 C57BL/6 and 3 MyD88−/− mice at 6 days p . i . with P . chabaudi or uninfected . Gene expression was accessed by microarray analysis using a gene chip from Affymetrix ( ∼23 , 000 transcripts ) . Genes were clustered by Tiger Multi Experiment Viewer software using the fold increase value obtained by the reason of the signal intensity values from infected vs . non-infected mice . Differences in gene expression between the 2 conditions were considered significant if p<0 . 05 as defined by unpaired t test . Detailed methodological and analysis for human microarrays are presented in Table S1 . Expression Omnibus ( http://www . ncbi . nlm . nih . gov/gds/ ) accession numbers are GSE35083 for mouse and GSE15221 for human microarrays . All data were analyzed using Graphpad Prism 5 . 0 Software . Cytokine measurements from human PBMCs were analyzed using two-tailed student's t test . Mann-Whitney testing was used for non-parametric analysis when data did not fit a Gaussian distribution . A p value≤0 . 05 was considered to be statistically significant .
|
Together Plasmodium falciparum and P . vivax infect approximately 250 million individuals , reaping life of near one million children every year . Extensive research on malaria pathogenesis has funneled into the consensus that the clinical manifestations are often a consequence of the systemic inflammation . Importantly , secondary bacterial and viral infections potentiate this inflammatory reaction being important co-factors for the development of severe disease . One of the hallmarks of malaria syndrome is the paroxysm , which is characterized by high fever associated with peak of parasitemia . In this study we dissected the mechanisms of induction and the importance of the pyrogenic cytokine , IL-1β in the pathogenesis of malaria . Our results demonstrate the critical role of the innate immune receptors named Toll-Like Receptors and inflammasome on induction , processing and release of active form of IL-1β during malaria . Importantly , we provide evidences that bacterial superinfection further potentiates the Plasmodium-induced systemic inflammation , leading to the release of bulk amounts of IL-1β and severe disease . Hence , this study uncovers new checkpoints that could be targeted for preventing systemic inflammation and severe malaria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunopathology",
"inflammation",
"immune",
"cells",
"cytokines",
"monocytes",
"immunity",
"innate",
"immunity",
"immunology",
"biology",
"immune",
"system"
] |
2014
|
Malaria-Induced NLRP12/NLRP3-Dependent Caspase-1 Activation Mediates Inflammation and Hypersensitivity to Bacterial Superinfection
|
Enteropathogenic Escherichia coli ( EPEC ) disease depends on the transfer of effector proteins into epithelia lining the human small intestine . EPEC E2348/69 has at least 20 effector genes of which six are located with the effector-delivery system genes on the Locus of Enterocyte Effacement ( LEE ) Pathogenicity Island . Our previous work implied that non-LEE-encoded ( Nle ) effectors possess functions that inhibit epithelial anti-microbial and inflammation-inducing responses by blocking NF-κB transcription factor activity . Indeed , screens by us and others have identified novel inhibitory mechanisms for NleC and NleH , with key co-operative functions for NleB1 and NleE1 . Here , we demonstrate that the LEE-encoded Translocated-intimin receptor ( Tir ) effector has a potent and specific ability to inhibit NF-κB activation . Indeed , biochemical , imaging and immunoprecipitation studies reveal a novel inhibitory mechanism whereby Tir interaction with cytoplasm-located TNFα receptor-associated factor ( TRAF ) adaptor proteins induces their proteasomal-independent degradation . Infection studies support this Tir-TRAF relationship but reveal that Tir , like NleC and NleH , has a non-essential contribution in EPEC's NF-κB inhibitory capacity linked to Tir's activity being suppressed by undefined EPEC factors . Infections in a disease-relevant intestinal model confirm key NF-κB inhibitory roles for the NleB1/NleE1 effectors , with other studies providing insights on host targets . The work not only reveals a second Intimin-independent property for Tir and a novel EPEC effector-mediated NF-κB inhibitory mechanism but also lends itself to speculations on the evolution of EPEC's capacity to inhibit NF-κB function .
The EPEC disease process depends on a protein delivery system , encoded by the Locus of Enterocyte Effacement ( LEE ) Pathogenicity Island , that transfers effector proteins directly into the cytoplasm of infected epithelia [1]–[3] . This delivery apparatus is composed of a Type Three Secretion System ( T3SS ) and a filamentous extension - formed by the EPEC secreted/signalling protein A ( EspA ) tipped by EspB/EspD to form a pore in the host plasma membrane - generating a conduit for transferring effectors into host cells [1] , [3] . The LEE region also encodes other factors , including the bacterial Intimin surface protein and six effectors: Translocated-Intimin receptor ( Tir ) , Mitochondrial-associated protein ( Map ) , EspF , EspG , EspH and EspZ ( with EspB also exhibiting effector functions ) [1] , [3] . Prototypic EPEC ( E2348/69 ) has at least fourteen additional non-LEE-encoded ( Nle or Esp nomenclature ) effector genes distributed on six horizontally-acquired mobile genetic elements [3] , [4] . The EPEC disease process is characterised by a number of histo-pathological events including ( i ) initial non-intimate attachment to epithelial cells , ( ii ) bacteria sinking into the microvillus surface , ( iii ) intimate interaction with the host plasma membrane , ( iv ) nucleation of actin beneath intimately-adherent bacteria and ( v ) extensive loss/effacement of microvilli [1]–[3] . Studies with the Caco2 small intestinal model have provided insights on these events and revealed a plausible mechanism to explain the rapid onset of EPEC-induced watery diarrhoea [5] . Moreover , the use of such models have uncovered EPEC's ability to disrupt cell-cell interactions [5]–[7] subsequently verified by in vivo studies [8] , [9] . While disruption of cell-cell interactions is an inflammatory event , human EPEC infections are normally associated with unexpectedly weak inflammation [2] thereby suggesting that the pathogen employs inhibitory mechanisms . Indeed , studies have revealed that EPEC inhibits Nuclear Factor κB ( NF-κB ) function - responsible for inducing the expression of anti-microbial and inflammation-related molecules - before barrier function is disrupted [10] . Host cell detection of foreign antigens ( by Toll-like receptors; TLR ) and cytokines ( such as TNFα and IL1β by TNFR and IL1R , respectively ) triggers a cascade of phosphorylation and ubiquitination events leading to IKK ( Inhibitor of KappaB kinase ) complex activation [11]–[13] . The activated IKK complex , composed of two kinases ( IKKα , IKKβ and a regulatory subunit ( NEMO/IKKγ ) , phosphorylates IκB ( Inhibitor of κB ) to induce its proteasomal-dependent degradation thereby releasing NF-κB for import into the nucleus to transcribe genes [14] . NF-κB function is regulated through many mechanisms including IκB re-synthesis , modification of NF-κB ( or accessory factors ) and altering NF-κB access to promoters [13]–[15] . In addition , NF-κB activity can be regulated at the level and/or function of signalling pathway components that includes kinases , phosphatases , ubiquitin ligases , de-ubiquitinases and adaptor proteins [13] , [16]–[18] . TLR , IL1R and TNFR signalling to the IKK complex depends on TNFα Receptor-Associated Factor ( TRAF ) adaptor proteins and TGFβ-Activating Kinase 1 ( TAK1 ) with pathway-specific components including kinases such as Receptor-Interacting Protein1 ( RIP1 ) and adaptors such as Myeloid Differentiation primary response gene ( 88 ) ( MyD88 ) [13] , [18]–[20] ( see Fig . 1 ) . EPEC inhibition of NF-κB activity triggered by flagellin ( recognised by TLR5 ) or cytokines ( including TNFα and IL1β ) in the Caco-2 small intestinal model depends on the pathogen possessing a functional effector-delivery system [10] , [21] . Previous work has argued against a need for all LEE effectors , Intimin and a subset of Nle effectors ( Orf3/EspG2 , NleA , NleF and NleH ) in this EPEC NF-κB inhibitory process thereby implicating other Nle effectors [10] . Indeed , screening programs by us and others have revealed novel NF-κB inhibitory activities for NleC and NleH with critical co-operative roles for NleE1 and NleB1 [22]–[29] . Here , we show that the LEE-encoded Translocated-Intimin receptor has a potent and specific Intimin-independent ability to inhibit NF-κB activation leading to the discovery of a novel inhibitory mechanism . Moreover , our work provides insights on the NleE1/NleB1 subversive process and on the possible evolution of EPEC's capacity to inhibit NF-κB activity .
EPEC requires a functional T3SS to inhibit antigens ( such as flagellin ) and cytokines ( such as TNFα and IL1β ) from activating NF-κB [10] , [21] . To gain insight on the inhibitory mechanism , we examined EPEC's ability to interfere with NF-κB activity driven by plasmid-expression of TLR/IL1R and TNFR signalling pathway components ( see Fig . 1 ) . Thus , HeLa cells were co-transfected with plasmids expressing luciferase under the transcriptional control of NF-κB - via five repeats of the κB consensus promoter [30] - and specific signalling pathway components [30]–[32] prior to infecting with EPEC strains and quantifying luciferase cellular activity ( see Materials and Methods ) . As EPEC can induce HeLa cells detachment [33] , we employed the eae mutant ( which lacks the Intimin surface protein ) that , like wild type EPEC , inhibits NF-κB activity [10] but has little capacity to detach HeLa cells ( Quitard et al . , unpublished ) . The luciferase assay revealed basal NF-κB activity within uninfected HeLa cells as reported [23] , with no significant change following infection with the eae or T3SS ( effector-delivery defective ) strains ( Fig . 1A ) . Plasmid expression of TNFR pathway-related kinase ( IKKα , IKKβ , TAK1 and RIP1 ) or adaptor ( TRAF2 ) proteins increased cellular luciferase levels by 8 to 24 fold . Interestingly , the T3SS mutant inhibited increases driven by plasmid-expressed RIP-1 ( Fig . 1A ) supporting the presence of T3SS-independent inhibitory mechanisms in the HeLa model [28] , [29] . By contrast , the eae mutant infection inhibited luciferase activity driven by plasmid-expression of all components except IKKα and IKKβ ( Fig . 1A ) . Parallel studies on TLR/IL1R pathway components revealed another T3SS-independent inhibitory mechanism relating to MyD88 ( Fig . 1B ) , with the eae mutant inhibiting signalling driven by plasmid-expressed TRAF6 and MyD88 ( Fig . 1B ) . Western blot analyses verified the T3SS-dependent delivery of effectors ( EspF and Tir; not shown ) and plasmid-expression of examined host proteins , with the latter revealing unexpected infection-related increases in the cellular level of Flag-tagged TRAF proteins ( Fig . 1C ) . Thus , consistent with previous studies [28] , [29] , EPEC infection of HeLa cells inhibits signalling to NF-κB by T3SS-independent and -dependent mechanisms . Moreover , the work implies that T3SS-dependent inhibitory mechanism ( s ) relates to effector ( s ) acting at or upstream of the IKKα/β complex . To identify Nle effectors postulated to inhibit NF-κB activity [10] , putative nle effector genes [3] , [4] were cloned into mammalian expression vectors and co-transfected into HeLa cells with the NF-κB luciferase reporter vector for screening . Indeed , this approach identified a NF-κB inhibitory activity for NleC leading to its definition as a zinc metalloprotease that degrades NF-κB complexes [23] as supported by independent studies [22] , [24] , [25] . Interestingly , inclusion of an available Tir-expressing construct [34] in the screening program indicated that this LEE effector could prevent TNFα from activating NF-κB . To investigate this putative NF-κB inhibitory activity in more detail , the tir gene was sub-cloned into pEGFP-N1 to generate a Tir-eGFP fusion protein with pEGFP-N1 serving as a negative control . Fig . 2A reveals similar basal luciferase activity in cells transfected with pEGFP or ptir-EGFP plasmids , with TNFα leading to a significant increase in NF-κB reporter activity for pEGFP , but not ptir-EGFP transfected cells . The relevance of this finding to NF-κB function was illustrated by Western blot analysis where expression of Tir-eGFP , unlike eGFP , inhibited TNFα from inducing the phosphorylation-associated activation of IKKα/β kinases and the NF-κB component , p65 ( Fig . 2B ) . Inhibition specificity was illustrated by unaltered total cellular levels of IKKα/β and p65 proteins ( Fig . 2B ) . Furthermore , fluorescent microscopy examinations revealed p65 within the nucleus of ∼18% of eGFP or Tir-eGFP expressing cells , with TNFα treatment increasing this to ∼70% for eGFP expressing cells but only ∼35% for Tir-eGFP expressing cells ( Fig . 2C ) . As IL8 secretion requires NF-κB activity [35] , we examined the extra-cellular levels of this chemokine ( see Materials and Methods ) . Consistent with the luciferase NF-κB reporter data ( Fig . 2A ) , pEGFP and ptir-EGFP transfected cells released similar basal levels of IL8 , with TNFα treatment increasing IL8 secretion levels from pEGFP but not ptir-EGFP transfected cells ( Fig . 2D ) . Thus , expressing Tir-eGFP within HeLa cells specifically prevents TNFα from transducing signals that activate NF-κB in a manner linked to a blockage in the phosphorylation-associated activation of IKK components needed to release NF-κB for nuclear import . The absence of other EPEC factors in these experiments illustrate that this novel property of the Translocated-Intimin receptor ( Tir ) effectors occurs independently of Intimin . To support the specific and Intimin-independent nature of the Tir inhibitory activity , studies evaluated Tir's ability to block TNFα-induced IL8 secretion following its delivery into HeLa cells by Yersinia pseudotuberculosis as previously described [34] . Importantly , the control Tir-negative Yersinia strain ( which lacks most of its own T3SS-delivered effectors ) failed to inhibit TNFα-induced IL8 secretion , whereas the Tir-expressing variant inhibited this process to a similar degree as EPEC-delivered effectors ( Fig . 3 ) . Western blot analyses verified Yersinia-delivery of Tir where it underwent partial host kinase-mediated modification , compared to EPEC-delivered Tir ( not shown ) , as previously described [34] . Given that TNFα augmentation of IL8 secretion requires NF-κB activity [35] , this work supports the premise that Tir ( in the absence of other EPEC factors , including Intimin ) possesses a potent and specific ability to prevent TNFR-induced signalling from activating NF-κB . To gain insight on how Tir inhibits TNFα-induced NF-κB activation , HeLa cells were co-transfected with plasmids encoding ( i ) the NF-κB luciferase reporter protein , ( ii ) TNFR signalling pathway components and ( iii ) eGFP or Tir-eGFP proteins prior to assaying cellular luciferase levels . This work revealed that Tir-eGFP , but not eGFP , inhibited luciferase activity driven by plasmid-expression of TRAF2 and RIP1 , but not TAK1 , IKKβ ( Fig . 4A ) or IKKα ( not shown ) . Fluorescence microscopy studies were undertaken to determine the cellular location of over-expressed signalling components and to assess if Tir-eGFP expression induced detectable changes . Staining for plasmid-expressed Tir and IKK kinase proteins revealed diffuse cytoplasmic signals in contrast to cytoplasmic aggregates/clusters for the TRAF2 ( Fig . 4B and Fig . S1 ) and MyD88 ( not shown ) adaptor proteins . Cytoplasmic clustering of plasmid-expressed TRAF2 has been reported [36] . Imaging of co-transfected cells revealed similar , distinct and partially-overlapping signals for Tir/IKK , Tir/MyD88 and Tir/TRAF signals , respectively ( Fig . 4B and Fig . S1 ) . Intriguingly , Tir-eGFP expression was associated with a loss of TRAF clusters ( Fig . S1B ) as supported by quantification studies ( Fig . 4C ) . Thus , Tir may inhibit plasmid-expressed TRAF2 from transmitting signalling to NF-κB by inducing the disaggregation and/or degradation of activation-associated clusters . To examine predicted Tir-TRAF interactions , GFP-Trap beads were used to isolate eGFP and Tir-eGFP proteins from cells co-transfected with the Flag-tagged TRAF2 expressing plasmid . Fig . 5A reveals eGFP and Tir-eGFP within input cellular extracts and their isolation by the GFP-Trap beads . While the IKKα/β proteins were present in the input pool , they did not co-isolate with eGFP or Tir-eGFP ( Fig . 5A ) . Probing for Flag-tagged TRAF2 revealed a prominent monomer-sized band with smaller amounts of a trimer-sized TRAF species in the input pool ( Fig . 5A ) . TRAF2 function is linked to the formation of homo- or hetero-trimers [13] , [37] . Intriguingly , the minor trimer-sized TRAF2 species preferentially isolated with Tir-eGFP , though some monomer was co-isolated ( Fig . 5A ) . This work suggests that Tir interacts ( either directly or indirectly ) with the activation-associated multimeric form of TRAF adaptor proteins . Examination of the input samples ( Fig . 5A ) suggested that Tir expression may decrease the cellular levels of multimeric ( and perhaps monomeric ) Flag-tagged TRAF2 protein ( Fig . 5A ) . This premise was supported by demonstrating that co-expression of Tir-eGFP with Flag-tagged TRAF2 could lead to the complete loss of TRAF2 from cell extracts , while the actin loading control protein remained unchanged ( Fig . 5B ) . As inflammatory signalling is commonly regulated by targeting components for proteasomal-dependent degradation [38] , we used the proteasomal inhibitor MG132 . Whilst MG132 inhibitory activity was confirmed , as per a parallel study [23] , it failed to prevent the Tir-mediated loss of Flag-tagged TRAF2 proteins ( Fig . 5B ) . Interestingly , probing the fate of endogenous TRAF2 suggested that it is not a substrate for Tir degradation , at least in cells expressing the Flag-tagged TRAF2 variant . Thus , similar levels of monomer and trimer-sized TRAF2 bands were evident in non-transfected and ptir-EGFP transfected cells ( Fig . 5C ) while the more prominent bands in pTRAF2-transfected cells correspond to the Flag-tagged variant ( Fig . 5C versus 5B ) . To investigate whether endogenous TRAF2 is a substrate for Tir-induced degradation , its fate was examined in cells that express Tir-eGFP , but not Flag-tagged TRAF2 proteins . Fig . 5D reveals that Tir expression can , in fact , induce the cellular loss of endogenous TRAF2 . Interestingly , TNFα treatment of control cells reduced the level of multimeric TRAF2 with an increase in the monomer species ( Fig . 5D ) that , presumably , reflects intrinsic host mechanism ( s ) for down-regulating cytokine-induced signalling . By contrast , TNFα treatment of ptir-EGFP transfected cells produced a small pool of TRAF2 protein ( monomer and trimer-sized forms; Fig . 5D ) that may explain why TNFα triggered some p65 relocation to the nucleus of Tir-eGFP expressing cells ( Fig . 2C ) . Ubiquitin-modified TRAF2 plays a key role in activating RIP1 which activates TAK1 [39] to , perhaps , explain why Tir inhibits NF-κB luciferase activity driven by plasmid-expressed RIP1 and , to a lesser extent , TAK1 ( Fig . 4A ) . Collectively , the work implies that Tir inhibits TNFα-induced NF-κB activation by interacting ( directly or indirectly ) with TRAF2 - a key component of the TNFR signalling pathway - to induce its proteasomal-independent degradation . Previous work [10] suggested that the Tir and NleH effectors are not required for EPEC to inhibit NF-κB activity , with recent studies reporting a non-essential role not only for NleH but also NleC [22κ27] . Indeed , HeLa cells infections with a tir mutant confirmed Tir's non-essential role in inhibiting TNFα-induced IL8 secretion [10] but also revealed a small , but statistically significant defect ( Fig . 6A ) . To examine the relationship of this defect to the absence of Tir/Intimin-mediated intimate EPEC-host cell interaction , assays were carried out with an eae ( Intimin-deficient ) mutant . Unexpectedly , these studies indicated that Intimin ( indirectly or directly ) induces NF-κB activity or suppresses effector-mediated inhibitory mechanism ( s ) , as the eae mutant inhibited TNFα-induced IL8 secretion to a greater extent than wild type EPEC ( Fig . 6A ) . Intimin alters host cellular processes by Tir-dependent and -independent mechanisms [40] . Infection studies with an eaetir double mutant suggest that this Intimin function relates to Tir-dependent and -independent mechanisms ( Fig . 6A ) . Interestingly , time course infection studies support a Tir-TRAF2 relationship as EPEC induced a dramatic loss in the levels of activation-associated multimeric TRAF2 proteins by a process dependent on Tir and Intimin ( Fig . 6B ) . The Intimin-dependent nature of this event , in contrast to that mediated by ectopically-expressed Tir ( Fig . 2 ) , implies that EPEC has evolved Intimin-dependent mechanisms for regulating this Tir activity . Interestingly , other effectors appear to contribute at early time points , with the eae mutant appearing to display an augmented ability to reduce TRAF2 levels ( Fig . 6B ) . Intriguingly , confocal microscopy studies of disease-relevant polarised cells infected with EPEC only detect Tir at the apical ( surface ) membrane whereas a transient pool is evident within the cytoplasm of eae mutant-infected cells ( Fig . S2 ) . This suggests that Intimin promotes Tir's rapid association with the plasma membrane , with the transient cytoplasmic pool perhaps promoting Tir-TRAF2 interactions to explain the eae mutant's Tir-dependent augmented ability to reduce the level of TRAF2 multimers and inhibit TNFα-induced IL8 secretion . Recent studies have described a prominent role for NleE1 , promoted by NleB1 or NleC , in the EPEC NF-κB inhibitory process , as nleCnleE1 and nleB1nleE1 double mutants behaved like a T3SS-defective strain , compared with partial defects for single mutants [22] , [25] , [28] , [29] . However , we and others have described T3SS-independent inhibitory mechanisms in the employed HeLa cell models ( Fig . 1 ) [28] , [29] that may obscure the contribution of effectors ( Fig . 1 ) [28] , [29] . Thus , an nleB1nleE1 double mutant was generated and evaluated in a small intestinal model where EPEC was confirmed to inhibit NF-κB function solely in a T3SS-dependent manner [10] ( Fig . 6C ) . Indeed , the nleB1nleE1 double mutant behaved akin to the effector-delivery defective ( T3SS ) strain ( Fig . 6C ) despite displaying no obvious defect in delivering EspB or Tir effectors ( not shown ) . By contrast , a nleB1nleE1tir triple mutant displayed a small ( significant ) capacity to inhibit NF-κB function - presumably due to remaining effectors . Interestingly , while EPEC and T3SS-mutant infected cells released ∼200 and ∼800 ng/ml of IL8 , respectively , in response to TNFα treatment only ∼400ng/ml was secreted from tir mutant infected cells ( Fig . 6C ) . Whilst these IL8 values support a non-essential contributory role for Tir in the inhibitory process , the difference between EPEC- and tir-infected cell was below the significance threshold ( p = 0 . 075 ) . Nevertheless , this work supports the idea that the NleB1/NleE1 effectors play a central role in enabling EPEC to inhibit NF-κB activity in intestinal cells , with the non-essential novel NF-κB inhibitory activities of NleC , NleH and Tir , presumably , playing evolutionary-advantageous roles in EPEC's lifecycle . Given NleE1 and NleB1's key roles in the EPEC NF-κB inhibitory process , with only speculations on their targets [28] , [29] , we investigated where the blockage occurred by co-expressing them with signalling pathway components for NF-κB luciferase reporter assays . Expression of NleE1 inhibited NF-κB reporter activity driven by plasmid-expression of components from the TNFR ( TRAF2 , RIP1 ) and IL1R/TLR ( TRAF6 , MyD88 ) pathways ( Fig . 7A ) consistent with reports of it inhibiting NF-κB activation by multiple pathways [28] , [29] . However , NleE1 failed to inhibit luciferase activity driven by plasmid-expression of TAK1 or IKK kinases ( Fig . 7A ) suggesting it inhibits TAK1 function to block signalling by TNFR , IL1R and TLR pathways . NleE1 may target TAK1 or factors needed for its activation , such as the TAB2/3 proteins which recruit TAK1 to ubiquitin-modified RIP1 and ubiquitin-modified TRAF6 proteins for activation in the TNFR and TLR/IL1R pathways , respectively [39] . By contrast , NleB1 inhibited luciferase activity driven by plasmid-expression of TRAF2 but not TRAF6 or IKKβ ( Fig . 7B ) supporting reports of it inhibiting signalling in TNFR but not TLR/IL1R pathways [28] , [29] . Interestingly , NleB increased luciferase activity driven by TRAF6 suggesting that it has functions that ( directly or indirectly ) activate TRAF6-mediated signalling . Increases in TRAF6 signalling , despite NleB inhibition of TAK1 function ( Fig . 7B ) , suggest that the effector may block RIP1-mediated activation of TAK1 to inhibit signalling in TNFR , but not TLR/IL1R pathways [39] . Interestingly , our screening program revealed that the NleE1 homologue , NleE2 , induced NF-κB luciferase activity as effectively as TNFα ( Fig . 7C ) leading to a similar high level of p65 relocation into the nucleus ( Fig . 7D ) . Indeed , both these NleE2-dependent alterations were inhibited by co-expressing Tir ( Fig . 7C and 7D ) suggesting that NleE2 activates NF-κB ( either directly or indirectly ) through a component at or upstream of Tir's target i . e . TRAF . While NleE2 is apparently not transferred into host cells [28] , our finding supports the idea that EPEC effectors have features or functions that can activate NF-κB signalling and can be blocked by Tir .
In this study we describe a new property for the most-extensively studied EPEC effector by demonstrating that the Translocated Intimin receptor ( Tir ) protein has a potent and specific ability to prevent HeLa cells from activating NF-κB in response to the cytokine TNFα . Whilst this discovery involved ectopic expression of Tir and an indirect NF-κB reporter assay , its relationship to transcription factor function was demonstrated by several lines of evidence . Firstly , expression of Tir-eGFP , unlike eGFP , blocked the phosphorylation-associated activation of IKKα/β and the NF-κB component , p65 - events required for the nuclear import and transcriptional activity of NF-κB , respectively [13] . Importantly , absence of these modifications was not due to cell loss as Tir-eGFP expression had no observable impact on the total cellular level of IKK kinases or p65 . Secondly , epifluorescent microscopy studies revealed that TNFα treatment induced some relocation of p65 into the nucleus of Tir-eGFP expressing cells but to a dramatically less degree than eGFP expressing cells . Indeed , as expected , these Tir-mediated inhibitory events translated into a dramatic deficiency in the NF-κB dependent event [35] of TNFα-augmented increases in IL8 secretion levels . Thirdly , use of a more physiologically relevant mechanism of introducing Tir into host cells ( via the T3SS of another pathogen , Yersinia ) inhibited TNFα-induced IL8 secretion to a similar level as control EPEC-infected cells , whereas the Tir-negative Yersinia strain had no inhibitory capacity . Finally , epifluorescent , biochemical and co-precipitation studies unearthed an inhibitory mechanism relating to Tir interaction with and subsequent cellular loss of a key component from the TNFR signalling pathway . These data illustrate that the EPEC Tir effector has a specific and potent ability to inhibit TNFα-induced NF-κB activation . The absence of additional EPEC factors in these ectopic and Yersinia-delivery Tir experiments illustrate the Intimin-independent nature of the NF-κB inhibitory process . Whilst over a decade of studies has re-enforced the idea that Tir's subversive activities require it to interact with the EPEC surface protein , Intimin ( and , thus , Tir's need to insert into the plasma membrane to act as a receptor for Intimin ) a recent study described an Intimin-independent function [41] . Our discovery of a second such activity raises the possibility that Tir possesses additional Intimin-independent functions and the need to consider their contribution to Tir's critical role in the virulence of attaching and effacing pathogens that include strains targeting humans ( EPEC and enterohaemorrhagic E . coli; EHEC ) , ruminants ( EHEC ) and various small mammals . Our work also revealed a novel mechanism for a pathogen effector to inhibit NF-κB activity as it demonstrated that Tir interacts , directly or indirectly , with TRAF2 proteins ( with a preference for activation-associated multimers ) inducing the proteasomal-independent loss of this adaptor protein from host cells . TRAF adaptor proteins play critical roles in signalling to NF-κB by multiple pathways including the TLR , TNFR and IL1R pathways inhibited by EPEC [10 , 13 , 19κ21] . Indeed , Tir inhibited NF-κB reporter activity driven by plasmid-expression of the TRAF2 ( Fig . 4 ) and TRAF6 ( not shown ) proteins of the TNFR and TLR/IL1R pathways , respectively . Moreover , TRAF2 and TRAF5 possess functionally redundant roles in TNFR-mediated signalling to NF-κB [37] suggesting that Tir also inhibits TRAF5 activity . It is speculated that Tir can inhibit signalling through other TRAF-dependent pathways . Six of the seven TRAF members carry amino-terminal zinc-binding motifs involved in their function as E3 ubiquitin ligases for activating downstream kinases , while TRAF1 ( lacks the ‘Really Interesting New Gene’ RING domain ) has regulatory functions [37] , [42] . Studies with a dominant-negative variant of TRAF2 [43] suggests that Tir is unable to induce its degradation ( Fig . S3 ) implicating a need for the absent RING domain in the degradation process . There are several examples of pathogens targeting TRAF proteins , including the poxvirus MC159 protein preventing TRAF2 sequestration into a signalosome [44] and the Yersinia YopJ effector deubiquitinating TRAF2 to inhibit signalling to NF-κB [45] . The Yersinia strain used in our studies has no detectable YopJ activity ( linked to a polar insertion mutation of the ypkA gene immediately upstream of yopJ; Prof Hans Wolf-Watz personal communication ) . As far as we are aware , the proteasomal-independent degradation of TRAF2 by Tir represents a novel pathogen-mediated mechanism for inhibiting NF-κB activity . Determining if Tir induces the cellular loss of all or a subset of TRAF members may provide insights on the breadth of cytokine- and antigen-signalling pathways it can inhibit and/or highlight conserved features involved in the TRAF interaction and/or degradation processes . Studies are underway to define the features and mechanism by which Tir induces the proteasomal-independent degradation of TRAF adaptors . Consistent with previous findings [10] , Tir was not required for EPEC to inhibit TNFα-induced NF-κB activation though infection studies revealed a small , but statistically significant inhibitory defect for a Tir-deficient strain . This defect was unlinked to Tir's role with Intimin in mediating intimate EPEC-host cell interactions , as an Intimin-deficient ( eae ) mutant inhibited NF-κB activity to a greater extent than EPEC . This Intimin-related activity was associated with Tir-dependent and -independent mechanisms thereby revealing a new property for this EPEC surface protein . Intriguingly , strains lacking Intimin or Tir displayed a dramatic deficiency in EPEC's ability to decrease cellular levels of activation-associated TRAF2 multimers suggesting that , in the context of an EPEC infection , Tir requires Intimin to reduce TRAF2 cellular levels . Interestingly , microscopy studies identified a transient pool of Tir within the cytoplasm of epithelia infected with the Intimin-deficient , but not wildtype EPEC strain . While Tir has been proposed to insert into the plasma membrane during the translocation process [46] , it is clear that it can insert from the host cytoplasm [47] , though an infection-associated cytoplasmic pool has , until now , only been supported by Western blot analyses [34] , [48]–[50] . It appears that Tir delivery into the host cytoplasm is normally followed by its rapid ( Intimin signalling-promoted ) association with the plasma membrane . The extended presence of Tir within the cytoplasm of eae-mutant infected cells may promote Tir-mediated loss of activation-associated TRAF2 multimers , as supported by the time course studies ( Fig . 6B ) , to perhaps explain the Tir-dependent increased capacity of the eae mutant to inhibit TNFα-induced IL8 secretion . While Tir's non-essential role in the EPEC NF-κB inhibitory process , like that of NleC and NleH [22]–[27] , could be due to functional redundancy with other effectors , this is not the case as illustrated by studies with an nleB1nleE1 double mutant . Thus , despite displaying no defect in delivering Tir into host cells , the double mutant had no significant ability to inhibit TNFα-induced IL8 secretion in HeLa or small intestinal models . This finding supports the reported key role for the NleE1/NleB1 effectors in blocking NF-κB function [25] , [28] , [29] and implies that the described novel NF-κB inhibitory activities of Tir , NleH and NleC effectors [22]–[27] ( this study ) are minor or transient during EPEC infections . As Yersinia-delivered and ectopically-expressed Tir proteins are potent inhibitors of TNFα-induced NF-κB activity , unlike EPEC-delivered Tir , this implies that EPEC possesses factors that suppress Tir's inhibitory function . Interestingly , ectopically-expressed Tir decreases TRAF2 cellular levels in an Intimin-independent manner ( Fig . 5 ) while decreases mediated by EPEC-delivered Tir depend on Intimin ( Fig . 6B ) . Ectopically-expressed ( and Yersinia-delivered ) Tir differs from the EPEC-delivered Tir by i ) being mainly cytoplasmic ii ) only undergoing partial host kinase-mediated modification and iii ) failing to interact with Intimin [34] ( Fig . S3 ) . Thus , EPEC suppression of Tir's ability to decrease TRAF2 cellular levels ( and presumably its ability to inhibit NF-κB activity ) is linked to undefined EPEC factors enabling Tir to insert into the plasma membrane to interact with Intimin . Further studies are required to define the putative EPEC factors and mechanisms involved in this regulatory process . Our screening program revealed NF-κB inhibitory activities for NleC [23] and Tir ( this study ) , a NF-κB activatory function of NleE2 ( this study ) and confirmed inhibitory activities [22]–[29] for NleH ( not shown ) , NleE1 ( this study ) and NleB1 ( this study ) effectors . By contrast , no significant NF-κB modulatory activity was evident from screening other LEE or non-LEE effectors , though it is possible that the findings included false negatives due to effector expression problems or expressing effector-fusion proteins . Indeed , our work supports the premise [22]–[27] that NleC and NleH inhibit NF-κB function by targeting components downstream of the IKK complex [23] ( not shown ) , with the inhibitory activities of NleE1 , NleB1 and Tir linked , respectively , to blocking the function of the TAK1 , RIP1 and TRAF components upstream of IKK . Interestingly , while NleB1 inhibited signalling by TNFR pathway components , it promoted that mediated by the TLR/ILIR pathway protein , TRAF6 ( but not downstream RIP1 ) suggesting that it has properties that induce TRAF6-mediated NF-κB activation . Indeed , the idea that EPEC effector features or properties can activate NF-κB is supported by the finding that ectopically-expressed NleE2 was as effective as TNFα at inducing p65 nuclear relocation . Interestingly , this NleE2-mediated event was blocked by co-expression of Tir suggesting that its activatory property , as per NleB1 , is transmitted through TRAF proteins - a defined target of Tir to inhibiting signalling to NF-κB . Our findings on LEE and Nle effectors , in light of published work , lend themselves to speculations on the evolution of EPEC's capacity to inhibit NF-κB . Genome sequencing projects suggest that pathogenic E . coli evolved from commensal E . coli through the horizontal-acquisition of new functions encoded on mobile genetic elements . Thus , enterotoxigenic E . coli ( ETEC ) virulence is linked to strains acquiring functional enterotoxins and an enterocyte-binding pilus [51] , whilst that of EPEC is linked to the acquisition of the effector-encoding mobile genetic elements [4] , [52] . As Nle effector genes are generally missing from non-pathogenic E . coli strains and require the LEE T3SS for delivery into host cells , it is reasonable to assume that the progenitor EPEC strain possessed the LEE , but not Nle-encoding genetic elements . EPEC factors ( including flagella ) and LEE subversive functions ( eg disrupting cell-cell interactions ) can activate NF-κB to induce the expression of anti-microbial and inflammatory molecules that inhibit EPEC's virulence-critical ability to colonise epithelia [10] , [21] . Thus , the LEE region presumably encoded factor ( s ) to inhibit this event , with our screening program defining Tir as the only LEE effector with significant NF-κB inhibitory activity . Indeed , our definition of Tir-TRAF interactions within the cytoplasm to inhibit NF-κB activity may explain why Tir transits through this host compartment prior to inserting into the host membrane . It is possible that Tir's inability to completely block signalling-induced relocation of NF-κB into the nucleus ( Fig . 2C ) provided a selective advantage to strains acquiring mobile genetic elements expressing effectors that promote the inhibitory process ( eg NleC degradation of nuclear NF-κB complexes ) . Undoubtedly , the key point in the evolutionary process relates to the acquisition of the NleB1/NleE1-encoding mobile genetic element ( Integrative element 6; IE6 ) given their critical roles in blocking NF-κB activation in HeLa cells [25] , [28] , [29] and disease-relevant small intestinal models . This premise is supported by the nleB1nleE1 genes being among the subset of nle genes found in all sequenced LEE-encoding pathogens [52] and their presence at the 3′ end of the LEE region in some enterohemorrhagic E . coli strains [53] . It is possible that this LEE/IE6 hybrid represents a minimal genetic unit required to provide strains with EPEC-like enteric pathogenic properties .
These studies used nalidixic acid resistant EPEC strains , specifically wild-type EPEC ( E2348/69 ) , eae ( Intimin-deficient ) and espA ( T3SS-deficient ) isogenic strains [54] , [55] . The nleB1nleE1 double mutant was generated using described standard allelic exchange procedures [56] , [57] to remove ( confirmed by PCR analyses ) the entire gene sequence ( and inter-gene region ) of the adjacent nleB1 and nleE1 genes . The nleB1nleE1tir triple mutant was generated using an available tir-deletion suicide vector as described [57] . Strains were grown in Luria-Bertani ( LB ) broth containing nalidixic acid ( 25 ug/ml final conc . ) from single colonies , without shaking , at 37°C in a 5% CO2 incubator overnight . The Yersinia strains and their usage was as previously described [34] . Hela cells ( ATCC CCL2 ) were grown at 37°C with 5% CO2 in Dulbecco's Minimal Eagles Medium ( DMEM ) supplemented with 10% heat-inactivated foetal calf serum and 2 mM L-glutamine . Caco2 parental or TC7 subclone cells were seeded at confluence onto Transwells ( Corning ) and polarised over 12–15 days as previously described [5] , [6] . Prof Luke O'Neill ( Trinity College , Dublin ) kindly provided plasmids relating to the NF-κB luciferase-reporter construct and expression of IKKα , IKKβ , TRAF2 , TRAF6 , and MyD88 [30] with those for RIP1 [31] and TAK1 [32] kindly provided by Prof's Jürg Tschopp ( University of Lausanne , Switzerland ) and Martin Dorf ( Harvard , USA ) , respectively . Tir-eGFP , eGFP-NleB1 , NleE1 and NleE2 proteins were expressed from pEGFP-N1 ( Clontech ) , pEGFP-C1 ( Clontech ) , pIRES ( Clontech ) and pcDNA3 ( Invitrogen ) vectors , respectively . Hela cells ( ∼2×105 ) seeded in 24-well plates were transfected the following day using JetPrime reagent ( PEQLAB Ltd , UK ) with a total amount of 250 ng DNA , comprising 100 ng of the NF-κB firefly luciferase reporter plasmid [30] , 40 ng of the Renilla reniformis luciferase plasmid plus 110 ng of empty , or effector gene-containing plasmid . Levels of firefly luciferase expression were normalised against Renilla luciferase activity as a control for transfection efficiency ( expressed as fold increase in luciferase activity over unstimulated control cells ) . When transfection efficiency was routinely found to be ∼65–80% , the Renilla luciferase plasmid was replaced with empty plasmid . High transfection efficiencies for pEGFP and/or pEGFP-tir experiments were routinely verified by visualising the eGFP signal . Twenty four hours post transfection , cells were incubated with or without TNFα ( 10 ng/ml ) for 30 minutes , lysed in 100 µl of passive lysis buffer ( Promega Ltd , Southampton , UK ) for 15 minutes at room temperature with cell extracts taken for assessment of firefly luciferase activity following standard protocols and a FLUOstar Optima 413-3266 plate reader ( BMG Labtech , Germany ) . LB grown EPEC cultures were first diluted ( 1∶10 ) in DMEM and incubated for 3 hours at 37°C in a 5% CO2 incubator . The typical optical density ( OD600 ) was between 0 . 2–0 . 3 with infections carried out at a multiplicity of infection , MOI , of ∼100∶1 . The HeLa cell medium was replaced with DMEM at least 2 hours prior to infection ( routinely 3 hours unless stated otherwise ) , with studies on transfected cells normally 24 hours post-transfection . Yersinia YIII MEKA strains were grown in modified brain-heart medium supplemented with 20 mM MgCl2 and 5 mM EGTA at 26°C without shaking , and used for infections as previously described [34] . When appropriate , cells were incubated with bactericidal levels of gentamycin ( 100 µg/ml final conc . ) for 1 hour prior to adding TNFα ( 10 ng/ml ) for between 30 minutes ( the routine ) and up to 2 hours . EPEC infections did not induce significant cell detachment under the employed experimental conditions . HeLa cells were washed with cold Phosphate Buffered Saline pH 7 . 4 ( PBS ) and lysed with 1% Triton X-100 in the presence of protease inhibitors ( 1/1000 dilution , Sigma cocktail ) , sodium fluoride , sodium orthovanadate and PMSF ( 1 . 2 , 1 . 2 and 1 mM final concentration , respectively ) . When appropriate centrifugation ( 13000 x g 5 minutes ) was used to separate insoluble ( contains host nuclei and cytoskeleton as well as adherent bacteria ) and soluble ( contains host cytoplasmic and membrane proteins as well as T3SS-delivered proteins ) . Samples were resolved on 10% SDS PAGE , transferred onto nitrocellulose , blocked in 5% Blotto milk powder/PBS/0 . 02% Tween and probed with antibodies against IKKα/β ( Santa Cruz ) , phospho IKKα/β ( Cell Signaling ) , NF-κB p65 ( Santa Cruz ) , phospho p65 ( Ser536 ) , TRAF2 ( Cell Signaling ) , actin ( Sigma ) , FLAG tag ( Sigma ) , Myc tag ( generous gift; Prof D . Mann , Newcastle University ) or GFP ( Zymed ) . Absence of reducing agents allowed the detection of TRAF2 multimeric bands . Primary antibodies were incubated overnight in a 5% bovine serum albumin ( BSA ) /PBS solution , washed extensively . Bound antibodies were detected using horseradish peroxidase-conjugated secondary antibodies and Super Signal West Pico chemiluminescent substrate ( Pierce ) with Hyperfilm ECL ( Amersham Biosciences ) following the manufacturer's recommendations . Supernatants ( 0 . 5 ml ) were taken from above the HeLa cells and assayed for the level of IL8 using an ELISA kit ( DB Biosciences ) following the manufacturer's recommendations . Immunoprecipitation of Tir-eGFP was performed using GFP-Trap A beads ( Chromotek ) according to the manufacturer's instructions . Briefly , 24 hours post-transfection , Hela cells were lysed in RIPA buffer , centrifuged and the supernatant incubated with GFP-Trap beads for 30 minutes at 4°C . Following centrifugation , the unbound material was harvested and the beads washed before being resuspended in sample SDS buffer for Western blot analyses . Following experimentation , Hela cells seeded on glass coverslips were washed three times with PBS prior to fixing ( 2 . 5% Paraformaldhyde - Sigma - in PBS ) for 30 minutes and permeabilisation of host membranes in Triton-X100/BSA ( 0 . 1% and 2 . 5% final conc , respectively ) / PBS solution for 30 minutes . Cells were then incubated overnight in the fridge with an appropriate primary antibody in 2 . 5% BSA/PBS solution , followed by multiple PBS washes and incubation with Alexa 488 or Alexa 555-conjugated secondary antibodies in a 2 . 5% BSA/PBS solution ( 1 hour; room temperature ) . Washed cells were mounted in DAPI-Vectashield ( Vector Laboratories ) and examined on a Zeiss Axioskop Epifluorescent or a Leica TCS SP2UV confocal microscopy . Nuclear p65 and TRAF clusters were counted in a semi-blind fashion i . e . slides were assessed without considering the slide order or orientation with obtained data mapped to labelling . For co-localisation studies , cells were visualised using the confocal microscope using an x63 objective lens with serial optical slices taken along the z-axis of cells within a field of view ( ∼20 cells ) , with signals analysed by Leica software and plotted to illustrate the degree of overlap .
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Enteropathogenic Escherichia coli ( EPEC ) remain an important cause of infant diarrhoea and death in developing countries . Undoubtedly , a key pathogenic event relates to the bacteria's ability to inhibit the expression of anti-microbial and inflammation-inducing molecules regulated by the NF-κB transcription factor . This inhibitory process was previously linked to undefined EPEC effectors blocking NF-κB function . In this manuscript , we report that the most extensively studied EPEC effector , Tir , possesses a potent and specific ability to inhibit the cytokine , TNFα from activating NF-κB . Moreover , this finding leads to the discovery of a novel inhibitory mechanism relating to the induced degradation of the TNFα Receptor-Associated Factor ( TRAF ) adaptor protein . Additional work supports reported NF-κB inhibitory activities for multiple non-LEE-encoded effectors and key roles for two ( identifying possible target proteins ) leading to speculations on the evolution of EPEC's multi-effector strategy for inhibiting NF-κB activity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"escherichia",
"coli",
"immunomodulation",
"bacterial",
"pathogens",
"medical",
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] |
2011
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The Enteropathogenic E. coli (EPEC) Tir Effector Inhibits NF-κB Activity by Targeting TNFα Receptor-Associated Factors
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Even when antiretroviral therapy ( ART ) is started early after infection , HIV DNA might persist in the central nervous system ( CNS ) , possibly contributing to inflammation , brain damage and neurocognitive impairment . Paired blood and cerebrospinal fluid ( CSF ) were collected from 16 HIV-infected individuals on suppressive ART: 9 participants started ART <4 months of the estimated date of infection ( EDI ) ( “early ART” ) , and 7 participants started ART >14 months after EDI ( “late ART” ) . For each participant , neurocognitive functioning was measured by Global Deficit Score ( GDS ) . HIV DNA levels were measured in peripheral blood mononuclear cells ( PBMCs ) and CSF cell pellets by droplet digital ( dd ) PCR . Soluble markers of inflammation ( sCD163 , IL-6 , MCP-1 , TNF-α ) and neuronal damage ( neurofilament light [NFL] ) were measured in blood and CSF supernatant by immunoassays . HIV-1 partial C2V3 env deep sequencing data ( Roche 454 ) were obtained for 8 paired PBMC and CSF specimens and used for phylogenetic and compartmentalization analysis . Median exposure to ART at the time of sampling was 2 . 6 years ( IQR: 2 . 2–3 . 7 ) and did not differ between groups . We observed that early ART was significantly associated with lower molecular diversity of HIV DNA in CSF ( p<0 . 05 ) , and lower IL-6 levels in CSF ( p = 0 . 02 ) , but no difference for GDS , NFL , or HIV DNA detectability compared to late ART . Compartmentalization of HIV DNA populations between CSF and blood was detected in 6 out of 8 participants with available paired HIV DNA sequences ( 2 from early and 4 from late ART group ) . Phylogenetic analysis confirmed the presence of monophyletic HIV DNA populations within the CSF in 7 participants , and the same population was repeatedly sampled over a 5 months period in one participant with longitudinal sampling . Such compartmentalized provirus in the CNS needs to be considered for the design of future eradication strategies and might contribute to the neuropathogenesis of HIV .
Human Immunodeficiency Virus ( HIV ) invades the central nervous system ( CNS ) early during the course of infection [1 , 2] providing the foundations for neurocognitive impairment ( NCI ) and potentially establishing a latent reservoir [3 , 4] . Newly infected individuals typically have homogeneous HIV populations in blood [5 , 6] that evolve during untreated infection to generate diverse viral variants [2 , 7 , 8] . Compartment-specific selective pressures can subsequently lead to the emergence of unique HIV populations in different anatomical sites during the course of infection , including the CNS [2 , 7 , 9–11] , the genital tract [12] , and other tissues [13 , 14] . HIV RNA variants can be sequestered from blood into the CNS early after infection ( within 2–6 months ) and give rise to a separate HIV RNA population in the cerebrospinal fluid ( CSF ) [2 , 8] , which remains genetically distinct from blood throughout the course of infection . Overall , these observations suggest that the CNS can be permissive for HIV replication from a very early period after HIV infection . The presence of compartmentalized HIV variants within the CNS has important implications: ( 1 ) compartmentalization of HIV RNA in CNS has been associated with greater inflammation and worse neurocognitive outcomes [15–17] and , ( 2 ) independent replication of HIV within the CNS might hinder HIV eradication efforts by providing a distinct reservoir of HIV persistence different from that found in peripheral CD4+ T cells . This has been suggested by previous observations reporting differential emergence of drug resistance mutations between CSF and blood during antiretroviral therapy ( ART ) failure [18–20] . Combination ART has markedly reduced the incidence of HIV-associated dementia [21 , 22] . However , the true impact of early ART initiation on HIV-associated neurocognitive impairment is still under investigation [23] . While the viral replication and evolution of HIV RNA in the CNS has been extensively studied even during early HIV infection [2 , 8 , 24 , 25] , little is known about the HIV DNA populations persisting in this anatomic compartment during the earliest phase of HIV infection , and especially during suppressive ART . Similar to blood [26 , 27] , initiation of ART during early HIV infection might limit the diversification of HIV DNA within the CNS , affecting the size and molecular diversity of the HIV reservoir , preventing inflammation , and limiting brain damage . But these features have not been evaluated yet for the CNS . Our study used a unique set of samples from a well-characterized cohort of HIV-infected individuals followed longitudinally from early HIV infection to investigate the effects of early ART initiation on the size and molecular and phylogenetic characteristics of the HIV DNA populations while on long-term suppressive ART . Additionally , since chronic inflammation has been associated with HIV persistence [28] , we evaluated the effects of early ART on selected inflammatory markers in blood and CSF supernatant .
Study participants ( n = 16 ) were all HIV-infected males with a median age of 41 years ( Inter Quartile Range [IQR]: 32 . 5–52 . 5 ) selected among participants of the San Diego Primary Infection Resource Consortium ( SD PIRC ) . At baseline ( pre ART ) , the median plasma viral load was 176 , 000 HIV RNA copies/μl ( IQR: 40 , 287–515 , 900 ) . Participants achieved viral suppression after a median of 76 days ( IQR: 47–256 ) ART start and remained undetectable during the entire follow-up ( median of 3 . 5 viral load measurements per participant , median of 168 days between visits , median % of time-points with suppressed HIV RNA during follow-up 100% ) . Participants received ART for a median duration of 2 . 6 years ( IQR: 2 . 2–3 . 7 ) and had suppressed levels of HIV RNA in blood plasma ( <50 copies/ml ) and in the CSF supernatant ( at single copy level ) at the time of sample collection . Six out of sixteen participants were on a protease inhibitor ( PI ) -based ART regimen , 6/16 were on a non-nucleoside reverse transcriptase inhibitor ( NNRTI ) -based regimen and 4/16 on an Integrase Strand Transfer Inhibitor ( INSTI ) -based regimen , all in combination with two nucleoside reverse transcriptase inhibitors ( NRTI ) . While we recruited participants with early and late ART initiation according to study design , the exact categorization ( <4 months or >14 months ) was performed retrospectively to participant enrollment , but a priori to any molecular data generation or interpretation . The “early ART group” ( n = 9 ) started ART within a median of 1 . 8 months from estimated date of infection ( EDI ) ( IQR: 1 . 5–3 ) while the “late ART group” ( n = 7 ) started ART within a median of 17 . 2 months from EDI ( IQR: 14 . 8–30 . 9 ) . Detailed demographic and clinical characteristics of the study population are summarized in Table 1 . No significant differences between the early and late ART groups were observed for any recorded demographic or clinical characteristics ( p>0 . 2 ) . Paired CSF and blood samples were obtained at baseline from all 16 participants . Two participants ( both belonging to the late ART group ) agreed to donate CSF and blood at a second ( T0338 and T0366 ) and a third ( T0366 ) longitudinal visit . These additional samples were obtained 5 and 3 months from the first evaluation and 2 months from the second evaluation , respectively . Overall , this study comprised 16 participants with baseline samples ( 9 early ART and 7 late ART ) and 3 extra time points from 2 participants ( both belonging to the late ART group ) . Among the 16 baseline samples , we detected HIV DNA from 6 CSF cell pellet samples ( 37 . 5% ) by ddPCR and amplified the HIV partial env gene ( C2V3 , HXB2 coordinates 6 , 928–7 , 344 ) in 8 CSF cellular samples ( 50% ) by nested PCR ( Summarized in Supplementary S1 Table ) . For the purpose of our study , we considered as “positive” any CSF sample with detectable HIV DNA by either ddPCR or nested PCR ( or both ) . This resulted in 10 HIV DNA positive CSF samples at baseline ( 62 . 5% , 5 in the early ART and 5 in the late ART group ) and 6 undetectable ( negative for both ddPCR and nested PCR ) . Of the 3 extra time point samples ( longitudinal ) , we detected HIV DNA from one CSF cellular sample by ddPCR ( T0338 TP2 ) but we were able to amplify C2V3 env in all 3 CSF cellular samples ( T0338 TP2 and T0366 TP2 and TP3 ) . Of note , only 5 samples ( out of the 13 with detectable HIV DNA ) had consistent detection of HIV DNA by ddPCR and nested PCR across both aliquots . This inconsistency across aliquots is not surprising because of the low number of infected cells which increases the proportional impact of unequal cell numbers across the two separate aliquots during processing . Also , the dilution of lysates before the ddPCR droplet generation may have significantly reduced the sensitivity of the ddPCR assay . When comparing the two groups , HIV DNA was detected in 5 out of 9 CSF cell pellet samples tested as part of the early ART group and in 5 of 7 in the late ART group , but this difference was not statistically significant ( 55% versus 71% , relative risk 0 . 78 , p = 0 . 63 ) ; HIV DNA was detected in all but one ( 93 . 8% ) of the 16 PBMC samples . To further characterize the HIV DNA population , we sequenced partial env from CSF cell pellets ( n = 8 ) and PBMCs ( n = 14 ) at baseline . For two participants , we also obtained partial env sequences from one additional time-point ( T0338 and T0366 ) . Detailed characteristics of the viral sequences are provided in supplementary S2 Table ( for PBMC ) and S3 Table ( for CSF cell pellets ) . Overall , participants in the early ART group presented a lower molecular diversity of the CSF HIV DNA population , as compared to the late ART group ( Fig 1; Median: 0 . 9% versus 2 . 5% , p = 0 . 11 ) . In contrast , no difference in molecular diversity was observed in the PBMC HIV DNA population between the two ART groups ( Fig 1 , Median: 2 . 1% versus 2 . 5% , p = 0 . 26 ) . The CSF/PBMC diversity ratio was 0 . 58 ( range: 0 . 31–0 . 69 ) for the early ART group and 0 . 84 ( range: 0 . 33–1 . 06 ) late ART group ( p = 0 . 12 ) . Next , we used a mixed-effects model where baseline viral diversity was predicted by log-transformed time to ART from EDI as a continuous variable to evaluate its association with percentage of diversity ( Fig 2 ) . We observed a higher percentage of diversity among participants with the longer time to ART from EDI , collapsed across blood and CSF ( b = 0 . 36 , p = 0 . 04 , η2p = 0 . 28 ) . When evaluating the compartments separately , this association was significant in CSF ( p = 0 . 05 , η2p = 0 . 22 ) , but not in blood ( p = 0 . 08 , η2p = 0 . 19 ) . Diversity was significantly higher in PBMC than in CSF by 0 . 8% ( p = 0 . 02 , η2p = 0 . 31 ) , regardless of time to ART . We also included five covariates ( age , peak viral load , CD4 , CD8 , and CD4/CD8 ratio ) separately in the model to examine their potential effects on diversity and the association between time to ART and diversity . None of the covariates was significantly associated with diversity ( all p-values>0 . 1 , all η2p<0 . 05 ) while the association between time to ART start and diversity remained consistently significant ( p-values<0 . 05 ) . The average number of input HIV DNA templates from CSF cells into the first round PCR reaction was estimated using the number of HIV DNA and RPP30 copies ( based on our ddPCR data ) . The median HIV DNA copies per million cells among HIV-positive CSF cell samples was 2 , 701 copies/million cells ( IQR: 1 , 119–4 , 526 ) . The median number of CSF cells for each ddPCR reaction ( estimated by RPP30 ) was 2 , 340 ( IQR: 1120 . 5–2700 cells ) . After adjusting for the different volumes ( 5 μl for ddPCR and 10μl for nested PCR ) and the dilution factor , we estimate that the average calculated HIV template input was 22 copies of HIV DNA ( range: 4–64 ) per reaction . It should be noted that these levels are likely an under-estimate , given the inherent dilution with the ddPCR methods , as described in the method section and above . To further evaluate if the low HIV DNA input for the sequencing reaction influenced our measures of molecular diversity , we performed additional sensitivity analyses based on our baseline model described above . We first assessed the potential impact of HIV DNA copies on diversity measures by including log-transformed HIV DNA levels ( measured in blood and CSF when available ) into our model; we found no statistical evidence that the number of HIV DNA copies was associated with any bias in molecular diversity ( p = 0 . 21 , η2p = 0 . 10 ) . Second , to take into account the lack of consistency across aliquots , we compared diversity measures between cases with consistent versus inconsistent detectability across aliquots ( assuming that cases with ddPCR+/nested PCR+ will have higher HIV DNA levels compared to cases with ddPCR-/nested PCR+ ) and we did not find a significant difference ( p = 0 . 46 , η2p = 0 . 04 ) . While the ability to detect a significant effect in our sensitivity analysis was surely limited by the small sample size , this analysis suggests that the effect size of our primary predictor ( time from EDI to ART , η2p = 0 . 28 ) on molecular diversity of partial env was greater than the effect sizes of each covariate , including the number of template HIV DNA copies ( η2p = 0 . 10 ) and the number of positive aliquots ( η2p = 0 . 04 ) . Finally , to test the consistency of the diversity measures across blood and CSF , we performed a correlation analysis , and found that molecular diversity in CSF pellets was significantly associated with molecular diversity in PBMC ( Pearson r = 0 . 78 , p = 0 . 02 ) , strongly supporting the validity of our conclusion and measurements within the context of all the aforementioned limitations . Paired HIV DNA sequences ( partial env ) from CSF cell pellets and PBMCs were obtained for 8 participants , 3 from the early ART group and 5 from the late ART group . Two individuals ( both from the late ART group ) had additional HIV DNA sequences from a second time-point available ( obtained 3 and 5 months from the first evaluation , respectively ) . One individual had a third time-point ( 2 months apart ) . Compartmentalization was assessed using three distinct methods: distance-based FST test with and without collapsed haplotypes and tree-based Slatkin-Maddison ( SM ) test . Applying our conservative definition ( i . e . significant compartmentalization for all three methods ) , we observed a significant genetic compartmentalization between the HIV DNA populations sampled from CSF cells and PBMCs in 6 of 8 participants , including 2 individuals in the early ART group ( T0104 and T0430 ) ( Table 2 ) . Of note , the Fst estimates were congruent between both distance-based approaches , with and without collapsed haplotypes ( Kendall τ test p<0 . 01 ) . Maximum likelihood ( ML ) phylogenetic trees were created to evaluate the structure of the HIV DNA populations for participants with paired env sequences from CSF cells and PBMCs ( Fig 3 and Fig 4 ) . Tree topologies revealed the presence of monophyletic HIV DNA populations in CSF for 7 participants ( Figs 3 and 4 , indicated with an asterisks ) . Two ( T0104 and T0430 ) of the six individuals with evidence of well-segregated viral populations in the CSF were part of the early ART group . The same monophyletic CSF virus population was sampled from longitudinal CSF pellets over a period of 5 months for the one individual with a second time-point ( T0338; Fig 4 , see asterisk ) . Next , we investigated the effect of early ART on inflammatory markers and a marker of neuronal damage . In our cross-sectional analysis ( including baseline samples ) , participants from the early ART group had lower levels of interleukin ( IL ) -6 ( Fig 5 and Table 1 , p = 0 . 03 ) and tumor necrosis factor ( TNF ) -α ( Fig 5 and Table 1 , p = 0 . 02 ) in CSF compared to participants from the late ART group . ART groups did not differ for any of the other soluble inflammatory markers in CSF ( sCD163 and MCP-1 ) or blood ( sCD163 , IL-6 , TNF-α and MCP-1 ) or for neurofilament light ( NFL ) in CSF ( p>0 . 1; Table 1 ) . We also used the time to ART as a continuous variable to evaluate its association with the levels of the four cytokines . We observed higher IL-6 levels among participants with the longest time to ART start from EDI , collapsed across blood and CSF ( b = 0 . 19 , p = 0 . 02 , η2p = 0 . 16 ) . When evaluated separately , this association was significant in CSF ( p = 0 . 02 , η2p = 0 . 16 ) , but not in blood ( p = 0 . 54 , η2p = 0 . 01 ) . Again the five covariates were included in the model to control for their potential effects . The CD4/CD8 ratio was significantly negatively correlated with IL-6 levels ( b = -0 . 37 , p = 0 . 05 , η2p = 0 . 12 ) , while the other four were not correlated ( all p-values>0 . 1 , all η2p<0 . 07 ) . Regardless of the covariate included in the model , the association between time to ART and IL-6 remained consistently significant ( p-values<0 . 05 ) . Since IL-6 levels and HIV DNA diversity showed a similar , positive association with time to ART , we performed an additional mediation analysis to test the hypothesis that time to ART might have influenced diversity through its effect on IL-6 levels ( Fig 6 ) . While the direct effect of time to ART on diversity was still significant ( p = 0 . 02 ) , its indirect effect through IL-6 levels was not ( p = 0 . 52 ) , suggesting that IL-6 is unlikely the main mechanism connecting shorter timing of ART initiation to lover HIV DNA diversity .
To cure HIV , all forms of viral persistence should be considered , including viral reservoirs in different tissues and anatomical compartments [2 , 17 , 29–31] . Strong evidence supports that HIV can independently replicate in the CNS during untreated infection [2 , 11 , 32] and that the virus can establish a latent reservoir in this anatomic compartment [33 , 34] , which may be distinct from the one in circulating CD4+ T cells . The exact timing of HIV compartmentalization within the CNS is uncertain but likely occurs soon after infection in at least some individuals [2 , 25] . Similarly to the periphery [35–38] , we hypothesized that initiation of ART during early HIV infection would reduce the size and diversity of the viral reservoir within the CNS . To test this hypothesis , we evaluated a unique cohort of 16 HIV-infected individuals with known EDI who were sampled while receiving long-term ART and with sustained HIV RNA suppression . As previously described [39] , we were able to detect HIV DNA in cells collected from the CSF , even in participants who started ART during early HIV infection ( within 4 months of EDI ) . We did observe that early ART was associated with less molecular diversity of HIV DNA in both CSF cells and PBMC compared to late ART . Molecular diversity was not associated with age , peak viral load , CD4 , CD8 and CD4/CD8 ratio . Interestingly , although early ART initiation was associated with lower molecular diversity of provirus , most participants presented evidence of genetic compartmentalization of HIV DNA within the CSF ( including 2 out of the 3 participants from the early ART group ) . Seven participants had a clear monophyletic population of HIV DNA in the CSF . Overall , our results are consistent with previous studies reporting the presence of compartmentalized HIV RNA in CSF of HIV-infected people very early after infection [2 , 25] . The detection of viral compartmentalization does not necessarily imply that the populations in CSF and in blood are completely segregated , but instead , distinct subpopulations can occur in each compartment . This can occur in two different ways . First , HIV RNA populations can be sequestered from blood and populate the CNS early after infection , giving rise to a HIV RNA population within the CSF that remains genetically distinct from blood throughout the course of infection [2] . Alternatively , HIV RNA can enter the CNS early and evolve over time as a consequence of isolated replication and differential selection pressures , creating a genetically complex population within the CNS [2] . Overall , these observations suggest that the CNS compartment is permissive for HIV replication in at least a subset of persons from a very early period after infection and likely originates a distinct reservoir from that found in the blood; however , it is noted in our study that we do not know if any of these HIV DNA sequences represented replication competent proviruses . Another open question is the cellular source of this genetically distinct HIV DNA isolated from CSF cells . In our study , we were not able to determine the exact cellular source of the HIV DNA due to technical limitations and the nature of the samples . It is possible that this genetically distinct HIV DNA population detected in CSF might be carried by macrophages or T cells into the CSF or that T-cells circulating in CSF could get infected through contact with HIV-infected macrophages residing in the brain tissue in proximity to the brain vessels [40] . Alternatively , this HIV DNA population might be originating from CD4+ T cells circulating in the CSF after crossing the blood brain barrier but this seems less likely , since HIV-infected CD4+ T cells trafficking from the periphery into the CNS should present an equilibrated viral population in comparison to blood , especially in the setting of suppressive ART . Alternatively , unrecognized isolated HIV replication within the CNS during the period before our study visit might be responsible for our observations . Unfortunately , we did not collect longitudinal CSF samples in time points previous our baseline study visits , as part of the study design . The novelty of our study derives from the fact that we evaluated the HIV DNA populations from cells circulating in CSF and we demonstrated the presence of compartmentalized monophyletic HIV DNA populations in CSF from HIV-infected participants receiving suppressive ART , including two participants who started ART during primary infection . Both participants with longitudinal sampling showed sustained compartmentalization at all time-points , and the same monophyletic population was repeatedly sampled from CSF over a period of 5 months in one participant . Despite several technical limitations ( described below ) , our findings are important for the design of future eradication strategies and also to improve our understanding of HIV pathogenesis in the CNS . In fact , the presence of compartmentalized HIV populations has been associated with neurocognitive impairment [15 , 41] . Several studies reported associations between circulating HIV DNA levels in blood and neurocognitive impairment with and without ART [42–46] . While this observation might hold true also for HIV DNA in CNS , this has not been consistently reported especially in the setting of suppressive ART . One previous study [3] , found higher levels of HIV DNA in brain tissue from people with HIV encephalitis and moderate neurocognitive impairment compared to HIV-positive controls dying without neurologic symptoms . However , this study was limited since it included autopsy material from people dying with advanced disease and variable ART exposure . Likely due to limitations in samples size and the fact that people treated early during HIV infection have overall less neurological complications , we did not find associations between HIV DNA levels and neurological impairment . Our study also evaluated the effect of early ART initiation on selected inflammatory biomarkers in CSF and blood . Increased inflammation has been extensively reported in the CNS during HIV and was often associated with neurocognitive impairment [47–49] even during suppressive ART [49 , 50] . In our study , the early ART group presented significantly lower levels of IL-6 and TNF-α in CSF ( but not in blood ) compared to the late ART group . We also explored the possible effect of IL-6 on molecular diversity and no mediation effect was observed . These data further support the concept that early ART initiation reduces the levels of at least some inflammatory mediators in CSF . This study has several limitations . First of all , even though we were able to collect the volumes of CSF necessary to recover sufficient cells by the lysis buffer protocol , the detection of HIV DNA from CSF has been challenging due the low number of cells typically present in CSF in the absence of neurological symptoms and when HIV is suppressed . The low number of input cells might increase the potential for error related to sampling bias , could possibly amplify the number of false positive events from the ddPCR assay and could affect our diversity and compartmentalization analysis . To partially evaluate its impact , we performed multiple sensitivity analysis to address a possible bias in our analysis . Although we acknowledge that the small samples size has limited our statistical evaluation , our primary predictor of interest ( i . e . the time to ART initiation ) appears to have a greater effect on molecular diversity than the assay-related covariates . Further , we significantly elevated the threshold of compartmentalization detection and specifically included computational tests to increase robustness against significant errors in frequency estimation . Template input was particularly low in some ( but not all ) CSF samples , which could negatively impact our capacity to find unique clades within the CSF: assuming we are simply resampling the most common variants , we are more likely to find that CSF sequences fall within better sampled blood variants . In contrast , despite the possible sampling bias in CSF , we were still able to observe monophyletic CSF variants at baseline in several participants . Also , the reproducibility of the phylogenetic trees with similar variants sampled across longitudinal CSF samples for one participant , suggests that our sequences are likely informative and not substantially affected by random error or sequencing bias . Despite this , and the fact that we are analyzing only a partial region of env gene ( ~400 bp ) , we found differences in molecular diversity of the HIV DNA populations in CSF between the early and late ART groups . Another limitation of the analysis is the lack of randomization for the timing of ART initiation , which might introduce some unrecognized biases in our study design . For example , people with more symptomatic infection ( including the presence of neurologic symptoms , which were not tested as part of our study ) will start ART earlier and might also be more likely to present compartmentalized HIV populations . The small sample size also limited our statistical power . Even though some comparisons did not reach statistical significance , effect sizes were medium to large in some cases , supporting that the study was underpowered to answer these questions . Another limitation is inherent in all CSF studies: CSF only approximates events in the brain . Despite this , CSF has provided many important insights into brain events in HIV and other diseases [51] . A high degree of HIV DNA compartmentalization within the CSF suggests that the sampled HIV DNA is originating from brain tissue , but it could also reflect a population of cells that preferentially migrate into CSF from blood . This will need to be evaluated in future studies using larger cohorts and post-mortem brain tissues . Finally , in this study , we were also not able to determine if the HIV DNA population sampled in the CSF is replication competent . Despite these limitations , our data provide a unique perspective by analyzing HIV DNA populations sampled using CSF prospectively collected from a unique cohort of individuals who started ART and with known EDI . Our study supports the idea that initiation of ART during early infection may limit the diversity of HIV populations and inflammation in CNS . Future studies may want to evaluate the CSF HIV DNA populations in bigger cohorts and include longitudinal assessments prior and after initiation of ART to characterize dynamics of the CNS as a HIV reservoir . Moreover , future studies need to assess the CNS replication competent HIV DNA populations . The presence of unique HIV DNA populations within the CSF during ART might be relevant for future eradication strategies .
The study was approved by the Institutional Review Board at the University of California . All adult participants ( age ≥ 18 years ) provided written informed consent . No children were included in this study . Study participants were selected among HIV-infected men who enrolled in the SD PIRC between 2001 and 2012 and were still engaged in follow-up [52] . All SD PIRC participants are recruited during primary infection and followed with longitudinal blood drawn . Per protocol , visits occur at weeks 1 , 2 , 4 , 8 , 12 , and 24 , and then every 24 weeks thereafter . The date of infection is estimated for each participant following an established algorithm ( summarized in supplementary S4 Table ) [36] . Although early ART initiation is encouraged for all SD PIRC participants , implementation is based on participants’ personal decision , primary care physician input and following the current ART guidelines at the time of recruitment . Participants started ART between 2003 and 2012 . Selection criteria for this study were: ( 1 ) HIV-infected males recruited during primary infection , ( 2 ) started ART during follow-up early or later during HIV-infection , ( 3 ) reached undetectable HIV RNA in blood plasma ( <50 HIV RNA copies/ml ) and remained undetectable during follow-up until the time of baseline CSF collection ( based on our longitudinal viral loads and participant self-report ) [53] . None of the participants had evidence of other inflammatory neurologic disorders or pleocytosis . Participants were divided in early ART versus late ART groups as follow: 9 were included in the early ART group ( ≤4 months from estimated date of infection [EDI] ) and 7 in late ART group ( >14 months from EDI ) . Paired blood and CSF samples were collected from each HIV-infected participant cross-sectionally . A subset of 2 participants provided a second pair of samples ( 3 and 5 months after their first evaluation , respectively ) and one participant provided a third pair of samples ( 2 months thereafter ) . We designed our study to maximize cellular recovery by collecting 40 ml of CSF fluid by lumbar puncture . Following standard procedures at the HIV Neurobehavioral Research Center ( HNRC ) , the LPs were performed using atraumatic needle by an experienced physician . None of our study participants reported any complication following the CSF collection . From this larger volume , we obtained a CSF cell pellet and split it into two separate aliquots . Cell pellet lysates ( containing HIV DNA ) were used for ddPCR and for C2V3 env nested PCR as described below ( see supplementary S1 Fig ) . CSF supernatant was used to measure levels of selected markers of inflammation and neuronal damage ( described below ) and to measure HIV RNA by Aptima HIV RNA assay ( Hologic ) , after concentrating 5 ml of supernatant ( with single copy sensitivity ) . The CNS penetration effectiveness ( CPE ) index for the most recent ART regimen was determined as previously described [54] . For all participants , blood CD4+ T-lymphocytes were measured by flow-cytometry ( CLIA certified local laboratory ) . Levels of HIV RNA in blood plasma were quantified by the Amplicor HIV Monitor Test ( Roche Molecular Systems Inc . ) . For each participant , neurocognitive functioning was assessed using a standardized clinical battery of seven ability areas consistent with Frascati recommendations for neuroAIDS research [55] and summarized using the validated global deficit score ( GDS ) [56] . The levels of selected markers of monocyte activation ( sCD163 ) , general inflammation ( IL-6 ) and ( TNF-α ) and monocyte trafficking monocyte chemoattractive protein ( MCP ) -1 as well as brain damage ( NFL chains were measured in all participants . Enzyme-linked immunosorbent assay ( ELISA ) was used to quantify the levels of sCD163 ( Trillium Diagnostics , Brewer , ME , USA ) from blood plasma and CSF , and NFL in CSF ( Uman Diagnnostics , Sweden ) . Electrochemiluminescence multiplex assay ( Meso Scale Diagnostics , Rockville , MD , USA ) was used to quantify the levels of IL-6 , TNF-α and MCP-1 in CSF supernatant and blood plasma . All assessments were performed according to the manufacturer’s procedures . Genomic DNA was extracted from 5 million PBMC for each participant ( QIAmp DNA Mini Kit , Qiagen , CA ) per manufacturer's protocol . Genomic DNA was also extracted from 1 ( out of 2 ) aliquot of cell pellets obtained from 20 mL of CSF ( in average , there were 34 , 000 white blood cells/aliquot , range: 20 , 000–60 , 000 ) using direct lysis as previously described [22 , 23] . Levels of HIV DNA ( pol gene region: HXB2 coordinates 2536–2662 ) were measured in triplicate by ( dd ) PCR [57] . Briefly , 5 μL of 1:2 diluted CSF lysates or 1000 ng of DNA from PBMC per replicate was digested with BANII enzyme ( New England Biolabs ) prior to ddPCR . Reactions were performed with the following cycling conditions: 10 minutes at 95°C , 40 cycles consisting of a 30 second denaturation at 94°C followed by a 60°C extension for 60 seconds , and a final 10 minutes at 98°C . For DNA from CSF cell pellets , we used 5 μL ( diluted 1:2 ) of lysate per replicate . A 1:10 dilution of the digested DNA was used for host cell RPP30 ( ribonuclease P30 ) ddPCR and cycled with same parameters described above . Copy numbers were calculated as the mean of the three PCR replicates measurements and normalized to one million of cells ( PBMC or CSF cells ) as determined by RPP30 levels . The limit of detection of the ddPCR assay for HIV DNA using the same primer-probe set was previously described as 0 . 7 copies per million of cells [57] . The detected number of RPP30 copies in each ddPCR reaction was used to estimate the number of cells per aliquot of CSF cellular pellet . We amplified the HIV-1 env C2-V3 ( HXB2 coordinates 6928–7344 ) region from DNA extracted from CSF cellular pellets and PBMC by nested PCR using specific primers [58] . Sequencing was performed using 454 GS FLX Titanium ( 454 Life Sciences , Roche , Branford , Connecticut , USA ) . Read ( FASTA ) and quality score files produced by the 454 instruments were further analyzed using a purpose-built bioinformatics pipeline [25–27] . The pipeline is available at https://github . com/veg/HIV-NGS and the key steps were summarized briefly bellow: Raw data were filtered by removing sequences of low quality ( q-score of less than 15 ) using the Datamonkey analysis tool [59] and aligned to a subtype B reference sequence [60] . High-quality reads were retained and aligned to HXB2 as a reference sequence ( without generation of contigs ) using an iterative codon-based alignment procedure implemented in Datamonkey . A Bayesian Dirichlet mixture of multinomials probabilistic model was used to distinguish sequencing error from true low-frequency variants ( posterior probabilities of ≥99 . 99% ) . For PBMC , we obtained a median of reads of 16927 . 5 [13725 , 23106 . 5] and for CSF , we obtained a median of reads of 16198 [9590 , 20157 . 5] . All sets of representative reads were screened for evidence of recombination using GARD [29] , APOBEC signatures , hypermutations and frame-shifts as part of our pipeline procedure . All sequences were screened for in-house cross-contamination using BLAST [61] . Identical sequence reads were clustered , allowing identification of non-redundant sequences . A minimum of 10 identical sequence reads were clustered into haplotypes , and the proportion of reads in each haplotype was provided . Hence , the output consists of a list of representative haplotypes and their relative frequencies . The average number of HIV DNA haplotypes recovered from the CSF is 21 ( range: 11–29 ) , while 27 ( range: 9–46 ) haplotypes were recovered from blood . For each sample , we computed the mean of all pairwise Tamura-Nei 93 distances between reads with at least 100 overlapping base pairs to quantify nucleotide diversity [62] . Viral compartmentalization was first assessed by the Fst approach defined as FST=1−πIπD , where πI is the estimate of mean pairwise intra-compartment genetic distance ( TN93 ) [28] , and πD is its inter-compartment counterpart [63] . Both quantities were computed by comparing all reads from blood and CSF compartments , subject to the requirement that they share at least 150 aligned nucleotide positions . The large number of pairwise comparisons ( 107−109 ) was handled computationally using an efficient implementation of the TN93 distance calculator ( github . com/veg/tn93 ) , which achieves a throughput of 107 distances/second on a modern multi-core desktop . Subsequently , to guard against inference of compartmentalization by skewing of allelic frequencies due to PCR amplification and other biases , we recomputed FST by discarding copy number counts for read clusters ( i . e . each cluster was counted as having only one sequence ) , i . e . all haplotypes are assigned a relative weight of 1 . Statistical significance of both tests was derived via 1 , 000 population-structure randomization/permutation test . Finally , we performed a second tree-based Slatkin-Maddison ( SM ) test for compartmentalization [64] . Conservatively , we defined a CSF sample as compartmentalized only if all of the following tests were consistent and significant: ( 1 ) distance based FST test , ( 2 ) sensitivity test FST with collapsed haplotypes and ( 3 ) tree- based SM test . Viral haplotypes were realigned using MUSCLE [65] , piped to FastTree 2 [66] for maximum likelihood trees reconstruction , and subjected to codon-based ( MG94 ) phylogenetic analyses in HyPhy [67] . Statistical differences between groups ( early versus late ART initiation ) were examined using linear mixed-effects models with individuals included as random intercepts . The time-to ART variable was dichotomized or log transformed , and outcome variables were rank-transformed when appropriate . When residual variance differed by a specific factor in analyzing untransformed outcomes , we allowed heterogeneous variances across levels of that factor . Differences for sparse variables were detected by Fisher exact test . Whenever possible , partial η2 ( η2p ) was provided as a measure of the strength of association . Statistical analyses were performed using the R statistical language ver 3 . 3 [68] and the nlme package [69] .
|
Human Immunodeficiency virus ( HIV ) enters the central nervous system ( CNS ) early after infection and provides the basis for the development of neurocognitive impairment and potentially the establishment of latent reservoirs . Early initiation of antiretroviral therapy reduces HIV reservoir size in the periphery , but no previous study has assessed whether this strategy can also affect the HIV reservoir in the CNS . In this study , we prospectively collected and evaluated cerebrospinal fluid ( CSF ) and peripheral mononuclear blood cells ( PBMC ) from a cohort of 16 HIV-infected participants on suppressive antiretroviral therapy ( ART ) who started ART early ( <4 months ) and late ( >14 months ) after the timing of HIV infection . We found that early ART initiation was associated with lower molecular diversity of HIV DNA and lower levels of inflammatory markers in CSF in comparison to late ART start . We also found evidence of compartmentalized HIV DNA populations between the CSF and blood in the majority ( 75% ) of the participants with available paired sequences , including two ( 66% ) participants from the early ART group . Such compartmentalized provirus in the CNS will be important for the design of future eradication strategies and could contribute to the neuropathogenesis of HIV .
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2017
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Early Antiretroviral Therapy Is Associated with Lower HIV DNA Molecular Diversity and Lower Inflammation in Cerebrospinal Fluid but Does Not Prevent the Establishment of Compartmentalized HIV DNA Populations
|
Whole-exome or gene targeted resequencing in hundreds to thousands of individuals has shown that the majority of genetic variants are at low frequency in human populations . Rare variants are enriched for functional mutations and are expected to explain an important fraction of the genetic etiology of human disease , therefore having a potential medical interest . In this work , we analyze the whole-exome sequences of French-Canadian individuals , a founder population with a unique demographic history that includes an original population bottleneck less than 20 generations ago , followed by a demographic explosion , and the whole exomes of French individuals sampled from France . We show that in less than 20 generations of genetic isolation from the French population , the genetic pool of French-Canadians shows reduced levels of diversity , higher homozygosity , and an excess of rare variants with low variant sharing with Europeans . Furthermore , the French-Canadian population contains a larger proportion of putatively damaging functional variants , which could partially explain the increased incidence of genetic disease in the province . Our results highlight the impact of population demography on genetic fitness and the contribution of rare variants to the human genetic variation landscape , emphasizing the need for deep cataloguing of genetic variants by resequencing worldwide human populations in order to truly assess disease risk .
Genetic variation in humans is a result of stochastic processes , selection and demographic history [1] . Modern humans show a reduced level of differentiation due to recent population dispersion less than 100 , 000 years ago , and differences between populations are thought to account for little more than 15% of all genetic variation across individuals [2] . However , this picture is based on the allele frequency differences of common and shared variants between populations , representing only a small fraction of the total number of variants . Recently , much effort has been put into the description of the total variation landscape in human populations by resequencing hundreds to thousands of individuals from the same population at particular loci or for complete exomes [3]–[8] . Additionally , the 1000 Genomes Project has characterized the complete genomic sequences of more than one thousand humans covering worldwide diversity [9] , [10] . Two important conclusions have arisen from studies deeply characterizing the allele frequency spectrum in human populations . First , the high number of low frequency variants is likely only explainable by models of recent demographic explosion [3]–[8] . Furthermore , low frequency variants are enriched for functional variants , particularly for nucleotide changes that affect protein function , and are therefore putatively more related to disease [3]–[8] , [11] . Second , most rare variants are private or show very little sharing among continents [7] , [8] , [12] , [13] . This may be particularly important in terms of genetic fitness , since rare variants are enriched for deleterious alleles . However , until now differences in the relative amount of detrimental variants have only been shown over relatively large timescales by comparing African and European populations [14] , [15] . Furthermore , these findings predict a lack of replication in association studies using rare functional variants across populations , since rare variants can show higher levels of stratification [16] , thus emphasizing the need of population-specific catalogues of genetic variation [12] . In this work , we analyze whole-exome sequence data from French-Canadian individuals , comparing various population level statistics to those for French and European populations , which allow us to make inferences about the fitness of a population with a unique demographic history . The current population of six million French-Canadians in Quebec are descendants of about 8 , 500 French settlers who colonized the province between 1608 and 1759 , before the English conquest [17] , [18] . Although colonization included emigrants from all of France , the migration event mostly originated from the Atlantic coast and Paris region . After 1760 , French immigration virtually stopped , and the French-Canadian population experienced rapid growth due to a high birth rate , and became genetically isolated from France with limited exchange with other non-French communities in the same geographical area [19] . Overall , French-Canadians have experienced a growth from 8 , 500 to six million individuals , which represents a population expansion of more 700% in less than 20 generations . While other colonized territories in America or Oceania may have experienced a similar growth , the uniqueness of the French-Canadian population is due in part to the reduced contribution of new immigration after the first settlers [20] and the founding population is estimated to have contributed 90% of the current French-Canadian genetic pool [21] . In addition , during the 19th century new territories were colonized by a reduced number of settlers , contributing massively to the genetic pool in these regions , giving place to several regional founder effects . This particular component of the demographic history of the French-Canadian population has resulted in a geographic heterogeneity of genetic diseases in Quebec , with more than twenty Mendelian diseases occurring at unexpectedly high frequencies in some areas of the province [19] , [22] . Here , we specifically test the theory that deleterious mutations accumulate and/or persist in a population that has undergone a demographic bottleneck and rapid expansion in a short period of time , potentially as a consequence of reduced selection , using the French-Canadian population of Quebec . It has been argued that colonists at the forefront of expansions have a fitness advantage [23] . Here we show that if this is the case , then this short-term fitness advantage may come at an overall long-term cost . We also aim to describe how this complex demographic scenario has shaped the genetic variation in a modern population; as of yet , no study has described how the original genetic bottleneck and subsequent population expansion have affected the full-spectrum of genetic variation among French-Canadians .
Through exome-sequencing , we set out to determine how the distribution of variants in a founder population differs both in overall frequency , and potential functional impact relative to the source or progenitor population . All major observations were replicated on two different sequencing platforms and with similar sample sizes ( see Material and Methods and results below ) . In total , we detect 64 , 631 high-quality SNPs in 109 individuals from the French-Canadian population with low error rates ( see Material and Methods ) . Using previously described data [24] , we find a total of 46 , 662 high-quality SNPs from 30 individuals in the French population . The difference in the number of SNPs detected is largely driven by the different sample sizes . The numbers of SNPs falling into each functional category are shown in Table S1 . Compared to French individuals , French-Canadians have lower levels of heterozygosity ( on average 19 . 2% and 11 . 5% of the variants per individual are heterozygous in French and French-Canadians , respectively ) and have lower average nucleotide pairwise diversity ( Table 1 ) . Reduced genetic diversity in the French-Canadian population is consistent with the historically documented population bottleneck . The French-Canadian population also exhibits an excess of low frequency variants in comparison to the French population ( Figure 1 ) , and the proportion of variants with MAF<5% is significantly higher in the French-Canadian population ( p<0 . 01 ) . The excess is not a consequence of different sample sizes; if we resample the same number of individuals from each population and include only sites where all individuals pass identical quality filters , we observe a similar excess of rare variants in the French-Canadian population compared to the French population ( Figure S1 ) . The distribution of allele frequencies is likely indicative of the population expansion undergone by French-Canadians after the bottleneck out of Europe and is supported by lower per locus Tajima's D values ( Table 1 ) when compared to the French population ( t-test p value = 6 . 51e-15 ) ( Figure S2 ) . As seen in previous studies , low frequency classes are enriched for nonsense and missense variants in relation to synonymous variants ( Figure S3 ) . Strikingly , among the total number of SNPs , only a relatively small fraction ( 36 . 5% ) are shared between the two populations ( Figure S4 ) and this fraction decreases for functional SNPs ( missense , nonsense , splice site ) , which are enriched for rare variants . When considering those variants shared between populations , we find a high level of agreement; of the 29 , 767 variants shared by both populations , the vast majority have extremely low FST scores ( 97 . 6% are less than 0 . 05 ) , indicating little population differentiation for most common variants . In order to compare the French-Canadian SNPs to a larger dataset , we extended the comparison to a list of variants discovered from high-coverage sequencing of exomes in 85 CEU individuals in the 1000 Genomes Project [9] , as well as 1 , 007 individuals from other populations from the same resource , and find that the French-Canadian population shows a high percentage of private variants not found in any other population ( Table 2 ) . The distribution of these non-shared variants is asymmetric , and is enriched for rare and missense variants . The proportion of private variants is lower than those reported in comparisons across different continents , but higher than proportions observed across populations in the same continent [12] , [13] . Roughly , populations in different continents share only about 10% of rare variants , while close populations in the same continent , such as individuals from the CEU and Tuscany ( Italy ) populations , share about 90% of rare variants [7] , [8] , [12] , [13] . Given that we observe an excess of rare variants at functional sites in the French-Canadian population , we consider the effect of these variants on fitness and selection using a number of different approaches . First , we test for differences in the ratio of missense to synonymous changes within the SFS ( Figure 2 ) . Whilst the missense to synonymous ratio in the French population for SNPs with MAF<5% ( 1 . 31 ) is very similar to that observed in other populations [11] , the French-Canadian ratio of 1 . 47 , points to a major fraction of deleterious SNPs in the population , which carries a significantly larger proportion of rare mutations at missense sites ( p<0 . 01 , chi-squared test ) . For the most common variants ( MAF>0 . 25 ) , the French and French-Canadian populations have identical missense to synonymous ratios ( 0 . 77 ) . Second , we consider the predicted effects of nonsynonymous variants using GERP scores [25] and find more evidence for an excess of potentially damaging mutations in the French-Canadian population . GERP is a measure of conservation that is calculated across 34 mammalian species [25] and since it inversely correlates with derived allele frequency ( DAF ) [26] , [27] , it can be used to classify genetic variants and is often used as part of a criteria to prioritize functional variants in disease studies [28] . Comparing missense and nonsense SNPs in the French and French-Canadian populations , we find that the average GERP score is significantly higher for mutations in the French-Canadian population ( Wilcoxon signed-rank test , p = 0 . 004 ) . The difference is particularly strong for SNPs at the lowest frequencies ( Figure 2 ) , which are enriched for mutations with a higher impact on protein function , but the average GERP score for variants with MAF>10% is also significantly higher in the French-Canadian population ( p<0 . 01 ) . Conversely , we do not observe significant differences between populations when synonymous changes are compared ( Wilcoxon signed-rank test , p = 0 . 846 ) . The same enrichment for higher GERP scores in the French-Canadian population is also seen when comparing the distribution of average GERP scores for alleles carried at missense sites within each individual ( Figure 2 ) , and overall French-Canadian individuals have significantly higher mean GERP scores than French Individuals ( Wilcox-rank sum test , p<0 . 001 ) . Third , a significantly higher proportion of missense variants are predicted to be damaging in the French-Canadians compared to the French population using Polyphen [29] ( 49 . 5% and 45 . 5% respectively , p<0 . 01 ) , indicating that on average variants segregating in the French-Canadian population tend to be putatively more damaging . The inference of a higher proportion of deleterious alleles in the French-Canadian population is not a consequence of different sample sizes; resampling thirty individuals from the French- Canadian population , we again find a significantly larger missense to synonymous ratio for rare alleles ( 1 . 44 , p<0 . 01 ) , a significantly higher average GERP score for rare alleles at missense sites ( 2 . 194 for resampled French-Canadians , 2 . 067 for French , p<0 . 01 ) and a significantly larger proportion of missense variants predicted to be damaging by Polyphen ( 49 . 6% , p<0 . 01 ) for French-Canadians when compared to the French population . Furthermore , it is unlikely that the excess of rare deleterious alleles in French-Canadians is driven by data quality since we estimate a false positive rate of ∼0 . 2% for singletons ( see Material and Methods ) , which are most likely to be enriched for error . To understand why there is an excess of putative damaging variants in the French-Canadian population , we analyzed the intensity of natural selection in both the French and French-Canadian populations . First , we estimated the demographic parameters and the population selection parameter ( γ = Ne ( s ) ) using the Poisson Random Fields method implemented in prfreq [14] . To estimate population demographic parameters , we used synonymous sites to test different demographic models and we find a significantly better fit for models that include a bottleneck and expansion compared to neutral stationary models for both the French and the French-Canadian populations ( Tables S2 and S3 , Kolmogorov-Smirnov ( KS ) tests , p>0 . 05 in both cases ) . Although these models are necessarily simplified to capture key demographic processes rather than a literal history of the populations , we used them as a correction factor when next attempting to infer selection parameters at nonsynonymous sites . To this end , models including both the three-parameter demographic history and negative selection have a significantly better fit to the observed site frequency spectrums for both the French and French-Canadian populations at nonsynonymous sites than models assuming neutrality or including demographic history alone ( Tables S2 and S3 and Figure S5 , p<0 . 001 ) . As expected , including selection does not significantly improve the fit to the site frequency spectrum at synonymous sites , which provides a good check on the demographic model . The estimated γ parameter in French-Canadians is substantially less negative than that in the French population ( γ = −115 in French population , γ = −82 in French-Canadian population , p<0 . 001 ) , which could be at least partially a result of smaller Ne in the French-Canadian population . Second , we estimated the distribution of fitness effects ( DFE ) of mutations segregating in French and French-Canadian populations using the DFE-alpha software ( http://homepages . ed . ac . uk/eang33/ ) , which predicts the effects of new deleterious mutations using the site frequency spectrum [30] . The DFE estimated for the French population is broadly similar to that predicted for the European population in a previous study [31] using the two epoch model ( Table 3 ) , and the mean selective effect ( Ne ( s ) ) is similar to the γ value predicted by prfreq . Interestingly , the DFE estimated for the French-Canadian population has a much lower mean selective effect for new deleterious mutations of 12 . 8 ( compared to 104 . 9 for the French population ) . Furthermore , the proportion of strongly selected deleterious mutations is much lower in the French-Canadian population compared to the French ( Table 3 ) , which could reflect a relaxation of selection in the French-Canadian population due either to a reduction in Ne or the new environment , that has subsequently led to an accumulation or the persistence of potentially harmful rare variants . Finally , to test whether the differences we observe between the two populations are driven by different sequencing platforms , we analyzed data from an additional 50 French-Canadian individuals sequenced on Illumina's HiSeq platform and compared the results to the French dataset; we replicate all of the major findings . First , we observe a significant excess of rare variants in the French-Canadian Illumina dataset compared to the French ( 57 . 4% and 45 . 3% of variants with MAF≤5% respectively , p<0 . 01 , Figure S6 , Table S4 ) . Similarly , comparing datasets sequenced on the SOLiD platform by considering a further European dataset ( CEU population from the 1000 Genomes Project ) , we again find an excess of rare variants in the French-Canadian population ( p<0 . 01 , Figure S6 ) . Second , we find a significantly larger missense to synonymous ratio for rare alleles ( 1 . 39 , p<0 . 01 , Table S4 ) and a significantly larger proportion of missense variants predicted to be damaging by Polyphen ( 48 . 2% , p<0 . 01 , Table S4 ) for the French-Canadian Illumina dataset compared to the French . Finally , rare alleles at missense sites have a significantly larger GERP score on average in the French-Canadian Illumina data ( 2 . 194 , p<0 . 01 , Table S4 ) when compared to the French population and when considering the distribution of average GERP scores at missense sites within these individuals , French-Canadians have significantly higher mean GERP scores than French Individuals ( p<0 . 01 , Figure S7 ) .
Recent deep resequencing of human populations has highlighted an accumulation of rare variants above that expected under Wright-Fisher models [3]–[8] . Using exome resequencing data from over a hundred French-Canadian individuals , we show that a human founding population that has undergone rapid expansion contains an excess of private and rare variants compared to the French population after a colonization event less than 20 generations ago . Genetic variants in French-Canadians tend to be putatively more deleterious than those in the French . On the population level , evidence for this comes from the fact that mutations in the French-Canadian population tend to occur at functional sites with higher conservation scores and/or sites predicted to be damaging , are located preferentially at missense sites , and have higher missense to synonymous ratios than in French and European populations . Furthermore , at the individual level , this potentially translates into an increased genetic burden , since although French-Canadians carry a similar number of derived alleles as the French , these alleles tend to occur at more putatively damaging sites , as indicated by alleles in French-Canadians occurring at sites that on average have higher GERP scores ( Figure 1C ) . Furthermore , since the French-Canadian population shows lower levels of heterozygosity ( and thus higher levels of homozygosity ) , this may have implications for disease susceptibility . It is known that the incidence of around twenty Mendelian diseases is higher in Quebec [19] , [22] and some hereditary diseases show a particular pattern in the French-Canadian population , with local enrichments within particular geographical areas originated by regional founder effects [19] , [22] , [32] . Although it is difficult to translate our results into specific population genetic risk estimates , it may be possible that the increase of rare deleterious variants and reduced heterozygosity in the French-Canadian population is leading to higher disease risk . Rare alleles that were present at damaging sites in the original population may subsequently have been removed in the French population , yet still persist in French-Canadian individuals due to sampling effects , smaller population sizes , less competition and a higher birth rate . Although this seems unlikely to impact upon diseases caused by recessive variants in homozygous form , damaging mutations that have arisen since the founder event may be dominant or serve as the second , and ultimately vital , mutation within an important gene under a compound heterozygous model of Mendelian disorders . Furthermore , we find some evidence that higher frequency variants ( MAF>10% ) are on average more damaging in the French-Canadian population when compared to the French , since they tend to have higher GERP scores ( see Results ) , which may impact upon the incidence of Mendelian diseases under a homozygous recessive model . It has previously been shown that there is proportionally more deleterious variation in European populations after the out of Africa expansion [14] , [15] . However , this process occurred over a much longer timeframe and also relies on a long bottleneck to explain the increase in deleterious variants in Europeans [14] , [15] . In French-Canadians we observe a similar increase of rare deleterious variants but over a markedly short time frame . Furthermore , since the French-Canadian population did not undergo a long population bottleneck , the excess of deleterious variants could be explained by a rapid expansion of the population as well as other demographic factors such as subsequent regional founder effects in Quebec . To test this we performed a number of forward simulations incorporating selection and the demographic history of Europe , as inferred in a recent study [14] , followed by a simple population bottleneck and rapid expansion in the French-Canadian population , and a less extreme expansion in the French population ( for details , see Materials and Methods ) . We modeled population bottlenecks of varying sizes , performing 100 replicates for each scenario , and then calculated the difference in the proportion of variants with MAF<5% between the French and French-Canadian populations ( Table S6 ) . The scenarios modeled likely represent a simplified version of the actual demographic history of the French and French-Canadian populations , however we use them here to test differences between populations undergoing different rates of expansion under selective constraint after sharing a large proportion of demographic history . Under these models , the largest increase in rare variants in French-Canadians occurs when the population did not undergo a bottleneck , showing differences as large as 5 . 23% across the 100 replicates , with an average shift of 1 . 09% . Furthermore , we also observe on average an additional 8 . 32 deleterious alleles per megabase ( defined as having a negative selection coefficient ) per replicate segregating in the French-Canadian population compared to the French population . For simulations including a bottleneck , the biggest increase of rare variants in the French-Canadian population occurs for a bottleneck of 75% , with differences as large as 5 . 74% across the 100 replicates , an average increase of 0 . 74% and an additional 5 . 86 deleterious alleles per megabase per replicate in the French-Canadian population . Although these simple models lead to an increase in the proportion of rare variants in the French-Canadian population , the shift observed in the empirical data , which shows an increase of 9 . 8% of variants with MAF<5% in the French-Canadian population compared to the French population when using the same sample sizes ( see above and Figure S1 ) , is larger than that generated by simulations; there are several possible explanations for this . First , it may be that current tools are not able to accurately model recent events such as a rapid population expansion . Second , it is likely that a more complex demographic scenario is needed to explain the size of the increase in rare variants in the French-Canadian population , that may also include changes in selective forces as a consequence of the reduced competition occurring between a small number of founders . In fact , the French-Canadian population is genetically stratified into subpopulations with differentiated demographic histories [19] , [21] , [22] . Independent settlements and expansions with partially reduced genetic exchange across subpopulations , unequal contribution to the current genetic pool , as well as some admixture with other populations could have also contributed to the shift in the site frequency spectrum . Consistent with these notions , a recent study focusing on a specific sub-founding population within Quebec presented evidence that individuals on the wave front of colonization events have a heritable advantage and a higher contribution to the current genetic pool [23] . In this study , we have not focused on specific regions within the population and have not tested this observation . However , our results do demonstrate that the recent founding event and subsequent colonization events may have had a substantial deleterious impact across genomes . To a lesser extent , rare variants could also arise from the inclusion of founders from different regions in France or other European countries , which could be also related to the level of genetic diversity in Quebec , similar to that reported for European populations [21] . Similarly , the unequal sex ratio of the Quebec settlers of more than ten times more men than women [18] , may also have contributed to a shift in the effective population size and loss of heterozygosity . Finally , although there is evidence of a population bottleneck in the French-Canadian population , such as reduced levels of heterozygosity , given the results of our simulations it seems unlikely that the bottleneck was particularly strong . In this study , we show that even in the case of two very close populations that are separated by only 400 years approximately , the differences in the landscape of genetic variation can be substantial under particular demographic conditions . Rare variants are presumed to explain some of the missing heritability not accounted for by common variants in genome wide association analyses for complex disorders [33] as well as most of the rare diseases . Furthermore , there is mounting evidence that coding rare variants are contributing to complex traits [34] . The high number of population private rare functional variants described in this study constitutes a challenge for genetic association studies , affecting the replicability and correlation of genetic risk factors across human populations . Indeed , even from a relatively limited number of French-Canadian chromosomes , we discovered a substantial number of missense mutations that are not found on the widely used Illumina exome-arrays built from SNPs ascertained across a number of major sequencing studies . One third of the missense SNPs we discovered from sequencing over one hundred exomes are not found on these arrays , variation that likely influences complex traits and disease phenotypes , but is missing from analysis of disease risk . Although we understand from population genetics that most variants will be rare , this observation speaks to the need for continued sequencing of isolated or semi-isolated populations . Beyond the particular case of the French-Canadian population , this study highlights the importance of local demographic events in shaping genetic variation , and the need for creating population-based catalogues of human genetic variation [12] .
This research has been approved by the CHU Sainte-Justine's ethical committee . Data was analyzed anonymously . One hundred and fourteen French-Canadians were selected for sequencing . French-Canadian samples are the healthy parents of four disease cohorts ( primary immunodeficiencies , acute lymphoblastic leukemia , schizophrenia and autistic spectrum disorder ) recruited at the Sainte-Justine Hospital ( Montreal ) . Additionally , sequences from 30 French samples previously analyzed were included in the study [24] . We used principal component analysis to identify and remove the genetic outliers ( see below ) . Exome capture was performed with the SureSelect Target Enrichment System from Agilent Technologies optimized for Applied Biosystems SOLiD sequencing , using the Agilent SureSelect All Exome Kit ( 38 Mb ) and the Human All Exon 50 Mb kit covering exons annotated in the consensus CCDS [35] . Analyses were performed considering the coding regions targeted in the Agilent SureSelect All Exome Kit ( 38 Mb ) . Briefly , 3–5 µg of DNA were sheared by sonication , 5′ ends repaired , and the resulting fragments were ligated to adaptors , which were then run in size-select gels to select fragments of 150–250 bp in size . The extracted DNA was amplified by PCR and hybridized to the capture library containing the human exome . Hybridization was performed in a solution at 65°C for a minimum of 24 hours , followed by washing and capture of the hybridized DNA through magnetic bead selection , PCR and purification . Quantification of DNA libraries was performed using a Bioanalyzer and qPCR instrument . Exome sequencing was performed using SOLiD 3 Plus and SOLiD 4 Systems ( Applied Biosystems ) , following the manufacturer's recommended protocols . Sequence reads were aligned to the human genome reference sequence ( hg18 , downloaded from http://genome . ucsc . edu ) with BioScope , the available mapping tool for the SOLiD technology . GATK recalibration [36] was applied after mapping , PCR duplicates removed with Picard ( http://picard . sourceforge . net ) and SNP calling was performed using Samtools [37] . In total , 61 gigabases of sequencing reads mapped to the reference genome , with an average of 86% of the targeted regions being covered by at least one sequencing read . Each individual had an average coverage of 17-fold ( see supplementary material , Table S5 ) . SNP annotation was performed using the SeattleSeq Annotation tool ( http://gvs . gs . washington . edu/SeattleSeqAnnotation/ ) . Variants from the French population were generated from exome sequencing of the same targeted exons using Illumina sequencing [24] . Stringent variant calling criteria were applied to produce a high quality dataset of both the French and French-Canadian populations , including only variants that satisfy all of the following conditions: ( i ) fall within the regions targeted by the Agilent SureSelect exome capture kit , ( ii ) with SNP consensus or variant quality of 30 or higher , ( iii ) with sequence coverage of 10-fold depth or greater and ( iv ) in Hardy-Weinberg equilibrium ( using a stringent p-value of 0 . 001 ) . Furthermore , variants were included only if these criteria were met in at least 20 individuals in both the French and French-Canadian populations . The average transition/transversion ratio for all the French and French-Canadian samples in the coding variants was 3 . 32 , as expected for exonic sequences [38] and we detected no significant difference between French and French-Canadian samples ( 3 . 38 and 3 . 30 , respectively ) . Similarly , frequencies of the twelve possible nucleotide changes are similar between the two populations ( Figure S8 ) . For the resampling analyses , we randomly choose thirty individuals from the French-Canadian population and applied the same filters as above . In order to use the most genetically homogeneous group of individuals in each population we performed principal component analysis ( PCA ) for each population sample using SmartPCA as implemented in the program eigenstrat [39] . First , PCA was performed within each population including variants called in at least 80% of the individuals in each population to avoid the effects of missing values; these variants totaled 13 , 035 positions for the French-Canadian population and 26 , 843 for the French population . Significant PCs were inferred using the TW-statistic ( p-value<0 . 01 ) and outlier individuals were identified based on their individual loading exceeding two standard deviations from the mean of each significant axis . This analysis revealed five outlier individuals in the French-Canadian population and none for the French samples ( Figure S9 ) . Removing outlier individuals based on population structure analysis of each population separately resulted in the retention of 109 French-Canadian and 30 French individuals for subsequent analyses . Next , we performed PCA combining both populations , including only positions called in at least 80% of the combined samples , and only individuals with missing data less than 1% . This represented a total of 4 , 588 SNPs in 89 samples . We find no obvious differences between the two populations ( Figure S9 ) , although the French-Canadian population seems to show a slightly lower level of diversity and represents only a subset of the total genetic variation in the French population . The joint frequency spectrum of genetic variation was represented using the δaδi software [40] . We performed a number of validation procedures to check the quality of our data . First , we performed Sanger sequencing on a total of 113 heterozygous calls detected in the individuals included in this study ( 89% of the 97 variants have MAF<5% and 54% were singletons ) . In total we confirmed 109 calls , giving a false positive rate of 3 . 5% . This figure probably represents an upper bound , since the variants selected for validation are enriched for rare variants which are known to be more prone to sequencing errors [41] . Second , we sequenced the offspring of 16 individuals from the French-Canadian population , using the same protocols and filtering steps as in the parents , in order to confirm the presence of certain alleles in the population . Thus , to check the false positive rate for variants that are likely to contain the most errors ( singletons ) , we isolated any positions in the parents that were singletons in our population and then checked to see if the variant is called in the child , only including the position if the same quality filters were met in the offspring ( variant quality>30 , coverage>10 ) . Under normal patterns of Mendelian inheritance we expect 50% of singletons to be inherited by the child . Overall , we observe 4 , 666 singletons across the 16 individuals , 2 , 328 of which are present in the offspring ( 49 . 89% ) , representing a false positive rate for singletons of ∼0 . 2% . Third , we also tested the quality of our data by comparing DNA and RNA sequences for three French-Canadian individuals using the same high quality filtering criteria in both datasets ( consensus or variant quality greater than 30 , coverage greater than 10 ) . For RNA sequencing , RNA was enzymatically fragmented , and cDNA generated by reverse transcription from adaptors ligated to ends of the RNA molecule . Then , the cDNA was amplified using primers complementary to adaptors and purified . Sequencing was performed in a single SOLiD slide containing barcoded samples . Sequence reads were aligned to the human genome reference sequence ( hg18 , downloaded from http://genome . ucsc . edu ) with SOLiD's BioScope mapping tool . Recalibration was performed with GATK [36] , and PCR duplicates were removed with Picard ( http://picard . sourceforge . net ) . SNP calling was performed using Samtools [37] . As differences may exist between DNA and RNA as a consequence of RNA editing [42]–[45] and allelic expression [46] , for positions that are heterozygous in DNA , we considered a site as successfully validated if at least one read was present in RNA for both alleles; we confirm 474 of 506 sites . Since it is known that approximately 28% of genes show greater than a 4-fold difference in the expression of two alleles in RNA [46] , it is likely that some differences between DNA and RNA are driven by allelic specific expression . Indeed , 5 out of the 32 sites that fail validation in one individual show evidence for being heterozygote ( displaying at least one read from each allele ) in the RNA of at least one of the other two individuals that were sequenced . Differences between DNA and RNA at heterozygous sites are not significantly enriched for rare variants; only 5 out of 32 sites that fail validation have MAF<5% ( variants with MAF<5% , 5/66 not validated , p = 0 . 92 ) . Furthermore , we also considered sites that contained homozygous non-reference alleles in DNA sequences and then checked the corresponding position in RNA . All 242 positions were validated , further confirming the quality of the data . Finally , to consider the quality of common variants , we compared the genotype frequencies at polymorphic sites obtained from our exome sequencing that overlapped with data from 521 French-Canadian individuals that were genotyped on Illumina's Omni 2 . 5M arrays . In each case we compared the number of homozygous reference , homozygous alternative and heterozygous calls in our exome data with the same number of randomly sampled individuals from the chip data . In total , 23 , 231 sites were overlapping , 99 . 94% of which were not significantly different between exome sequencing and array data ( p>0 . 05 , after Bonferroni correction ) . To estimate the strength of purifying selection in the French and French-Canadian populations we applied two methods . First , we used prfreq , a program that uses Poisson random fields [14] to estimate the maximum likelihood values for different scenarios given an observed site frequency spectrum ( SFS ) . For the French and French-Canadian populations , we projected the SFS down to 60 alleles by randomly sampling individuals from the French-Canadian population and including only sites with 0% missing data . The ancestral allele was inferred from the homologous chimpanzee sequence obtained from Seattleseq annotation ( http://gvs . gs . washington . edu/SeattleSeqAnnotation/ ) and since mutation rates vary across the genome as a function of neighbouring nucleotides [47] , we corrected for the uncertainty of the ancestral sequence following the method of Hernandez et al [48] . Maximum likelihood values for each scenario were obtained with a multinomial calculation that estimates the probability of each SNP segregating at a given derived allele frequency . P-values associated with various demographic and selective models were estimated using likelihood-ratio tests . Demographic parameters were inferred from the site frequency spectrum of synonymous variants comparing three scenarios: a stationary population , contraction/expansion , and a population bottleneck and expansion ( Tables S2 and S3 ) . Finally , the selective parameters were obtained by comparing the likelihood of the missense SFS using the demographic model inferred from synonymous variants ( see above ) to the likelihood for the same demographic model incorporating a selection parameter ( γ = 2Ne ( s ) ) . To compare the γ values estimated in the French and French-Canadian populations we compared the likelihoods estimated in each case with the likelihoods computed using the γ values from the other population . Second , to calculate the distribution of fitness effects associated with mutations occurring in the French and French-Canadian populations we used the DFE-alpha software [31] ( http://homepages . ed . ac . uk/eang33/ ) . To construct the unfolded site frequency spectrums for the two populations we included variants and sites in the targeted region in which at least 30 and 90 individuals passed the high quality filters for the French and French-Canadian populations respectively . These numbers were chosen to reduce the amount of missing data at each site , whilst retaining the majority of polymorphic sites for analysis . We then counted the number of sites that had zero to 180 derived alleles in the French-Canadian population , where derived alleles represent sites that have diverged from chimpanzee . The same approach was applied for the French population using 60 chromosomes . For the French-Canadian population , ninety individuals were sampled randomly without replacement at sites where the number of alleles passing quality filters exceeded 180 . Derived alleles were inferred from chimpanzee sequences and human and chimpanzee pairwise alignments were downloaded from the UCSC website ( http://hgdownload . cse . ucsc . edu/downloads ) . As in the original DFE analysis [31] , intronic sites served as the neutral standard , the distribution of fitness effects was fit to zero-fold degenerate sites and any sites that were part of a CpG dinucleotide were removed . Confidence intervals were generated by bootstrapping; sites were selected randomly across the site frequency spectrum with replacement to generate 100 new datasets for each population . To replicate the major findings of this study we analyzed data from a cohort of fifty French-Canadian individuals sequenced on the Illumina platform representing the unaffected parents from different disease projects ( developmental delay and fetal malformations ) . Exomes were captured from 3 µg of blood genomic DNA , using the Agilent SureSelect Human All Exon Capture kit ( V3 and V4; Mississauga , ON ) , and sequenced paired end using the Illumina Hi2000seq technology . Raw sequencing data was processed using the same pipeline and filtering process as described above , including only those sites that are sequenced in all datasets . PCA was performed as before , taking SNPs with MAF>5% and missing data<5% - zero outliers were removed ( Figure S10 ) . For the CEU population , we obtained BAM files for 35 individuals from the 1000 Genomes Project ftp site ( ftp://ftp-trace . ncbi . nih . gov/1000genomes/ftp/ ) sequenced on the SOLiD platform and applied the same pipeline and filters as detailed above . To test for an increase in rare variants in the French-Canadian population we simulated a number of demographic scenarios under selection using the forward simulator SFS_code [49] . First , we implemented timing and population size scaling for the European demographic history , as detailed in the SFS_code documentation ( http://sfscode . sourceforge . net/SFS_CODE_doc . pdf , figure 2 , model taken from [14] ) . This model includes an initial burn-in period with a population size of 7 , 895 , followed by a bottleneck at time zero to a population size of 5 , 699 . Following this , the population remains at constant size for 7 , 703 generations before an instantaneous growth to 30 , 030 , which remains for a further 874 generations . We scaled this model using an ancestral population size of 1 , 000 . Then , we simulated a population split and a bottleneck of 50% , 75% and 100% ( no bottleneck ) for one of the populations to represent the founding of Quebec , scaled using the initial population size to occur twenty generations ago . This was then followed by exponential growth over twenty generations in the European and Quebec populations to increase their size by 3 and 600 respectively ( as documented in historical records ) , using 100 replicates for each scenario . In total , we simulated 360 unlinked genes per replicate , each consisting of five 400 bp exons separated by introns of size 2 kb ( similar to the average exon and intron sizes documented in [50] ) . We used a mutation rate per site of 1 . 5×10−8 and an average recombination rate of half this value . We ignored positive selection since it is likely to be rare and used an average selection coefficient of −0 . 03 , as inferred in [14] , sampled from a gamma distribution . In each replicate and for each population , we selected 100 individuals and then compared the proportion of variants with MAF<5% . The results of these simulations are shown in Table S6 .
|
Recent resequencing of the whole genome or the coding part of the genome ( the exome ) in thousands of individuals has described a large excess of low frequency variants in humans , probably arising as a consequence of recent rapid growth in human population sizes . Most rare variants are private to specific populations and are enriched for functional mutations , thus potentially having some medical relevance . In this study , we analyze whole-exome sequences from over a hundred individuals from the French-Canadian population , which was founded less than 400 years ago by about 8 , 500 French settlers who colonized the province between the 17th and 18th centuries . We show that in a remarkably short period of time this population has accumulated substantial differences , including an excess of rare , functional and potentially damaging variants , when compared to the original European population . Our results show the effects of population history on genetic variation that may have an impact on genetic fitness and disease , and have implications in the design of genetic studies , highlighting the importance of extending deep resequencing to worldwide human populations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Whole-Exome Sequencing Reveals a Rapid Change in the Frequency of Rare Functional Variants in a Founding Population of Humans
|
The influence of the mammalian retinal circadian clock on retinal physiology and function is widely recognized , yet the cellular elements and neural regulation of retinal circadian pacemaking remain unclear due to the challenge of long-term culture of adult mammalian retina and the lack of an ideal experimental measure of the retinal circadian clock . In the current study , we developed a protocol for long-term culture of intact mouse retinas , which allows retinal circadian rhythms to be monitored in real time as luminescence rhythms from a PERIOD2::LUCIFERASE ( PER2::LUC ) clock gene reporter . With this in vitro assay , we studied the characteristics and location within the retina of circadian PER2::LUC rhythms , the influence of major retinal neurotransmitters , and the resetting of the retinal circadian clock by light . Retinal PER2::LUC rhythms were routinely measured from whole-mount retinal explants for 10 d and for up to 30 d . Imaging of vertical retinal slices demonstrated that the rhythmic luminescence signals were concentrated in the inner nuclear layer . Interruption of cell communication via the major neurotransmitter systems of photoreceptors and ganglion cells ( melatonin and glutamate ) and the inner nuclear layer ( dopamine , acetylcholine , GABA , glycine , and glutamate ) did not disrupt generation of retinal circadian PER2::LUC rhythms , nor did interruption of intercellular communication through sodium-dependent action potentials or connexin 36 ( cx36 ) -containing gap junctions , indicating that PER2::LUC rhythms generation in the inner nuclear layer is likely cell autonomous . However , dopamine , acting through D1 receptors , and GABA , acting through membrane hyperpolarization and casein kinase , set the phase and amplitude of retinal PER2::LUC rhythms , respectively . Light pulses reset the phase of the in vitro retinal oscillator and dopamine D1 receptor antagonists attenuated these phase shifts . Thus , dopamine and GABA act at the molecular level of PER proteins to play key roles in the organization of the retinal circadian clock .
The vertebrate retina is both a sensory organ and an endogenous circadian clock . As the locus of visual phototransduction , the retina initiates many organismal responses to light , and as a circadian clock , the retina expresses many physiological or functional circadian rhythms , including photoreceptor disc shedding [1–4] , visual sensitivity [5–8] , rod–cone balance[9 , 10] , electroretinogram ( ERG ) b-wave amplitude [11–13] , extracellular pH [14] , melatonin release [15–18] , dopamine synthesis [19 , 20] , gamma-aminobutyric acid ( GABA ) turnover rate and release [21] , PKC level [22] , intraocular pressure [23 , 24] , and gene expression [13] . In mammals , the retina is the sole site for circadian phototransduction , and its output modifies the rhythmicity , period , and developmental organization of the central biological clock , the suprachiasmatic nucleus ( SCN ) of the hypothalamus [25–27] . In addition , the mammalian retinal clock and its outputs influence trophic processes in the eye , including the susceptibility of photoreceptors to degeneration from light damage [28] , photoreceptor survival in animal models of retinal degeneration [29] , and the degree of refractive errors in primate models of myopia [30] . Despite the widespread influence of the mammalian retinal circadian clock on retinal , visual , and circadian function , the cellular elements and mechanisms comprising the retinal pacemaker remain to be elucidated . The mammalian retina contains all the major neurotransmitter systems , and to a large extent , retinal function is mediated by neurotransmission . Glutamate is the prominent excitatory neurotransmitter of the neuron types of vertical pathways through the retina , including photoreceptors , bipolar , and ganglion cells [31 , 32] . The other major excitatory neurotransmitter , acetylcholine ( ACh ) , is produced by a mirror-symmetric pair of amacrine cells [33] . The inhibitory neurotransmitter GABA occurs in many different subtypes of amacrine cells and in one or more subtypes of horizontal cells [34] . The inhibitory neurotransmitter glycine is present in most of the small-field types of amacrine cells [35] . Besides these four fast neurotransmitters , there are two globally modulatory neurotransmitters , melatonin and dopamine , which are mutually inhibitory neurochemical outputs of the retinal clock and are rhythmically released by photoreceptors and dopaminergic amacrine cells , respectively [19 , 36 , 37] . Melatonin and dopamine rhythms have served as the principal experimental measures of the retinal circadian clock , and previous models of retinal circadian organization have postulated that communication through these two transmitters is critical for overall retinal rhythmicity [15 , 19 , 20 , 38 , 39] . However , these neurochemical assays of clock output have left open questions of whether dopamine or melatonin modulates the retinal molecular clock mechanism itself and what roles they may play in the organization of retinal clockworks . Circadian clocks in mammalian tissues generate molecular circadian rhythms through coupled transcription/translation feedback loops in which the positive gene elements Clock and Bmal1 interact with the negative gene elements Period ( Per ) 1 and 2 , and Cryptochrome ( Cry ) 1 and 2 , and in which casein kinases , CKIε and CKIδ , set circadian period by phosphorylating PER proteins to regulate their degradation and nuclear localization [38 , 40] . Circadian rhythms of Per1 expression have been reported in the rat and mouse retina [41 , 42] . We have previously shown that in photoreceptor-degenerate mouse retinas , Per1 , Per2 , Cry1 , Cry2 , and Bmal1 mRNA levels exhibit statistically significant variations over a 24-h sampling period under constant darkness conditions [43] . Moreover , in a recent microarray study , 277 genes representing a wide range of functions were found to show a circadian rhythm of expression in the mouse retina in constant darkness [13] . However , it is unclear from these in vivo studies whether circadian rhythms of retinal gene expression are self-sustained or driven by the master SCN clock . In the current study , we developed an in vitro retinal explant culture protocol to monitor circadian rhythms in clock gene expression in intact retinas of adult mice , which harbor a knockin PERIOD2::LUCIFERASE ( PER2::LUC ) fusion protein as a real-time reporter of circadian gene dynamics [44] . Using this protocol , we studied the properties and location of retinal PER2::LUC rhythms in vitro , the influence of major retinal neurotransmitters , the roles of sodium-dependent action potentials and connexin 36 ( cx36 ) -containing gap junctions in retinal PER2::LUC rhythms , and light-induced phase resetting of retinal PER2::LUC rhythms . Our findings support a model of retinal circadian organization in which rhythm generation occurs in the inner nuclear layer independently of major forms of neural communication , but in which dopamine and GABA play critical roles in setting the phase and amplitude of the retinal circadian clock .
To study the retinal circadian clock in the intact retina , mPer2Luc mice congenic on the C57BL/6J background , in which melatonin levels are greatly reduced due to point mutations in synthetic enzymes for melatonin [19 , 45] , were crossed to C3H rd1/rd1 mice to obtain F1 hybrid B6C3 mice . These mice have one wild-type allele at the melatonin and rd1 loci , synthesize and secrete normal levels of melatonin [46] , and do not undergo photoreceptor degeneration . When retinal explants from B6C3 F1 mice were cultured under standard conditions ( medium 199 or DMEM , air , 37 °C ) , they exhibited only low-amplitude PER2::LUC oscillations ( Figure S1 ) . However , when retinal explants were first cultured in neurobasal medium in 5% CO2 at 37 °C for 24 h in vitro and subsequently transferred to medium 199 as before , PER2::LUC expression was robustly rhythmic for numerous circadian cycles ( Figure 1A ) . Peak-to-trough amplitude typically increased on the second cycle in medium 199 , after which the amplitude gradually decreased over 9–10 d ( Figure 1A ) . The first peak of PER2::LUC expression occurred at hour 14 . 81 ± 0 . 29 of projected Zeitgeber time ( ZT; i . e . , 2 . 81 h after the time of lights off in the mouse colony; mean ± standard error of the mean [SEM]; n = 11 ) . This was essentially identical to the phase of PER2::LUC rhythms in photoreceptor-degenerate retinas [43] , and phase delayed by 2 h relative to the peak times of SCN PER2::LUC rhythms [44] . Retinal Per2 mRNA abundance is rhythmic in vivo , as sampled at 4-h intervals by quantitative reverse transcriptase ( RT ) -PCR ( Figure S2 ) , indicating that the luminescence signals recorded in vitro faithfully reflect in vivo rhythmic processes . The phase of the in vivo Per2 and of the in vitro PER2::LUC rhythms were similar , suggesting that the culture procedure itself does not greatly affect the phase of retinal molecular oscillations . However , the limited resolution of the in vivo data ( 4-h intervals ) prevented a more precise definition of the expected delay between Per2 transcription and PER2::LUC luminescence . As with the cultured SCN [47] , a media change partially restored the amplitude of luminescence rhythms ( Figure 1C ) . With media changes every 10 d , retinal rhythms could be readily sustained for a month or more . To control for whether bioluminescence light emission produced by the PER2::LUC fusion protein influenced retinal rhythms , we performed assays in which luciferin substrate was absent from the medium , and thus , there was no retinal light emission for the first two circadian cycles in vitro . Luciferin ( 0 . 1 mM ) was then added to the medium of experimental explants , and an equal volume of vehicle was added to the medium of control explants at the beginning of the rising phase of the third cycle recorded in the LumiCycle . The delayed addition of luciferin revealed rhythms of PER2::LUC luminescence in the experimental explants that were similar in amplitude and peak time to the ongoing rhythms of control explants ( n = 4; Figure 1B ) , indicating that bioluminescence light emitted by the PER2::LUC reporter in these retinas did not influence retinal PER2::LUC rhythms in our in vitro assay . Histological examination of vertical slices from cultured retinas confirmed that all retinal layers were intact in these preparations , including photoreceptor cell bodies and outer segments ( Figure 1D ) . In addition , immunocytochemistry with an antibody to tyrosine hydroxylase ( TH ) , the rate-limiting enzyme of dopamine synthesis , confirmed that cultured retinal explants retained a normal complement of dopamine neurons ( Figure 1E ) . Further immunocytochemical analysis revealed that all the major retinal cell types , including cone photoreceptors and horizontal , bipolar , amacrine , and ganglion cells , were present and morphologically intact in cultured retinal explants ( Figure S3 ) . Multiple approaches , including in situ hybridization , immunocytochemistry , and single-cell reverse-transcriptase PCR ( RT-PCR ) , have established that the Per2 clock gene is expressed , with varying levels and frequencies , in all the major subtypes of retinal neurons [13 , 43 , 48] . Thus , all retinal cell subtypes could potentially contribute to the tissue-level PER2::LUC luminescence rhythms recorded in our assays . To localize the PER2::LUC signal within the retina , we imaged vertical retinal slices for both bright-field and luminescence with a cooled charge-coupled device ( CCD ) camera . The bioluminescence signal was concentrated in the mid-retina , in the inner nuclear layer that contains the nuclei of the horizontal , bipolar , and amacrine cells , with low levels of luminescence detected in the photoreceptor and the ganglion cell layers ( Figure 2A ) . To confirm that inner retinal PER2::LUC is rhythmic , populations of retinal slices maintained in constant culture conditions were imaged at 6-h intervals ( n = 2 independent runs; Figure 2B ) . PER2::LUC bioluminescence in the inner nuclear layer exhibited a circadian variation with a peak at projected ZT 8–14 , close to the peak time observed in whole retinal explant preparations . In addition , individual retinal slices subject to time-lapse imaging for 48 h showed similar circadian rhythms in PER2::LUC bioluminescence emanating from the inner nuclear layer ( n = 3 independent runs; Figure 2C ) . Similar inner nuclear layer localization of PER2::LUC expression was observed in retinas from C57BL/6J mice as well ( unpublished data ) . This indicates that the primary source of rhythmic PER2::LUC luminescence signals in our retinal rhythms assay is the inner nuclear layer , with minor contributions from the other retinal layers . Although the inner nuclear layer was the primary source of rhythmic PER2::LUC signals in our vertical retinal slice imaging , signaling from photoreceptors or ganglion cells might play an important role in generating or sustaining these rhythms . To test this possibility , we examined the influence of the main forms of cell communication from photoreceptors ( melatonin and glutamate ) and ganglion cells ( glutamate , sodium-dependent action potentials , and cx36-containing gap junctions ) on retinal PER2::LUC rhythms . To determine whether melatonin signaling is required to maintain retinal rhythmicity in clock gene expression , we assayed luminescence rhythms from retinal explants derived from mPer2Luc C57BL/6J mice , in which melatonin synthesis is genetically blunted [19 , 45] . Circadian rhythms of PER2::LUC expression in C57BL/6J retinas were similar in their sustained nature to those of B6C3 F1 retinas ( n = 6 each; Figure 3A and 3B ) , which synthesize and secrete normal levels of melatonin [46] . Similarly , B6C3 retinas , treated with continuous application of 10 nM melatonin or continuous blockade of melatonin MT1 receptors with 5 μM luzindole , also exhibited robust circadian gene expression rhythms ( n = 6 each; Figure 3C and 3D ) . Taken together , these results indicate that the mouse retina requires neither normal levels of melatonin nor melatonin rhythmicity to maintain PER2::LUC rhythms . Glutamate is an excitatory neurotransmitter used by photoreceptors , ganglion , bipolar , and amacrine cells . To examine the influence of glutamate on retinal PER2::LUC rhythms , we either activated its receptors with l-glutamate ( 1 mM; n = 4; Figure 3E ) , or blocked its receptors with kynurenic acid ( 0 . 5 mM; n = 4; Figure 3F ) , a broad-spectrum glutamate receptor antagonist . Neither of these treatments exhibited marked effects on the amplitude or period of retinal PER2::LUC rhythms ( n = 3 each ) , indicating that glutamatergic neurotransmission is not required for maintenance of retinal PER2::LUC rhythms . In the mammalian retina , ganglion cells and dopaminergic amacrine cells require sodium-dependent action potentials for evoked neurotransmitter release and subsequent chemical communication [49] . In addition , in the SCN pacemaker , sodium-dependent action potentials are critical for molecular rhythms generation in individual neurons and for cellular synchronization [50] . Thus , we tested the role of sodium-dependent action potentials in the retinal circadian pacemaker with the sodium channel blocker tetrodotoxin ( TTX ) , previously shown to damp tissue and cellular gene expression rhythms in in vitro SCN cultures [50 , 51] . Retinal PER2::LUC rhythms persisted undiminished upon continuous application of 1 μM TTX , and there was no significant difference in e-fold damping rate between TTX treatment and vehicle ( 3 . 92 ± 0 . 22 d vs . 3 . 82 ± 0 . 34 d; p > 0 . 05; n = 5 each; Student's t-test; Figure 3G ) . This concentration of TTX was confirmed to block spontaneous action potentials in ganglion cells and in dopaminergic neurons ( unpublished data ) . Therefore , sodium-dependent action potentials are not required for retinal PER2::LUC rhythms generation . Ganglion cells form extensive electrically coupled networks via gap junctions . To test for the role of gap junctions , we applied 100 μM carbenoxolone , a general gap junction blocker , to mPer2Luc retinal explants . Carbenoxolone did not influence the amplitude or damping rate of retinal PER2::LUC rhythms ( damping rate = 3 . 54 ± 0 . 18 days; n = 5; Figure 3H ) . In addition , we examined the role of the gap junction channel protein , cx36 in retinal PER2::LUC rhythms . Cx36 is widely distributed in retinal gap junctions , is essential for transmission of rod-mediated visual signals in the mammalian retina [52] , and comprises most of the junctions between alpha ganglion and amacrine cells [53] . Interneuronal communication via cx36 gap junctions also promotes synchronous electrical activity in SCN neurons and coherent circadian behavioral rhythms [54] . To test whether cx36-mediated electrical coupling plays a role in the retinal biological clock , we crossed mPer2Luc mice with cx36−/− mice [55] to produce cx36 knockout mPer2Luc mice . PER2::LUC rhythms in retinas from both cx36−/− and cx36+/− mice were similar to mice wild type at the cx36 locus , and there was no significant difference in the damping rate between cx36−/− mice and cx36+/− mice ( 2 . 62 ± 0 . 12 d vs . 2 . 74 ± 0 . 33 d; p > 0 . 05; n = 8 each; Figure 3I and 3J ) , suggesting that neuronal synchrony was not degraded by cx36 knockout . Taken together , these results suggest that communication through gap junctions is also not required for retinal PER2::LUC rhythms generation or for circadian neural synchrony in the mouse retina . The wide distribution of PER2::LUC expression across the inner nuclear layer suggests the possibility that multiple inner nuclear layer neuron types , including horizontal , bipolar , and amacrine cells , could be sources for the rhythmic PER2::LUC signals . Rhythms generation could be a network property driven by a specific population of pacemaker neurons located in the inner nuclear layer , which establish PER2::LUC rhythmicity by neurotransmission . To test this possibility , we examined the role of the principal neurotransmitters of the inner nuclear layer: dopamine , acetylcholine , GABA , and glycine . We first examined whether dopamine is required for retinal PER2::LUC rhythms generation . PER2::LUC rhythms persisted during depletion of retinal dopamine by 100 μM α-methyl-l-tyrosine ( L-AMPT ) and 10 μM reserpine , an inhibitor of dopamine synthesis and of vesicular dopamine uptake , respectively ( n = 4; Figure 4A ) . Dopamine depletion at 48 h posttreatment was confirmed with high-performance liquid chromatography ( HPLC ) . Whereas control retinas contained 0 . 93 ± 0 . 08 ng/mg protein dopamine , L-AMPT–treated retinas contained 0 . 50 ± 0 . 06 ng/mg protein , and L-AMPT– and reserpine-treated retinas contained undetectable levels of dopamine ( n = 4 each ) . In addition , circadian PER2::LUC expression rhythms persisted during continuous application of the long-lasting dopamine agonist ( ± ) -2-amino-6 , 7-dihydroxy-1 , 2 , 3 , 4-tetrahydronaphthalene ( ADTN; 100 μM; in vehicle containing 100 μM l-ascorbic acid [L-AA , an antioxidant]; n = 4; Figure 4B ) . However , ADTN produced perturbations in the waveform during the first few cycles of treatment and indeed changed the phase of retinal PER2::LUC rhythms ( further analyzed below ) . Retinal PER2::LUC rhythms also persisted during continuous application of the D1 receptor agonist SKF-38393 and the D2/D4 receptor agonist quinpirole hydrochloride ( 50 μM each; n = 4; Figure 4C ) ; or continuous blockade of dopamine receptors with the D1 receptor antagonist SCH-23390 and the D2/D4 receptor antagonist sulpiride ( 50 μM each; n = 4; Figure 4D ) . These data , along with our above-mentioned results demonstrating that retinal PER2::LUC rhythms persisted in the presence of TTX , form convergent lines of evidence suggesting that neither dopaminergic transmission nor its rhythmicity is necessary for ongoing retinal PER2::LUC circadian rhythms generation . The excitatory neurotransmitter ACh is employed by specialized circuits and produced by a mirror-symmetric pair of amacrine cells . Neither activation of ACh receptors with the nonselective cholinergic agonist carbamoylcholine chloride ( 100 μM; n = 4; Figure 4E ) , nor blockade of ACh receptors with a variety of antagonists including the nicotinic acetylcholine receptor antagonist ( + ) -tubocurarine ( 100 μM ) alone ( unpublished data; n = 4 ) , the muscarinic ACh receptor antagonist atropine ( 100 μM ) alone ( unpublished data; n = 4 ) , or ( + ) -tubocurarine and atropine together ( n = 4; Figure 4F ) , had a noticeable effect on the amplitude or period of retinal PER2::LUC rhythms . The inhibitory neurotransmitter GABA is synthesized and released by horizontal cells and several subtypes of amacrine cells including dopaminergic amacrine cells . Activation of GABA receptors with 1 mM GABA substantially suppressed the amplitude of retinal PER2::LUC rhythms ( n = 4; Figure 4G ) . However , retinal PER2::LUC rhythms persisted during blockade of GABA receptors with the GABAA receptor antagonist SR-95531 ( 40 μM ) along with the GABAB receptor antagonist CGP-35348 ( 100 μM ) and the GABAC receptor antagonist 1 , 2 , 5 , 6-tetrahydropyridine-4-yl methyl phosphinic acid ( TPMPA; 100 μM; n = 4; Figure 4H ) , indicating that GABAergic transmission is not required for retinal PER2::LUC rhythms generation . Manipulation of another inhibitory neurotransmitter glycine ( 3 mM; n = 4; Figure 4I ) or the glycine receptor antagonist strychnine ( 50 μM; n = 4; Figure 4J ) did not exhibit marked effects on the phase , amplitude , or period of retinal PER2::LUC rhythms , although the baseline of PER2::LUC rhythms was modestly reduced . Manipulation of dopaminergic neurotransmission produced perturbations in the waveform of retinal PER2::LUC rhythms at the onset of treatments with dopaminergic agonists , but did not damp or disrupt rhythms generation ( Figure 4A–4D ) . To test the effects of dopamine on retinal phase , we applied dopaminergic reagents in a step protocol beginning at different circadian times during the second cycle , using the peak time as a phase reference point of approximately circadian time ( CT ) 15 . We then compared the transient change in period ( phase shift ) of PER2::LUC rhythms in reagent-treated explants relative to those of vehicle-treated explants on the cycle ensuing initiation of treatment ( see Figure 5C legend for detail ) . Application of the nonselective dopamine receptor agonist ADTN ( 100 μM; in vehicle containing 100 μM L-AA ) , or the D1 dopamine agonist SKF 38393 ( 50 μM ) beginning in the early retinal subjective day ( CT 3 ) , phase-advanced retinal rhythms by approximately 1 . 5 h ( Figure 5A and 5C ) . In contrast , application of the D2/D4 receptor agonist quinpirole ( 50 μM ) at CT 3 , did not reset retinal phase ( Figure 5C ) , whereas coapplication of the two agonists together produced phase advances similar in amplitude to D1 agonist alone ( Figure 5C ) . Application of D1 agonist , beginning at CT 18 , induced phase delays of approximately 1 h ( Figure 5B and 5C ) , whereas application of the D2 agonist at that phase did not affect retinal phase ( Figure 5C ) . Phase advances and delays induced by dopamine agonists were stable and persisted for multiple cycles . Although dopaminergic agonists were applied as a bolus and thus were potentially active for multiple circadian cycles , the periods of molecular rhythms of treated explants were only altered relative to controls for two cycles following administration of dopaminergic reagents and then stabilized , indicating that these reagents likely have a limited effective half-life of about 48 h in our culture system . Acute pulse application experiments , performed in an attempt to define more precisely the phase dependence of the resetting action of dopamine , did not yield useful data due to artifactual phase shifts induced by the repeated media changes themselves . Manipulation of melatonin transmission did not have any noticeable effects on the waveform of retinal PER2::LUC rhythms ( Figure 3C and 3D ) and did not significantly affect the phase of retinal molecular rhythms . Neither application of melatonin ( 10 nM ) , nor the melatonin receptor antagonist luzindole ( 5 μM ) significantly altered the phase of retinal PER2::LUC rhythms ( Figure 5D ) . Melatonin and luzindole were each applied beginning at two different phases of the retinal rhythm , at approximately CT 3 or CT 18 , but none of these treatments resulted in a statistically significant change in retinal phase ( Figure 5D ) . In addition , application of a higher dose of melatonin ( 10 μM ) to B6C3 retinas , or application of 10 nM melatonin to C57BL/6J retinas also failed to alter retinal phase ( Figure 5D ) . To test the effects of light pulses on the phase of retinal PER2::LUC rhythms , we prepared retinal explant cultures under infrared illumination to preserve photoreceptor function and subjected them to 1-h pulses of white light ( 500 lux ) at different circadian times during the second cycle in vitro . Light pulses beginning in the early retinal subjective night ( CT 13 ) phase delayed retinal PER2::LUC rhythms by approximately 2 . 3 h , whereas light pulses beginning in the late retinal subjective night ( CT 19 . 5 ) induced phase advances of approximately 1 . 5 h ( Figure 6A–6C ) . To determine whether dopamine acting through D1 receptors mediates light resetting of retinal PER2::LUC rhythms , we applied the D1 receptor antagonist SCH-23390 ( 50 μM ) beginning 15 min before starting 1-h light pulses at CT 13 . Light-induced phase delays were significantly attenuated in the presence of SCH-23390 compared to light alone ( 1 . 2 h , p < 0 . 01 , Figure 6D ) . Pretreating retinal explants with the D1 antagonist SCH-23390 and the D2 receptor antagonist sulpiride ( 50 μM ) together prior to light pulse resulted in phase delays similar in amplitude to D1 antagonist pretreatment alone ( Figure 6D ) . Application of the nonselective dopamine receptor agonist ADTN ( 100 μM; in vehicle containing 100 μM L-AA ) or the D1 agonist SKF 38393 ( 50 μM ) beginning at CT 13 in the absence of light exposure , induced phase delays of approximately 1 h ( Figure 6D ) , which were significantly lower in amplitude than the phase delays produced by 1-h light pulses ( p < 0 . 01 ) . Application of the D2 agonist quinpirole at CT 13 did not reset retinal phase ( Figure 6D ) . In Figure 4G , we found that 1 mM GABA greatly suppressed the amplitude of retinal PER2::LUC rhythms . We further tested different concentrations of GABA on our retinal explant cultures . Application of 0 . 1 mM GABA did not obviously alter PER2::LUC rhythms compared with vehicle ( Figure 7A ) ; however , at a higher concentration ( 0 . 5 mM ) , GABA acutely suppressed the level of PER2::LUC bioluminescence signals and greatly reduced the peak-to-trough amplitude of PER2::LUC oscillations on subsequent circadian cycles ( Figure 7B ) . At 2 mM or 3 mM , GABA further inhibited PER2::LUC luminescence levels and resulted in rapid damping of PER2::LUC rhythms ( Figure 7C and 7D ) . Media changes that removed GABA restored the damped oscillations ( see below ) . To further characterize the inhibitory action of GABA on retinal rhythms , we calculated the ratio of the peak-to-trough amplitude of the fourth circadian cycle ( second cycle after treatment ) to the amplitude of the second cycle ( pretreatment control cycle , A4/A2 ) . This ratio was plotted as a function of GABA concentration ( Figure 7E ) . The A4/A2 for 0 . 1 mM GABA was 0 . 82 ± 0 . 06 ( n = 5 ) , which was similar to that for vehicle treatment ( 0 . 75 ± 0 . 08; n = 5 ) . As GABA concentration was increased , A4/A2 gradually decreased , and at 3 mM GABA , most explants lacked clear rhythms , and the few cycles that could be measured were of very low amplitude ( 0 . 07 ± 0 . 01; n = 7 ) , indicating that GABA inhibits PER2::LUC rhythmic amplitude in a dose-dependent manner . However , GABA did not significantly change the period of retinal PER2::LUC rhythms compared with vehicle ( τ = 23 . 80 ± 0 . 16 h for 0 . 5 mM GABA , n = 6; τ = 24 . 33±0 . 22 h for 1 mM GABA , n = 7; and τ = 24 . 05 ± 0 . 09 h for vehicle , n = 5; p > 0 . 05 for both comparisons to vehicle ) . To test whether the inhibitory action of GABA is specific to the retina , or common to other neural clock tissues , we applied 1 mM or 3 mM GABA to mouse mPer2Luc SCN explants . GABA did not significantly change the A4/A2 of SCN PER2::LUC rhythms compared to vehicle ( 0 . 91 ± 0 . 08 for 1 mM GABA; 0 . 86 ± 0 . 07 for 3 mM GABA; 0 . 84 ± 0 . 11 for vehicle treatment; p > 0 . 05 for both; n = 3 each; Figure S4 ) ; neither did GABA have significant effect on the period of SCN PER2::LUC rhythms ( τ = 23 . 56 ± 0 . 26 h , 23 . 96 ± 0 . 32 h , and 23 . 63 ± 0 . 23 h for 1 mM GABA , 3 mM GABA , and vehicle , respectively; p > 0 . 05 for both comparisons to vehicle; n = 3 each ) . Therefore , GABA-induced inhibition of ensemble PER2::LUC rhythms is retina specific . Next , we characterized the receptors responsible for the inhibitory action of GABA . GABA receptors are classified as ionotropic , chloride-conducting GABAA and GABAC receptors , or as metabotropic GABAB receptor . All three types of GABA receptors are present in the mammalian retina [31] . When the GABAA receptor agonist muscimol ( 200 μM ) or the GABAC agonist cis-4-aminocrotonic acid ( CACA; 50 μM ) was applied individually to the retinal explants , the amplitude of retinal PER2::LUC rhythms was significantly reduced compared to that of vehicle-treated samples ( see Table 1 for A4/A2 ratios of our GABA pharmacological treatments; Figure S5A and S5C ) . However , the amplitude of PER2::LUC rhythms was largely unaffected by the GABAB agonist baclofen ( 200 μM; Figure S5B ) . When muscimol and CACA were applied together , they mimicked the inhibition of 1 mM GABA on retinal PER2::LUC rhythms ( Figure 8A ) . Baclofen coapplied with either muscimol or CACA , or baclofen coapplied with both muscimol and CACA , did not enhance the inhibition of the GABAA and GABAC agonists ( Figure S5D–S5F ) . trans-4-Aminocrotonic acid ( TACA; 80 μM ) , an agonist for both GABAA and GABAC receptors , inhibited luminescence rhythms to a degree similar to 1 mM GABA ( Figure 8B ) . These results indicate that activation of both GABAA and GABAC receptors is necessary to mimic fully the action of GABA on retinal rhythms , with GABAB receptors playing no apparent role , either alone or in combination with the other receptors . We further examined whether specific antagonists could block the action of GABA . The inhibitory effect of 1 mM GABA persisted when it was applied in the presence of the GABAA receptor antagonist SR-95531 ( 40 μM ) alone , the GABAB receptor antagonist CGP-35348 ( 100 μM ) alone , and the GABAC receptor antagonist TPMPA ( 100 μM ) alone ( Figure S6A–S6C ) . However , the effect of 1 mM GABA was greatly attenuated by coapplication of SR-95531 and TPMPA ( Figure 8C ) . Coapplication of either SR-95531 with CGP-35348 , or TPMPA with CGP-35348 did not block the action of 1 mM GABA ( Figure S6D and S6E ) . CGP-35348 also did not significantly enhance the blocking ability of SR-95531 and TPMPA when it was coapplied with both antagonists ( Figure S6F ) . These results indicate that blockade of both GABAA and GABAC receptors is necessary to attenuate GABA inhibition of retinal rhythms with , again , no dependence on GABAB receptors . To assess the role of endogenous GABA in retinal rhythms generation , we next applied various GABA receptor antagonists to cultured mPer2Luc retinal explants . When GABAA or GABAC antagonists SR-95531 ( 40 μM ) and TPMPA ( 100 μM ) were applied individually , each modestly increased the peak-to-trough amplitude of luminescence rhythms ( Figure S7A and S7C ) , whereas application of the GABAB antagonist CGP-35348 ( 100 μM ) alone did not change the amplitude ( Figure S7B ) . However , antagonism of both GABAA and GABAC receptors with SR-95531 and TPMPA significantly increased the peak-to-trough amplitude of retinal ensemble PER2::LUC luminescence rhythms compared with vehicle ( Figure 8D and 8E ) , indicating that endogenous GABA indeed suppresses the amplitude of retinal PER2::LUC rhythms . Again , CGP-35348 did not enhance the effect of SR-95531 and TPMPA ( Figures 4H , S7D , and S7E ) . Taken together , these pharmacological studies indicate that endogenous retinal GABA reduces PER2::LUC signals and the amplitude of molecular retinal circadian rhythms through activation of both GABAA and GABAC receptors . To further test whether GABAergic suppression of PER2::LUC luminescence rhythms results from an effect on the core function of the retinal clock , we tested whether prolonged application of GABA can halt the molecular oscillation of the retinal clock . GABA ( added to a final concentration of 3 mM in 1 μl of water vehicle ) was applied to retinal cultures for different durations ( 1 , 7 , 13 , 19 , 25 , 31 , 37 , and 43 h ) , starting at the beginning of the third cycle in vitro ( Figure 9A and 9B ) . We hypothesized that if retinal rhythms generation was stopped by prolonged GABA application , and restored upon washout , then the time of washout should predict the subsequent phase of restored rhythms . In contrast , if the observed suppression of PER2::LUC luminescence rhythms during GABA application did not have an effect on the core clock , but merely on luminescence output , then the phase of restored rhythms should be predicted by the projected phase of the baseline rhythm obtained prior to treatment . Figure 9C shows the peak times of retinal PER2::LUC rhythms following GABA treatment and washout at the specified time intervals . For retinas exposed to GABA for 19 h or greater , the first peak of the restored luminescence rhythm always appeared ca . 22 h following GABA washout , with subsequent peaks occurring approximately at 24-h intervals . The trend lines of peak times show that the phase of restored rhythms is indeed predicted by the time of washout , but not by projected continuation of previous rhythms . In a control experiment , 1 μl of water vehicle was applied to retinal cultures for 37 h and then the medium was changed , as in the GABA application experiments . In this case , the first peak of the ongoing PER2::LUC rhythms occurred ca . 14 h after the media change , not ca . 22 h after , as when GABA was washed out ( n = 3 ) . Thus , GABA affects the retinal clock , not just its output , and its action in this experiment is consistent with having the ability to stop the molecular clock mechanism at a certain phase . To determine the mechanisms by which GABA suppressed retinal PER2::LUC expression and rhythmicity , we first quantified Per1 , Per2 , Clock , and Bmal1 mRNA levels 8 h after 3 mM GABA treatment using quantitative real-time PCR . Interestingly , GABA did not change the mRNA levels of Per2 and Clock , but did increase the mRNA levels of Per1 and Bmal1 ( Figure 10A ) . Therefore , the substantial reduction in PER2::LUC expression upon GABA treatment is likely due to posttranscriptional regulation . The finding that GABA acts on the retinal PER2::LUC rhythms through ionotropic , Cl−-conducting receptors suggested that GABAergic modulation of the clock could be mediated by membrane hyperpolarization or , in the case of prolonged GABA application , by alteration of membrane ionic gradients leading to tonic depolarization [56] . Depolarization with elevated K+ media ( 4 mM ) during 1 mM GABA application partially restored the rhythmic amplitude of PER2::LUC expression ( A4/A2 = 0 . 70 ± 0 . 04 for vehicle , 0 . 20 ± 0 . 05 for GABA , and 0 . 45 ± 0 . 07 for 4 mM K+ + GABA; n = 5 each; Figure 10B and 10C ) , indicating that GABA acts in part through membrane hyperpolarization . Prolonged application of high K+ alone modestly increased the amplitude of PER2::LUC rhythms ( A4/A2 = 0 . 90 ± 0 . 10; n = 5; Figure 10D ) , indicating that the inhibitory effect of GABA on retinal PER2::LUC rhythms is not due to tonic depolarization . The effect of GABA is not mediated through the simple cessation of neuronal spiking due to membrane hyperpolarization , because blocking neuronal spiking with TTX ( 1 μM ) did not affect retinal PER2::LUC rhythms ( A4/A2 = 0 . 67 ± 0 . 09; n = 5; Figure 3G ) . Depolarization with elevated K+ media in the presence of GABA only partially rescued the amplitude of retinal PER2::LUC rhythms , indicating that other mechanisms contribute to the inhibitory action of GABA . The epsilon and delta isoforms of CKI are important regulators of PER protein stability that phosphorylate PER2 and target it for ubiquitin-mediated proteasomal degradation [57] . In addition , casein kinases associate with GABAA and GABAC receptor subunits [58 , 59] . To test whether casein kinases are involved in the inhibitory effect of GABA on PER2::LUC levels and retinal rhythms , we applied the casein kinase inhibitor CKI-7 ( 50 μM ) along with 1 mM GABA to mPer2Luc retinal explant cultures . CKI-7 partially rescued rhythmic amplitude in the presence of GABA ( A4/A2 = 0 . 35 ± 0 . 03 for CKI-7 + GABA vs . 0 . 20 ± 0 . 05 for GABA; p < 0 . 05; n = 5 each; Figure 10E ) . When KCl ( 4 mM ) and CKI-7 ( 50 μM ) were coapplied with 1 mM GABA , the rescue of rhythmic PER2::LUC amplitude was complete ( A4/A2 = 0 . 93 ± 0 . 15; n = 4; Figure 10F ) . When applied alone , CKI-7 did not significantly change the amplitude of PER2::LUC rhythms; however , it substantially lengthened retinal free-running period from 24 . 05 ± 0 . 09 h ( n = 5 ) to 25 . 77 ± 0 . 38 h ( n = 6; p < 0 . 005; Figure 10G ) . Taken together , these results suggest that GABA acts , in part , by stimulating casein kinase to suppress PER2 levels .
Information regarding the mechanisms of the mammalian retinal clock has been limited by the challenge of long-term culture of the mammalian retina and the interpretational caveats imposed by using melatonin or dopamine neurochemical output rhythms as proxies for observing the clock mechanism . The protocol we have developed here , a modification of the explant culture method for SCN luminescence recording [60] , allows continuous readout of the rhythmic abundance of a putative molecular component of the circadian clock mechanism , PER2 protein , as bioluminescence intensity emitted by isolated , intact mouse retinal explants . Within our preparations , all layers of the retina were anatomically intact , and the retinas exhibited robust circadian rhythms of PER2::LUC expression as well as functional light responses in the form of phase resetting . A key empirical finding in developing this culture protocol was that incubation of the retina in bicarbonate-buffered medium for the first 24 h in vitro greatly enhanced the amplitude and sustainability of retinal PER2::LUC rhythms . The precise mechanism by which the transition through bicarbonate-based medium preserves retinal rhythmicity is unknown; however , bicarbonate balance has wide-ranging effects on retinal physiology , and increased bicarbonate concentration has previously been shown to increase the amplitude of circadian rhythms in photoreceptor disc shedding in amphibian retinas [61] . Imaging of PER2::LUC luminescence signals from retinal vertical slice cultures of both B6C3 and C57BL/6J mice showed that PER2::LUC rhythms were predominantly localized to the inner nuclear layer of the retina , which contains the nuclei of retinal horizontal , bipolar , and amacrine cells . The pattern of PER2::LUC expression in the current study is consistent with previous single-cell RT-PCR and in situ hybridization studies demonstrating concentration of clock gene transcripts in the inner nuclear layer of the mouse [42 , 43 , 62] , and immunocytochemistry studies showing predominant localization of PER1 , PER2 , and CLOCK proteins in the inner nuclear layer as well [13 , 42] . Localization of rhythmic PER2::LUC expression to the inner nuclear layer is consistent with the persistence of PER2::LUC rhythms in photoreceptor-degenerate mouse retinas [43] , and with the persistence of Per2 rhythms in photoreceptor-degenerate RCS retinas [63] . The fact that disruption of communication through glutamate , sodium-dependent action potentials , and cx36-containing gap junctions did not disrupt retinal rhythms further suggests that communication through ganglion cells is not necessary . The wide distribution of PER2::LUC expression across the inner nuclear layer suggests that multiple inner nuclear layer cell types are sources for the rhythmic PER2::LUC signal and are likely participants in the inner retinal clock mechanism . Cell-type–specific mapping of coordinate expression of the core circadian clock genes has demonstrated that significant proportions of horizontal , rod bipolar , and dopaminergic amacrine neurons of the inner nuclear layer express all six core clock genes and thus are candidate cell types for self-sustained circadian rhythms generation [43] . Although , our results suggest that the inner nuclear layer of the intact mouse retina contains a circadian clock , our study does not preclude the potential for circadian rhythms generation in the photoreceptor or ganglion cell layers . Indeed , Tosini et al . [64] have recently reported gene rhythms from photoreceptor layers isolated from rat retinas that carry a transcriptional transgenic reporter for Per1 , rather than the knockin fusion protein reporter for PER2 used here . Our findings support cell-autonomous rhythms generation in the inner nuclear layer clock rather than rhythms generation as a network property dependent upon cell communication . None of the major neurotransmitter systems in photoreceptors ( melatonin and glutamate ) , ganglion cells ( glutamate ) , or the inner nuclear layer ( dopamine , acetylcholine , GABA , glycine , and glutamate ) was required to support generation of retinal circadian PER2::LUC rhythms , nor was intercellular communication through sodium-dependent action potentials and cx36-containing gap junctions , although dopamine and GABA exhibited important effects on retinal PER2::LUC rhythms . A question that arises for interpretation of these negative findings regarding cellular communication is the duration over which pharmacological treatments were effective in vitro . For GABA agonists and antagonists , it is clear that they are effective for at least 7 d ( Figures 4 , 7 , and 8 ) . For many other target transmitter systems , we have included genetic or chemical alternative tests that mitigate against this concern . For example , melatonin has been depleted genetically , as well as manipulated pharmacologically; dopamine has been depleted chemically , as well as manipulated pharmacologically; and gap junctional communication through the principal neural connexin , cx36 , has been lesioned genetically to complement pharmacological disruption of electrical synapses . All of these alternative manipulations gave negative results consistent with the pharmacology . Blockade of sodium-dependent action potentials with TTX has previously been shown to be effective in disrupting circadian rhythms in the SCN cultured under similar conditions for several days [50 , 65] . In addition , TTX blockade of sodium-dependent action potentials itself would have significantly reduced the spike-mediated release of GABA , glycine , glutamate , acetylcholine , and dopamine by amacrine and ganglion cells of the inner retina , reinforcing the negative findings with the pharmacological blockers specific to each of these transmitter systems . Thus , we cannot exclude all possibility that these transmitter systems contribute to circadian rhythms generation in the retina; however , the consistent results we have obtained with overlapping types of treatments suggest that these negative results are robust . We also cannot rule out the possibility that multiple transmitter systems play redundant roles , or the possibility that some untested neurochemical signals are critical , but our data do exclude previous models of retinal circadian organization in which a limited population of cells using one of the modulatory neurotransmitters acts as a pacemaker for all retinal neurons , such as the 500 dopaminergic amacrine cells in each retina driving all other retinal oscillator cells through dopamine secretion , or the photoreceptors driving all other retinal oscillators through melatonin release [38] . It is well established that dopamine and melatonin are important neurochemical messengers of the retinal circadian clock that act at multiple sites within the retinal circuitry to shape retinal function into “day” and “night” states [38 , 39 , 66 , 67] . Our results distinguish the roles of these neurotransmitters in retinal circadian organization , indicating that whereas dopamine has the additional role of regulating the phase of the core clock mechanism , melatonin's role is primarily that of an output messenger of the clock . Our finding that PER2::LUC rhythms persist in C57BL/6J mouse retinas with genetically blunted melatonin production and following pharmacological manipulation of melatonin signaling is consistent with previous studies showing that melatonin is not required for circadian photoreceptor disk shedding in constant darkness [4] , and that photoreceptors , the primary source of retinal melatonin , are not required for retinal PER2::LUC rhythms generation [43] . In addition , in the present study , neither manipulation of melatonin nor blockade of MT1 receptors significantly altered the phase of retinal PER2::LUC rhythms . Taken together , these results suggest that melatonin is an output messenger of the clock , which can influence dopamine release and other aspects of retinal physiology , but which does not directly affect or feed back onto the molecular clock mechanism . In contrast , dopamine is a key regulator of the endogenous retinal clock mechanism . Stimulation of dopamine D1 receptors reset the phase of retinal PER2::LUC rhythms , whereas blockade of dopamine D1 receptors reduced the amplitude of phase delays induced by light pulses given in the early subjective night . These effects on circadian phase demonstrate that retinal dopamine influences the core clock mechanism of mammalian retinas . Our findings suggest D1 dopamine receptors are key to the action of dopamine on the retinal circadian pacemaker , whereas D2 receptors have been suggested to play a role in light-induced Per gene induction by dopamine [68] . Both studies suggest that dopamine targets retinal cell types in the inner nuclear layer clock to reset the retinal circadian oscillator . The potential targets of phase resetting include multiple cell types that are the primary sites of D1 receptor expression in the inner nuclear layer: horizontal , AII amacrine , and bipolar cells [69] . The lack of phase setting of the inner retinal oscillator by the D2/D4 agonist quinpirole and melatonin suggests that photoreceptors and melatonin do not participate in the dopaminergic resetting process , as D4 receptors are expressed predominantly in retinal photoreceptors where they influence melatonin synthesis [69] . The D1-dependent resetting mechanism , which targets neurons of the inner nuclear layer , is distinct from dopamine-mediated light resetting of amphibian retinal melatonin rhythms , which involves D2-like receptors on photoreceptor cells [70 , 71] . Our demonstration of light resetting of in vitro retinal PER2::LUC rhythms is consistent with previous findings that the melatonin secretion rhythms of the mouse retina could be synchronized to light cycles in vitro [17] . All retinal photoreceptive cell types—rods , cones , and melanopsin ganglion cells—are anatomically intact in the retinal explants we used , and thus could contribute to the circadian light responses we recorded . In this regard , it is interesting to note that dopaminergic amacrine neurons are light responsive [72] and that a subpopulation of them exhibit sustained light responses driven by melanopsin-expressing intrinsically photoreceptive ganglion cells in a novel intraretinal photic pathway [73] . Thus , melanopsin ganglion cells may contribute to the entrainment of both retinal and SCN neural oscillators . Based on our results , we propose that endogenous retinal dopamine serves to synchronize the inner retinal circadian clock to the external light/dark cycle . In vivo , retinal dopamine release is rhythmic , exhibiting a peak near dawn each day , due to both circadian and light-driven regulation of retinal dopamine [74] . Light increases the activity of retinal tyrosine hydroxylase , the key enzyme in dopamine synthesis , within 15 min of light onset [75] , and evokes a sharp burst of dopamine synthesis and utilization within 30 min of light onset [76] . On a molecular level , dopamine mediates acute light induction of the Per1 gene in the mouse retina [68] and resets the Xenopus photoreceptor clock through induction of xPer2 [70 , 71] . The phase dependence of dopamine-induced phase shifts we have observed corresponds with the likely phase-resetting response to light , with phase advances induced in the early subjective day and phase delays in the early subjective night . Phase delays induced by light pulses in the early subjective night were significantly reduced by blockade of dopamine D1 receptors . The circuitry of mammalian retinal dopaminergic amacrine cells [72 , 73] , the molecular action of dopamine on retinal clock genes [68] , and our direct demonstration of dopamine effects on clock phase and light resetting of retinal clock phase , all indicate that dopamine transmission likely mediates light entrainment of the inner retinal mammalian circadian clock through its action on Per gene levels and rhythms . Importantly , light-induced phase shifts were not completely blocked by D1 antagonists and dopamine agonists could not mimic the full amplitude of light-induced phase shifts , suggesting that other neurotransmitters are also involved in the light entrainment process . In addition to setting the phase of the retinal clock , dopamine is a key output of the retinal clock , inducing many of the functional changes in retinal neurons and circuits that define the “day” state of retinal function , such as cone-dominated processing in visual circuits and ERG amplitude [13 , 66 , 77] , and mediating circadian rhythms in behaviorally measured visual sensitivity [78] . Thus , retinal dopamine apparently plays dual roles in the circadian organization of the retina , serving as an output signal that mediates many of the physiological , morphological , and trophic rhythms in the retina , as well as an input signal to regulate the phase of the molecular clock mechanism in relation to the external light/dark cycle . As both a key input to and output from the mammalian retinal clock , dopamine plays a central role in retinal circadian organization and is an important point for mechanistic intervention in ocular processes and pathologies regulated by the circadian clock . In the current study , we found that GABAA and GABAC receptor blockade significantly increased the amplitude of retinal PER2::LUC rhythms , whereas application of exogenous GABA damped rhythmic amplitude , and with prolonged application , stopped the retinal circadian clock . The high concentrations of exogenous GABA ( >100 μM ) necessary to modulate retinal PER2::LUC rhythms in these experiments are likely due to uptake by high-affinity GABA transporters present on a range of retinal neurons , as well as on retinal Müller cells [79] . Previous electrophysiological experiments have demonstrated , for example , that application of 100 μM GABA produces near-maximal effects on isolated retinal ganglion cells , but higher concentrations are necessary to produce detectable effects in retinal slice preparations [80] . Our finding that the effects of GABA can be mimicked or blocked by GABAA and GABAC receptor agonists and antagonists indicates that the effects of GABA on retinal PER2::LUC rhythms are receptor mediated and specific . Our data establish that GABAergic regulation is a common mechanism across neural circadian clocks because blockade of GABA receptors in SCN explants also increases Per1::luc rhythmic amplitude [65] , and application of GABA resets the phase of spike frequency rhythms in isolated SCN neurons [81] . However , the ability of exogenous GABA to suppress substantially PER2::LUC rhythms is unique to the retina , as similar experiments with SCN slices do not yield similar results . The increased effectiveness of GABA as a modulator of retinal PER2 expression is likely due to the specific retinal expression of the GABAC receptor , which engenders nondesensitizing responses and prolongs greatly the effectiveness of any GABA stimulation [82–84] . The inhibitory action of GABA on the amplitude of retinal PER2::LUC rhythms could act through damping of circadian rhythms in individual retinal neurons , or through desynchronizing individual oscillators , and differentiating these mechanisms will require single-cell resolution studies of retinal circadian rhythms . A functional consequence of reducing rhythmic amplitude , predicted by limit cycle models of circadian oscillators , is to increase sensitivity to phase-resetting stimuli . Thus , a potential role of GABA in neural circadian oscillators is to enhance the effectiveness of endogenous phase-communicating substances , such as dopamine in the retina [70] , or vasoactive intestinal polypeptide ( VIP ) and gastrin-releasing peptide ( GRP ) in the SCN [85 , 86] , in resetting the rhythms of neuronal oscillators by limiting the amplitude of the intracellular molecular clock oscillations . On a mechanistic level , our data suggest that GABA acts to suppress the amplitude of retinal PER2::LUC rhythms through membrane hyperpolarization and casein kinase activation . The mechanism by which GABA may stimulate retinal casein kinases is unknown at this point . CKI has been shown to physically associate with GABAA receptors in a rhythmic manner in the SCN [58] , and CKII has been shown to phosphorylate GABAC receptor subunits [59] . These types of physical associations between GABA receptors and casein kinases have been assumed to be for receptor modulation , but could provide a link between persistent activation of GABA receptors and activation of casein kinases as well . It is noteworthy that the circadian association between CKIε–CKIδ and GABAA receptors in the rat SCN peaks at CT 6–7 [58] , when GABA content in the SCN is high [87] and PER levels are low [88 , 89] , and then drops to low levels in early subjective night when PER levels peak . This may imply a novel clock regulatory mechanism in which PER and GABA receptors compete for association with casein kinases . It will be of importance to test this hypothesis in the mouse retina , where GABA exhibits significant effect on PER2::LUC rhythms . Alternatively , casein kinases are regulated by protein phosphatases and by metabotropic glutamate receptors [90 , 91] , and perhaps persistent GABA stimulation alters these pathways . It will be of interest in the future to elucidate more fully the mechanistic relationship of GABA to casein kinase and its effect on the retinal clock . Our finding that GABA plays key roles in the retinal circadian clock draws attention to GABAergic neurons as sources of these retinal circadian signaling molecules and potential sites of rhythms generation . The full complement of core clock genes is expressed in GABAergic horizontal cells and dopaminergic amacrine cells , which also express GABA [43] . In addition , transcription of the Per1 clock gene has been shown to occur in most GABAergic amacrine cells , and rhythmically in the dopaminergic and nitric-oxide synthase ( NOS ) -positive subtypes of GABAergic amacrine cells [42 , 92] . Thus , there is a strong correlation of core clock gene expression within GABAergic retinal neurons , suggesting a molecular basis for circadian regulation of this neurotransmitter . Retinal GABA turnover rate and release show rhythmic variations under constant darkness condition , with their levels higher in the subjective night [21] . Furthermore , GABA increases melatonin content in a dose-dependent manner in the hamster retina [93] . Our results suggest that GABA could , through membrane hyperpolarization and activation of casein kinase , stimulate the degradation of accumulated PER proteins on the falling phase of their rhythm ( which roughly corresponds to the night state ) , acting to remove feedback inhibition on Per transcription and thereby preparing retinal oscillators for transcriptional activation that characterizes the “day” state . Importantly , GABA is not acting merely through suppression of dopamine release , as dopamine depletion does not mimic the effects of GABA . Taken together , these data are consistent with the notion that one role of GABA in the retinal circadian clock is to function as an analog for darkness and enforce the “night” state in the mammalian retina , along with melatonin . Our finding that the GABAC receptor is a critical co-mediator of GABA influence on retinal PER2::LUC rhythms suggest roles for neurons in the outer half of the retina in circadian clock function . The GABAC receptor has a restricted distribution in the mammalian retina , with the primary site of expression on bipolar cell synaptic terminals in the inner plexiform layer where it mediates recurrent feedback from GABAergic amacrine cells [94] , and a secondary site of expression in mouse cone photoreceptors [95] . Thus , the balance of results suggests that retinal bipolar cells , some of which coordinately express all the core clock genes [43] , and perhaps photoreceptors , are important targets of GABAergic regulation in the retinal circadian clock network . Convincing evidence for the presence of GABAC receptors on rod photoreceptors and horizontal , amacrine , and ganglion cells in the mammalian retina is still lacking; however , GABAA receptors may mediate the action of GABA on these cell types , because the GABAA receptor agonist muscimol alone partially reduced rhythmic PER2::LUC amplitudes ( Figure S3A ) , and GABAA receptors have been detected in these retinal cell types [31] . In summary , the present data are consistent with the hypothetical model shown in Figure 11 for circadian organization of the mammalian retina in which GABAergic and dopaminergic neurons play key roles through rhythmic secretion of dopamine and GABA , and the subsequent molecular actions of dopamine and GABA on PERs , via transcriptional activation and degradation . In this model , dopamine , which exhibits both circadian and light-induced elevation in the day phase [19 , 75] , reinforces the rising phase of Per molecular rhythms during the day through up-regulation of Per genes via the mitogen-activated extracellular signal-regulated kinase ( ERK ) kinase ( MEK ) and the cAMP-responsive element-binding protein ( CREB ) binding protein ( CBP ) [68] . In terms of phase resetting , increasing dopaminergic stimulation on the initial rising phase of the PER2 rhythms , corresponding to the early day , resulted in phase advances , whereas increasing dopaminergic stimulation near the peak in PER2 expression , corresponding to the early night , resulted in phase delays . We propose that light stimulation of dopaminergic neurons through M-cones and ON bipolar cells , as well as intrinsically photosensitive retinal ganglion cells ( ipRGCs ) [73] at these phases would act to synchronize molecular retinal circadian rhythms to the light/dark cycle . In contrast , retinal GABA exhibits elevated turnover and release at night [21] , perhaps from excitatory input from OFF bipolar cells to GABAergic amacrine cells , and in this model , stimulates the degradation of accumulated PER proteins through casein kinase activation , acting to remove feedback inhibition on Per transcription , and thereby preparing retinal oscillators for transcriptional activation in the next “day” state . GABA might also facilitate the molecular resetting effects of dopamine through negatively regulating the amplitude of retinal PER rhythms . Thus , GABA transmission acts to reinforce the “night” state of the retinal clock . The idea that GABA serves as a “dark” signal is consistent with previous findings that GABA and the GABAA receptor agonist muscimol cause dark-adaptive retinomotor movements in the Xenopus and fish retinas [96 , 97] and that GABA suppresses tyrosine hydroxylase activity [98] and inhibits dopamine release [49] . Future studies employing our in vitro retinal explant culture system and other strategies can test our working hypothesis regarding the organization of the mammalian retinal circadian clock , and further elucidate the cellular and molecular mechanisms of retinal circadian clocks and their influence on visual function . The highly ordered and well-characterized property of retinal circuitry will facilitate elucidation of the general principles of the circadian pacemaking system .
mPer2Luc knockin mice , which initially were on a 129SvEv X C57BL/6J genetic background [44] , were maintained as a continuous backcross to C57BL/6J for 13 generations . The resulting mPer2Luc mice were crossed with C3H rd1 mice ( The Jackson Laboratory ) to produce mPer2Luc mice that are heterozygous for the rd1 gene mutation and are genetically capable of producing melatonin . Unless otherwise specified , the mice used in the experiments were B6C3F1 mPer2Luc mice . All animal use was conducted in accordance with the guidelines of the Vanderbilt University Animal Care Division and the Association for Research in Vision and Ophthalmology ( ARVO ) Statement for the Use of Animals . mPer2Luc mice 28–60 d of age were killed at approximately ZT 4 during the day phase ( 4 h after lights on in the mouse colony ) . Eyes were enucleated and placed in Hank's balanced salt solution ( HBSS; Invitrogen ) . Retinas were isolated and cut into two pieces . Each piece of retina was placed with the ganglion cell layer up on Millicell culture membrane ( Millipore ) and gently flattened with two end-blunted glass pipettes . The membrane was first transferred to 1 . 0 ml of neurobasal medium ( Invitrogen ) supplemented with 2 mM l-glutamine ( Sigma-Aldrich ) , 2% B27 ( Invitrogen ) , 25 units/ml penicillin , and 25 μg/ml streptomycin ( Invitrogen ) , incubated in 5% CO2 incubator at 37 °C for 24 h , and then transferred to 1 . 0 ml of medium 199 ( Sigma-Aldrich ) supplemented with 0 . 7 mM l-glutamine , 4 mM sodium bicarbonate ( Sigma-Aldrich ) , 10 mM Hepes ( Sigma-Aldrich ) , 20 mM d-glucose ( Sigma-Aldrich ) , 2% B27 , 0 . 1 mM beetle luciferin ( Promega ) , 25 units/ml penicillin , and 25 μg/ml streptomycin . Bioluminescence was measured with a LumiCycle ( Actimetrics ) . Cultures were maintained at 37 °C . LumiCylce ( Actimetrics ) software was used to calculate peak-to-trough amplitude , and ClockLab software ( Actimetrics ) was used to determine the peak time ( maximum phase ) of PER2::LUC rhythms from the raw data . At approximately ZT 4 , mPer2Luc knockin mice were killed by cervical dislocation , the brains rapidly extracted following decapitation and immediately placed in cold HBSS . Coronal brain sections ( 250-μm thickness ) were cut with a Vibratome ( World Precision Instruments ) , transferred to cold HBSS , and sorted under a dissecting microscope . Slices containing the SCN were trimmed to rectangles 3 × 3 mm with a pair of scalpels , and then cultured on Millicell culture membranes with 1 . 0 ml of DMEM ( Sigma ) , supplemented with 4 mM sodium bicarbonate , 10 mM Hepes , 20 mM d-glucose , 2% B27 , 0 . 1 mM beetle luciferin , 25 units/ml penicillin , and 25 μg/ml streptomycin . Bioluminescence was continuously measured with a LumiCycle . Cultures were maintained at 37 °C . Freshly isolated retinas were attached tightly to Millipore membrane filters , with the ganglion cell layer facing the filters , sliced into 150-μm-thick sections with a tissue slicer ( Stoelting ) , transferred onto Millicell membrane inserts , and cultured with 1 . 0 ml of neurobasal medium , and then incubated in 5% CO2 incubator at 37 °C for 1–2 d . Immediately before imaging , 0 . 1 mM beetle luciferin was added to the culture medium . Images of 30-min exposure duration were collected using a 20× objective ( NA 0 . 35 ) coupled directly to a PIXIS camera system ( Princeton Instruments ) cooled to −70 °C . Signal-to-noise ratio was improved by 2 × 2 binning of pixels . Data were analyzed with Metamorph software ( Molecular Devices ) . Retinal explant whole mounts were fixed in phosphate-buffered saline ( PBS ) containing 4% paraformaldehyde ( PFA ) , blocked with 1% bovine serum albumin ( BSA ) and 0 . 3% Triton X-100 in 0 . 1 M PBS for 2 h , and then incubated in the primary antibodies at 4 °C for 48 h . The primary antibodies used were: 1:500 sheep anti-tyrosine hydroxylase ( Chemicon ) ; 1:1 , 000 rabbit anti-cone arrestin antibody Luminaire junior; 1:1 , 000 rabbit anti-M-opsin; 1:1 , 000 rabbit anti-S-opsin ( cone antibodies were kind gifts from Dr . Craft , University of Southern California ) ; 1:1 , 000 mouse anti-calbindin ( Chemicon ) ; 1:1 , 500 rabbit anti-CaBP5 ( a gift from Dr . Haeseleer , University of Washington ) ; 1:3 , 000 mouse anti-calretinin ( Chemicon ) ; and 1:2 , 000 rabbit anti-melanopsin ( a gift from Dr . Provencio , University of Virginia ) . After rinsing , a secondary incubation was performed for 2 h with either 1:500 Alexa Fluor 488 donkey anti-sheep IgG , 1:500 Alexa Fluor 488 donkey anti-rabbit IgG , or 1:500 Alexa Fluor 594 donkey anti-mouse IgG ( all purchased from Molecular Probes ) . Samples were mounted and cover slipped using Vectashield ( Vector Laboratories ) and then visualized using Zeiss LSM5 PASCAL confocal microscopy ( Carl Zeiss ) . The total content of dopamine from retinal explants was determined by HPLC in the Vanderbilt Neurochemistry Core Lab . The HPLC system consisted of a 515 HPLC pump ( Waters ) , a 717 plus autosampler ( Waters ) , a Decade II ( oxidation: 0 . 5 ) electrochemical detector ( Antec Leyden ) , and a Nucleosil ( 5 μm , 100 Å ) C18 HPLC column ( 150 × 4 . 6 mm; Phenomenex ) . The retinal samples were first homogenized in 200 μl of 0 . 1 M TCA , which contains 10 mM sodium acetate , 0 . 1 mM EDTA , and 10 . 5% methanol ( pH 3 . 8 ) , and then centrifuged at 10 , 000 g for 20 min , filtered on a 0 . 2-μm pore size filter . A total of 20 μl of each sample was directly injected into the HPLC system . The mobile phase was composed of 89 . 5% 0 . 1 M TCA , 10 mM sodium acetate , 0 . 1 mM EDTA , and 10 . 5% methanol ( pH 3 . 8 ) . The mobile phase flow rate was set to 0 . 6 ml/min and the electrochemical detector to 33 °C . HPLC control and data acquisition were managed by Millennium 32 software ( Waters ) . To determine the transcript levels of core clock genes in GABA- and vehicle-treated retinal explants , retinal explants were homogenized 8 h after treatment started at the beginning of the third circadian cycle , and total RNA extraction and real-time RT-PCR were performed as previously described [43] . The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) was used as a reference gene for normalization . For data analysis , the threshold cycle ( Ct ) was converted to absolute amount of transcript ( E−Ct ) and presented as E Ct GAPDH -Ct gene of interest . The mean value of E Ct GAPDH -Ct gene of interest for vehicle was normalized to 1 . 0 . A Student's t-test was applied to compare clock gene expression levels between treatment and vehicle . Student's t-test was routinely performed to compare the difference in the damping rate , period , phase , and A4/A2 ratio between drug- and vehicle-treated samples . For GABAA and GABAC antagonists treatment , two-way repeated-measures ANOVAs were run for both drug and vehicle control in which the variances were homogeneous , as indicated by a significant Levene test . For post hoc analysis of significant interactions , independent t-tests with a Bonferroni-corrected alpha ( the alpha was Bonferroni corrected by the number of comparisons ) were used to compare the effects of drug for each cycle .
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The circadian clock in the mammalian retina regulates many retinal functions , and its output modulates the central circadian clock in the brain . Details about the cellular location and neural regulation of the mammalian retinal circadian clock remain unclear , however , largely due to the difficulty of maintaining long-term culture of adult mammalian retina and the lack of an ideal experimental measure of the retinal clock . We have circumvented these limitations by developing a protocol for long-term culture of intact mouse retinas to monitor circadian rhythms of clock gene expression in real time . Using this protocol , we have localized expression of molecular retinal circadian rhythms to the inner nuclear layer . We find molecular retinal rhythms generation is independent of many forms of signaling from photoreceptors and ganglion cells , or major forms of neural communication within the inner nuclear layer , and have characterized light-induced resetting of the retinal clock . Retinal dopamine and GABA , although not necessary for the generation of molecular retinal rhythms , were revealed to regulate the phase and amplitude of retinal molecular rhythms , respectively , with dopamine participating in light-induced resetting . Our data indicate that dopamine and GABA play prominent roles in the organization of the retinal circadian clock .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroscience"
] |
2008
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An Autonomous Circadian Clock in the Inner Mouse Retina Regulated by Dopamine and GABA
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A major goal of contemporary studies of embryonic development is to understand large sets of regulatory changes that accompany the phenomenon of embryonic induction . The highly resolved sea urchin pregastrular endomesoderm–gene regulatory network ( EM-GRN ) provides a unique framework to study the global regulatory interactions underlying endomesoderm induction . Vegetal micromeres of the sea urchin embryo constitute a classic endomesoderm signaling center , whose potential to induce archenteron formation from presumptive ectoderm was demonstrated almost a century ago . In this work , we ectopically activate the primary mesenchyme cell–GRN ( PMC-GRN ) that operates in micromere progeny by misexpressing the micromere determinant Pmar1 and identify the responding EM-GRN that is induced in animal blastomeres . Using localized loss-of -function analyses in conjunction with expression of endo16 , the molecular definition of micromere-dependent endomesoderm specification , we show that the TGFβ cytokine , ActivinB , is an essential component of this induction in blastomeres that emit this signal , as well as in cells that respond to it . We report that normal pregastrular endomesoderm specification requires activation of the Pmar1-inducible subset of the EM-GRN by the same cytokine , strongly suggesting that early micromere-mediated endomesoderm specification , which regulates timely gastrulation in the sea urchin embryo , is also ActivinB dependent . This study unexpectedly uncovers the existence of an additional uncharacterized micromere signal to endomesoderm progenitors , significantly revising existing models . In one of the first network-level characterizations of an intercellular inductive phenomenon , we describe an important in vivo model of the requirement of ActivinB signaling in the earliest steps of embryonic endomesoderm progenitor specification .
Most animal embryos are patterned through a series of cell–cell interactions that shape and refine maternally inherited axial asymmetries . Cells are directed to assume specific fates through the phenomenon of embryonic induction , a process that has been intensely studied by developmental biologists for a number of decades . Understanding the mechanisms of embryonic induction is also of vital clinical importance , especially in the field of regenerative medicine , in which pluripotent stem cells are induced to differentiate into specific lineages of therapeutic interest . The widespread availability of sequenced eukaryotic genomes and microarray-based tools has greatly facilitated large-scale analyses of genes expressed at various times in the course of an embryonic induction . There is now a critical need to understand the regulatory interactions among these gene products that ultimately determine the outcome of the induction process . By integrating regulatory interactions among large sets of gene products , gene regulatory networks ( GRNs ) provide uniquely global perspectives on biological and disease processes . Network-based approaches have therefore been adopted to describe Drosophila embryonic patterning [1] , immune cell function [2] , nematode vulval development [3] , vertebrate retinal development [4] , metabolic pathways [5] , and malignant transformations [6] . The sea urchin endomesoderm–gene regulatory network ( EM-GRN ) was one of the first large-scale integrations of maternal and zygotic regulatory information in early embryonic development [7] and is currently one of the most detailed biological networks in existence [8] . A number of recent studies employing the sea urchin EM-GRN have demonstrated that , in addition to describing the regulatory blueprint underlying embryogenesis , changes in developmental GRN structure can suggest the molecular basis of the evolution of species-specific cell types and body plans across distant phyla [9 , 10] . An asymmetric localization of nuclear β-catenin is observed in most deuterostome embryos during early stages of development [11–15] . In sea urchin embryos , this anisotropy occurs along the primary ( animal–vegetal ) axis [13] . The vegetal wave of nuclearization of β-catenin during early cleavage stages is essential for endomesoderm formation , since embryos that lack nuclear β-catenin function develop as primarily ciliated ectoderm without any endomesodermal tissue [13] . These findings provided the initial experimental leverage for constructing the overall sea urchin pregastrular EM-GRN [7] . The constituent subnetworks of the overall EM-GRN include core sets of transcription factors that function cell autonomously and interact within specific blastomeres that eventually differentiate into primary ( primary mesenchyme cell–GRN [PMC-GRN] ) and secondary mesoderm ( secondary mesoderm–GRN ) as well as early endoderm ( early endomesoderm/endoderm–GRN [E-EM/En-GRN] and late endoderm-GRN ) derivatives ( Figure 1A and [8] ) . Whereas regulatory interactions within each component network of the overall EM-GRN have been studied in considerable detail , critical connections between blastomeres , and thus between individual subnetworks , are not completely understood . One of the earliest inductive phenomena reported in experimental embryology was provided by the vegetal micromeres of the sea urchin embryo . Almost a century ago , Hörstadius reported that micromeres placed at the animal pole of the sea urchin embryo could induce adjacent presumptive ectoderm to differentiate as endodermal vesicles [16] . Several decades later , a molecular analysis confirmed that transplanted micromeres induce expression of the endomesoderm marker endo16 and eventually formation of a complete secondary archenteron from adjacent presumptive ectoderm ( Figure 1B ) [17] . Subsequent analyses have provided evidence in the normal embryo for a signal sent by micromeres and their progeny between fourth and sixth cleavages , which is required for normal endomesoderm specification , as monitored by endo16 expression , as well as for timely gastrulation ( Figure 1B ) [18] . Micromeres continue to emit archenteron-inducing signals through late cleavage/early blastula stages [19] . Despite several decades of efforts , however , neither the molecular identity ( s ) of these micromere-originated signals nor their specific regulatory contribution to early endomesoderm specification has been elucidated . Collectively , this constitutes a set of major unanswered questions in echinoderm embryo development . The β-catenin–dependent determinant Pmar1 is transiently expressed in micromere progeny during the initial stages of specification of this cell lineage [20] . Pmar1 is sufficient for micromere-originated endomesoderm inducing signals and can regulate most aspects of micromere differentiation through activation of the PMC-GRN [21 , 22] . Pmar1′s ability to activate the PMC-GRN in any blastomere provides a unique opportunity to understand how signaling outputs of the PMC-GRN are interpreted through the regulatory logic of the recipient E-EM/En GRN ( Figure 1A ) . In this study , we resolve the regulatory connections between the Pmar1-activated PMC-GRN and the E-EM/En-GRN by testing the responsiveness of every zygotically activated core factor of the E-EM/En-GRN to the ectopic expression of the micromere determinant Pmar1 . Our studies define a subset of the E-EM/En-GRN that responds to signals from the Pmar1-activated PMC-GRN and show that a significant fraction of the E-EM/En-GRN , including the recently identified Wnt8/Blimp1/β-catenin subcircuit [23] , is insensitive to signals emitted by Pmar1-misexpressing blastomeres . We show that the transforming growth factor-β cytokine , ActivinB , is required both in Pmar1-expressing cells that emit endo16-inducing signals and in blastomeres that respond to this induction , making it an essential component of both emitting and responding GRNs . Furthermore , we demonstrate that ActivinB function is necessary for activation of the Pmar1-inducible subset of the E-EM/En-GRN in early endogenous endomesoderm development , whereas the Pmar1-unresponsive portion of the E-EM/En-GRN operates in vegetal endomesoderm precursors independently of ActivinB signaling . These key findings strongly suggest that ActivinB has an essential role in endomesoderm specification mediated by micromeres and their progeny and unexpectedly reveal the existence of a new micromere-derived signal ( s ) . Collectively , we provide a critical global view of the regulatory interactions that constitute a classic embryonic induction response .
Previous studies have shown that the micromere determinant Pmar1 , through activation of the PMC-GRN , is sufficient for most aspects of micromere differentiation [20–22] . Pmar1 misexpression is sufficient to confer properties of the micromere/PMC lineage to all blastomeres in the embryo , including the ability to induce endo16 expression and formation of a complete ectopic gut from adjacent blastomeres [20 , 21] . Therefore , in order to determine which core factors of the E-EM/En-GRN respond to outputs of the PMC-GRN , we generated clones of cells expressing Pmar1 and GFP under the control of the hatching enzyme ( SpHE ) promoter ( Figure 2A ) , which is active in nonmicromere lineages of the sea urchin embryo during early cleavage and blastula stages [24] . Consistent with previous studies [21] , ectopic Pmar1-misexpressing cells ingressed into the blastocoel at the same time as endogenous primary mesenchyme cells . Furthermore , at the mesenchyme blastula stage , endo16 transcripts accumulated in ectopic nonvegetal blastomeres adjacent to gfp-positive , Pmar1-expressing cells , as well as in endogenous endomesoderm progenitors ( [21]; see below ) . Collectively , these observations validate the use of the SpHE-pmar1 construct to examine ectopic E-EM/En-GRN activation in response to Pmar1-dependent induction signals . The E-EM/En-GRN includes the core regulatory factors , Z13 [25] , Blimp1 [26] , Wnt8 [27] , Eve [28] , FoxA [29] , and Brachyury [30] , all of which are zygotically expressed in endoderm and secondary mesoderm precursors during early cleavage and blastula stages ( Figure 1A ) . We tested for transcripts encoding each of these core factors in cells adjacent to gfp-positive , Pmar1-misexpressing cells . At late cleavage and blastula stages , Pmar1 induced ectopic expression of z13 ( Figure 2B , panels e–h vs . a–d ) , foxA ( Figure 2C , panels e–h vs . a–d ) and eve ( Figure 2D , panels e–h vs . a–d ) mRNAs within two to three cell diameters of gfp-labeled blastomeres . As for the late endomesoderm marker , endo16 , each of these early genes was ectopically induced in presumptive ectoderm at the same developmental time that its transcripts accumulated in normal vegetal endomesoderm progenitors . In contrast to this set , no ectopic expression of blimp1 ( Figure 3B , panels e–h vs . a–d ) , wnt8 ( Figure 3C , panels e–h vs . a–d ) or brachyury ( Figure 3D , panels e–h vs . a–d ) was detectable in cells adjacent to Pmar1-misexpressing clones at developmental times ranging from 10 to 22 h postfertilization ( p . f . ) , when these transcripts accumulated in presumptive vegetal endomesoderm . These observations strongly suggest that Pmar1-misexpressing blastomeres respecify presumptive ectoderm as endomesoderm by activating only part of the E-EM/En-GRN , consisting of the genes encoding the core regulatory factors Z13 , Eve , and FoxA . Ectopic activation of blimp1 , wnt8 , and brachyury was not detectable , thereby defining a significant part of the E-EM/En-GRN that is not activated by Pmar1-dependent induction . Members of the Nodal/Activin/TGFβ class of transforming growth factor-β cytokines have been implicated in endomesoderm development in vertebrate embryos [31] , making them promising candidates for ligands that regulate micromere-dependent endomesoderm induction . The Strongylocentrotus purpuratus genome encodes several candidate TGFβ-related factors that are expressed before the hatching blastula stage [32] . Two of these , Nodal and Univin , are required for oral–aboral specification in the sea urchin embryo , but neither is necessary for endomesoderm formation [33 , 34] . In addition to these , transcripts encoding an ortholog of ActivinB ( GenBank accession number: EU526314 ) were detected at low levels at developmental times ( see below ) when micromere progeny emit endomesoderm-inducing signals [18 , 19] . When ActivinB translation was blocked with either of two different antisense morpholino oligonucleotides ( MO ) , gastrulation was consistently delayed by 12–18 h compared to buffer-injected controls ( Figure 4A , panels d and g vs . a ) , and embryos contained supernumerary pigment cells at 3 d p . f . ( Figure 4A , panels e and f , and h and i vs . b and c ) . Furthermore , we found that expression of endo16 , the cardinal marker of micromere-mediated ectopic [17] and endogenous [18] endomesoderm induction , was significantly reduced in veg2 progeny at the mesenchyme blastula stage in ActivinB morphants ( Figure 4C , panel c vs . a ) . We observed a similar gastrulation defect ( Figure 4B , panels c and d vs . a and b ) and inhibition of endo16 mRNA expression ( Figure 4C , panel d vs . b ) when we used the small molecule inhibitor , SB-431542 ( SB; 5 μM ) [35] , to block signaling through Activin-like kinase-4/5/7 ( ALK-4/5/7 ) during the first day of embryogenesis . Since ALK4/5/7 is a type I TGFβ receptor that transduces Activin/Nodal/TGFβ-like signals in the sea urchin embryo [34 , 36] , these findings suggest that ActivinB signaling through the SB-sensitive ALK4/5/7 complex is necessary for normal archenteron formation in the sea urchin embryo and could constitute at least part of the signaling machinery required for endomesoderm induction in response to micromere signals . To determine whether ActivinB is necessary for ectopic endomesoderm induction , we assessed its requirement for endo16 induction adjacent to blastomeres expressing the micromere determinant Pmar1 . Consistent with previous studies [21] , in embryos injected with SpHE-gfp and SpHE-pmar1 , endo16 mRNA accumulated at the mesenchyme blastula stage in ectopic nonvegetal patches of cells adjacent to Pmar1-expressing blastomeres , as well as in its normal vegetal territory ( Figure 5A , panels e–h vs . a–d ) . When ActivinB MO was coinjected with the SpHE-gfp and SpHE-pmar1 constructs , both ectopic and endogenous endo16 expression were significantly reduced ( Figure 5A , panels i–l vs . e–h ) . This finding identifies ActivinB as an essential component of Pmar1-dependent endomesoderm induction . Pmar1 is sufficient to activate all known inductive properties of micromeres , and cells misexpressing this determinant express Delta [20] , which signals from micromeres through the Notch receptor to induce expression of the pigment cell marker , gcm , in neighboring blastomeres ( Figure 5B , panels e–h vs . a–d ) [37 , 38] . In contrast to endo16 induction , both endogenous and ectopic gcm induction were unaffected by coinjection of ActivinB MO along with the SpHE-gfp and SpHE-pmar1 constructs ( Figure 5B , panels i–l vs . e–h ) . Thus , ActivinB is specifically required for Pmar1-mediated respecification of ectoderm as endomesoderm and not for micromere Delta-dependent secondary mesenchyme induction [37] . To test whether ActivinB function is required within Pmar1-expressing cells or within the cells that receive signals from them , we microinjected pmar1 and gfp mRNAs into one blastomere of a two-cell embryo and ActivinB MO into either the same blastomere or its sister . When ActivinB translation was inhibited exclusively within the Pmar1-misexpressing cells , induction of ectopic endo16 mRNA ( at 26 h p . f . ) in the other half of the embryo was significantly compromised ( Figure 6 , panels g–j vs . b–e ) . A small region of residual endogenous endo16 transcript accumulation persisted in the uninjected embryo half , demonstrating that the morpholino did not diffuse to the progeny of the uninjected blastomere ( arrowheads in Figure 6 , panels i and j ) . The descendants of the injected blastomere , on the other hand , adopted a primary mesenchyme cell fate and therefore did not express endo16 . When ActivinB translation was blocked only in the recipient blastomere , both ectopic and endogenous endo16 mRNA expression were significantly down regulated in the progeny of that cell ( Figure 6 , panels k–o vs . a–e ) . These findings demonstrate that ActivinB is an essential component of the PMC-GRN that is activated in Pmar1-expressing blastomeres , as well as of the responding EM-GRN that operates in neighboring cells . This dual requirement suggests that ActivinB from Pmar1-expressing cells probably activates an autoregulatory loop in recipient cells that is necessary for amplifying its own response , as has been shown for other members of the Nodal/Activin subfamily of TGFβ cytokines [31] . Consistent with its function in both regulatory subnetworks , as well as the ability of all early blastomeres to respond to induction by micromeres , activinB transcripts accumulate throughout the embryo at early cleavage and blastula stages of embryogenesis ( Figure 7 , panels a–c ) when micromere-derived endomesoderm inducing signals are sent [16 , 17 , 39] . Although Pmar1 induction of early endomesoderm specification requires ActivinB function , ectopic Pmar1 expression does not induce activinB mRNA expression ( unpublished data ) . Additional studies will be required to determine the molecular basis of the interaction between Pmar1 and ActivinB . The data described thus far resolve a section of the E-EM/En-GRNs that is activated in response to Pmar1-derived endomesoderm-inducing signals . Furthermore , we demonstrate that Pmar1-dependent ectopic induction of the cardinal endomesoderm marker , endo16 , requires ActivinB . Previous studies have shown that micromere-derived signal ( s ) regulate endo16 accumulation , not only in presumptive ectoderm , but also in the endogenous vegetal plate [17 , 18] . Therefore , our finding that ActivinB is required for endo16 accumulation in both ectopically induced and endogenous locations strongly suggests that it mediates micromere-dependent endomesoderm specification in the vegetal plate . Furthermore , as in micromereless embryos , gastrulation is significantly delayed in ActivinB morphants and in embryos treated with SB , which inhibits ALK4/5/7 function [35 , 36] . ALK4/5/7 function in promoting timely gastrulation is required from early cleavage to blastula stages when micromere progeny signal to overlying veg2 macromere descendants [19] because pulses of 5 μM SB for at least a 4-h interval any time prior to hatching at 18 h p . f . delay gastrulation by 12–18 h , whereas treatment after 18 h p . f . had no effect on the timing of gut development ( unpublished data ) . These results are consistent with a role for ActivinB signaling in early endoderm development . To determine whether the Pmar1-responsive EM-GRN resolved by the above experiments accurately describes the GRN response to ActivinB signaling in vegetal blastomeres during normal sea urchin development , we characterized the requirement for ALK4/5/7-ActivinB function in pregastrular endomesoderm specification . Whole-mount in situ hybridizations showed that SB treatment significantly inhibits the accumulation of transcripts encoding the core regulatory factors , Z13 ( Figure 8A , panel g vs . a ) , Eve ( Figure 8A , panel h vs . b ) , and FoxA ( Figure 8A , panel i vs . c ) in the veg2 tier of blastomeres ( see fate map in Figure 8C ) prior to the hatching blastula stage . The reduction in z13 and eve expression in drug-treated embryos was observed as early as 8 h ( ∼seventh cleavage ) following fertilization . Similarly , a MO-mediated block of ActivinB translation strongly inhibited the expression of z13 ( Figure 8A , panel j vs . d ) and foxA ( Figure 8A , panel l vs . f ) mRNAs when assayed at the corresponding stages . We did not detect any difference in the expression of eve mRNA in veg2 blastomeres in ActivinB morphants at cleavage stages ( Figure 8A , panel k vs . e ) , suggesting that this transcription factor may be regulated by a TGFβ cytokine other than ActivinB . In contrast , the accumulation of transcripts encoding the remaining members of the E-EM/En-subnetworks , brachyury , blimp1 , and wnt8 , did not depend on ALK4/5/7 ( Figure 8B , panels g , h , and i vs . a , b , and c , respectively ) or ActivinB ( Figure 8B , panels j , k , and l vs . d , e , and f , respectively ) function . Pigment cells ( a secondary mesoderm derivative ) were formed at similar times in controls and in embryos lacking ALK4/5/7 or ActivinB function . Consistent with this observation , we found that the first phase of Delta-Notch–mediated secondary mesoderm specification , as assayed by expression of gcm mRNA in veg2 progeny [37 , 38] was independent of ActivinB-ALK4/5/7 activity ( Figure 9A , panel e vs . a and panel g vs . c ) . Similarly , gataE was expressed normally in veg2 secondary mesoderm precursors of SB-treated embryos ( Figure 9A , panel f vs . b ) as well as ActivinB morphants ( Figure 9A , panel h vs . d ) . However , at 34 h p . f . , ActivinB morphants contained 40% more gcm-positive pigment cell precursors than did buffer-injected controls ( Figure 9B , panels d–f vs . a–c ) , probably due to suppression of early FoxA expression , which represses gcm transcription in veg2 endoderm precursors [29] . Thus , similar to its regulatory properties in ectopic Pmar1-mediated induction , normal vegetal activation of the secondary mesoderm ( pigment cell ) -GRN does not require ActivinB or ALK4/5/7 function . By the late mesenchyme blastula stage , z13 and foxA transcripts accumulate in veg1 derivatives in addition to veg2 descendants ( see fate map in Figure 10B ) and the corresponding proteins are essential elements of the late endoderm-GRN . Unlike their earlier expression in the veg2 tier , this later phase of expression does not depend detectably on ALK4/5/7 or ActivinB function ( Figure 10A , panel e vs . a , f vs . b , g vs . c , and h vs . d ) . This finding is consistent with the fact that SB treatment after hatching does not hinder gastrulation and demonstrates that ActivinB signaling through the SB-sensitive ALK4/5/7 complex regulates core elements exclusively within the E-EM/En-GRNs , which function in veg2 blastomeres overlying the micromeres . The findings described above demonstrate that the gastrulation defect observed in SB-treated embryos and ActivinB morphants can be entirely attributed to compromised early specification of veg2 endomesoderm progenitors . Interestingly , the GRN response to ActivinB-ALK4/5/7 signaling in normal pregastrular endomesoderm development is almost identical to that of ectopic Pmar1-driven induction . Collectively , these observations strongly suggest that a micromere-derived , ActivinB-dependent signal is necessary for early veg2 endomesoderm progenitor specification through the outputs of a discrete set of core factors of the E-EM/En subnetworks . Previous studies have shown that micromeres emit signals during early cleavage stages that clear the β-catenin antagonist SoxB1 from veg2 macromere progeny , a process that has been suggested to be important for the activation of the E-EM/En-subnetworks [21 , 27 , 40] . Similarly , when transplanted to animal locations , micromeres also clear SoxB1 protein from surrounding ectoderm cells during their respecification as endomesoderm [21] . Pmar1 misexpression is sufficient to induce SoxB1 down-regulation in adjacent blastomeres in both animal and vegetal contexts [21] , and it has been proposed that this clearance occurs in response to the same Pmar1-dependent endomesoderm induction signal that activates endo16 expression in adjacent blastomeres [21 , 27] . Since ActivinB is an essential component of this Pmar1-derived induction signal and regulates both ectopic and endogenous endo16 accumulation , we examined its role in clearing SoxB1 from veg2 secondary mesoderm progenitors . If SoxB1 clearance requires ActivinB , then SoxB1 should persist at high levels in secondary mesenchyme of ActivinB morphants . Surprisingly , however , SoxB1 clears normally from this region , which is marked by gcm mRNA accumulation ( Figure 11A , panels d–f vs . a–c ) . We conclude that ActivinB and an unidentified SoxB1-clearing signal are separate , early outputs of micromeres . The specific role of SoxB1 clearance in the EM-GRN remains to be elucidated . Although SoxB1 clearance is indicative of micromere signaling , it is gradual and not completed in veg2 progeny until the early mesenchyme blastula stage , after initial veg2 specification has occurred [41 , 42] . Consistent with this observation , we found that at cleavage stages , genes encoding the Pmar1-responsive endomesoderm core factors , z13 and eve , are expressed in blastomeres that contain high levels of SoxB1 ( z13 shown in Figure 11B; eve data not shown ) , confirming that micromere-mediated early veg2 endomesoderm specification does not require SoxB1 clearance . This is also the case in presumptive endoderm since endoderm-specific genes are expressed normally when the later phase of micromere-dependent SoxB1 clearance is prevented [40] . Furthermore , although endogenous SoxB1 limits nuclear β-catenin activity and SoxB1 misexpression strongly antagonizes it , SoxB1 morphants have greatly reduced endoderm and fail to gastrulate [42] , demonstrating that some level of SoxB1 is required for normal endoderm development . To test whether down-regulation of SoxB1 might instead be a consequence of early specification , we blocked Delta-mediated secondary mesoderm specification by use of a Delta-MO . This approach inhibited gcm expression , as expected [37 , 38] , but had no effect on the extent of SoxB1 clearance from veg2 blastomeres ( Figure 11A , panels g–i vs . a–c ) . Thus , micromere-dependent SoxB1 clearance occurs normally when at least two Pmar1-mediated veg2 specification signals ( ActivinB and Delta ) are blocked . We conclude that the signal for SoxB1 down-regulation , ActivinB , and Delta make largely separate Pmar1-dependent contributions to the development of endomesodermal tissues within the veg2 tier of blastomeres .
This study describes one of the first systematic demonstrations of the developmental GRNs activated in blastomeres that emit and respond to embryonic endomesoderm induction signals . We ectopically activated the Pmar1-dependent PMC-GRN in nonvegetal cells and demonstrate that adjacent blastomeres are specified as endomesoderm through a responding GRN that includes genes encoding the core factors Z13 , Eve , and FoxA . In contrast , a significant part of the sea urchin EM-GRN , consisting of the regulatory outputs of Wnt8 , Blimp1 , and Brachyury , is not activated through Pmar1-mediated signals . Two members of this Pmar1-insensitive set , Wnt8 and Blimp1 , have recently been proposed to constitute a self-sustaining cis-regulatory subcircuit that maintains nuclearization of β-catenin in endomesoderm progenitors [23] . Since ectopic Pmar1-misexpressing cells do not detectably induce wnt8 or blimp1 expression in adjacent animal blastomeres , our work implies that these responding cells are specified as endomesoderm without accumulating β-catenin in their nuclei . This conclusion is consistent with previous studies demonstrating the absence of detectable nuclear β-catenin in animal blastomeres specified as endomesoderm through ectopic micromere induction signals [13] . Using endo16 expression , the molecular hallmark of micromere-mediated endomesoderm specification [16 , 17 , 43] , we show through localized loss-of-function analyses that the TGF-β cytokine , ActivinB , is required in both Pmar1-expressing cells that send endomesoderm-inducing signals as well as in the responding EM-GRN in blastomeres that transduce this signal . The requirement for ActivinB in Pmar1-dependent endomesoderm induction strongly suggests that it plays a similar essential role in endomesoderm formation in response to signals from ectopic micromeres , a phenomenon first described almost 70 y ago in the sea urchin embryo [16] . ActivinB signaling through ALK4/5/7 during early cleavage and blastula stages also is necessary for endogenous vegetal activation of almost the entire E-EM/En-GRN induced by ectopic Pmar1 expression . This critical finding , along with the fact that , like micromere progeny , ActivinB is required for normal endo16 expression in veg2 endomesoderm progenitors , and for timely gastrulation , strongly suggests that this TGFβ ligand plays an essential role in early micromere-dependent endomesoderm specification . In contrast to the Pmar1-dependent EM-GRN factors , ActivinB is not required for normal vegetal expression of any of the Pmar1-insensitive EM-GRN core factors . Furthermore , both the Delta-dependent secondary mesoderm–GRN and the late endoderm-GRN ( 17 to 30 h p . f . ) that is subsequently activated in veg2 and veg1 macromere progeny are independent of ActivinB function . Thus , the primary cellular targets of ActivinB signaling during pregastrular endomesoderm development are the more vegetal ( veg2 ) macromere derivatives . The exact state of specification of these cells in ActivinB morphants is not clear , but some of them transfate to pigment cells , presumably as a result of reduced FoxA activity [29] . Whether any of these cells adopts other mesodermal fates is not yet known . If indeed they do not contribute to definitive endoderm , then the gut that eventually forms would arise from veg1 progeny that are specified much later than veg2 derivatives [44] , through the regulatory outputs of the ActivinB-independent late endoderm-GRN . Alternatively , if veg2 endoderm progenitors in ActivinB morphants do not acquire other secondary mesoderm fates , then they probably participate in gut formation . In this case , either they eventually express all of the core EM-GRN factors through micromere-independent mechanisms or , in a later vegetal developmental context , the Wnt8/Blimp1/nuclear β-catenin subcircuit [23] is sufficient to support this process . Either scenario would require considerable regulatory cross talk between GRN components in order to achieve threshold concentrations of critical core factors that drive definitive endoderm development . Recent models of pregastrular development in echinoderm embryos have proposed that a single micromere signal specifies veg2 macromere progeny as endo16-expressing endomesoderm progenitors and also causes the gradual clearance of the β-catenin antagonist , SoxB1 , from nuclei of these blastomeres [21 , 27 , 40] . This is an attractive model because it invokes a causal linkage between activation and maintenance of the EM-GRN that depends on nuclear β-catenin accumulation and removal of an antagonist . However , the results of several experiments presented here argue strongly that micromere-mediated early endomesoderm induction and micromere-regulated SoxB1 removal are independent processes . First , SoxB1 down-regulation occurs normally in embryos lacking the early endomesoderm-inducing ( endo16-inducing ) signal ActivinB . Second , SoxB1 clearance is not required for activation of the early EM-GRN because macromere progeny express early Pmar1-responsive core factors in this subnetwork before SoxB1 protein levels are detectably reduced in nuclei of the same cells . Third , ectopic micromere-mediated EM-GRN activation and complete archenteron formation occur in cells that lack detectable nuclear β-catenin [13] but still clear SoxB1 [21] . Our findings are consistent with other studies showing that endoderm specification can occur without SoxB1 clearance [40] . The first phase of SoxB1 clearance in presumptive secondary mesoderm also does not depend on other known micromere signals , because it occurs normally in either Delta ( this work ) or Wnt8 morphants [27] . Therefore , although SoxB1 clearance depends on an early micromere signal ( s ) , it is a gradual process that is not completed until the mesenchyme blastula stage and relies on an unknown pathway ( s ) distinct from the one that regulates endo16 induction through ActivinB ( Figure 12 ) . Therefore , we favor the view that fine-scale patterning of endoderm and mesoderm in different blastomere tiers probably requires micromere-dependent regulation of the level and duration of SoxB1 expression , but early micromere-mediated endomesoderm specification is independent of this process ( Figure 12 ) . This work is the first report , to our knowledge , of the requirement of ActivinB in the earliest steps of endomesoderm specification in any developmental model system and of the involvement of a TGFβ cytokine of the Activin/Nodal/TGFβ class in endomesoderm formation in an invertebrate embryo . Activin was initially proposed to be a mesoderm-inducing agent because exogenous Activin can induce mesoderm in amphibian animal cap explants [45] . Activin can also direct endoderm differentiation in human and mouse embryonic stem cells , which , interestingly , transit through an endomesoderm-like primordium prior to expressing markers of definitive endoderm [46 , 47] . Recent studies in which ActivinB was knocked down with MOs in amphibian embryos suggest that it does play some role in axial mesoderm formation , possibly by regulating convergent extension movements of gastrulation through the activities of other mesoderm-inducing factors [48] and/or the timing of cell cycle transitions in involuting dorsal axial mesoderm [49] . However , even though ActivinB is expressed during early development of amphibian embryos [50] , there is , at present , no evidence suggesting that it functions in early endomesoderm specification per se . By analyzing the EM-GRN response to ActivinB , we show that it is exclusively required for the earliest steps of endomesoderm specification , eventually leading to the formation of definitive endoderm and timely gastrulation in the sea urchin embryo . This work , therefore , provides a critical in vivo model of the requirement of ActivinB function in early embryogenesis . Furthermore , the remarkable developmental plasticity of the sea urchin embryo allows us to demonstrate that ActivinB is the cardinal primary mesenchyme-derived signal that can activate an ectopic endomesoderm GRN . We thus describe an important experimental paradigm that elucidates the GRNs that drive endogenous and ectopic endomesoderm induction . By defining major regulatory connections between individual networks of the overall sea urchin EM-GRN , we have built a new framework for future studies of endomesoderm specification . In the sea urchin embryo , endomesoderm development is highly regulative and eventually occurs even in the absence of micromeres [18 , 43] . Similarly isolated macromeres also express endoderm markers [51] . Our identification of a Pmar1-insensitive set of core regulatory factors may significantly inform future work addressing how the network responds in these situations , potentially leading to an understanding of the molecular basis of such regulative endomesoderm development . Our work also postulates the existence of an uncharacterized micromere signal that is emitted during early cleavage stages to clear the β-catenin antagonist SoxB1 from endomesoderm precursors . Identifying this signal and its GRN properties will be necessary to achieve a comprehensive understanding of pregastrular development in echinoderm embryos . We show that endomesoderm is ectopically induced through the regulatory outputs of a set of Pmar1-responsive EM-GRN core factors , making it important to understand how this core set activates and stabilizes downstream GRN circuits . Since this Pmar1-responsive GRN potentially drives formation of a complete archenteron without detectable nuclear β-catenin , it is also conceivable that additional uncharacterized β-catenin–independent GRNs exist . Interestingly , recent studies have demonstrated the existence of such a β-catenin-independent JNK/Axin dorsalization pathway in zebrafish embryos ( e . g . , [52] ) . The sea urchin embryo , with its well-known developmental plasticity , would be an ideal system for characterizing such nascent GRNs and examining their interactions with the currently understood endomesoderm regulatory networks .
Adult sea urchins ( S . purpuratus ) were obtained from Marinus Scientific ( Garden Grove , California ) or The Cultured Abalone ( Goleta , California ) . After removal of fertilization envelopes , embryos were cultured at 15 °C in artificial seawater ( ASW ) at fewer than 300 embryos/ml of ASW . A stock solution of the ALK4/5/7 inhibitor SB-431542 ( Tocris Biosciences ) in dimethylsulfoxide was added to cultures to a final concentration of 5 μM . Inhibitor treatments were terminated by pipetting embryos through five ASW-containing 60 × 15-mm Petri dishes . Only cultures in which more than 90% control embryos were morphologically normal were used . Fertilized S . purpuratus eggs in 60 × 15-mm Petri dishes coated with 1% ( w/v ) protamine sulfate were microinjected using an Eppendorf Femtojet-Injectman NI2 micromanipulator attached to a Leica inverted microscope . Microinjections were performed in PABA-ASW followed by three rinses in ASW . Injection solutions containing 25% glycerol and 10 mM Tris-HCl ( pH 8 . 0 ) were filtered through 0 . 22 μM PVDF filters ( Millipore ) and briefly incubated at 50 °C before loading to minimize clogging within the injection needles ( Femtotips I; Eppendorf ) . For single-blastomere injections , embryos were cultured in ASW until 40 min after the first cell division and rinsed four times with ice-cold calcium-magnesium–free sea water ( CMFSW ) . Embryos were microinjected within 10 min of the last CMFSW rinse , washed four times with ice-cold ASW , and cultured to the desired developmental stage at 15 °C . The SpHE-GFP construct contains the SpHE minimal promoter sequence ( from −310 to +96 , [24] ) inserted between the EcoRI and SalI sites of pGreenLantern ( pGL ) . For the SpHE-pmar1 construct , the GFP fragment from SpHE-GFP was replaced with the Pmar1 coding region ( GenBank accession number: AF443277 ) and the first six base pairs upstream of its initiation codon . Both constructs were linearized with PstI prior to microinjection . pmar1 and gfp mRNAs were synthesized from linearized pCS2+-pmar1 and pCS2+-gfp templates using the mMessage mMachine kit ( Ambion ) . RNA concentrations were measured by spectrophotometry , and RNA integrity was verified by electrophoresis through agarose-formaldehyde gels prior to microinjection . To determine the 5′-end of activinB mRNA , RACE ( rapid amplification of cDNA ends ) -ready cDNA was synthesized from 1 μg of DNA-free total RNA extracted from eggs using the SMART RACE cDNA synthesis kit ( BD Biosciences ) per the manufacturer's instructions . A touchdown-PCR protocol recommended by the manufacturer was used to amplify an activinB 5′ RACE product with the activinB gene–specific reverse primer , ( 5′-TCACGAACACCCACAACTCAAGATCCTCATTTC-3′ ) and the kit's SMART oligonucleotide forward primer mix . RACE products were cloned into PGEM T-Easy ( Promega ) , and 20 clones were sequenced completely in both directions to determine the 5′-end of activinB . The activinB mRNA sequence has been deposited in GenBank with the accession number of EU526314 . Sequences were as follows: SpActivinB MO1: 5′-gcaggtacagagcttctcggtcaac-3′; SpActivinB MO2: 5′-TATCAAGCTGGCCCACGACATGAAA-3′; and Delta MO: 5′-GCCGATCCGTTATTCCTTTCTTATC-3′ . MOs were dissolved in 100 μl of nuclease-free water to give 3 mM stock solutions , which were diluted to the concentrations indicated in the figure legends . Embryos were fixed in 4% paraformaldehyde-ASW containing 10 mM MOPS ( pH 7 . 0 ) and 0 . 1% Tween-20 for 1 h at room temperature ( RT ) in 96-well flat-bottomed plates . Hybridization and detection procedures using colorimetric alkaline phosphatase-conjugated digoxigenin antibodies were carried out as described previously [53] . For single-color fluorescent in situ hybridizations ( FISH ) using a digoxigenin-conjugated RNA probe and tyramide signal amplification ( TSA-Plus kit; PerkinElmer ) , the above procedure was modified as follows . After posthybridization washes , embryos were incubated for 1 h at RT in a blocking buffer consisting of 10% normal goat serum and 5 mg BSA/ml in MOPS wash buffer [53] , followed by 30 min at RT in horseradish peroxidase ( POD ) -conjugated anti-digoxigenin antibody ( Roche ) ( 1:1 , 500 ) in blocking buffer . Embryos were washed eight times over the course of 2 h at RT with MOPS wash buffer . Hybrids were detected with a 1:100 dilution of TSA-stock solution ( FITC TSA-Plus kit or Cy3-TSA Plus kit; PerkinElmer ) in diluting buffer at RT for 6–8 min , followed by five washes in MOPS wash buffer at RT . For dual-color FISH with fluorescein- and digoxigenin-conjugated RNA probes , after the blocking step , embryos were incubated in POD-conjugated anti-fluorescein antibody ( Roche ) ( 1:750 ) at RT overnight , followed by eight washes with MOPS wash buffer . Duplexes were detected by incubating embryos in Cy3 TSA ( 1:100 dilution ) for 6–8 min , followed by five washes with MOPS wash buffer . The POD activity of the bound anti-fluorescein antibody was quenched with 0 . 1% ( w/v ) sodium azide in MOPS wash buffer for 30 min at RT , followed by six washes with MOPS wash buffer for a total of 60 min . Digoxigenin-labeled transcripts were detected subsequently using FITC-TSA as described above . The dual-labeled FISH procedure was modified to allow hybridization of probes to endogenous transcripts and mRNA synthesized from injected SpHE promoter-containing plasmids but to exclude formation of hybrids with plasmid DNA [54] . To prevent DNA denaturation during hybridization , the formamide concentration was reduced to 50% and the hybridization temperature lowered to 45 °C . After 4–7 d of hybridization , unhybridized probe was removed with five washes in MOPS wash buffer at 50 °C for 3 h [54] . Residual binding of the probe to DNA was eliminated by two washes in standard hybridization buffer ( 70% formamide ) for 30 min at 50 °C . For single-color FISH and SoxB1 immunostaining double labeling , FISH was performed first as described above . Embryos were incubated overnight at 4 °C with SoxB1 primary antibody that had been filtered by centrifugation through a 0 . 22-μm PVDF mesh ( Millipore ) and diluted 1 , 000-fold in a buffer containing 5% normal lamb serum , 1× phosphate-buffered saline ( PBS ) , and 0 . 05% Tween-20 ( PBST blocking buffer ) . After five washes for a total of 1 h at RT with PBST wash buffer ( 1× PBS , 0 . 05% Tween-20 ) , bound primary antibody was detected with goat anti-rabbit Alexa-488 secondary antibody ( 1:750; Invitrogen ) in PBST blocking buffer for 1 h at RT . Images were captured on a Zeiss Axiovert200 inverted microscope equipped with an Axiotome axial tomography device and differential interference contrast ( DIC ) optics in Axiovision Release 4 . 6 . Images were processed in Adobe Photoshop 8 . 0 and Canvas X . Z-stack composites were generated using Imaris Release 5 . 7 . 2 ( Bitplane ) .
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In recent years , “gene regulatory networks” ( GRNs ) have provided integrated views of gene interactions that control biological processes . One of the earliest networks to be activated in the developing zygotes is the one controlling endomesoderm development . In the sea urchin , this network includes several subnetworks that function in adjacent tiers of cells that form the endoderm and mesoderm of the developing embryo . Although classic embryological manipulations have shown that the precursors of the embryonic skeleton induce endomesoderm fate in adjacent cells , the GRNs regulating this interaction are not understood . To investigate these networks , we ectopically activated a GRN that operates in skeletogenic precursors and characterized the responding GRN in neighboring cells , which adopt an endomesoderm fate . By testing the responsiveness of every core factor in the responding GRN , which allowed us to identify a subset that executes the response to the induction , we demonstrated that the signaling molecule , ActivinB , is an essential component of this induction and that its function is physiologically relevant: it is required during normal embryonic development to activate the same GRN that responds to signals from skeletogenic precursors . Furthermore , the network response to ActivinB signaling reveals greater complexity in an additional uncharacterized inductive signal emitted by skeletogenic precursors . Our results thus highlight how interacting GRNs can be used to understand a fundamental signaling process .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology",
"developmental",
"biology"
] |
2009
|
Gene Regulatory Network Interactions in Sea Urchin Endomesoderm Induction
|
A comparative analysis of cavities enclosed in a tertiary structure of proteins and interfaces formed by the interaction of two protein subunits in obligate and non-obligate categories ( represented by homodimeric molecules and heterocomplexes , respectively ) is presented . The total volume of cavities increases with the size of the protein ( or the interface ) , though the exact relationship may vary in different cases . Likewise , for individual cavities also there is quantitative dependence of the volume on the number of atoms ( or residues ) lining the cavity . The larger cavities tend to be less spherical , solvated , and the interfaces are enriched in these . On average 15 Å3 of cavity volume is found to accommodate single water , with another 40–45 Å3 needed for each additional solvent molecule . Polar atoms/residues have a higher propensity to line solvated cavities . Relative to the frequency of occurrence in the whole structure ( or interface ) , residues in β-strands are found more often lining the cavities , and those in turn and loop the least . Any depression in one chain not complemented by a protrusion in the other results in a cavity in the protein–protein interface . Through the use of the Voronoi volume , the packing of residues involved in protein–protein interaction has been compared to that in the protein interior . For a comparable number of atoms the interface has about twice the number of cavities relative to the tertiary structure .
Close atomic packing is an important metric for characterizing protein structures—the average packing density for the interior of proteins is similar to that for crystals of small organic molecules [1] . While the average value of packing density in the protein interior is close to 0 . 75 , the efficiency may not be uniform over the whole structure , the density varying in the range 0 . 66 to 0 . 84 [2]–[4] . The localized defects in packing show up as cavities [5] , and when present they can reduce the stability of the structure [6] . Protein binding has many similar features common to folding , such as the presence of a core in the interface region [7] , [8] and complementarities of chemical characteristics of residues in contact across the interface and the nature of the specific interactions linking them [9]–[11] . Although the surfaces that form the interface in protein-protein interaction have complementary shape [12] , [13] , an issue that has not been addressed is whether the interface can harbor cavities , and their features relative to those present in the protein interior . Voronoi [14] procedure has been used to assign a unique volume to individual atoms in a collection of atoms , such as in proteins . This has been used to calculate the volumes occupied by amino acid residues and their variation at individual sites [3] , [15]–[17] . As to associate a volume of space to an atom the procedure relies on the location of all its neighbors , it works well when applied to atoms in the protein interior . Distinct from the surface atoms those in the interface have surrounding atoms from the interacting protein chains , and as such one can calculate the Voronoi volumes associated to interface atoms [18] , and compare these to those in the protein core . Cavities in structures have also been looked into from the perspective of protein hydration [19]–[21] . Water molecules are also located in the interfaces [22] . Buried water molecules are often conserved among members in a homologous family and are integral structural component of these proteins [23] . When located in cavities they can compensate for the destabilization of reduced hydrophobic and van der Waals interactions [24] . It is of importance to know the average volume occupied by water molecules in protein interior and interfaces and the nature of their interactions with the surrounding protein atoms . Clefts or pockets on the surface are important for molecular recognition and protein function [25] . Distinct from them are the cavities , defined as enclosed space in the interior of the protein . However , they are sometimes considered together , for example , for defining interior and surface packing densities [26] . Internal cavities have been analyzed separately for monomeric proteins [19] , [21] , as well as protein interfaces [27] , but no attempt has been made to generalize their features from a common perspective . In this work we use a vastly enlarged repertoire of structures to quantify the geometrical characteristics of the cavities found in protein tertiary structures and interfaces—the latter being of two types—those involving obligate homodimeric assemblies and the non-obligate protein-protein heterocomplexes . Other features studied are the occurrence of solvent molecules in the cavities and their hydrogen bonding , the participation of different secondary structural elements in the cavities , the environment of cavity water in the interface , etc . Rather than being mere “packing defects” cavities are also known to play a role in assisting conformational changes between domains or subunit interfaces and in controlling binding and catalysis [28] . Thus a comprehensive analysis of cavities would provide insight into our understanding of protein structure and function .
On average ∼15 cavities occur in the individual subunits in the monomer and the homodimer datasets and consequently these two categories were considered together to represent Ter_str ( Table 1 ) . Compared to the tertiary structure the interfaces have about a third and a sixth number of cavities in homodimers and complexes , respectively . However , the total cavity volume is reduced to two-third and one-fifth in the two types of interfaces , indicating that the cavities in interfaces are larger in size than those in the tertiary structure . Proteins are of different sizes; besides there is a lot of variation in the numbers of atoms constituting the tertiary structure and the interface . Consequently , we have also expressed the number of cavities and their total volume relative to an average-sized protein of 2000 atoms . Compared to Ter_str the interfaces have about 1 . 6 times the number of cavities , but the increase in total volume is 3 . 4 and 2 . 1 times in homodimers and heteocomplexes , respectively , again indicating the larger size of cavities in the interface , especially for homodimers . In general , the packing of residues in the interface leaves more cavities compared to that in the tertiary structure . The total volume of Ter_str cavities in a subunit is well correlated to the size , as given by the protein volume ( or the number of atoms ) ( Figure 2A ) . If the size of an interface is defined by the number of atoms belonging to it , the correlation with the total cavity volume is poor for interface cavities ( Figure 2B and 2C ) . However , when individual cavities are considered the correlation of volume is very strong with both the numbers of cavity lining atoms and residues in all the three categories of cavities ( Figure S1 ) . Both linear relationships and equations using power law can fit the data equally well ( Table 2 ) , but the former would suggest a negative cavity volume when the number of CL atoms/residues is <4 . Using the latter set of equations about 5 atoms or 4 residues are needed to enclose a volume ( ∼11 . 5 Å3 ) large enough to accommodate one water molecule . The histogram of the distribution of volume in three different classes of cavities is shown in Figure 3A . Interfaces contain higher percentage of larger cavities ( 14 . 3% Inter_H and 10 . 2% of Inter_C with volume>100 Å3 ) than tertiary structure . The cavities were further divided into empty and solvated cavities and the distribution of their volume ( Figure 3B and 3C ) indicates that the large cavities ( volume>50 Å3 ) are usually solvated . 50% of all Ter_str cavities are solvated , the corresponding values for Inter_H and Inter_C being 61% and 62% , respectively . The percentages calculated based on volume are 61 , 83 , and 79% , respectively for the above three categories . Examples of water molecules in cavities belonging to the tertiary structure and interface can be seen in Figure 4B–D . Rvs ( defined in Methods ) indicates how spherical a cavity is—for a perfect sphere the value would be 1 . 0 , and would reduce in value as the cavity deviates from being spherical . The distribution of Rvs ( Figure 5A ) indicates that more than 50% of all cavities are nearly spherical . To investigate if the aspherical shape of the cavity can result from the size we plotted the distribution for the cavities having volume greater than 100 Å3 ( Figure 5B ) . A peak near 0 . 75 indicates that such cavities are quite irregular in shape . Two small , spherical cavities ( labels: 1 and 2 ) are illustrated in Figure 4A and 4B , in which the largest cavity ( label , 5 ) deviates from the spherical shape . As cavities are embedded within the protein structure ( or interface ) we compared the distribution of the CL residue types with that observed over all the proteins ( or interfaces ) . Amino acid preferences for the CL and NCNS ( non-cavity-non-surface ) regions across all the three classes are shown in Figure 6A and 6B . A large , positive ( or negative ) value indicates preference ( or avoidance ) , and a value close to zero suggests an occurrence close to the general population . Charged residues ( Lys , Glu , Asp , and Arg ) are avoided in general . Ter_str cavities prefer hydrophobic residues , such as Cys , Leu , Ile , Phe , Met , and Val . The preference for the branched aliphatic side chains seems to be the common feature for all the categories of cavities . However , in contrast to Ter_str , interface cavities avoid Cys , Phe and Trp , and prefer Thr , Gln , Gly—possibly due to a higher percentage of interface cavities being solvated . Unlike the CL region the trend in propensities is quite similar in the NCNS region in both types of interfaces and tertiary structure . The amino acid preference for solvated and empty cavities across all three cavity classes is shown in Figure 6C and 6D . Residues which are preferred in empty cavities are Leu , Ile , Met , Phe , and Val , while Gly , Thr , and Tyr are more preferred in solvated cavities . Additionally , Ser is also found in greater number in the solvated cavities of interfaces in heterocomplexes , and His in those of homodimers . The propensities of different atom types to occur in the CL , NCNS regions , solvated and empty cavities in the three cavity classes are shown in Figure 7 . There is a distinct pattern in the atom preference for Ter_str cavities—main-chain atoms ( C , N , and CA ) are disfavored and the side-chain atoms ( aromatic carbon , hydroxyl oxygen and amide nitrogen ) are favored in CL as compared to NCNS regions . In the NCNS region all three categories exhibit similar features , for example , polar side-chain atoms ( Oa , Oh , Na and Nc ) are not preferred and the preference is for C , N , CA and aliphatic carbon ( Cc ) . Polar atoms like oxygen and nitrogen are more preferred in solvated cavities than empty cavities ( Figure 7C and 7D ) . Figure 4D illustrates a cavity with two water molecules , and out of six CL atoms 2 are oxygen and 3 nitrogen . There is a considerable variation in the cavity volume as a function of the number of water molecules contained in it . To discern any underlying trend we considered the Ter_str cavities , averaged the cavity volume containing a particular number of the solvent molecule ( Figure 8 ) and based on the average numbers one can derive a linear relationship . Roughly , one water molecule can be accommodated in a volume of 15 Å3 ( observed value ) , and an increment of ∼40–45 Å3 is needed for each additional molecule . On average a water molecule participates in 3 . 4 hydrogen bonds ( the number includes those to other water molecules also; if hydrogen bonds to only protein atoms are considered the number is 2 . 6 for Ter_str cavities and 2 . 3 for interfaces ) . 15 Å3 is about the smallest volume that can enclose a water molecule , and such a volume would need about 5 CL atoms ( based on equations in Table 2 ) . Figure 4D and 4E , however , provides an example where a rather small cavity had six CL atoms , which could enclose two water molecules that participated in 4 and 2 hydrogen bonds , respectively . We first compare the Voronoi volume of the NCNS atoms in the interface to those in the protein tertiary structure ( values are provided in Table S3 ) . Most of the 13 atom types show an increase of value in the interface , though the change is usually <5% ( Figure 9B ) . It should be mentioned here that for simplification we have grouped atoms together , for example all the aromatic atoms as Cr . However , it is known [17] that there can be some variation between the volumes of these atoms within a given aromatic residue or between any two of them . As such the result would be affected by the atom composition in the datasets . Under these limitations , cases where the difference is more are worth mentioning . In complexes , S and aromatic atoms have smaller values in the interface , indicating that these are better packed relative to the tertiary structure . Fleming and Richards [4] observed that in protein structures Cys and aromatic residues are better packed than the aliphatic ones . It appears that these residues ( containing atoms types Cr and S ) are still better packed in interfaces . On the other hand , N of Lys and Arg are lesser packed . For homodimers , CB atoms that link the main chain to the functional part of the side chain are also packed less efficiently . As expected , if we compare the CL atoms instead of the NCNS atoms , there is an increase in volume ( 11–35% ) compared to the atoms in the tertiary structure ( Figure 9A ) . From Figure 9C one can see the difference in the Voronoi volume of CL atoms in solvated cavities , calculated including and excluding water molecules ( blue and red bars , respectively ) . The difference in the volume of the polar atoms is to the extent of 12–25% as compared to 5–7% by the non-polar atoms . However , on including water the values of the polar atoms come to within 5% , indicating that the cavity water molecules are located closer to these CL atoms . One would have expected the bars corresponding to the empty cavities should match with the ones calculated without considering waters for the solvated cavities . But this is not quite correct , as they tend to have different sizes ( solvated ones are bigger ) and the propensities of atom-types ( for example , compare Cr in Figure 7C and 7D ) to occur in them are also different . The percentage composition of occurrence of CL atoms , as well as the ones in the whole data set , in three types of secondary structural elements is provided in Figure S3 and the propensities calculated from these numbers are shown in Figure 10 . Strands are preferred in all three cavity classes , more so in Ter_str and Inter_H . Structures other than helices and strands are less inclined to form cavities . Two examples of cavities being located on top of β-sheets can be seen in Figures 11 and 12A . Even the structure shown in Figure 4C has 18 cavities ( out of a total of 52 ) having more than 50% CL atoms coming from β-sheet . There is not much distinction between the cavity types based on the occurrence of the main- and side-chain atoms—Figure S4 indicates that when a helix or sheet contributes to a cavity , ∼70% of the atoms are from the side-chain; however , for ‘Others’ the value comes down to the range 56–63% .
Hubbard & Argos [27] analyzed three classes of cavities: within domains , between domains and between protein subunits . Ter_str cavities considered here would include the first two classes , whereas the interfaces between subunits in obligate homodimers and protein-protein heterocomplexes substituted the last class . Interdomain and intersubunit cavities were on average found to be larger than those located within domains [27] . Our results ( Figure 3A ) comparing cavities in the tertiary structures and interfaces indicate that interface cavities , especially Inter_H , are indeed larger . However , if we distinguish between intradomain and interdomain cavities ( of the 219 individual subunits considered by us 158 were single domain proteins and the rest multidomain ) there is not much difference in the distribution ( Figure S2 ) . It can be mentioned that cavities with atomic surface components arising from more than one domain were deemed to be interdomain in the earlier study; however , we used a more stringent criterion of having at least 20% of CL atoms coming from a different domain . Nevertheless , Figure 2A indicates that the total cavity volume is a function of the protein volume , irrespective of the number of domains present . It has been reported that the wide cavities form 0 . 002–1 . 55% of the volume of a protein ( in a dataset of 75 monomeric proteins ) ; however , no quantitative relationship could be established linking the two [21] . A linear relationship was noted between the number of voids and pockets plotted against the number of residues in each protein , although the total pocket volume did not correlate well [26] , [29] , possibly because no distinction was made between single and multiple subunit proteins , the latter containing tunnels or holes of large size [26] . From our analysis we could derive a linear relationship between the total volume of the cavities present and the total protein volume ( Figure 2A ) . From this one can derive that for two proteins of volume 30 , 000 and 50 , 000 Å3 , the cavities will constitute 0 . 56 and 0 . 99% of the volume ( the two values are 0 . 61 and 0 . 91 , using the power law ) . The observed minimum and maximum values were 0 . 06 and 2 . 26% , respectively . Cavities are usually located close to the protein surface—considering the CL atoms , most of them belong to the surface of the molecule . Cavities were found to cover 10% of a typical interface [27] . Comparing the number of CL atoms to the total ( Table S1b ) we find that 5 . 5% atoms of the tertiary structure and 13 . 8 and 10 . 5% of homodimeric and hetercomplex interfaces form cavities . That for a given number of atoms the interfaces have about twice the number of cavities as the tertiary structure can also be seen from Table 1 . For some structures the resolution of the data may be rather low , or the quality of the electron density too poor for the bound water molecules to be seen . Figure S5 indicates that there is an increase in the number of solvated cavities as the resolution improves from 2 . 5 Å till about 1 . 8 Å . If the water molecule is partially or completely disordered it cannot be located . Even with high resolution data detection of water molecules in cavities , especially if they are mobile due to the hydrophobic nature of the cavity , is rather tricky by conventional crystallographic analysis that neglects low resolution data [30] and as such , the average number of water molecules obtained could be an underestimate . Nevertheless , the average number of hydrogen bonds involving water in the solvated cavities—the number is 2 . 6 with protein atoms , and 3 . 4 if hydrogen bonding with other water molecules is also included—matches with the typical value of 3 hydrogen bonds made by a buried water molecule reported in literature [20] , [21] , [31] . The cavity volume needed to enclose one water molecule is ∼15 Å3 , however , each additional water requires an extra volume of ∼40–45 Å3 ( Figure 8 ) . The propensity of the secondary structural elements to be associated with cavities indicates that β-strands have a high tendency and the non-regular regions ( ‘Others’ ) are disfavored ( Figure 10 ) . Interestingly , the packing densities of residues in turns , helices and strands were found to be 0 . 794 , 0 . 744 , and 0 . 723 , respectively [4] , indicating the β-strands to be packed least efficiently , possibly due to the greater occurrence of cavities associated with them , examples of which can be seen in Figures 11 and 12A . Loops and turns with higher flexibility can adjust the structure locally to avoid/minimize any local packing defects . It has been suggested that Cβ atoms do not cover an antiparallel β-sheet by a tightly packed layer , leaving holes equivalent to the size of a methyl group or water molecule [32] . These holes are possibly not included in our analysis because of the volume cut-off used in the definition of cavities . Additionally , these would have had all the CL atoms residing on the β-sheet; however , the percentage of cavities exclusively lined by β-sheet atoms is very low ( <5% ) . The higher involvement of β-sheet residues in lining the cavities may have implications for the energetics of interaction . It has been observed that for protein-protein interactions , those having interfaces mostly made up of β-sheet have , on average lower free energy of binding compared to those having α- or αβ ( mixed ) classes of interfaces ( Guharoy and Chakrabarti , unpublished ) . This observation may be understood in terms of the lowest packing efficiency of interfacial β structures , leading to lower van der Waals contacts and therefore lower binding free energies as well . Figure 4A shows that when there is a surface groove that is not matched by a bulge on the surface of the interacting protein this would result in the formation of a cavity in the interface . Water molecules in the groove cannot be squeezed out and remains trapped inside the interface . When we analyzed if the water molecules can have direct hydrogen bond contact with both the subunits ( Table 3 ) we observed that such molecules are just 37% and 51% in Inter_H and Inter_C , respectively , a smaller number ( 10% and 5% ) of water molecules do not form any bond with either subunit . Indeed , one can see from Figure 12A , where the cavity can be considered as a casket of water molecules , the majority of which form hydrogen bonds between themselves . Even when the cavities contain one or two water molecules , the large size of the cavity may preclude the solvent molecules to interact with both the protein components . However , if we consider contacts ( instead of hydrogen bonds ) made with both the sides , a greater number ( 72% and 84% ) of water molecules bridge the two subunits . For the water molecules having direct hydrogen bonding with both the protein subunits we considered the involvement of main- and side-chain atoms and how important the solvents are in neutralizing the destabilizing effect of like-charges from the two subunits coming close to each other . It appears from Table 4 that water molecules sitting between like and opposite charges ( in the side chain ) occur to similar extent in Inter_H , but these are in 3∶4 ratio in Inter_C . A residue close to the two-fold axis in homodimeric interfaces can be in contact with the same residue from the other subunit – the so-called self contacts [33] , which may explain some of the occurrences of like charges around water molecules in Inter_H . The packing density at interfaces has been computed by comparing the Voronoi volume of the buried atoms in the interface to the reference atomic volume [34] . Such a plot is shown in Figure S6 , which also includes the distribution for the atoms in the tertiary structure in individual files—as a reference for the normal distribution . When Vr is larger than unity it indicates that the packing density at interfaces is lower than that in protein interiors , and a smaller value indicates a higher density . The average values of Vr for the interfaces in homodimers and heretocomplexes are slightly higher than 1 . 01 ( ±0 . 06 ) reported in [34] for heterocomplex interfaces . Overall , the volumes of the interface atoms are within 3% of those in the protein interior . The existence of any small molecule , other than water , in the cavities was found out ( Table S4 ) . In about 30% cases only 1–3 atoms of a much larger ligand are found to be inside the cavity , which are usually <20 Å3 in volume . These cavities cannot be considered as having a small molecule entrapped . Heterocomplex interfaces have just two cases where molecules used in the crystallization procedure found their way into the cavity . In general , biologically relevant molecules are not found in interface cavities—only two cases of cofactor molecules are found in Inter_H cavities . In the tertiary structure , there is an example of Mg ion being located in a volume of 16 Å3; cavities having Ca ion usually have a volume in the range of 17–18 Å3 ( one example is shown in Figure 12B ) and a K ion is observed in 20 Å3 . Metals such as Hg , Ni , and iron-sulfur clusters occupy a much larger volume . Water molecules usually accompany the ligand in the cavity; the largest of such a cavity is displayed in Figure 12C . In summary , in this work we have delineated the total volume expected to be occupied by cavities in a protein or a protein-protein interface of a particular size . A quantitative relationship has been derived for the volume of a cavity and the atoms/residues lining it . Of the secondary structural elements , β-strands have a higher inclination to be associated with cavities . For a comparable ensemble of atoms the interfaces contain about twice the number of cavities relative to the tertiary structure . It has been shown recently that a cavity of an appropriate size is the basis of peptidyl-prolyl-isomerase ( PPIase ) activity of an important class of enzymes ( human FK506-binding protein 12 ) and that it is possible to create artificial PPIase activity by introducing such a cavity on barnase , a bacterial nuclease [35] . A comprehensive understanding of the features of cavities in protein interiors and interfaces , as presented here , would facilitate such protein design experiments .
Atomic coordinates of the proteins were extracted from the Protein Data Bank ( PDB ) [36] . The dataset consisted of 97 monomeric proteins [13] , 122 homodimers [8] and183 protein-protein complexes [37] , mostly determined to a resolution of 2 . 5 Å or better ( only16 structures are in the resolution range 2 . 5–3 . 0 Å ) . 219 independent subunits from the first two categories were used to identify cavities in the tertiary structure . The atoms that lose at least 0 . 1 Å2 of the accessible surface area ( ASA ) in the complex/dimer structure as compared to that in the isolated subunit were considered as interface atoms [7] , [8] . The calculation of protein volume was done by ProGeom , based on the Alpha-Shape theory ( server: http://nook . cs . ucdavis . edu/~koehl/ProShape/download . html ) . Quite a few algorithms/softwares exist for the calculation of cavities—VOIDOO [38] , MS package [39] , [40] , VOLBL [41] , [42] , CAST [29] ( now rechristened as CASTp ) , a Monte Carlo ( MC ) procedure [43] , etc . Of these the last two performed in a more consistent way [43] . For our work the cavities for each protein are identified using the CASTp ( Computed Atlas of Surface Topography of proteins ) server [44] located at http://sts . bioengr . uic . edu/castp/ . The basic ingredients of computational geometry applied in CASTp are: Delaunay triangulation , alpha shape , and discrete flow [45]–[48] . CASTp provides a full description of protein pockets and cavities , including volume , surface area , protein atoms that line the concavity , and features of pocket mouth ( s ) including identification of mouth atoms as well as measurement of mouth area and circumference . The default probe radius of 1 . 4 Å has been used for our calculations . Three classes of cavities were identified: ( a ) Ter_str ( cavities in monomeric proteins and within one subunit of homodimeric proteins ) ; ( b ) Inter_H ( those within homodimer interfaces ) ; and ( c ) Inter_C ( within protein-protein complex interfaces ) . Surface pockets and cavities belonging to the subunit or interface are illustrated in Figure 1 . Any residue contributing one or more atoms to the cavity-lining ( CL ) region is considered as a CL residue; the same is true for the NCNS ( non-cavity-non-surface ) region . Interface cavities should have at least 20% of the cavity-lining atoms from a different subunit . The same condition was also used to identify if any Ter_str cavity belonged to the interdomain region , after identifying the individual domain residues from SCOP [49] . As we have used the option in CASTp that defines cavities based on molecular surface ( rather than ASA ) , a few atoms not identified by us as belonging to the interface were also found lining the interface cavities and these were counted as being associated with the cavity ( as well as the interface ) . Only the cavities with volume>11 . 5 Å3 ( the volume of a probe with radius 1 . 4 Å ) were retained for analysis . Further the cavities were classified as solvated or empty based on the presence or the absence of crystallographically determined water molecules in them . Cavities were considered for the existence of embedded water molecules starting from the smallest one . When two cavities , one small and the other large and irregular have some common CL atoms , there could be ambiguity is assigning a water molecule that may lie close to the shared atom ( s ) . As such the cavities were considered in the ascending order of volume . The location of water in a cavity was found out as follows . ( i ) It has to be within 4 . 5 Å of a CL atom . ( ii ) If such water exists , all CL atoms within 4 . 5 Å from the water are found . ( iii ) If the distance from the center of mass of the cavity to the water molecule is less than that to any of the CL atoms in contact ( as obtained in ii ) , the water is assumed to belong to the cavity . The existence of any ligand in a cavity was found in a similar fashion . Hydrogen bonding involving a water molecule ( to protein atoms , as well as to other water molecules in the cavity ) was determined using HBPLUS [50] . The surface representation of the cavities was made using MSMS [51] and displayed with VMD [52] . Based on chemical characteristics the atoms in the PDB files were grouped into thirteen classes . Following are the atom labels ( and their definition ) . N , CA , C , O , CB , S ( sulfur of Met and Cys ) , Oh ( the hydroxyl group of Ser , Thr and Tyr ) , Oa ( both the carboxylate oxygen atoms of Asp and Glu , and the amide oxygen of Asn and Gln ) , Na ( the amide nitrogen of Asn and Gln ) , Nc ( side-chain N atoms of Lys and Arg ) , Nr ( ring N atoms of His and Trp ) , Cr ( aromatic C atoms of Phe , Tyr , His and Trp ) , Cc ( aliphatic C atoms excluding CB of Val , Ile , Leu , Met , Lys , Pro , Gln , Glu , Arg and Thr ) . In the first 5 cases the labels are the same as the atomic labels used in PDB . The Voronoi [53] procedure for the determination of volume of atomic groups was applied to proteins by Richards [2] . By constructing the minimally sized polyhedron ( called a Voronoi polyhedron ) around each atom , this procedure allocates the space within a structure , to its constituent atoms . The original program , as modified and extended by Harpaz et al . [54] and Voss et al . [55] ( available at http://www . molmovdb . org/geometry/ [17] ) , has been used in this study . Two parameters need to be provided for the program – the atomic van der Waals radii and Voronoi plane positioning method ( method B used ) . The propensity of a residue to be in the CL region is given as ln P , whereNx is the number of atoms of amino acid residue of type X lining the cavities and ∑Nx is its total number in the dataset ( consisting of all the subunits for Ter_str , and all the interfaces , for Inter_H and Inter_C ) ; Na and ∑Na are the corresponding numbers considering all residue types together . This method is based on counting the atoms , rather than residues , as it is supposed to provide values that are independent of the size of the residue [56] . The propensity was also calculated in a similar fashion considering different types of atoms ( instead of residues ) , as also for the occurrence of secondary structural elements ( helix , strand and the rest , termed ‘Others’ ) lining the cavities . Secondary structure assignments were made using the DSSP program [57] . Rvs provides an estimate of the surface:volume ratio for a cavity relative to that for a sphere having the same volume as the cavity . The following formula is used for its calculation:
|
During protein folding a polypeptide chain takes up a three-dimensional structure that is characterized by close packing of atoms . For cellular processes proteins need to interact , and the binding is also characterized by packing of complementary surfaces . Two types of binding can be envisaged—obligate and non-obligate—the former is exhibited by homodimeric molecules ( in which two polypeptide chains are held together in permanent association ) and the latter by protein–protein complexes ( such as antigen–antibody , enzyme–inhibitor , etc . ) , which are more transient in nature . Cavities are observed as defects in atomic packing . We present an analysis of cavities within the structure of a protein chain , as well as interfaces formed by the association of two protein chains . For a comparable number of atoms the interface has about twice the number of cavities relative to the tertiary structure . The interfaces contain a higher percentage of larger cavities , which tend to be solvated . We have determined the relationships between the protein volume and the total volume of all the cavities in the structure , the volume of the cavity and the number of atoms ( residues ) lining it , and the size of the cavity and the number of waters in it .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/macromolecular",
"structure",
"analysis"
] |
2008
|
Cavities and Atomic Packing in Protein Structures and Interfaces
|
African trypanosomes constrain livestock and human health in Sub-Saharan Africa , and aggravate poverty and hunger of these otherwise largely livestock-keeping communities . To solve this , there is need to develop and use effective and cheap tsetse control methods . To this end , we aimed at determining the smallest proportion of a cattle herd that needs to be sprayed on the legs , bellies and ears ( RAP ) for effective Human and Animal African Trypanosomiasis ( HAT/AAT ) control . Cattle in 20 villages were ear-tagged and injected with two doses of diminazene diaceturate ( DA ) forty days apart , and randomly allocated to one of five treatment regimens namely; no treatment , 25% , 50% , 75% monthly RAP and every 3 month Albendazole drench . Cattle trypanosome re-infection rate was determined by molecular techniques . ArcMap V10 . 3 was used to map apparent tsetse density ( FTD ) from trap catches . The effect of graded RAP on incidence risk ratios and trypanosome prevalence was determined using Poisson and logistic random effect models in R and STATA V12 . 1 respectively . Incidence was estimated at 9 . 8/100 years in RAP regimens , significantly lower compared to 25 . 7/100 years in the non-RAP regimens ( incidence rate ratio: 0 . 37; 95% CI: 0 . 22–0 . 65; P<0 . 001 ) . Likewise , trypanosome prevalence after one year of follow up was significantly lower in RAP animals than in non-RAP animals ( 4% vs 15% , OR: 0 . 20 , 95% CI: 0 . 08–0 . 44; P<0 . 001 ) . Contrary to our expectation , level of protection did not increase with increasing proportion of animals treated . Reduction in RAP coverage did not significantly affect efficacy of treatment . This is envisaged to improve RAP adaptability to low income livestock keepers but needs further evaluation in different tsetse challenge , HAT/AAT transmission rates and management systems before adopting it for routine tsetse control programs .
African trypanosomes transmitted by tsetse flies ( Diptera: Glossinidae ) pose one of the biggest constraints to animal and human health and livestock-crop integration in Sub-Saharan Africa [1] , [2] . They cause a debilitating disease in domestic animals ( nagana ) and humans ( sleeping sickness ) [3]–[5] . In the south-eastern part of Uganda , cattle are the main reservoir of Trypanosoma brucei rhodesiense the causative agent of the acute form of African human trypanosomiasis ( HAT ) [6]–[9] . The chronic form of the disease caused by T . b . gambiense , whose main reservoir is yet unknown , exists in West Nile Districts of Uganda extending to most parts of South–Sudan [10] , [11] . However , active case detection and management have been shown to be effective in T . b . gambiense control indicating that humans are very important in maintaining disease transmission [12]–[14] . The distance between the two forms of HAT has been decreasing threatening a merger as a result of massive cattle restocking in south-eastern Uganda following 20 years of unrest in this region [10] , [15] , [16] . This merger has recently been temporarily halted by the Stamp-out sleeping sickness ( SOS ) program-led preventive chemotherapy and pyrethroid insecticide spraying of about 0 . 5 million cattle [17] , [18] . However , this halt remains temporary unless control efforts are sustained [18] , [19] . The above notwithstanding , The World Bank estimates that about 25% of the population in Sub-Saharan Africa and Uganda in particular , subsists on less than US $ 1 . 25 per day . This poverty level is compounded by food insecurity that affects over 34% of the population [20] , [21] and ill-health caused by HAT in addition to other endemic human diseases in this region . However , the majority of the poor people in Ugandan communities afflicted by HAT own cattle [22] , [23] whose production is also constrained by AAT . This implies that improving livestock production has potential to reduce poverty and improve food security [1] , [2] , [20] , [24] . Before this can be achieved , there is need to put in place effective and sustainable HAT/AAT control methods . Such control methods to be effective and sustainable in small holder crop-livestock production systems need to be commensurate to inelastic budgets of small holder livestock keepers . In addition , they need to be environmentally benign and target more than one of the endemic diseases that are known to occur in these areas . Previously , restricting pyrethroid insecticides to the belly and legs had proved cheap , environmentally benign and unequivocally effective on tsetse and trypanosomiasis control compared to other control methods [25]–[27] . It has also been suggested that RAP is unlikely to disrupt endemic stability to tick-borne diseases ( TBDs ) ; an epidemiological equilibrium that is known to maintain a large population of cattle protected against TBDs [27] . However , it had been suggested that RAP needs to be optimized in the field setup so as to further reduce its cost and make it commensurate to inelastic budgets of small holder livestock keepers [27] . To this end , a cluster randomized trial was carried out to determine the smallest proportion of a village herd that needs to be sprayed by restricting pyrethroid insecticides to the bellies , legs and ears of cattle and effectively control HAT/AAT . To achieve this , bovine trypanosome prevalences were determined by molecular techniques before and after spraying ( by RAP ) 0% , 25% , 50% , 75% of village cattle herds in 20 villages in Tororo , district; eastern Uganda . RAP was initially developed basing on a body of research that indicated that tsetse land and feed mostly on legs , bellies and ears of the larger compared to smaller/younger cattle [27]–[29] . Restricting insecticides to the legs , bellies and ears reduces the amount of the insecticide by 5 fold; reducing on the cost of application and environmental effects to the dung fauna that break down dung into manure [25] , [30] . It is upon this background that we sought to further optimize RAP .
This study was carried out in Tororo district , south-eastern Uganda for 18 months between June 2012–December 2013 . Glossina fuscipes fuscipes and Glossina pallidipes are the main tsetse fly vectors of trypanosomiasis in this area [9] , [31] . The location , livestock production systems , climate and vegetation of Tororo district have been described elsewhere [32] , [33] . The 20 intervention villages were selected from 57 villages of a larger survey of trypanosome ( D Muhanguzi; unpublished ) and T . parva [33] prevalence in Tororo district by molecular techniques . Fifty seven villages were screened for eligibility and data collected on basic socio-demographics and trypanosome prevalence by molecular techniques . Twenty-seven villages fulfilled the eligibility criteria of i ) a cattle population of > = 50 and ii ) a trypanosome prevalence of > = 15% . A village cattle population of 50 was used so as to make sure that cattle population is large enough not to be depleted in 18 months of follow-up . Baseline trypanosome prevalence of 15% was used for village inclusion in order to provide a wide enough range to be able to measure the effect of graded RAP on trypanosome prevalence . In order to select 20 villages , 100 unique allocation sequences were generated which fulfilled the condition of a minimum distance of 2 km between neighbouring villages . This was to minimize contamination effects from different intervention arms . Finally , one allocation sequence was selected randomly . Each of the 20 study villages was randomized to one of five different treatments . All cattle in 20 study villages were ear tagged for ease of identification at follow-up . They were then treated with a short acting diminazene diaceturate ( DA ) containing cyanocobalamin ( vitamin B12 ) and hydroxocobalamin ( Vitamin B12a ) ( Veriben B12; Ceva santé animale , France ) at the beginning of the trial . Another DA dose was administered 40 days later to all cattle in the 20 study villages to clean them of residual trypanosome infections and be able to monitor the rate at which re-infection took place . Diminazene diaceturate was administered at a dose of 0 . 01 g/kg live body weight ( bwt ) by deep intramuscular injection . In order to assess , herd structure ( age , sex , breed , exits/entries ) at each sampling time , livestock-keepers , their household particulars ( village , parish , county ) and cattle demographics were entered on a herd structure register at the time of introduction into the intervention . This register was updated once three monthly for 15 months . In regimens 2–4; different proportions ( 25% , 50% and 75% ) of the village herd were sprayed once every 28 days in what is referred to here as graded RAP . This was to determine the effect of spraying different proportions of a village cattle herd on the rate of transmission of different trypanosomes . An emulsifiable deltamethrin concentrate ( Vectocid , Ceva Interchem , Tunis ) spray was applied in the recommended concentration of 1; 1000 ( Vectocid to water parts ) on legs , belly and ears as previously described [27] . The first 25% , 50% and 75% of all the registered cattle to be presented in the respective RAP regimens were spayed at each of the monthly spraying . Cattle in regimen 5 were in addition given an Albendazole 10% drench at a dose rate of 0 . 008 g/kg bwt once after three months . This was to create a replica non-RAP regimen where a non tsetse and-trypanosomiasis effective treatment was administered as an incentive for farmers to present cattle for trypanosome testing for 18 months . This was introduced in the design of this study in order to reduce the risk of excessive losses to follow-up in Regimen 1 . As such , regimens 1 and 5 were planned control regimens for RAP regimens 2–4 . Blood samples were taken 14 days post the last Veriben B12 injection and repeated once three monthly for 18 months of the trial in order to monitor the rate of re-infection with different trypanosomes . For ethical reasons , all cattle in the non-RAP villages were administered with Veriben B12 injections at the end of the trial since they were at a higher risk of infection during the trial . About 125 µl of blood were collected from the middle ear vein and applied onto designated sample area of the classic Whatman FTA cards ( Whatman Bioscience , Cambridge , UK ) avoiding cross contamination [34] , [35] . Blood samples were then allowed to air-dry , labelled with cattle ear tag numbers , treatment regimen , sampling number , village name , parish , sub County , County and date of collection . They were packed in foil pouches with a silica gel desiccant ( Sigma Aldrich , Co . , Life sciences , USA ) prior to shipping to the University of Edinburgh , UK for analysis . DNA was extracted and eluted in Chelex-100 resin ( Sigma Aldrich , Co . , Life sciences , USA ) from five 3 mm FTA sample discs according to a previously described protocol [35] , [36] . Eluted DNA samples were kept at −20°C for long-term PCR analyses or 4°C if they were to be analysed within a few days after extraction . Eluted DNA samples were screened for different trypanosome species using a single pair of primers ( CR and BR ) and thermo cycling conditions as previously described [37] . The ITS1- PCR was done in 25 µl reaction volume; 20 µl of which were the PCR master-mix and either 5 µl of the test sample or negative control eluate or positive control DNA . The master-mix was made of 10×-reaction buffer ( 670 mM Tris-HCl pH 8 . 8 , 166 µM ( NH4 ) 2SO4 , 4 . 5% Triton X-100 , 2 mg/ml gelatin ) ( Fisher Biotech ) , 1 . 0 mM MgCl2 , 200 µM of each dNTP , 5 µM each of the CF and BR primers , 0 . 5 U of Taq DNA polymerase ( Fisher Biotech ) and 15 . 2 µl RNase-free ( molecular grade ) water . To determine which samples were infected with either T . brucei or T . b . rhodesiense , multiplex PCR [38] was carried out on each of the samples from which a 450 bp fragment was detected on ITS1-PCR . Multiplex PCR was done in 25 µl reactions using primers and conditions as previously described [38] . In order to determine the commonest T . congolense genotype circulating in Tororo district , all samples from which a ≥600 bp fragment was amplified on ITS1-PCR were initially tested for T . congolense savannah using a single pair of primers ( TCS1 & TCS2 ) and thermo cycling conditions as previously described [39] . All samples that were positive for T . congolense DNA on ITS1-PCR were positive for T . congolense savannah . For this reason , no more T . congolense genotype-specific ( Kilifi , Tsavo , forest ) PCRs were performed although a few co-infections with different T . congolense genotypes could have been possible . The PCR was done in 25 µl reaction volume; 20 µl of which were the PCR master-mix and either 5 µl of the test sample or negative control eluate or positive control DNA . The master-mix was made of 10×-reaction buffer ( 670 mM Tris-HCl pH 8 . 8 , 166 µM ( NH4 ) 2SO4 , 4 . 5% Triton X-100 , 2 mg/ml gelatin ) ( Fisher Biotech ) , , 4 . 5% Triton X-100 , 2 mg/ml gelatin ) ( Fisher Biotech ) , 0 . 75 mM MgCl2 , 200 µM of each dNTP , 12 . 5 µM each of the TCS1 & TCS2 primers , 1 U of Taq DNA polymerase ( Fisher Biotech ) and 13 . 05 µl of RNase-free water . PCR products for the three sets of PCRs were electrophoresed in 1 . 5% agarose ( Bio Tolls Inc . Japan ) , stained in GelRed ( Biotium , Inc . , USA ) and visualised on a UV transilluminator for fragment size determination . Pyramidal traps [40] were set in 161 locations by Tororo District Entomology Department between June and September 2012 . Individual tsetse trap catches were used to determine pre-intervention FTD . About 12 traps were set per Km2 to cover at least a square km ( km2 ) of each sub county . Tsetse fly catches were monitored , emptied and species and sex determined after every 24 hours . Apparent tsetse density was determined as the number of tsetse flies per trap per day . The primary analysis investigated the impact of RAP on the incidence risk ratios of any trypanosome infection using generalized linear mixed models with a Poisson distribution and a logarithmic link function . To account for correlation within clusters , villages were included as gamma distributed random effects . The logarithm of the time under observation , i . e . the time period between the first and last time an individual animal was sampled , was included as offset variable . To assess the intervention effect over time , prevalences after 12 and 18 months of follow up were compared using mixed models with binary outcome and logit link function . Additional analyses at other sampling points are provided in supporting information S1 . The original idea of modelling the proportion of animals treated with RAP as a dose response relationship was abandoned because incidence did not decrease with increasing proportion of treated animals . Therefore , the results for the different treatment regimens compared to the control regimens are presented . Apparent tsetse density was determined as the number of tsetse captured per trap per day . To determine the spatial distribution of tsetse flies ( G . pallidipes and G . fuscipes ) in Tororo district an FTD map was generated using the Inverse Weighing Distance Extension ( IDW ) [41] of ArcMap 10 . 3 of 161 individual trap catches . Interpolation was done at two spatial resolutions ( grid cell sizes of 1 km2 and 25 km2 ) and raster values were extracted for each village at each spatial resolution . A default exponent value of 2 was chosen . Although there was little evidence of spatial autocorrelation ( Moran's I = −0 . 11 , −0 . 08 , −0 . 10; all P >0 . 2 , for baseline trypanosome prevalence and FTDs at 1 km2 and 25 km2 resolution , respectively . The association between FTD and trypanosome prevalence was adjusted for potential spatial dependence . We used a generalized least squares model with a Gaussian spatial correlation structure to quantify the effect . Statistical analyses were performed using R v 3 . 0 . 2 ( packages ‘lme4’ , ‘nlme’ and ‘ape’ ) except Poisson random effect models which were performed in STATA v 12 . 1 . This study was reviewed and approved by the Makerere University College of Veterinary Medicine Animal Resources and Biosecurity ( COVAB ) research and ethics committee for consistency to animal use and care . Upon approval ( number VAB/REC/10/105 ) the COVAB research and ethics committee forwarded it to the Uganda National Council for Science and Technology ( UNCST ) and it was further approved and registered under registration number HS1336 .
Over all seven time points , eleven thousand blood samples ( 11 , 087 ) were collected from 3 , 677 cattle . One thousand nine hundred eighty one cattle ( 54% ) were sampled 14 days post the second Veriben B12 injections and examined to determine trypanosome residual infections . Almost half the investigated animals ( 46% ) were newly introduced into the herd during the 18 months of follow up ( Figure 1 ) . Pre-intervention trypanosome prevalence ranged from 20–27% in different regimens . The Boran and African short horn zebu hybrid was the most predominant cattle breed ( 98% ) and well balanced among the five treatment groups ( range 93%–100% ) . Treatment groups were slightly imbalanced with respect to age and sex composition . Roughly half of the animals were above 3 years of age ( Table 1 ) . Fourteen days post the second dose of diminazene diaceturate ( denoted as time 0 ) , trypanosome prevalences generally increased in all regimens up to month 6 when they started decreasing ( Regimen 2 , 3 and 4 ) over time . In regimens 1 and 5 trypanosome prevalences increased up to about 12 and 15 months respectively and started decreasing thereafter . The slope of curves representing trypanosome prevalences over time in different regimens is in increasing order of Regimen 2<3<4<1<5 ( Figures 2& 3 ) . T . vivax was the most predominant species detected in any regimen while T . brucei s . l . was the least predominant species detected over the study period ( Table 2 ) . At the end of follow-up , we observed an incidence of 9 . 8 per 100 animal years in the RAP regimens which was significantly lower compared to the 25 . 7 in the non RAP regimens ( incidence rate ratio: 0 . 37; 95% CI: 0 . 22–0 . 65; P<0 . 001 ) . Likewise , trypanosome prevalence after one year of follow up was 15% in the non-RAP regimens compared to 4% in the RAP animals ( OR: 0 . 20 , 95% CI: 0 . 08–0 . 44; P<0 . 001 ) . The effect was lower but statistically significant after 18 months of follow up ( 9% vs 4%; OR: 0 . 38; 95% CI: 0 . 14–0 . 93; P = 0 . 03 ) . Adjustment for sex , age category , FTD at 1 km2 spatial aggregation ( FTD-1000 m ) and for animal treatment at baseline did not noteworthy change the estimates ( Table 3 ) . There was some indication that FTD-1000 m had an impact on the treatment effect , but the association was only statistically significant in one of the 3 models . Newly introduced animals had slightly lower risk but it was only marginally significant . Of note , newly introduced cattle were generally younger ( median age 2 . 3 years compared to 4 . 0 years ) . As such , trypanosome infections were persistently higher in isolated villages in central , northern and western parts of Tororo District especially in Kirewa , Nagongera and Paya sub counties ( Figure 4 ) . Details of the models on the other sampling dates as well as time×treatment interaction are provided in supporting information S1 . The relative risk of infection with any trypanosome species measured here by the incidence risk ratios was highest in regimen 5 over the 18 months of the study . Cattle in regimen 2 presented with incidence of 5 . 1 per 100 animal years which was significantly lower compared to the 20 . 9/100 years observed in the control group ( regime 1 ) ( IRR: 0 . 24; 95% CI: 0 . 11–0 . 52; P<0 . 001 ) ( Table 4 ) . The risk of infection with different trypanosome species was in order of regimen 5>4>3>2 ( Table 5 ) . Contrary to our expectation there was no evidence that protection increases with increasing proportion of animals treated . The number of tsetse flies caught per trap per day were summarised into FTD which was highly variable between traps ( Figure 5 ) . About 88% of all tsetse caught during the period were of G . f . fuscipes while 12% were G . pallidipes from Paya and Mulanda sub counties . G . pallidipes was localised at one site in Lwala Parish , Mulanda Sub County but fairly distributed in each of the 4 selected parishes of Paya Sub County ( Table 6 ) . We observed a 2 . 7%-points increase in the baseline trypanosome prevalence with each 1 unit increase of FTD ( 95% CI: 0 . 6–4 . 7%-points , P = 0 . 02 ) using the prediction of the 1 km2 spatial aggregation . On a higher spatial aggregation level ( 25 km2 grid cell size ) the observed effect was with 1 . 7%-points smaller and statistically not significant ( 95% CI: −1 . 2–4 . 7%-points , P = 0 . 26 ) . In Table 7 the baseline prevalences and the corresponding FTD are presented for all villages .
This study complements the available literature to demonstrate that RAP is effective in controlling African trypanosomiasis . To our knowledge , this study provides the first field based longitudinal study to demonstrate that spraying only as low as 25% of a village cattle herd in stable African trypanosomiasis transmission area is sufficient in the control of T . brucei s . l . In high tsetse challenge areas where tsetse mainly feed on cattle , control of nagana ( T . vivax and T . congolense ) would probably require increasing village RAP herd coverage to 50–75% without reducing RAP efficacy . This is particularly important because T . vivax and T . c . savannah persist under moderate ( <50% RAP ) tsetse control over a long period of time . In such areas , treatment of all cattle with a curative trypanocide once yearly for the first 1–2 years of the control program would leverage tsetse control by reducing parasitaemia . Reducing RAP coverage to 25% ( T . brucei s . l control ) or 50–75% ( T . vivax/congolense control ) would further reduce the amount of insecticides used compared to that used in whole body spraying . This will further reduce cost of application of RAP and improve uptake by small holder farmers in many crop-livestock production systems . Before these findings are integrated in routine tsetse control programs we recommend that the performance of different RAP herd coverage levels is evaluated in varied tsetse challenge , trypanosome transmission rates and management systems .
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Poverty , hunger and human ill-health aggravated by trypanosomiasis in Sub-Saharan Africa can only be reduced by developing and using cheap and effective tsetse control methods . To further reduce the cost of tsetse control by restricting insecticides to the legs , belly and ears ( RAP ) we set out to determine the lowest RAP coverage that can effectively control tsetse . Cattle in 20 south-eastern Uganda villages were randomly allocated to 5 treatment groups , ear-tagged for ease of follow-up and treated twice forty days apart with a trypanocide at the beginning of the trial . Cattle in regimens 2–4 received monthly graded RAP ( 25% , 50% and 75% of village herd respectively ) , while those in regimens 1 and 5 received no more treatment and deworming once every three months respectively . Molecular techniques were used to check for trypanosome infections , while tsetse apparent density was determined by traps at 161 locations in the district . About 25% RAP coverage was effective at controlling T . brucei s . l . while 50–75% RAP coverage would need to be used for effective T . vivax and T . congolense nagana control . Use of RAP at lower herd coverage is envisaged to reduce its cost , damage to the environment and improve its uptake in resource poor communities .
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2014
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Improvements on Restricted Insecticide Application Protocol for Control of Human and Animal African Trypanosomiasis in Eastern Uganda
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Intellectual disability ( ID ) , one of the most common human developmental disorders , can be caused by genetic mutations in Cullin 4B ( Cul4B ) and cereblon ( CRBN ) . CRBN is a substrate receptor for the Cul4A/B-DDB1 ubiquitin ligase ( CRL4 ) and can target voltage- and calcium-activated BK channel for ER retention . Here we report that ID-associated CRL4CRBN mutations abolish the interaction of the BK channel with CRL4 , and redirect the BK channel to the SCFFbxo7 ubiquitin ligase for proteasomal degradation . Glioma cell lines harbouring CRBN mutations record density-dependent decrease of BK currents , which can be restored by blocking Cullin ubiquitin ligase activity . Importantly , mice with neuron-specific deletion of DDB1 or CRBN express reduced BK protein levels in the brain , and exhibit similar impairment in learning and memory , a deficit that can be partially rescued by activating the BK channel . Our results reveal a competitive targeting of the BK channel by two ubiquitin ligases to achieve exquisite control of its stability , and support changes in neuronal excitability as a common pathogenic mechanism underlying CRL4CRBN–associated ID .
Intellectual disability ( ID ) , formerly known as mental retardation ( MR ) , is a generalized neurodevelopmental disorder defined as substantial impairment of cognitive and adaptive functions with a diagnosis of IQ score of less than 70 [1] . ID can occur as an isolated phenomenon or may frequently be accompanied by congenital malformations or other neurological features such as sensory impairment , seizures , and autism spectrum disorders ( ASD ) [2] . With an estimated 1% of the world’s population affected [3] , ID has become a major social problem and resulted in an enormous economic burden in all countries [4 , 5] . Many inheritable genetic mutations have been identified in ID patients [5–8] , but the underlying mechanisms remain to be elucidated [9] . Cul4B was identified as one of the most frequently mutated genes in X-linked ID families by whole-exon sequencing [10–12] . Cul4B shares 80% sequence homology with Cul4A and they are functionally redundant [13] . Cul4A/B functions as a scaffold to bridge the RING finger protein Rbx1/Roc1 and the adaptor protein Damaged DNA Binding protein 1 ( DDB1 ) to form the E3 ubiquitin ligase complex , Cul4 RING ligase ( CRL4 ) [14] . CRL4 targets diverse substrates to regulate cell cycle , DNA damage repair , and chromatin functions via substrate receptors called DDB1 and Cul4 associated factors ( DCAFs ) [13 , 15 , 16] . Cereblon ( CRBN ) , a DCAF protein , can recruit two ion channels to CRL4 , the ClC-1 chloride channel for turnover [17] and the BK channel for trafficking [18] . Based on the study designed to overexpress CRBN and BK channel in heterologous cells , wild-type ( WT ) CRBN can restrict excessive BK channel in ER for a balance of BK activity in cell membrane to prevent induced epileptogenesis [18] . However , whether genetic inactivation of CRBN can influence endogenous BK channel activity need further clearly addressed . Mutations in both CRBN and BK channel have been implicated in the pathogenesis of ID . BK channel regulates the general excitability of neurons and the transmitter release at presynaptic terminals [19] . Some ID patients with ASD and epilepsy were found to harbor a loss-of-function mutation in α subunit ( SLO1 ) of BK channel [20] . Inactivation of BK channel by transcriptional silencing of the Fmr1 gene causes the most common inheritable ID , known as Fragile X syndrome ( FXS ) [21 , 22] . Autosomal recessive CRBN mutations have also been identified in ID patients in two unrelated families [23 , 24] . Genetic deletion of Slo1 [25] or Crbn [26] in mouse brain leads to impaired spatial learning or conditioned fear memory , respectively . Given our recent finding that CRL4CRBN ubiquitinates Slo1 [18] , these human and animal studies suggest that the ubiquitination of BK channel may play an important role in learning and memory . Here using DDB1 and CRBN mutant mouse strains , we found similar cognitive defects in them and by surprise found that the total BK channel level is significantly reduced in these mutant mice brains . We further identified the ID-associated CRL4CRBN mutations redirect both CRBN and BK channel to another new Cullin E3 ligase , SCFFbxo7 , for ubiquitination and turnover . We finally demonstrated that BK channel activity is critical for restoring the learning and memory defects observed in CRL4CRBN-deficient mice . Thus , our study offers a new regulatory point to control BK channel activity via two distinct ubiquitin ligase , which can be targeted for mitigating intellectual disability .
To investigate the role of CRL4 in cognitive function , we generated the Ddb1F/F;Camk2α-Cre mouse strain , in which DDB1 is conditionally deleted in postnatal neurons in the hippocampus and cerebral cortex [18] . Whole-brain deletion of DDB1 causes neonatal death in Ddb1F/F;Nestin-Cre mice [27] . We first assessed the general locomotor and visual capabilities of the Ddb1F/F;Camk2α-Cre mice . There was no statistically significant difference between WT and Ddb1F/F;Camk2α-Cre mice in ambulation , rearing , center and total horizontal activities ( S1A Fig ) . In a light/dark transfer test , Ddb1F/F;Camk2α-Cre mice exhibited slightly more frequent light/dark transitions as a sign of mild anxiety but no obvious visual disability ( S1B Fig ) , the latter corroborated by another vision test ( S1C Fig ) . This model therefore would allow us to perform cognitive tests that rely on functional locomotion and vision . To test texture-associated short-term memory , we performed a novel object recognition task and found that Ddb1F/F;Camk2α-Cre mice had difficulty in differentiating a novel object from a familiar one compared to WT ( Fig 1A ) . Additionally , in a cued and contextual fear conditioning task , which assesses the ability to learn and remember an association between environmental cues and aversive experiences , Ddb1F/F;Camk2α-Cre mice also exhibited significant deficits ( S2A Fig ) . To investigate spatial learning and memory , we performed the Barnes maze and Morris water maze tests on these animals . In the Barnes maze test , Ddb1F/F;Camk2α-Cre mice spent significantly more time and made more errors than WT during the 9-day training period ( Fig 1B and 1C ) . WT mice were more likely to use a spatial-reference search strategy than either a sequential or random search strategy during the training process , but Ddb1F/F;Camk2α-Cre mice were inclined to use a random search strategy ( Fig 1D ) . On the 10th day ( probe test ) , the escape tunnel was removed and the maze was divided into 4 quadrants . Ddb1F/F;Camk2α-Cre mice spent significantly less time in the target quadrant , where the escape tunnel was located , than WT mice did ( Fig 1E ) . These data indicate that Ddb1F/F;Camk2α-Cre mice exhibited defects in both reference and working memory . In the Morris water maze test , mice were released from the edge of a water pool to locate a submerged hidden platform during a 4-day training period . Ddb1F/F;Camk2α-Cre mice took longer time to arrive at the platform in all trials . When analyzing their performance over the course of the experiment , Ddb1F/F;Camk2α-Cre mice showed no significant improvement , indicating their impaired spatial learning ( Fig 2A ) . To evaluate the spatial memory , a probe trial was administered on the 5th day . With the original platform removed , Ddb1F/F;Camk2α-Cre mice crossed the platform location much less frequently than WT mice , indicating their defective spatial memory ( Fig 2B ) . Put together , these behavioral tests demonstrate that DDB1 deletion in mouse brain results in ID-like defects such as impaired learning and memory . CRBN is a substrate receptor for CRL4 and its mutations are found in ID patients . It was reported previously that deletion of CRBN in the CrbnF/F;Camk2α-Cre or Crbn-/- mice did not affect normal locomotor activity but caused a associative learning defect in a contextual fear-conditioning test [26] . We generated whole-brain CRBN KO mutant CrbnF/F;Nestin-Cre mice and verified this behavioral defect ( S2B Fig ) , as well as the defect in DDB1 mutant mice ( S2A Fig ) . Like the DDB1 mutant mice , CrbnF/F;Nestin-Cre mice spent more time reaching the platform and showed less improvement over the trials during a 5-day training period in the Morris water maze test ( Fig 2C ) . On the probe day , CrbnF/F;Nestin-Cre mice exhibited significantly fewer platform crossings ( Fig 2D ) , suggesting that they were less able to remember the location of the platform compared to their WT littermates . We conclude that CRBN and DDB1 deficient mice displayed similar impairment in learning and memory , suggesting that this ID-like phenotype may be regulated by the CRL4CRBN ubiquitin ligase . To understand the functional crosstalk between DDB1 and CRBN , we examined the CRBN protein level in the brain or hippocampus of WT and Ddb1F/F;Camk2α-Cre mice . We observed that CRBN levels were reduced in the absence of DDB1 in mouse brain lysates ( Fig 3A ) . To further confirm this result , we performed RNA interference ( RNAi ) in several cell lines , including human glioma cell lines LN229 , U87mG and U118mG . Knockdown of DDB1 also resulted in reduced protein level of CRBN ( Fig 3B ) . Therefore , DDB1 is required for the steady-state level of CRBN protein . A nonsense mutation in CRBN with a premature stop at amino acid 419 , CRBNR419X , was identified in a large kindred to cause non-syndromic ID [23] . Recently , another missense variant in the CRBN , CRBNC391R , was identified in five individuals with severe ID from a consanguineous family [24] . By immunoprecipitaion assays , we found that both CRBNR419X and CRBNC391R associated very weakly with DDB1 ( Fig 3C ) . Likewise , CRBND249Y , a mutation located in close proximity to the DDB1 binding domain [28 , 29] and found in the lenalidomide-resistant ANBL-6 MM cell line [30] , completely abolished its binding to DDB1 ( Fig 3C ) . A further truncation analysis revealed that CRBN truncations ( residues 1–317 and 1–370 ) exhibited a similar DDB1-binding defect to CRBNR419X , while a different truncation ( residues 1–429 ) showed binding to DDB1 as strong as the full length protein ( Fig 3D ) . This result suggests that loss of a 10 amino segment ( 419–429 ) in the DDB1 C-terminus might adversely affect the CRBN domain structure critical for DDB1 interaction , as would be predicted by the structure of the human CRBN-DDB1-lenalidomide complex [29] ( S3A Fig ) . To investigate the mechanism underlying the decreased CRBN level caused by DDB1 deletion , we performed protein stability assays using the DDB1-binding-defective CRBN mutants . Compared with the WT protein , these mutant CRBN protein levels were decreased but can be restored by treating the cells with the proteasome inhibitor MG132 ( S3B Fig ) . The cycloheximide ( CHX ) chasing assays confirmed a reduction of the protein stability of these CRBN mutants ( S3C and S3D Fig ) . These results suggest that the mutant proteins are targeted by proteasomal degradation . The reduced stability of CRBNR419X was also previously reported , but the CRL4 ligase was proposed to account for its turnover [31] . Since mutant CRBN fails to bind CRL4 and the protein level can also be restored by MLN4924 , a neddylation inhibitor of all Cullin ubiquitin ligases ( S3E Fig ) , we hypothesized that another Cullin family ubiquitin ligase other than CRL4 might target these mutants for degradation . Out of the seven human Cullins [32] , Cul1 was found to interact with CRBN , though much more weakly compared to Cul4 , when overexpressed in 293T cells ( S3F Fig ) . The CRBN-Cul1 interaction was greatly enhanced when CRBN was abolished of its interaction with DDB1 or stabilized by MG132 ( S3G Fig ) . Cul1 forms the SCF complex ( also called CRL1 ) with Skp1 and Rbx1 to modify a large variety of substrates using various F-box adaptor protein [32 , 33] . It was reported that lenalidomide can increase CRBN stability [34] and also down-regulate the expression of a F-box protein Fbxo7 , even to a greater extent than its down-regulation on IKZF1 and IKZF3 levels [35] . We found that Fbxo7 bound to the CRBN mutants ( S3H Fig ) and mediated the interaction of CRBNR419X with Cul1 and Skp1 ( Fig 3E ) . Overexpression of both Fbxo7 and Skp1 greatly enhanced the interaction between CRBNR419X and Cul1 , and the ubiquitination level of CRBNR419X ( Fig 3E; S3I and S3J Fig ) . Conversely , knockdown of Cul1 decreased the ubiquitination level of CRBNR419X ( Fig 3F ) , and increased the steady-state level of CRBNR419X ( Fig 3G ) . Taken together , we have identified a distinct E3 ligase that is not active on CRBN unless CRBN fails to bind DDB1 . Considering DDB1 functions as a critical linker for CRL4 E3 ubiquitin ligase , these DDB1-binding defects in CRBN ID-associated mutations can disrupt their protein stability and the normal E3 function of CRL4CRBN as well as DDB1 deletion . However , the question remains how DDB1 deletion or these CRBN mutations cause learning and memory defect . Loss of the BK channel α subunit ( Slo1 ) was reported to cause impairment in spatial cognitive function [25] . Considering Slo1 is a direct substrate of CRL4CRBN , we examined the Slo1 level in DDB1 or CRBN deficient mouse brain lysates . Slo1 protein abundance was statistically reduced in mouse brain with either DDB1 or CRBN depleted ( Fig 4A; S4A and S4B Fig ) . Consistent with the animal study , knockdown of DDB1 or Cul4A/4B expression in 293T cells dramatically reduced the abundance of exogenously expressed Slo1 protein ( S4C Fig ) . To further confirm the reduction by endogenous Slo1 , we checked the expression of endogenous Slo1 in a series of cell lines , using glioma cells transfected with sgRNA for Slo1 as a negative control . As expected , Slo1 is not expressed in 293T and MM1S cells , but highly expressed in glioma cells such as U87mG and LN229 ( S4D Fig ) . Knockdown of DDB1 led to reduced endogenous CRBN and Slo1 protein abundance in both U87mG and LN229 cells ( Fig 4B ) . Therefore , like CRBN , the steady-state level of Slo1 is also maintained by an intact CRL4 complex in cells and mouse brain . To determine the role of CRBN and its ID-associated mutations in Slo1 regulation , we generated CRBN KO 293T cells using the CRISPR-Cas9 gene editing tool ( S4E Fig ) . Deletion of CRBN resulted in the dissociation of Slo1 from binding to CRL4 ( Fig 4C ) and a profound decrease of the half-life of the exogenously expressed Slo1 ( Fig 4D ) . Re-introducing WT CRBN but not mutant CRBNR419X in these KO cells restored both the binding to CRL4 and the half-life of Slo1 ( Fig 4C; S4F Fig ) . Endogenous Slo1 level was also decreased after CRBN was knocked down by RNAi or knocked out by CRISPR-Cas9 gene editing in glioma cells ( Fig 4E and 4F; S4G Fig ) . As BK channel functions as a neuronal excitability regulator on the cell membrane , we extracted total membrane proteins and verified that the membrane Slo1 levels were also decreased in CRBN KO U87mG cells ( Fig 4G ) and LN229 cells ( S4H Fig ) . Stable expression of WT CRBN but not mutant CRBNR419X in the CRBN KO cells could obviously restore the Slo1 levels . ( Fig 4H ) . Put together , we conclude that CRBN and its interaction with CRL4 are essential for the steady-state level of Slo1 . Considering CRBN deletion has little effect on Slo1 mRNA level in both U87mG and LN229 cells ( S5A Fig ) , the decreased Slo1 level is achieved via a posttranslational mechanism . We then tested whether Slo1 was redirected to SCFFbxo7 for turnover along with CRBNR419X , when unbound to CRL4 . Immunoprecipitation assays revealed that Slo1 interacted with Fbxo7 strongly when CRBN was deleted or mutated ( Fig 5A; S5B Fig ) . WT CRBN instead diminished the interaction of Slo1 and Fbxo7 ( Fig 5A ) and of Slo1 and Cul1 ( S5C Fig ) . We further found that Fbxo7 bound to the RCK1 and RCK2 domains of Slo1 ( S5D Fig ) , the same domains interacting with CRBN [18] , which is consistent with the competitive targeting of Slo1 by Fbxo7 and WT CRBN ( Fig 5A ) . In the ubiquitination assay , Slo1 formed a complex with SCFFbxo7 and was still ubiquitinated in CRBN KO 293T cells ( Fig 5B ) . The ubiquitination level of Slo1 increased along with overexpression of SCFFbxo7 components ( Fig 5B ) , and decreased after knockdown of Fbxo7 or Cul1 ( Fig 5C ) . Importantly , reduced Slo1 protein level in CRBN KO U87mG and LN229 cells could be restored by knockdown of Fbxo7 or Cul1 ( Fig 5D and 5E ) . Likewise , treatment with proteasome or neddylation inhibitors ( MG132 , bortezomib [36] or MLN4924 [37] ) in CRBN KO glioma cells could also increase the Slo1 levels , though treatment with bortezomib or MLN4924 in WT cells for excessive time led to decreased Slo1 levels , likely due to compound cytotoxicity ( Fig 5F and 5G ) . Put together , these results demonstrate that Slo1 , upon dissociated from CRL4 by CRBN mutations , is redirected to SCFFbxo7 for proteasomal destruction . To explore the physiological significance of the Slo1 turnover by CRBN mutations , the whole cell currents of CRBN KO U87mG and LN229 cells were measured by voltage clamp . BK currents were presented as the difference between the currents under baseline condition and after treatment with paxilline , a BK channel blocker [38 , 39] ( Fig 6A; S6A Fig ) . The BK currents were significantly decreased in CRBN-depleted cells compared to isogenic WT cells , and the difference was largely offset by treating the mutant cells with MLN4924 ( Fig 6A ) , likely as a result from an increased expression of Slo1 ( Fig 5G ) . However , MLN4924 treatment did not affect BK currents in the WT cells ( S6B Fig ) . Additionally , the decreased whole cell currents in CRBN KO cells could also be significantly enhanced by stable expression of WT CRBN , but not as much by expression of the mutant CRBNR419X ( Fig 6B ) . To rule out individual BK channel activity defect , BK single-channel currents were recorded using the excised inside-out configuration . No significant difference of single channel conductance and open probability was found between WT and CRBN KO cells ( Fig 6C ) . These results suggest that CRBN mutations decrease BK channel activity on cell membrane by destabilizing the protein . To demonstrate the reduced BK activity accounts for the impaired learning and memory of the CRBN mutant mice , we performed the Morris water maze tests on CrbnF/F;Nestin-Cre mice treated with a BK channel opener , BMS-204352 [20 , 40 , 41] . This compound was validated to be able to activate the residual membrane BK channels to elevate the currents in CRBN KO cells ( Fig 7A ) . In a bigger platform and with a longer training period for better quantitative comparison , BMS-204352-treated Crbn mutant mice spent significantly less time than vehicle-treated mutant mice to reach the platform , especially in the last 2 training days ( Fig 7B ) and exhibited more frequent platform crossings on the probe day ( Fig 7C ) . Unfortunately , restoring Slo1 protein levels in the CRBN mutant mouse brain with bortezomib or MLN4924 cannot be achieved , due to these two compounds’ toxicity and failure to pass blood-brain barrier .
In a physiological context , BK channel is recruited by CRBN to the CRL4 ubiquitin ligase for ubiquitination and retention in the ER compartment [18] . When this post-translational modification is disrupted in ID patients with CRBN mutations , BK channel is rerouted to an unrelated SCFFbxo7 ubiquitin ligase for ubiquitination and , instead of ER retention , proteasomal degradation ( Fig 7D ) . The differential targeting under normal and pathological conditions would ensure the limited distribution of the BK channel in the cell surface and thus the exquisite control of its activities and neuronal excitability . The dual ubiquitination of the same substrate is not an unusual mechanism for regulation of protein stability . For example , Myc oncoprotein can be ubiquitinated by the SCFβ-TrCP ubiquitin ligase to prevent its turnover by SCFFbw7-mediated ubiquitination [42] . Cand1 can promote exchange of Fbw7 for β-TrCP in SCF complex [43] , further modulating the cellular repertoire of SCF complex for control of Myc stability and cell proliferation . While many details on the differential targeting still need working out , it is clear that critical factors including BK channel and Myc are subjected to highly orchestrated regulation even just at the level of post-translational modification . BK channel is composed of four α subunits ( Slo1 ) , which form the ion channel pore , and auxiliary subunits , including four regulatory β ( β1–4 ) subunits that are expressed in various tissues [19] . The β1 subunit , primarily distributed in smooth muscle cells , controls cell surface trafficking by distinct domains of N-terminus [44] . The β1 subunit is ubiquitinated for proteolysis in diabetes-like conditions [45] , which may weaken its ability to dynamically assemble with Slo1 at the membrane [46] . However , ubiquitination of BKβ1 alone may not necessarily cause a reduced level of Slo1 , especially in brain cells [47] . Here we report for the first time that the protein level of the pore-forming Slo1 was directly regulated by the ubiquitin-proteasome system , and that this post-translational destruction of Slo1 decreases BK current . It will be interesting to examine the effects and conditions of SCFFbxo7-mediated ubiquitination and turnover of Slo1 in the alteration of the action potential ( AP ) shape and duration in neurons [47 , 48] as well as the Ca2+-dependent modulation of neurotransmitter release at presynaptic terminals [49] . ID is a clinically diverse disorder with heterogeneous genetic inheritance . We first prove that DDB1 is a critical regulator in learning and memory using mouse model . DDB1 and CRBN deletion in mouse brain can cause similar ID-like phenotypes . Then we provide mechanistic insight into ID pathogenesis in patients with CRBN mutations by demonstrating that they are DDB1-binding defective , which makes them unstable due to SCFFbxo7-mediated degradation . At last , we identify that the protein levels and activities of the BK channel are dysregulated by mutant CRBN . Moreover , a BK channel opener can significantly improve the learning and memory impairment in CRBN mutant mice . Therefore , BK channel may be one of the several substrates of CRL4CRBN that are disrupted by changes in protein ubiquitination [50–52] , due to CRBN or Cul4B ID mutations . However , whether enhancing BK level in brain is efficient and sufficient to rescue ID-like phenotypes in CRL4CRBN deficient mice , still remains to be further elucidated . Whether low BK channel activity is found in ID patients , particularly those with CRBN or Cul4B mutations , still needs in-depth clinical investigation . Since there is currently no effective treatment for ID , targeting BK channel activity might provide a potential therapeutic intervention for this neurological disorder .
Animal experiments were performed in Zhejiang University and the Scripps Institute with approval from Institutional Animal Care and Use Committee from both institutions ( approval number 15614 for Zhejiang University ) and comply with the Guide for the Care and Use of Laboratory Animals ( NIH publication no . 86-23 , revised 1985 ) . MG132 ( S2619 , Selleck Chemicals ) , bortezomib ( S1013 , Selleck Chemicals ) , MLN4924 ( M2189 , Abmol ) , Cycloheximide ( R750107 , Sigma-Aldrich ) , Paxilline ( P2928 , Sigma-Aldrich ) and BMS-204352 ( SML1313 , Sigma-Aldrich ) were dissolved in DMSO . Anti-CRBN ( SAB045910 , Sigma-Aldrich ) , anti-DDB1 ( 37–6200 , Invitrogen ) , anti-actin ( 1844–1 , Epitomics ) , anti-β-actin ( 4967 , cell Signaling Technology; 5779–1 , Epitomics ) , anti-β-tubulin ( 32–2600 , Invitrogen ) , anti-Pan-Cadherin ( 4068 , Cell Signaling Technology ) , anti-ubiquitin ( sc-271289 , Santa Cruz ) , anti-Cul1 ( AP16324b , Abgent ) , anti-Fbxo7 ( ab84129 , Abcam ) , anti-Cul4A ( A300-739A , Bethyl Laboratories ) , anti-Cul4B ( 2527–1 , Epitomics ) , anti-Slo1 ( NBP1-48250 , Novus Biologicals ) , anti-mSlo1 ( Millipore , MABN70 ) , anti-HA ( H6908; H3663 , Sigma-Aldrich ) , anti-Flag ( F3165 , Sigma-Aldrich ) , anti-Myc ( 2272 , Cell Signaling Technology ) , anti-GFP ( sc-8334 , Santa Cruz ) , anti-HA agarose ( E6779 , Sigma-Aldrich ) , anti-Flag M2 agarose ( A2220 , Sigma-Aldrich ) , goat anti-mouse IgG-HRP ( sc-2005; Santa Cruz ) and goat anti-rabbit IgG-HRP ( sc-2004; Santa Cruz ) were used following the manufacturers’ protocol . Animals were bred in a pathogen-free and temperature-controlled barrier facility with a 12-h-light/dark cycle and free access to food and water . All mice were bred on a mixed 129×C57BL/6J background . Generation and characterization of Ddb1F/F and Ddb1F/F;Camk2α-Cre mice have been described previously[18 , 27] . CrbnF/F mice [26] were purchased from The Jackson Laboratory ( Bar Harbor , ME ) and crossed with Nestin-Cre strains . The efficiency and specificity of CRBN deletion in the brain was confirmed by PCR ( forward primer: 5’-CAG TCA GAT GGG TAA GGA GCA-3’; reverse primer: 5’-AAG CAG CTC CGT AAT GCT G-3’ ) and further confirmed by Western blotting . The homozygous KO mouse would generate a PCR product of 467 bp while the heterozygous and WT mouse had a 387 bp PCR product . Ddb1F/F ( n = 16 , 10 males and 6 females , 5–8 month ) and Ddb1F/F;Camk2α-Cre ( n = 15 , 8 males and 7 females , 5–8 month ) mice were tested for locomotor activity in polycarbonate cages ( 42×22×20 cm ) placed into frames ( 25 . 5×47 cm ) mounted with two levels of photocell beams at 2 and 7 cm above the bottom of the cage ( San Diego Instruments , San Diego , CA ) . These two sets of beams allowed for the recording of both horizontal ( locomotion ) and vertical ( rearing ) behaviors . A thin layer of bedding material was applied to the bottom of the cage . Each mouse was tested for 60 minutes to evaluate its locomotor activity . Ddb1F/F ( n = 16 , 10 males and 6 females , 5–8 month ) and Ddb1F/F;Camk2α-Cre ( n = 15 , 8 males and 7 females , 5–8 month ) mice were subjected to the light/dark transfer procedure to assess anxiety-like behaviors by capitalizing on the conflict between exploration of a novel environment and the avoidance of a light open field . The apparatus was a rectangular box made of Plexiglas divided by a partition into two environments . One compartment ( 14 . 5×27×26 . 5 cm ) was dark ( 8–16 lux ) and the other compartment ( 28 . 5×27×26 . 5 cm ) was highly illuminated ( 400–600 lux ) by a 60 W light source located above it . The compartments were connected by an opening ( 7 . 5×7 . 5 cm ) located at floor level in the center of the partition . Mice were placed in the dark compartment to start the five minutes test . The time spent in the light compartment was recorded . Duration in the light compartment was used as indicator of anxiety-like behavior . The visual cliff test provided a measure of visual acuity by evaluating the ability of Ddb1F/+;Camk2α-Cre ( n = 5 , 3 males and 2 females , 5 month ) and Ddb1F/F;Camk2α-Cre ( n = 5 , 3 males and 2 females , 5 month ) mice to see a drop-off at the edge of a horizontal surface . In this apparatus , there was the visual appearance of a cliff but in fact the Plexiglas provided a solid horizontal surface . If the animal saw the cliff , it would step down onto the “safe” side ( the horizontal checkered surface ) in most trials . A blind animal would just as often step down onto the “negative” side ( the vertical appearing surface ) , i . e . make 50% correct and 50% incorrect choices . Each mouse was placed onto the center ridge , and the side onto which the animal stepped down was recorded . Six consecutive trials were used for each mouse and the percent of correct choices was calculated for each mouse . Ddb1F/F ( n = 11 , 7 males and 4 females , 6–9 month ) and Ddb1F/F;Camk2α-Cre ( n = 10 , 5 of either sex , 6–9 month ) mice were individually habituated to a 51×51×39 cm open field for 5 minutes and then tested with two identical objects ( A and B ) placed in the field ( two clear plastic cylinders 6×6×16 cm half filled with glass marbles ) . Individual animal was allowed to explore for 5 minutes in the present of objects . After two such trials ( each separated by 1 minute in a holding cage ) , the mouse was tested by object novelty recognition test in which a novel object ( C ) replaced one of the familiar objects ( B ) . All objects and the area were thoroughly cleaned with 70% ethanol between trials to remove odors . Behavior was video recorded and scored for contacts ( touching with nose or nose pointing at object within 4 cm of object ) . Ddb1F/F ( n = 15 , 8 males and 7 females , 5–8 month ) and Ddb1F/F;Camk2α-Cre ( n = 16 , 7 males and 9 females , 5–8 month ) or CrbnF/F ( n = 9 , female , 5–6 month ) and CrbnF/F;Nestin-Cre ( n = 9 , female , 4–7 month ) mice were trained to associate a novel environment ( context ) and a previously neutral stimulus ( conditioned stimulus , a tone ) with an aversive foot shock stimulus . Testing was performed in the absence of aversive stimulus . Conditioned animals , when exposed to the conditioned stimuli , were tended to refrain from all but respiratory movements by freezing . Freezing responses could be triggered by exposure to context in which the shock was received ( context test ) or conditioned stimulus ( CS+ test ) . Conditioning took place in freeze monitor chambers housed in sound-proof boxes . The conditioning chambers ( 26×26×17 cm ) were made of Plexiglas with speakers and lights mounted on two opposite walls . The chambers were installed with a shockable grid floor . On day 1 , mice were placed in the conditioning chamber for five minutes in order to habituate them to the apparatus . On day 2 , mice were exposed to the context and conditioned stimulus ( 30 seconds , 3000 Hz , 80 dB sound ) in association with foot shock ( 0 . 70 mA , 2 second , scrambled current ) . Specifically , mouse received two shock exposures , both in the last 2 seconds of a 30 second tone exposure , during a 5 . 5-minute session . On day 3 , contextual conditioning ( as determined by freezing behavior ) was measured in a 5-minute test in the chamber where the mice were trained ( context test ) . On day 4 , the mice were tested for cued conditioning ( CS+ test ) . The mice were placed in a novel context for 3 minutes , after which they were exposed to the conditioned stimulus ( tone ) for 3 minutes . For this test , the chamber was disguised with new walls ( black opaque plastic creating a triangular-shaped compartment in contrast to a clear plastic square compartment ) , a new floor ( black opaque plastic in contrast to metal grid ) and a novel odor ( drop of orange extract under the floor ) . Freezing behavior , i . e . the absence of all voluntary movements except breathing , was measured in all sessions by a validated computer-controlled recording of photocell beam interruptions . The Barnes maze apparatus is an opaque Plexiglas disc 75 cm in diameter elevated 58 cm above floor by a tripod . Twenty holes , 5 cm in diameter , were located 5 cm from the perimeter and a black Plexiglas escape box ( 19×8 ×7 cm ) was placed under one of the holes . Distinct spatial cues were located all around the maze and kept constant throughout the study . A training session was performed by placing Ddb1F/F ( n = 11 , 7 males and 4 females , 7–10 month ) and Ddb1F/F;Camk2α-Cre ( n = 10 , 5 of either sex , 7–10 month ) mice in the escape box for one minute in the first day . Testing session started after the habituation period . At the beginning of each session , the mouse was placed in the middle of the maze in a 10 cm high cylindrical black start chamber . After 10 seconds the start chamber was removed to free mouse exploring maze with a buzzer ( 80 dB ) and a light ( 400 lux ) turning on . The session ended when mouse entered the escape tunnel or after 3 min elapse . When mouse entered the escape tunnel , buzzer was turned off and mouse was allowed to remain in the dark for one minute . If the mouse failed to enter the tunnel by itself it was gently put in the escape box for one minute . The hole to place underneath tunnel was fixed for the same mouse but randomly determined . Mice were tested once a day in a 9 days’ acquisition portion with video typing . Latency to escape , the number of errors made and strategies chosen per session were measured from record . Errors are defined as nose pokes and head deflections over any hole that does not have tunnel . Strategies were determined by examining each mouse's daily session and classifying it as either “Random”—localized hole searches separated by crossings through the center of the maze ( no systematic search pattern ) , “Sequential”—systematic hole searches ( every hole or every other hole ) in a clockwise or counterclockwise direction , “Random/Sequential”—systematic hole searches separated by crossings through the center of the maze , or “Spatial”—reaching the escape tunnel directly with both error and distance ( number of holes between the first hole visited and the escape tunnel ) scores of less than or equal to 3 . For the 10th test ( probe test ) , escape tunnel was removed and mouse was allowed to freely explore the maze for 3 minutes . The time spent in each quadrant was determined and the time spent in the target quadrant ( the one originally containing the escape box ) was compared with that in the other three quadrants . Morris water maze was comprised of a circular tank ( 150 cm diameter , 50 cm height ) with 34 cm deep warm milky water ( 22±1°C ) , a platform submerged 1 cm below the water surface , a video camera and a computer . During the training phase , mice were placed facing the wall of the pool and permitted to swim for 60 or 90 seconds to locate the platform . If the mice failed , they were placed in the platform for 10 seconds to learn the location . 3–4 trials with different starting positions per day were performed for each mouse with recording of time to locate the platform . After training , test was repeated in the pool where the platform was removed on the probe day . Test parameters were listed as follows ( Table 1 ) . Patched cells were perfused with the bath solution containing 10 μM BMS-204352 and used to detect the whole cell currents . For mouse work , BMS-204352 was dissolved in the vehicle solution ( DMSO 1/80; Tween 80 1/80; 0 . 9% NaCl ) and administered with a 10 ml/kg single intraperitoneal ( i . p ) injection . In the test 3 of Morris water maze , mouse was pretreated with vehicle or BMS-204352 ( 2mg/kg ) during every training days . Behavioral tests were performed at the maximal BMS-204352 brain concentration , i . e . , 30 min after injection [40] . The full-length cDNAs ( Human ORFeome Collection , MA ) of CRBN and FBXO7 were amplified and subcloned into pXF4H expression vector ( 2xHA in the N-terminus ) from Xin-Hua Feng ( Zhejiang University , China ) between the HindIII and XbaI restriction sites , and named as HA-CRBN and HA-Fbxo7 . The full-length of SLO1 and FBXO7 were amplified and subcloned into pXF6F expression vector ( 3xFlag in the N-terminus ) from Xin-hua Feng between the BamHI and EcoRI restriction sites , and named as Flag-Slo1 and Flag-Fbxo7 . The full-length of CUL1 and SKP1 were amplified and subcloned into pXF3HM expression vector ( His-6xMyc in the N-terminus ) from Xin-hua Feng between the HindIII and XbaI restriction sites , and named as Myc-Cul1 and Myc-Skp1 . The siRNAs were synthesized by Ribobio Company ( Guangzhou , China ) and transfected using lipofectamine RNAiMax , Lipofectamine 2000 or Lipofectamine 3000 ( Invitrogen ) . Protein were harvested at 48h after transfection . The sequences of siRNA are listed as follows: ( Table 2 ) Cultured cell samples were lysed in RIPA buffer ( Sigma-Aldrich ) or NETN buffer ( 150 mM NaCl , 1% NP-40 , 50 mM Tris–HCl , pH 8 . 0 ) supplemented with complete set of protease inhibitors ( Roche ) for 20 min in ice . After centrifugation at 17 , 950 rpm for 15 min , supernatants were collected and subjected to protein quant ( Pierce BCA Protein Assay Kit , Thermo ) . Mouse brain samples were homogenized in the same RIPA buffer , followed by incubation on ice for 30 min , and centrifuged for 15 min at 12 , 000 g , 4°C to collect proteins in supernatants . Equal amounts from 10–100 μg of proteins were separated by SDS-PAGE and transferred onto PVDF or nylon membranes ( Invitrogen ) . After blocking in 5% non-fat dry milk and incubation with indicated primary antibodies in 5% BSA at 4°C overnight followed by HRP-linked secondary antibodies for 1 hour , bands on membranes were detected using chemiluminescent substrates ( Thermo Scientific ) . For transient transfection , 293T cells were seeded at 2 x 106 cells per dish ( 60 mm diameter ) . On the next day , expression constructs ( usually 1 μg for each construct , total constructs ≤ 5 μg ) were introduced into cells ( 70–90% confluent at transfection ) using Lipofectamine 2000 or Lipofectamine 3000 . 48 hr later , Cells were lysed in RIPA buffer or NETN buffer supplemented with complete protease inhibitors cocktail . After centrifugation at 17 , 950 rpm for 15 minutes , supernatants were subjected to incubation with anti-Flag M2 Affinity agarose or anti-HA Affinity agarose overnight at 4°C . After washing 3 times with lysis buffer , beads were added with SDS sample buffer , boiled and analyzed by Western blot . To detect ubiquitination status of protein , 293T cells co-transfected with indicated plasmids were lysed after treatment with MG132 ( 10 μM ) for 6h . Lysates were subjected to co-immunoprecipitation and analyzed by Western blot using indicated antibodies . The genome editing plasmids were prepared by cloning sgRNAs into pX459 plasmid ( Addgene ) digested by BbsI ( NEB ) following Zhang’s lab protocol ( http://www . addgene . org/crispr/zhang/ ) . 293T , U87mG and LN229 cells cultured in 6-well plates were transiently transfected with 2 μg of pX459 genome editing plasmids using Lipofectamine 3000 ( Invitrogen ) . Two days after transfection , cells were treated with growth media containing puromycin ( 0 . 6–1 μg/mL ) for 3 days . After recovery in complete media for 2 days , cells were collected to check the SLO1 editing by Western blot . Cells for CRBN editing were transferred into 96-well plate via serial dilution for single cloning ( 0 . 3 cells/well ) . Single clones were screened and expanded to investigate CRBN editing . sgRNA sequences: ( Table 3 ) Integral membrane proteins and membrane-associated proteins were enriched using the Thermo Scientific Mem-PER Plus Membrane Protein Extraction Kit ( 89842 ) following manufacture’s protocol . WT and R419X CRBN sequences were subcloned into pcDNA3-YFP vector . Then YFP-CRBN ( WT and R419X ) sequences were subcloned into pLVX-IRES-Neo vectors ( Clontech ) . The cloned pLVX vectors were transfected into 293FT cells along with psPAX2 and pVSVg using Lipofectamine 2000 ( Invitrogen ) for virus packaging . The viral supernatant was collected 72 hours later and condensed by ultra-centrifugation at 25 , 000 rpm at 4°C for 2 hours . CRBN KO U87mG cells were treated with 6 μg/ml Polybrene ( Sigma ) during the viral infection ( MOI = 0 . 5 ) . Two day after infection , cells were selected with 500 μg/mL G418 for 2 weeks . Stable cells were transferred into 96-well plates with limiting dilution . Single clones were expanded and collected for immunoblot analysis . RNA was extracted with trizol regent ( Invitrogen ) and reverse transcripted using PrimeScript RT Master Mix ( Takara , RR036A ) . Relative expression level of Slo1 transcripts was determined by real time RT-PCR using SYBR Green ( Life Technology ) with GAPDH as internal control . Primers are listed below: F: GGCAGCAGTCTTAGAATGAGTAG; R: AAAGCCCACCACATGCGTT . Signals were recorded with an Axopatch 200B amplifier ( Axon Instruments ) . Conventional whole-cell and inside-out configurations of the patch-clamp technique were used in the electrophysiological study . Records were filtered at 2 kHz and digitized at 20 kHz . Patch electrodes were pulled from a horizontal micropipette puller ( P-1000 , Sutter Instruments ) and flame polished to final tip resistance of 4–6 MΩ when filled with internal solutions . The pipette solution ( 130 mM potassium aspartate ( Sigma-Aldrich ) , 10mM KCl , 1 mM MgCl2 , 10 mM HEPES , 5 mM EGTA at pH 7 . 3 ( adjusted with KOH ) ) and bath solution contained ( 140 mM NaCl , 5 . 4 mM KCl , 1 . 8 mM CaCl2 , 1 mM MgCl2 , 10 mM HEPES , 10 mM glucose at pH 7 . 4 ( adjusted with NaOH ) ) were used for whole-cell recordings . For single-channel recordings , the pipette solution ( extracellular ) contained 120 mM KCl , 20 mM KOH , 11 mM EGTA and 10 mM HEPES ( pH 7 . 4 with KOH ) , and the bath solution ( intracellular ) contained 120 mM KCl , 20 mM KOH , 11 mM EGTA , 1 mM MgCl2 , and 10 mM HEPES ( pH 7 . 4 with KOH ) . All of the experiments were performed at room temperature ( 22± 1°C ) . Data acquisition and analysis were carried out using pClamp 10 . 2 ( Axon Instruments ) . Records were filtered at 2 kHz and digitized at 20 kHz . The whole-cell currents were elicited from a 0 mV holding potential to test potentials from −100 to +100mV in 10 mV increments ( see inset ) . The BK currents were paxalline-subtracted currents and normalized to membrane capacitance . Single-channel events were analyzed by pclampfit 10 . 2 ( single-channel search-in-analyze function ) . NPo , the product of the number of channels and open probability , was used to measure channel activity within a patch . In total , 50% threshold cross-method was used to determine valid channel openings . Results were presented as mean±standard error of mean ( SEM ) . Results for different groups were compared by the two-tailed unpaired Student’s t test with Welch’s correction . Results for Barnes maze , locomotor activity test and training days of Morris water maze were performed with two-way repeated-measures ANOVA tests . Asterisks in the Figures were used to indicate statistically significant differences ( *P < 0 . 05; **P < 0 . 01;***P < 0 . 001 ) .
|
Our study first demonstrates intellectual disability associated proteins , CRBN and BK channel pore-forming α subunit , are targeted by SCFFbxo7 for ubiquitination and proteolysis , which can be further regulated by their binding to DDB1 and the expression of CRL4 complex . We also first find DDB1 or CRBN deletion in mouse brain causes similar spatial cognitive defects . By cellular electrophysiology , human ID-related mutation and genetic mouse model experiments , we confirm the role of this regulatory pathway in controlling BK channel activity and modulating learning and memory .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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] |
2018
|
CRL4 antagonizes SCFFbxo7-mediated turnover of cereblon and BK channel to regulate learning and memory
|
A longstanding question in molecular biology is the extent to which the behavior of macromolecules observed in vitro accurately reflects their behavior in vivo . A number of sophisticated experimental techniques now allow the behavior of individual types of macromolecule to be studied directly in vivo; none , however , allow a wide range of molecule types to be observed simultaneously . In order to tackle this issue we have adopted a computational perspective , and , having selected the model prokaryote Escherichia coli as a test system , have assembled an atomically detailed model of its cytoplasmic environment that includes 50 of the most abundant types of macromolecules at experimentally measured concentrations . Brownian dynamics ( BD ) simulations of the cytoplasm model have been calibrated to reproduce the translational diffusion coefficients of Green Fluorescent Protein ( GFP ) observed in vivo , and “snapshots” of the simulation trajectories have been used to compute the cytoplasm's effects on the thermodynamics of protein folding , association and aggregation events . The simulation model successfully describes the relative thermodynamic stabilities of proteins measured in E . coli , and shows that effects additional to the commonly cited “crowding” effect must be included in attempts to understand macromolecular behavior in vivo .
While reductionist biophysical studies continue to contribute important insights into the properties and functions of biological macromolecules , research attention is increasingly being directed at uncovering the extent to which behavior observed in vitro is likely to reflect that occurring in vivo [1] , [2] . In a physiological setting , all biomolecules must inevitably experience non-specific , unintended interactions with the intracellular milieu and there are good theoretical reasons to expect that , even if such interactions are only steric in nature , significant alterations in macromolecular folding and association equilibria may result [2] , [3] . In order to allow macromolecules to be directly interrogated in vivo therefore , a number of important developments have been made in the experimental fields of hydrogen exchange [4] , nuclear magnetic resonance [5] , [6] , and fluorescence spectroscopies [7]–[9] . An alternative to the use of experimental techniques is to assemble a molecular model of an intracellular environment in silico and to use molecular simulation techniques to explore its behavior; if such a model could be shown to be realistic – and that is a big ‘if’ – it would have the important advantage of allowing the simultaneous , direct observation of all molecules in the system . In fact , at least two simulation studies that attempt to model the bacterial cytoplasm have already been reported [10] , [11] , producing a number of intriguing results . Both of these previous studies , however , modeled all cytoplasmic molecules as spheres and it is perhaps to be anticipated therefore that simulations that include structurally detailed macromolecular models might lead to additional insights . In pursuit of this strategy , we have chosen the gram-negative prokaryote Escherichia coli as a test system , combining quantitative proteomic [12] and high-resolution structural data [13] to build a first structurally detailed molecular model of the bacterial cytoplasm .
Starting from three different randomized initial configurations of the cytoplasm model ( all shown in Figure S1 ) , we performed independent Brownian dynamics ( BD ) simulations [14] to explore diffusive behavior . A variety of energetic descriptions of intermolecular interactions were explored , ranging from a simple steric-only model – which provides an opportunity to directly test the predictions of excluded-volume ‘crowding’ theories [2] , [3] – to models that include both long-range electrostatic interactions and short-range potential functions that mimic hydrophobic interactions between exposed non-polar groups . In order to determine the most realistic energy model , the long-time translational diffusion coefficients , DLtrans , of the ‘tracer’ GFP molecules were computed from the BD simulations and compared with previously reported experimental estimates obtained by fluorescence-recovery-after-photobleaching ( FRAP ) analysis of GFP in the E . coli cytoplasm [15]–[18] . A comparison of the computed GFP DLtrans values obtained with the different energy models is shown in Figure 2A . For simulations in which only steric interactions operate between macromolecules the computed GFP DLtrans value is 3–6 times higher than the experimental estimates , and although this value decreases somewhat when electrostatic interactions between macromolecules are added , it remains 2–5 times too high relative to experiment . A more realistic model of macromolecular interactions would allow favorable short-range attractions to occur between exposed hydrophobic atoms and one simple way of approximating such interactions is to use a Lennard-Jones potential , with the well-depth of the potential , ε , being treated as an adjustable parameter ( see Methods ) . As shown in Figure 2A , the inclusion of such a term results in computed GFP DLtrans values that decrease monotonically as the well-depth , ε , increases in magnitude . The best agreement with experiment is obtained with ε = 0 . 285 kcal/mol: at this value of ε the computed value of DLtrans – which is ∼10% of its value at infinite dilution – is within the experimental error of all in vivo estimates [15]–[18] including a very recent report for diffusion in cells growing in minimal media [18] . As noted in the Discussion , this optimal value of ε is very similar to the values obtained in our previous efforts to model the interaction thermodynamics of single-component protein solutions [19] . Having determined that good agreement with experiment could be obtained using a so-called ‘full’ energy model that included steric , electrostatic and short-range attractive hydrophobic interactions , we extended each of three independent simulations performed with this energy model to 20µs ( see Figure S2 for plots of the system's energy versus time ) . In order to provide a useful baseline for comparative purposes we also performed extended simulations with the purely ‘steric’ energy model ( i . e . one that neglects the electrostatic and hydrophobic interactions ) ; the latter simulations were performed for simulation times of 17 . 5µs . Each BD simulation using the ‘full’ energy model required more than a year ( clock-time ) to complete . For both energy models , snapshots taken from the last 15µs of each simulation were used for detailed analysis . An informative , albeit non-quantitative , impression of the simulation behavior can be obtained by viewing movies of the simulations ( Supporting Information ) . In some respects , these movies can be considered a key result of this work: they represent , in effect , dynamic analogs of the highly influential pictorial representations pioneered by Goodsell [20] . Examination of a typical movie obtained from a simulation performed with the ‘steric’ energy model shows the simulated motion to be rapid , chaotic and obviously Brownian . For the more realistic ‘full’ model , on the other hand , motion is somewhat slower-paced , and molecules can be seen to fluctuate between engagement in short-lived associations and periods of relatively free diffusion . We can place these observations on a more quantitative footing , and obtain an indication of the extent of sampling achieved in 15µs of simulation , from the remaining panels of Figure 2 . Figure 2B shows the maximum distances moved , on average , by each molecule type during simulations performed with the ‘full’ and ‘steric’ energy models; all distances are expressed relative to the diameter of the diffusing molecule . In the case of GFP with the ‘full’ energy model , for example , each molecule travels , on average , approximately 6 molecular diameters ( i . e . 320Å ) from its position at the beginning of the simulation . Since the data in Figure 2B are plotted versus molecular weight it is apparent that 15µs of simulation is sufficient for the smaller macromolecules to move very significant distances , while for the largest macromolecules ( the 30S and 50S ribosomal subunits ) , little motion away from the initial position is achieved . On this basis alone , therefore , we expect the estimates of diffusional behavior for the smaller macromolecules to be somewhat more precise than those of the larger macromolecules . A second measure of the extent of sampling achieved during each simulation period is provided by plotting the number of unique interaction partners encountered by each type of macromolecule as a function of the simulation time ( Figure 2C ) . Encouragingly , most molecule types encounter many unique neighbors over the course of 15µs: during a typical simulation with the ‘full’ model , for example , each GFP molecule encounters ∼80 different neighbors . Just as importantly , the total numbers of unique neighbors continues to increase even toward the end of the simulation period: this indicates that the cytoplasm model remains highly dynamic and does not tend to ‘freeze’ as the simulation progresses . As might be expected , the average numbers of neighbors that a macromolecule possesses at any instant scales essentially monotonically with its molecular weight: the average number of macromolecules in the immediate neighborhood of a GFP molecule , for example , is only ∼5 while for the 50S ribosomal subunit it is more than 25 ( Figure 2D ) . The time constants for the dissociation of these neighboring interactions – which in all cases are in the microsecond range – also scale straightforwardly with the molecular weight ( Figure 2E ) , indicating that molecules remain in the neighborhood of larger macromolecules for somewhat longer periods of time than they do with smaller macromolecules . The data shown in Figures 2C and 2D can be combined to provide an estimate of the number of times that each molecule's entire complement of neighbors is replaced during the simulations ( Figure 2F ) . Interestingly , while the overall trend is such that smaller macromolecules encounter a more dynamic constellation of neighbors even the largest macromolecules experience a significant number of environmental changes during the 15µs simulation period . While each GFP molecule , for example , effectively ‘shed its skin’ of neighbors a total of ∼14 times , even the 50S ribosomal subunit undergoes ∼5 such transformations ( Fig . 2F ) . This observation suggests that the limited diffusional exploration carried out by the largest macromolecules evident in Figure 2B may , in at least one important respect , give a misleadingly low indication of the extent of configurational sampling achieved in the simulations: it is in fact , possible for a completely static macromolecule to rapidly encounter widely different microenvironments simply by virtue of the dynamic exchange of its smaller , more mobile neighbors . While it was noted above that the long-time DLtrans value of GFP obtained with the ‘full’ energy model is in good agreement with in vivo measurements ( Figure 2A ) , there are other aspects of diffusional behavior in the simulations that warrant examination . One question that is of interest is how the observed Dtrans values of macromolecules depend on the observation interval , δt , over which their diffusion is monitored ( see Methods ) . The answer to this question is plotted in Figures 3A and 3B for the three most abundant members of the cytoplasm model ( MetE , TufA and CspC ) ; these proteins have been chosen for closer examination because their high abundance yields the most statistically robust numbers , but very similar results are obtained for the other constituents of the model . Figure 3A plots the computed Dtrans values of the three proteins versus δt for both the ‘full’ and ‘steric’ energy models . The clear variation of Dtrans with δt seen for all three proteins is indicative of ‘anomalous’ diffusion [21]–; the magnitude of the anomaly is conventionally expressed by the anomality exponent , α , ( Methods ) which is plotted for the same proteins , again versus δt , in Figure 3B . Examination of this figure shows that with the ‘steric’ energy model , the diffusion of all three proteins progresses from being normal ( α∼1 ) , to transiently subdiffusive ( α<1 ) , to normal again as the observation interval increases from δt∼100ps to δt∼10ns to δt∼1µs . With the ‘full’ model , in contrast , macromolecules exhibit transiently anomalous subdiffusion even at the shortest observation intervals; again however , a slow , but unequivocal return toward normal diffusion occurs on a high microsecond timescale . The same qualitative features are seen for all other molecule types although , for the largest macromolecules or those with the very lowest copy numbers , it is not always clear that sampling is sufficient to be absolutely certain of a return to normal diffusion at the longest δt values . At very short values of δt however we can obtain quite precise values of α for all molecule types; when these are plotted versus molecular weight ( Figure 3C ) it is apparent that while there is a clear difference between the values obtained with the two energy models , and a clear size-dependence of α with the ‘steric’ model , there is no such obvious trend with the ‘full’ model . For both energy models , the plots of α versus δt fit well to an analytical function ( solid lines in Figure 3B ) that , when integrated , enables an asymptotic long-time translational diffusion coefficient , DLtrans , to be estimated ( see Methods ) . The observed DLtrans values of all molecule types are expressed relative to their translational diffusion coefficients at infinite dilution ( D0trans ) and plotted versus molecular weight in Figure 3D . For both energy models , the ratio DLtrans/D0trans decreases with increasing molecular weight , which is qualitatively consistent with experimental studies of tracer protein diffusion in simple single-component protein solutions [24] and of Ficoll diffusion in the cytoplasm of mouse 3T3 cells [25] . The poorer correlation obtained for the ‘full’ model ( which does not appear to be solely due to incomplete sampling ) suggests that translational diffusion in vivo should not be predictable with arbitrary precision solely from knowledge of molecular weight; again , this is in line with the often significant variations observed in the in vivo diffusion coefficients of similarly-sized GFP-constructs [15] , [26] . It is perhaps worth noting , however , that the computed diffusive behavior of the heterologous GFP – marked by an asterisk in the ‘full’ model data points – is consistent with the general trend established by the endogenous E . coli macromolecules . The rotational motion of macromolecules is also significantly affected by immersion in the cytoplasm model . In the case of the ‘full’ energy model , the rotational behavior can be fit equally well by either a double-exponential function or a model that describes transiently anomalous rotational diffusion [27] . Since it is the rotational behavior on a nanosecond timescale that is more relevant to experimental measurements ( see Methods ) , we plot the short-time rotational diffusion coefficient , DSrot of all molecule types , relative to their rotational diffusion coefficients at infinite dilution , D0rot , in Figure 3E . As would be anticipated given the translational behavior shown above , rotational diffusion is significantly slower with the ‘full’ model than it is with the ‘steric’ model . Notably , a comparison of Figures 3D and 3E shows that with both energy models rotational diffusion is slowed less by immersion in the cytoplasm than is translational diffusion . This can be viewed as indicating that the two kinds of motion experience different relative viscosities ( ηrelT and ηrelR for translational and rotational diffusion respectively ) . Figure 3F plots the ratio of these relative viscosities , ηrelT/ηrelR , versus molecular weight for all molecule types . For the abundant proteins MetE , TufA , and CspC , and the less abundant GFP , we find the ratio of these relative viscosities , ηrelT/ηrelR , to be 3 . 6 , 3 . 0 , 3 . 2 and 2 . 5 , respectively using the ‘full’ model; perhaps surprisingly , similar numbers are also obtained with the ‘steric’ model ( Figure 3F ) . These computed ratios are in quite good agreement with the value of ηrelT/ηrelR of 2 . 6±0 . 2 obtained from in vitro data for apomyoglobin diffusion in human serum albumin [28] ( see Methods ) and the value of ηrelT/ηrelR of 2 . 1±0 . 3 reported for GFP in Chinese hamster ovary cells [29]; the lower value obtained in the latter case is consistent with the lower macromolecular concentration of the mammalian cytoplasm relative to that of E . coli . In addition to the simulations providing direct views of diffusive motions in the cytoplasm , snapshots extracted from the simulations offer an important opportunity to explore the thermodynamic consequences of the cytoplasm on macromolecular stability . Using a variant of Widom's ‘particle-insertion’ method [30] , the free energy change that accompanies the insertion of a molecule into the cytoplasm can be rigorously computed by subjecting the molecule to millions of randomized placements ( see Methods ) . We used this approach to compute the cytoplasm's effects on the folding equilibria of selected proteins by performing separate insertion calculations on their native state structures and on ensembles of 1000 unfolded structures generated by a sophisticated conformational sampling method [31] . We focused initially on the only two proteins for which experimental estimates of thermodynamic stability in the E . coli cytoplasm are available: ( 1 ) a construct of the λ-repressor N-terminal domain , λ6-85 [4] , which has been found to have essentially identical stability in vivo and in vitro , and ( 2 ) the cellular retinoic acid binding protein [7] , [32] ( CRABP ) , which has been found to be thermodynamically destabilized in vivo relative to in vitro . Both of these findings – the latter in particular – are non-trivial results to capture since they are inexplicable in terms of conventional macromolecular crowding theory [2] , [3] , [7] , [33] , [34] ( see below ) . We performed thermodynamic calculations under a total of four different scenarios . The first scenario that we examined involved taking cytoplasm snapshots sampled during the ‘steric’ BD simulations , and computing the cytoplasm-interaction energies of the folded and unfolded conformations with the same ‘steric’ energy model: this scenario corresponds to that considered in conventional models of macromolecular crowding effects [2] . In this case , the differences between the folding free energies in vivo and in vitro are computed to be +1 . 3±0 . 0 and +2 . 2±0 . 1 kcal/mol for λ6-85 and CRABP respectively ( blue bars in Figure 4A ) , with the positive signs indicating that the folding free energies of both proteins are calculated to be more favorable in vivo than in vitro . When compared to the experimental values ( red bars in Figure 4A ) , these results are in poor quantitative agreement for λ6-85 and are qualitatively wrong for CRABP . In a second scenario , we took cytoplasm snapshots sampled during the ‘full’ model BD simulations , but computed the cytoplasm-interaction energies of folded and unfolded conformations using the simpler ‘steric’ energy model . In this case , the differences between the folding free energies in vivo and in vitro are computed to be +1 . 0±0 . 0 and +1 . 6±0 . 0 kcal/mol for λ6-85 and CRABP respectively ( cyan bars in Figure 4A ) . The smaller crowding effects obtained in this situation reflect the fact that during the ‘full’ BD simulations transient clustering of molecules creates bigger voids in the system; again however , these computed results are in poor quantitative agreement with experiment for λ6-85 and are in qualitative disagreement with experiment for CRABP . A third scenario that we examined involved taking cytoplasm snapshots sampled during the ‘steric’ BD simulations and computing the cytoplasm-interaction energies with the ‘full’ energy model . In this case , the differences between the folding free energies in vivo and in vitro are computed to be +0 . 1±0 . 5 and −1 . 8±1 . 4 kcal/mol for λ6-85 and CRABP respectively ( green bars in Figure 4A ) , both of which , notwithstanding the larger error bars , are in rather good agreement with the experimental results . Finally , we took cytoplasm snapshots sampled during the ‘full’ model BD simulations and computed the cytoplasm-interaction energies with the same ‘full’ energy model . In this fourth scenario – which on the basis of the diffusional properties described above would be hoped to provide the most realistic description ( Figure 2A ) – the computed changes in stability amount to +0 . 3±0 . 1 and −0 . 9±0 . 4 kcal/mol for λ6-85 and CRABP respectively ( yellow bars in Figure 4A ) ; again , these results are in close quantitative agreement with the experimental results . The overall picture that emerges , therefore , is that the experimental results cannot be reproduced , even qualitatively , when the ‘steric’ energy model is used to score the interactions between the folding protein and the cytoplasm environment , but they can be reproduced – and with a perhaps surprisingly high degree of quantitative accuracy – when the ‘full’ energy model is used in the particle-insertion calculations . Furthermore , the fact that similarly good results are obtained regardless of which energy model was used in the BD simulations suggests that , for such calculations , the method of sampling the cytoplasm's configurations is perhaps less important than the nature of the energy function used to describe the protein of interest's interaction with it . Histograms of the computed interaction energies of the folded and unfolded state with the cytoplasm explain why the predictions of the ‘full’ model successfully reproduce experiment , and deviate so significantly from the predictions of the purely steric model: for both proteins , but especially so in the case of CRABP , the unfolded state conformations are computed to have somewhat more favorable energetic interactions with the cytoplasm than the folded state conformations ( Figure 4B ) . The consequence is that while the excluded-volume ( crowding ) effect experienced by both proteins undoubtedly significantly stabilizes their folded states relative to their unfolded states ( e . g . see the blue and cyan bars in Figure 4A ) , the effect is counterbalanced by the more favorable energetic interactions engaged in by the unfolded state conformations . To explore the potential generality of this latter result , we performed identical calculations for a number of other monomeric proteins using snapshots taken from the ‘full’ model BD simulations; histograms illustrating the size distributions of the unfolded states of the tested proteins are shown in Figure 4C . The computed changes in their folding free energies are plotted in order of increasing molecular weight in Figure 4D . As before , when the ‘steric’ energy model is used to compute the cytoplasm-interaction energies the proteins' stabilities are computed to increase ( white bars in Figure 4D ) ; the computed stability changes scale broadly with the molecular weight of the protein , reflecting the greater relative difference between folded and unfolded state dimensions of larger proteins . In contrast , when the ‘full’ energy model is used to compute the cytoplasm-interaction energies , the molecular weight dependence is lost ( dark grey bars in Figure 4D ) : some proteins are computed to be stabilized and others destabilized in vivo relative to in vitro ( in no case however is the extent of destabilization sufficient to predict that the proteins will be predominantly unfolded in vivo ) . These results suggest that differences between the in vitro and in vivo thermodynamic stabilities will vary significantly with the identity of the protein . We performed similar calculations to explore the potential thermodynamic effects of immersion in the cytoplasm on a variety of protein-protein associations . For the formation of homo-dimeric complexes ( Figure 4E ) , we again find that the excluded-volume crowding effect , which alone stabilizes dimers relative to separated monomers by on average 1 . 1±0 . 3 kcal/mol , is largely cancelled by the more favorable energetic interactions that the monomers form with the cytoplasm constituents: when the ‘full’ energy model is used the stabilization of the dimeric forms by the cytoplasm is computed to be , on average , only 0 . 1±0 . 3 kcal/mol . For the assembly of the trimeric nucleus [35] of the bacterial cytoskeletal protein ParM from three separated monomers , we find that the stabilization predicted with the ‘full’ energetic model is also significantly lower than that predicted from the crowding effect alone ( Figure 4F ) ; again , the smaller value appears more consistent with the close similarities between the polymerization behavior of ParM observed in vitro and in vivo [36] . Finally , we performed calculations on the assembly of two published ( but putative ) structural models of amyloid-like aggregates [37] , [38] , each formed by association of 8 monomer units ( Figure 4F ) . For one of these two cases , the aggregation of an SH3 domain [37] , we find that the use of the ‘full’ model predicts a slightly greater stabilization than that predicted solely on the basis of the crowding effect; the additional stabilization observed in this case results from the protein's interactions with the cytoplasm being dominated by repulsive electrostatic interactions , which , on average , are diminished in the aggregated state ( see Figure S3 ) .
Developing working computational models of intracellular environments is one potential route to understanding differences between biomolecular behavior observed in vitro and in vivo . The simulations and calculations described here represent the first attempt to build such a model for the bacterial cytoplasm using atomically detailed structures of the constituent molecules , and represent the first attempt to directly model the consequences of immersion in the cytoplasm on the thermodynamics of protein stability and protein-protein interactions . It is worth noting that these innovations have been made possible in large part due to the immense progress made by the structural biology community in recent years: in constructing our model it was a major surprise to us to find that , of the 50 most abundant cytoplasmic E . coli proteins identified in the study of Link et al . [12] , it was possible to produce complete or near-complete structural models for more than 45 ( see Supporting Information ) . Since large-scale structural genomics initiatives continue to map out the structural proteomes of organisms with ever increasing detail [39] it will be possible to make future generations of cytoplasm models even more compositionally complete . Before considering the strengths and weaknesses of the present model , and the implications of the results reported here , it is important to reiterate that at least two other cytoplasm models have already been reported in the literature . The first such model was described by Bicout and Field [10] some thirteen years ago . Owing to the comparative paucity of both structural information and computer power then available , the model was restricted to only three types of macromolecule , each of which was modeled as a sphere: their modeled system contained 12 ribosomes , 188 copies of a generic protein of molecular weight 160kDa , and 136 tRNAs . Langevin dynamic simulations were used to model behavior over a timescale of 7 . 5µs , and four different electrostatic approximations were investigated in an attempt to cover a range of possible simplified descriptions of the ribosome's electrostatic properties . With all four models , the long-time translational diffusion coefficient of the modeled protein was slowed by only ∼40% relative to its infinite-dilution value . Since their work pre-dated the first reports of Dtrans values measured in vivo , Bicout and Field could not know at the time that this simulated diffusion was too fast relative to experiment; they were therefore not in a position to more fully calibrate their model . Despite this issue , it should be clear to readers that the work of Bicout and Field was far ahead of its time . It should also be apparent that , like the influential work of Goodsell [20] , it was a direct inspiration for the work reported here . A second and much more recent model for the bacterial cytoplasm has been developed by Ellison and co-workers [11] . Relative to Bicout and Field's work , the model of Ridgway , Broderick et al . provides an enormous step forward in terms of compositional complexity: >100 different types of proteins are represented , and thanks to the availability of the authors' own proteomic data [40] , are present in copy numbers that are likely to much more closely reflect their relative abundances in vivo . On the other hand , all macromolecules are treated as spheres , and intermolecular interactions are assumed to be purely steric in nature . In addition , the actual modeling of motion is somewhat simplified: particles take steps of uniform length in randomly chosen directions , with the steps being accepted only if no collision – or reaction – with a neighboring molecule occurs . While somewhat approximate , this approach has the significant advantage of allowing reactive events to be rapidly modeled , making the simulation model applicable to a more general set of problems than that considered here . The resulting model of the cytoplasm was used to investigate the effects of crowding on the translational diffusion of macromolecules and on the rate of the diffusion-limited association of the barnase-barstar protein-protein complex . As noted by the authors , the diffusional simulations produced only a two-fold decrease in the translational diffusion coefficients of GFP-like molecules , suggesting , in common with the results reported here , that ( steric ) crowding effects alone are insufficient to explain the ∼10-fold slowed diffusion of GFP observed in vivo . Relative to these two previous cytoplasm models , therefore , the present approach offers a significant increase in both structural and energetic complexity: all macromolecules are modeled in atomic detail and interact with one another via an energetic model that accounts for the two major types of interaction that drive protein-protein associations ( i . e . electrostatic and hydrophobic interactions ) . It does so , of course , at very significant computational expense: each of the simulations performed with our ‘full’ energy model required more than a year of clock-time to complete . But even with its associated expense it should not be thought that the present model represents the pinnacle of sophistication in terms of its description of reality . Leaving aside the fact that the model is incomplete in terms of the types of macromolecules ( and small molecules ) that it includes , there are several key assumptions of the modeling that are both important to stress and which represent obvious candidates to address further in future work . A first simplification of the present approach , and one shared by the previous models described above , is that all macromolecules have here been treated as rigid bodies . This simplification has two consequences . First , it immediately precludes us from making any meaningful attempt to simulate the ( presumably very interesting ) diffusive behavior of highly flexible macromolecules such as mRNAs and intrinsically unstructured proteins . While this is undoubtedly a limitation , it is to be noted that in terms of their contributions to the overall mass content of the cytoplasm , such molecules play a comparatively minor role relative to that played by the folded , globular macromolecules examined here [10] . It is also to be noted that there are currently very serious technical obstacles to be overcome if the diffusive behavior of flexible macromolecules is to be simulated with any degree of realism: we have shown recently , for example , that the inclusion of hydrodynamic interactions ( HI ) , which are computationally very expensive to compute , is essential if flexible protein models are to adequately reproduce translational and rotational diffusion [41] . A second consequence of the rigidity of the present model is that it is not immediately suited to describing conformational changes that might potentially occur in highly crowded conditions , and for which interesting experimental and simulation results have recently been reported [42] , [43] . As shown in the second part of this paper however , this limitation can be overcome , at least for the purposes of calculating thermodynamic effects , by the use of particle-insertion calculations . In fact , the use of such an approach has enabled us to explicitly evaluate the cytoplasm's thermodynamic consequences on both folding and association equilibria , something that would currently be prohibitively expensive to achieve through the direct dynamic simulation of flexible protein models . A second , but not unrelated simplification adopted in the present approach concerns the energy model used to describe intermolecular interactions . On the one hand , the model is comparatively sophisticated in that it includes descriptions of electrostatic and hydrophobic interactions , and models both at an atomic , or near-atomic level of resolution: in this respect it is a clear improvement over previous models used to simulate the cytoplasm . On the other hand , the model assumes that electrostatic desolvation effects can be neglected ( which may lead to an overestimation of the strength of electrostatic interactions; [44] ) and treats hydrophobic interactions as pairwise additive [45] , [46] and of equal strength for aliphatic and aromatic groups . We assume that the effects of these missing features are at least partly subsumed , in an implicit fashion , within our single hydrophobic parameter , ε . For this reason , we should be careful not to attach too much importance to the absolute value of ε found here ( 0 . 285 kcal/mol ) : it is , nevertheless , encouraging that it is very similar to the range of values that we previously obtained [19] when modeling the thermodynamics of simple dilute protein solutions ( 0 . 22–0 . 28 kcal/mol ) . This is perhaps especially notable given the enormous difference between the protein concentration studied here ( 275mg/ml ) and that studied in the previous work ( 10mg/ml ) . In future , it should be possible to increase the sophistication of the energy model without incurring an exorbitant additional computational cost: if one stays with a rigid-body approach , for example , a number of grid-based methods might be used that allow electrostatic desolvation [44] and/or hydrophobic interactions [47]–[50] to be rapidly calculated . It should be remembered , however , that a more complicated functional form will not necessarily lead to better results , and that , at least for now , it is highly likely that some degree of empirical adjustment of energy terms will be required in order to reproduce experimental behavior . This will be especially true if the intention is to use a similar model to explore , for example , macromolecular crowding effects on specific protein-protein interactions: despite significant advances , no current computational method is capable of accurately predicting the strength or geometry of specific protein-protein interactions with any generality [51] . To model such situations , therefore , it may be necessary to supplement the energy model with additional short-range forces to drive the formation of known intermolecular contacts , in the same way that such terms ( commonly known as Gō-potentials; [52]–[54] ) are often used in the modeling of protein folding processes; an alternative might simply be to use different ε values for different protein-protein interactions . A third limitation of the present model concerns its very simplified description of macromolecular hydrodynamics . In particular , while the basic hydrodynamic properties of all macromolecules ( i . e . their translational and rotational diffusion coefficients at infinite dilution ) are properly accounted for , the BD simulations reported here do not allow for the presence of hydrodynamic interactions ( HI ) between macromolecules; again this is true also of the two previously reported cytoplasm models [10] , [11] . The immense expense associated with HI calculations remains a major stumbling block to their inclusion in large-scale simulations [55] and a number of attempts have therefore been made to accelerate their computation ( see , e . g . [56] , [57] for very recent and potentially important examples ) . This expense would be further increased in the present case if , as would in principle be necessary , an Ewald summation technique was used to properly account for HI in periodic boundary conditions [58] . While simply stating that HI are expensive to calculate does not constitute a compelling reason for leaving them out of the simulations , it is pertinent to note that the omission of HI seems unlikely to be the cause of the gross overestimation of the diffusion coefficient of GFP obtained with the ‘steric’ energy model ( Figure 2A ) . It is certainly true , as noted elsewhere [18] , that for hard-sphere-like colloidal particles – where the interactions between particles are extremely short-range – theoretical work has established that the inclusion of HI should cause decreases in Dtrans values over both short [59] and long timescales [60] , [61] . Such decreases are , however , unlikely to bridge the ∼5-fold gap necessary to bring the ‘steric’ energy model behavior into quantitative agreement with experiment: in an interesting recent simulation study , for example , it was found that an approximate description of HI in crowded hard-sphere solutions resulted in only a ∼40% additional decrease in the diffusion coefficient relative to simulations without any description of HI [62] . In addition , it is also to be noted that for colloidal particles with long-range repulsive electrostatic interactions , theory indicates that the inclusion of HI causes modest increases in Dtrans values at both short [63] , [64] and long timescales [64] , [65] . Since the current model has macromolecules interacting with each other not only by short-range steric forces and long-range repulsive electrostatic forces , but also by short-range attractive interactions between exposed hydrophobic residues it is difficult to predict the effects that the inclusion of HI might ultimately cause , other than to say that we think they may be comparatively modest . In keeping with the caveat given above about our energy model , however , we clearly must leave open the possibility that the hydrophobic parameter , ε , is also , in part , serving as an implicit correction for the omission of HI from the simulations . Having produced in the preceding paragraphs a litany of shortcomings of the model one might be tempted to view it as so fundamentally limited that its practical utility is in doubt . Perhaps the strongest argument against such a view comes from the results of the particle-insertion calculations aimed at computing the thermodynamics of protein folding in vivo ( Figure 4A ) . It is important to note that these thermodynamic calculations should be considered bona fide predictions of the simulation model since it was calibrated to reproduce a quite different experimental observable , i . e . the translational diffusion coefficient of GFP . Because of this , we can rule out the possibility that the calibration of the model predisposes it to trivially reproduce experimental protein stability effects . To our knowledge , the calculated results reported here with our ‘full’ energy model are the first to provide a quantitative rationalization of the experimental observation that CRABP is destabilized in vivo ( relative to in vitro ) and that λ6-85's relative stability is essentially unchanged . As noted earlier , the experimental CRABP result is inexplicable with conventional macromolecular crowding theory ( as exemplified by the results obtained here when the ‘steric’ energy model is used in the particle-insertion calculations ) since the dimensions of its unfolded state are greater than those of its native state . Use of the ‘full’ energy model , on the other hand , produces results in close agreement with experiment because it explicitly allows for the two states of the protein to engage in differential , favorable energetic interactions with the rest of the constituents of the cytoplasm . Interestingly , good results are obtained when the ‘full’ energy model is used in the particle-insertion calculations regardless of whether the cytoplasm snapshots were sampled from the ‘steric’ BD simulations or sampled from the ‘full’ BD simulations . Although the most internally consistent approach is obviously to use the same energy model in both the BD simulations and the particle-insertion calculations , the fact that good results can apparently also be obtained using snapshots from the ‘steric’ BD simulations is intriguing since such simulations are much faster to conduct than those using the ‘full’ energy model . Our model's predicted effects on the folding free energies of the six other proteins investigated ( Figure 4D ) await experimental testing of course , but regardless of how quantitatively accurate such predictions might eventually turn out to be we feel reasonably confident in suggesting that future attempts to understand a protein's folding thermodynamics in vivo will need to describe its interactions with the cytoplasm with more realism than is provided by simple steric interactions . Other findings from the simulations , while probably more difficult to directly test experimentally , provide examples of the kinds of new information that can be obtained from simulation approaches that attempt to model intracellular environments . Examples include the observation that the immediate neighbors of individual proteins exchange rapidly on a microsecond timescale – even for the very largest macromolecules – and that diffusion is transiently anomalous even on a sub-nanosecond timescale . The latter observation is especially interesting given the current interest in anomalous subdiffusion as an efficient mechanism of search and association in physiological situations [8] , [66] . Finally , one might also point to the fact that the simulation model correctly reproduces the cytoplasm's relative translational and rotational viscosities as an important favorable result since differential effects on translational and rotational motion appear to have interesting effects on protein-protein association rates in crowded solutions [67]–[69] . It should be remembered , however , that a similarly good reproduction of the relative translational and rotational viscosities is also obtained with the otherwise poorly performing ‘steric’ energy model . An examination of all of the dynamic and thermodynamic results described above shows , we think , that it is possible to leverage the existing structural biology and quantitative proteomic data to produce a meaningful , working molecular model of the bacterial cytoplasm . The actual simulation model used here has a number of limitations , of course , but continuing increases in computer power and/or the development of faster simulation methodologies , will likely allow many of these drawbacks to be eliminated in the not too distant future . Given the continuing progress in the fields of structural biology and quantitative proteomics it is likely that the same basic approach might be used to model other intracellular environments .
When this work was initiated , the only large-scale quantitative study of the E . coli proteome was that reported by Link et al . [12] who experimentally measured levels of >200 of the most abundant proteins present in E . coli . A number of other quantitative proteomic studies of E . coli have since been reported [40] , [70] , [71] , and , since this work was completed , quantitative estimates of metabolite concentrations have also become available [72] . Restrictions on computer memory ( 4GB of RAM for all servers used ) meant that the total number of different types of macromolecules that could be realistically modeled was limited to 51: these would be 50 types of E . coli macromolecule plus the Green Fluorescent Protein ( GFP ) . Although including only 50 different types of macromolecules means that the model underestimates the structural diversity of the cytoplasm , it is important to note that the macromolecules selected for inclusion account for 85% ( by number of protein chains ) and 86% ( by mass ) of all the cytoplasmic proteins quantified and identified in Table 4 of Link et al . [12] . Of the 50 types of E . coli macromolecules to be included in the model , 45 would be proteins . These were selected by working down the list identified by Link et al . in order of decreasing abundance , selecting only those proteins ( a ) for which high-resolution structures were then available in the Protein Data Bank [13] ( PDB ) or for which reasonable homology models could be constructed ( see below ) , and ( b ) for which the cytoplasm was unambiguously identified as the major cellular location in the EcoCyc [73] and/or CCDB [74] databases . A full list of all potentially cytoplasmic proteins identified and quantified in Table 4 of Link et al . ( under minimal media conditions ) , arranged in decreasing order of chain-abundance , is shown in Table S1; asterisks in the columns headed ‘Mod . ’ denote those proteins included in our cytoplasm model . It is an indication of the tremendous coverage of the structural proteome that has been achieved by the structural biology community that we were able to obtain , or build , reasonable structural models for all of the 30 most abundant cytoplasmic proteins identified by Link et al . [12] . In addition to the 45 different types of proteins , 5 types of macromolecule were RNAs or RNA-protein complexes: these were the two ribosomal subunits ( 50S and 30S ) , and three typical tRNAs for which structures were available: ( tRNA-Gln , tRNA-Phe and tRNA-Cys ) . It is to be noted that we did not model complete ( translating ) 70S ribosomes owing ( a ) to the inherent difficulties in modeling the flexible mRNA , and ( b ) to the absence – at the time this work was begun – of a three-dimensional structure showing the arrangement of multiple 70S ribosomes in a polyribosome [75] . The total number of molecules in the simulations was set to 1008 ( eight copies of GFP and 1000 E . coli macromolecules ) . This number was chosen so that the eventual assembled cytoplasm model would be large enough to provide a reasonable representation of the environment while still allowing simulations of up to 20µs to be performed ( albeit over the course of more than a year clock-time ) . The linear dimensions of the final modeled system ( 808 . 4Å in each of the x , y and z directions ) correspond to approximately one-twelfth of the diameter of a typical E . coli cell [76] . A summary of the macromolecules selected , their subunit compositions , the PDB codes of their originating structures , and the degree of sequence coverage achieved by the structural models , is presented in Table S2 . Using composition estimates provided by Neidhardt et al . [76] as a guide , we set the total concentration of macromolecules in the model ( excluding the ‘tracer’ GFP ) to 275 g/l; this is slightly on the low side of the rough values of 300–340 g/l estimated independently by Zimmerman and Trach [77] . Of this , 55g/l ( i . e . 20% of the total ) is contributed by RNA , with 15% of the RNA dry weight contribution being made by tRNA and the remainder being made by ribosomal RNA [76] . mRNA , which accounts for only ∼4% of the total dry weight of RNA in the cell , is omitted from the present model . The remaining 219g/l ( i . e . 80% ) of the model is contributed by proteins; this percentage is deliberately set somewhat higher than the 55% contribution to the dry weight of the entire cell estimated by Neidhardt et al . [76] in order to compensate for the missing volume of components that are not explicitly represented in the model ( DNA , mRNA , lipid , lipopolysaccharide , murein , and glycogen ) . If one takes the specific volumes of proteins and RNA to be 0 . 73ml/g and 0 . 58ml/g respectively [77] , the total volume fraction occupied by macromolecules in the model is 0 . 19; if instead , an ‘effective’ specific volume of macromolecules suggested by Zimmerman and Trach is used [77] ( 1 . 0ml/g ) , the total volume fraction occupied by the macromolecules in the model amounts to 0 . 27 . Structures for all selected proteins were identified by performing a BLAST search [78] of the protein's FASTA sequence ( as reported in the EcoCyc database ) against the entire PDB and selecting the structure with the closest identity to the query sequence using the program BioEdit [79] . The quaternary structure of each selected structure was determined using the PQS web server [80] and was verified , where possible , with the EcoCyc database; it should be noted that correct identification of a protein's quaternary structure is a non-trivial undertaking , and the PQS predictions are unlikely to be 100% reliable [80] , [81] . Homology modeling was used for all proteins for which either no E . coli structure was directly available in the PDB , or for which a significantly greater coverage of the sequence could be obtained through the use of a non-E . coli structure . All homology modeling was performed using the SWISS-MODEL web server [82] via the so-called “First Approach mode”; for oligomeric proteins each individual chain was homology-modeled independently . Any sidechains missing from a structure were built in using the molecular modeling program WHATIF [83] . Hydrogens were then added , and partial charges and radii were assigned to atoms using the program PDB2PQR [84] . For proteins , partial charges and atomic radii were taken directly from the PARSE parameter set [85] . For nucleic acids , which are not represented in the PARSE parameter set , partial charges were instead assigned from the CHARMM23 parameter set [86]; partial charges for the modified bases of tRNAs , such as pseudouridine , were assigned based on similarity to functional groups already represented in the parameter sets . The protonation states of all protein ionizable residues were assigned using the fast empirical algorithm PropKa [87]; for these calculations , the pH was set to 7 . 6 , the estimated pH of the E . coli cytoplasm [76] . With each structure complete , infinite-dilution translational and rotational diffusion coefficients – which are necessary input parameters for BD simulations [14] – were calculated with the program HYDROPRO [88] using default parameters . For the latter calculations we assumed a solvent viscosity , η , of 0 . 89cP , which corresponds to the viscosity of pure water at 25°C; given that the most recent estimate of the total metabolite concentration in the E . coli cytoplasm is ∼300mM [72] we do not anticipate , based on what we currently know , that the viscosity of the solvent environment will be hugely altered from the pure water value . The final stage of preparation for each molecule involved the calculation of electrostatic potential grids; these were computed in all cases by using the APBS software [89] to solve the non-linear Poisson-Boltzmann ( PB ) equation [90] . As in our previous BD study of single-component protein solutions [19] , two cubic electrostatic potential grids were computed for each type of macromolecule: ( a ) a comparatively fine grid , of spacing 2Å , with dimensions sufficient to encompass a 20Å shell around the macromolecular surface , and ( b ) a coarse , long-range grid , of spacing 4Å , that extends at least 50% further in each direction than the smaller grid . The use of a 2Å grid spacing for the higher resolution grids , rather than the 1Å grid spacing used in our previous simulations [19] , was necessary in order to fit all potential grids into the available 4GB of RAM . This spacing is , however , sufficiently detailed that at least two grid points always intervene between interacting atoms even when they are at the closest possible separation distance ( 4 . 5Å ) ; significant numerical instabilities in the calculated electrostatic forces do not , therefore , arise . In all PB calculations the solvent dielectric was set to 78 . 0 and the internal dielectric of the macromolecule was set to 12 . 0 , with the boundary between the two being determined by the cubic-spline surface [91] implemented in APBS [89] . Use of an internal dielectric of 12 . 0 is intended to provide a simple , averaged description of the different dielectric responses of macromolecular interiors and exteriors [19] , [92] , [93] . The ionic strength in all PB calculations was set to 150mM . With the electrostatic potentials in hand , ‘effective charges’ were computed for each molecule type using the procedure established by Gabdoulline & Wade [94] , [95] . Finally , as in our previous work [19] , simulations were accelerated by retaining , in addition to the effective charges , only those non-hydrogen atoms that were solvent-exposed: these atoms were identified using the ACC tool within APBS [89] , with a 4Å solvent probe . The BD software used for the simulations is an extension of the methodology developed and tested in our previous work on pure protein solutions [19] . Modifications were made to the software to improve memory usage so that 102 electrostatic potential grids could be simultaneously held in memory; in addition , toward the end of this study , loop-level parallelization of a number of key loops was implemented with OpenMP ( http://www . openmp . org ) to accelerate computations by a factor of ∼4 . All simulations were performed under periodic boundary conditions [96] in a cubic cell with edges of 808 . 4Å . The initial configuration of each system had eight GFP molecules evenly positioned at the center of the eight octants of the cell; all other macromolecules were initially positioned by performing random translations and rotations within the cell subject to the requirement that there was at least a 10Å separation between the surfaces of all neighbors . Three independent configurations were generated in this way by use of different random seeds; views of each system before and after 15µs of simulation are shown in Fig . S1 . As in our previous work , BD simulations were conducted using the Ermak-McCammon algorithm [97] with a time step of 2 . 5ps , with additional algorithmic measures being taken to ensure that no atom-atom distances at the completion of each timestep were less than 4 . 5Å . For subsequent analysis of the simulations , the 3D translational vector and the 3×3 rotational matrix necessary to specify the position of each macromolecule were recorded every 100ps . The form of the energy model used to describe intermolecular interactions was identical to that used in our previous work [19]: the effective charge method [94] was used to calculate electrostatic interactions , and a Lennard-Jones potential ( comprising 1/r12 and 1/r6 terms ) was used to provide a simple combined description of steric , van der Waals and hydrophobic interactions . To accelerate the simulations , the combined non-electrostatic interactions were computed only between atom pairs separated by less than 12Å; a list of all such pairs was continually updated every 40 timesteps ( i . e . every 100ps ) . As in our previous work , we treated the strength of these non-electrostatic interactions , which are determined by the well-depth , εLJ , of the Lennard-Jones potential , as the only adjustable parameter of the model . In order to determine the best setting , three independent BD simulations of at least 6µs duration were performed with each of the following εLJ values: 0 . 190 , 0 . 285 , 0 . 3325 and 0 . 380 kcal/mol . Finally , for comparison purposes , two additional sets of three BD simulations were also performed: these were ( a ) simulations in which the only the repulsive ( 1/r12-dependent ) steric interactions operated ( these are the ‘steric’ simulations discussed in the main text ) and ( b ) simulations in which only steric plus electrostatic interactions acted . The effective translational diffusion coefficients , Dtrans , of molecules were calculated from the simulations using the Einstein equation: ( 1 ) where < δr2 > is the mean-squared distance traveled by the molecular center of mass in the observation interval , δt; all Dtrans values reported in Results are mean values for each molecule type averaged over the number of copies of each type . In cases of ‘normal’ diffusion , the computed Dtrans values are independent of δt; in certain cases of diffusion in vivo and in vitro however , anomalous sub-diffusion is observed [8] , [21]–[23] , [66]; in such cases , the apparent Dtrans value is dependent on δt , decreasing with increasing δt . A common way of describing anomalous diffusion involves writing it in the form: ( 2 ) where the apparent translational diffusion coefficient Dtrans is now written to indicate that it depends on the observation interval and α is the so-called anomalous diffusion ( anomality ) exponent; α = 1 corresponds to normal diffusion since it leads to Dtrans being independent of δt , and α<1 indicates anomalous ( sub ) diffusion . Taking logarithms and differentiating with respect to log ( δt ) allows us to write: ( 3 ) This enables us to obtain α by numerically differentiating Dtrans values computed over a range of δt values; in practice we computed Dtrans at δt values of 100 , 200 , 300 , 600 , 1000 , … ps , and obtained α at the logarithmic mid-point , δtmid , of these time-intervals , δtmid = 141 , 245 , 424 , … ps . Plots of α versus log ( δtmid ) for macromolecules simulated with both the ‘steric’ and ‘full’ energy models all indicated that α itself was dependent on δtmid , thus signifying that diffusion was transiently anomalous . To our knowledge , there is no explicitly derived functional form that describes the expected dependence of α on δt for transient anomalous diffusion . We found however that the data fit well to the following empirical functional form ( see Fig . 3B ) : ( 4 ) where α0 is a constant , a and b are parameters that describe the amplitude of the δt-dependent changes to α , and τshort and τlong are , respectively , the timescales over which α first decreases , and then returns to one , with increasing δt . Plots of α versus δt for all molecule types were fit to the above functional form with SigmaPlot [98]: fits were performed using all datapoints from the shortest δtmid value up to the first datapoint that had a percent error exceeding ∼25% ( obtained by comparing the α values computed from the three independent BD simulations ) , or that deviated qualitatively from the trend . To ensure that the latter criterion did not drastically affect the results , the fits were repeated retaining even those datapoints that qualitatively deviated; essentially the same behavior was obtained but with slightly greater values of τlong . Regressed values of τshort and τlong are plotted versus molecular weight for all molecule types in Figs . S4 and S5 respectively . Having fit a function to the observed dependence of α on δt , it was numerically integrated to obtain an extrapolated , asymptotic long-time Dtrans value using the Dtrans value at δt = 100ps as the starting point for the integration . The quality of fits of the integrated Dtrans values ( for the most abundant proteins ) is indicated by the solid lines in Fig . 3A . Effective rotational diffusion coefficients were computed from the time-dependent behavior of the 3×3 rotational matrix recorded every 100ps for every molecule during the simulations . For each of the three rotational axes , an autocorrelation function , θ ( δt ) , was calculated as: ( 5 ) where e ( 0 ) and e ( δt ) are unit vectors pointing along one of the rotational axes at time t = 0 and t = δt respectively , and the brackets indicate an average over all possible initial timepoints; the three computed autocorrelation functions were averaged to give a single decay function consistent with the isotropic rotation that we assumed for all molecule types at infinite dilution . Since the resulting averaged autocorrelation function for the ‘full’ energy model did not fit well to a single-exponential decay , and given that translational diffusion was clearly transiently anomalous , we decided to use the following functional form proposed recently for transiently anomalous rotational diffusion [27]: ( 6 ) where θ0 is the value of the autocorrelation function at δt = 0 ( always 1 ) , a is a parameter , τrot is a long-time rotational correlation time ( which dominates as δt→∞ ) , and τrel is the timescale over which a faster , short-time rotational relaxation gives way to the slower rotation characterized by τrot . The above functional form was fit to computed values of θ for each molecule type over a range of δt values up to 1µs; the r2 values for these fits were all in excess of 0 . 999 . An example of such fits for the most abundant proteins is shown in Fig . S6 . The long-time rotational diffusion coefficient , DLrot , is then obtained using the relationship: ( 7 ) and the short-time rotational diffusion coefficient , DSrot , is obtained from [27]: ( 8 ) The computed ratios DLrot/D0rot and DSrot/D0rot obtained with the ‘full’ energy model are plotted for all molecule types versus their molecular weights in Fig . S7; a plot of the parameter a versus molecular weight shows no obvious relationship ( not shown ) . Comparison of the simulated translational and rotational diffusion coefficients with the infinite-dilution values that are input parameters for the simulations provides an indication of the relative viscosities experienced during the two types of motion . From studies of GFP diffusion in Chinese hamster ovary cells , the Verkman group reports [29] a relative viscosity experienced by translational motion , ηrelT = 3 . 2±0 . 2 , and a relative viscosity experienced by rotational motion , ηrelR = 1 . 5±0 . 1 . Combining these numbers gives a ratio , ηrelT/ηrelR of 2 . 1±0 . 3 , indicating that the effective relative viscosity experienced by translational motion is roughly twice that experienced by rotational motion in mammalian cells . A second estimate of the ηrelT/ηrelR ratio can be obtained from the work of Zorrilla et al . [28] , [99]: these authors have reported measurements of the translational diffusion coefficients of apomyoglobin ( 17kDa ) using fluorescence correlation spectroscopy ( FCS ) measurements [28] and have compared them with rotational diffusion coefficients that they had previously measured [99] for the same system using time-resolved fluorescence depolarization experiments . They report measurements for two different background proteins , RNaseA and human serum albumin ( HSA ) ; we focus on the data reported for the latter since its molecular weight ( 67kDa ) is much closer to the number-averaged molecular weight of the macromolecules in our cytoplasm model ( 87kDa ) , than is the molecular weight of RNaseA ( 14kDa ) . The data reported by Zorrilla et al . are expressed relative to the macroscopic viscosity , ηm , of the protein solution ( measured with an Ostwald viscometer ) . They report that ηm fits to the following functional form , ηm = η0 exp ( Ac/ ( 1−Bc ) ) , where η0 is the viscosity of pure water , c is the background protein's concentration in mg/ml , and A and B are background-dependent constants: A = 2 . 7×10−3 ml/mg and B = 1 . 3×10−3 ml/mg for HSA [99] . Using these values we obtain a macroscopic viscosity for a 275 mg/ml HSA solution of 3 . 155 η0 . Using the data given in Table 2 of ref . 49 , the effective viscosity experienced by the translational motion of apomyoglobin in HSA is expressed as ηrelT = ( ηm/η0 ) 1 . 28 , which from above means that we can write ηrelT = 3 . 1551 . 28 = 4 . 35; following similar calculations the effective viscosity experienced by the rotational motion is ηrelR = ( ηm/η0 ) 0 . 44 = 3 . 1550 . 44 = 1 . 66 . Together , these numbers translate into a value of ηrelT/ηrelR of 2 . 6±0 . 2 . As noted in the main text , we find that both the translational and rotational diffusion coefficients of molecules vary with the time interval , δt , over which diffusion is observed . While the observation of this transient anomalous diffusion is significant in its own right it takes on added significance when comparing the relative viscosities experienced by translational and rotational motion . This is because the timescales over which the two types of experiments are conducted are quite different: translational diffusion coefficients are obtained from FCS experiments by fitting to an autocorrelation function over a timescale extending from microseconds to seconds [21] , [22] , [66] while rotational diffusion coefficients are obtained from fits to data obtained over a nanosecond timescale [28] , [29] . We therefore compare the experimentally derived relative viscosities quoted above with diffusion coefficients computed from the BD simulations on the same timescales , i . e . we compare with the ratio of the long-time translational diffusion coefficient DLtrans and the short-time rotational diffusion coefficient , DSrot ( see Fig . 3F ) . The intermolecular contacts engaged in by each molecule were recorded every 100ps during the BD simulations and subsequently analyzed to determine: ( a ) the average number of neighbors of each molecule type at any given time , ( b ) the number of unique neighbors encountered by each molecule type during the course of the entire simulations , and ( c ) the rate of dissociation of intermolecular interactions . The definition of ‘neighbor’ was kept somewhat loose in order to detect all molecules in the immediate environment of the molecule being probed: molecules were assigned as neighbors if any of their atoms were within ∼12Å of each other . The rates at which the neighbors of a particular molecule dissociated were obtained from plots of the fraction of its neighbors , initially present at t = 0 , that remained after some time t = δt , averaged over all possible initial timepoints . In order to obtain the characteristic neighbor-decay rate for each particular type of molecule , such plots were averaged over all molecules of that type . The resulting plots are found to follow biexponential kinetics: ( a ) a very fast decay process ( τfast ) that typically has an amplitude of ∼0 . 7 and is due to loss of neighbors that interact only peripherally with the molecule of interest , and ( b ) a slower decay process ( τslow ) that has an average amplitude of ∼0 . 3 and is due to loss of those neighbors that form bona fide intermolecular contacts . Typical fits for these data are shown in Fig . S8 . The effects of immersion in the cytoplasm on the thermodynamics of protein folding and protein-protein association were computed using the particle insertion technique first outlined by Widom [30] . For small perturbations , the free energy change , ΔG , for transferring a molecule from an environment free of any interacting macromolecules to the cytoplasm environment can be rigorously expressed as: ( 9 ) where Eint is the interaction energy of the molecule with the constituents of the cytoplasm , R is the Gas constant , T is the temperature , and the brackets indicate an average over randomly selected insertion positions and configurations of the cytoplasm environment . In order to assess the likely effects of the cytoplasm on a thermodynamic process ( such as protein folding ) therefore , separate particle-insertion calculations are required for both the initial state ( e . g . unfolded protein ) and the final state ( e . g . folded protein ) . Such calculations give the free energy changes for the vertical processes in the thermodynamic cycle shown below: ( 10 ) Since free energy is a state function , the difference between the free energy changes of the horizontal processes is equal to the difference between the free energy changes of the vertical processes . We can therefore write the difference between the free energy change for the process in vivo and in vitro , ΔΔG , as: ( 11 ) The effect of the cytoplasm on the free energy change for a process can therefore be calculated without needing to know the actual value of the free energy change for the process in vitro . A conceptually similar but different approach to computing thermodynamics in crowded solutions has recently been outlined by Zhou and co-workers [100] . Code for performing particle-insertion calculations was generated by modifying the existing BD simulation program; prior to performing large-scale explorations of protein folding and association thermodynamics , the code's correctness was first checked by comparing its predictions for the free energy cost of placing a sphere into a solution of spheres with the corresponding predictions of scaled particle theory [101] , [102] . Calculations of the cytoplasm's thermodynamic effects initially focused on protein folding equilibria . In addition to calculating the folding thermodynamics of six proteins already present in the cytoplasm model ( Adk , Bcp , CspC , Efp , GFP and PpiB ) , we examined two other proteins that have been subject to direct experimental study in vivo: these were the 80-residue λ6-85 construct studied experimentally by Ghaemmaghami and Oas [4] and the 136-residue cellular retinoic acid binding protein ( CRABP ) investigated by Ignatova , Gierasch and co-workers [7] , [32] . The structure of the folded state of λ6-85 was taken from its crystal structure in complex with operator DNA ( pdbcode: 1LMB [103] ) ; the G46A & G48A mutations present in the experimental construct were made using the rotamer-sampling method SCWRL3 [104] . The structure of the folded state of CRABP ( pdbcode: 1CBI [105] ) was altered to include the R131Q mutation used in the experimental construct [7] , but in the absence of direct structural information no attempt was made to model the experimentally-incorporated fluorophore . The unfolded states of all eight proteins were modeled as ensembles of 1000 unfolded conformations generated using the conformational sampling method developed by the Sosnick group [31]; the code was kindly made available by Dr . Abhishek Jha . This method has been shown to produce models with dimensions in good agreement with experimental estimates [31] . Prior to calculations , the structures of all conformations were completed by adding sidechains with SCWRL3 [104] and by adding hydrogens with the PDBTOPQR utility [84] of APBS [89] . In order to ensure consistency between the BD simulations and the Widom particle-insertion calculations , effective charges and electrostatic potential grids were calculated for all conformations ( both folded and unfolded ) using the exact same protocol employed with the rigid protein models of the cytoplasm model ( see above ) . For each protein , a large number of random trial positions were attempted with both the single , folded state structure and the 1000 unfolded state conformations; each trial consisted of a different randomly selected translation and rotation . For the folded state structure , a total of 25 million trials were attempted; for the unfolded state , 250 , 000 trials were attempted for each of the 1000 conformations ( to give a total of 250 million trials for each cytoplasm ‘snapshot’ studied ) . For each trial position , the interaction energy of the protein with the surrounding cytoplasm was calculated with ( a ) the ‘full’ energetic model , which includes electrostatic , steric and hydrophobic contributions , and ( b ) the ‘steric’ energetic model . To simplify the latter calculations , only two possible energies were allowed: the interaction energy , Eint , was set to +∞ if any of the protein's atoms came within 4 . 5Å of any of the cytoplasm atoms , and was set to zero if not; this binary scoring method is effectively identical to that used in most examinations of excluded-volume ( crowding ) effects . Due to the very significant computational expense associated with the particle-insertion calculations , they were applied only to the final ‘snapshot’ of the three independent BD simulations performed with the ‘full’ and ‘steric’ models . Error bars for all reported free energy changes were therefore calculated as the standard deviation of the computed values obtained from the three different system ‘snapshots’ . The total number of unfolded and folded-state trial positions that were accepted and rejected for each protein , for each of the three ‘full’ model cytoplasm ‘snapshots’ are listed in Table S3 . A very similar protocol was used to calculate the effects of the cytoplasm on a variety of protein association reactions . Calculations on each assembled protein complex were performed exactly as described above . Calculations on each disassembled complex – e . g . two separated protein monomers in the case of a dimerization reaction – were carried out by performing insertions of all components simultaneously; importantly , each randomized placement was first screened to ensure that there were no steric clashes between any of the inserted components before their interactions with the cytoplasm were evaluated . As might be expected , the requirement of simultaneously placing multiple molecules into the cytoplasm meant that in some cases very large numbers of trial positions were required in order to obtain reasonably converged results . Owing to the significant computational expense , therefore , calculations were only performed on snapshots taken from BD simulations performed with the ‘full’ energy model . In addition , since the Boltzmann-weighting of the sampled interaction energies can contribute significant noise in cases where the number of accepted placements are comparatively low , the cytoplasm-interaction energy distributions were first smoothed by fitting to sums of three Gaussians using SigmaPlot [98] ( see Fig . S9 for a typical fit ) . The total numbers of accepted and attempted insertions for the various association reactions studied are listed in Table S4 . Dimerization equilibria were investigated by performing separate particle-insertion calculations on the dimeric forms and the monomeric forms; for such calculations it was assumed that no structural change ( e . g . unfolding ) occurs when the two monomers are separated . The trimerization equilibrium of ParM was investigated in analogous fashion , by performing calculations on a trimer extracted from the ParM filament model ( pdbcode: 2QU4 [106] ) . The aggregation of a poly-Q-inserted RNaseA to form an amyloid fiber was studied using the theoretical model developed by Eisenberg and co-workers ( pdbcode: 2APU; [38] ) . The model deposited in the PDB contains 56 aggregated monomeric units; the largest aggregate for which we could obtain reasonably precise free energy estimates however contained eight monomeric units ( Fig . 4F ) . Since formation of the amyloid structure involves a significant change in conformation , the use of monomeric structures extracted without modification from the aggregate model would be inappropriate . Instead , the structure of the monomeric poly-Q-inserted RNaseA was taken from the crystal structure reported by the Eisenberg group ( pdbcode: 2APQ [38] ) . In order to ensure sequence-consistency with the amyloid model , a A131H mutation was made with SCWRL3 [104] . In addition , since the monomeric structure has no resolved coordinates for the inserted GQQQQQQQQQQGNP stretch this region was model-built using the loop-building program Loopy [107] . The second aggregate structure studied was a theoretical model of SH3 domain aggregation proposed by the Shakhnovich group [39] and kindly made available to the authors by Dr . Feng Ding ( UNC; personal communication ) . This structure contains only Cα atoms so complete backbone coordinates were first constructed using the SABBAC webserver [108] ( http://bioserv . rpbs . jussieu . fr/cgi-bin/SABBAC ) before sidechain positions were constructed using SCWRL3 . Owing to the structure's origins being a Cα-only model we were unable to add sidechains in such a way that the assembled aggregate model was free of internal steric clashes; this , however , does not significantly affect our ability to estimate the model's interaction with the cytoplasm environment . As with the RNaseA amyloid model , it would be inappropriate to assume that the conformations of unaggregated monomeric units are identical to those found in the amyloid model; instead therefore the conformation of the monomeric SH3 domain was taken from the crystal structure ( pdbcode: 1NLO [109] ) . Two movies , each showing 1 . 8µs of simulation , are provided as separate Quicktime . mov files . Video S1 shows a BD simulation performed with the ‘full’ energy model; Video S2 shows a BD simulation performed with the ‘steric’ energy model . File size restrictions at the PLoS website have limited the size and resolution of the uploaded movies to be used for review . Higher resolution movies are available to readers at the authors' website: http://dadiddly . biochem . uiowa . edu/Elcock_Lab/Movies . html .
|
The interior of a typical bacterial cell is a highly crowded place in which molecules must jostle and compete with each other in order to carry out their biological functions . The conditions under which such molecules are typically studied in vitro , however , are usually quite different: one or a few different types of molecules are studied as they freely diffuse in a dilute , aqueous solution . There is therefore a significant disconnect between the conditions under which molecules can be most usefully studied and the conditions under which such molecules usually “live” , and developing ways to bridge this gap is likely to be important for properly understanding molecular behavior in vivo . Toward this end , we show in this work that computer simulations can be used to model the interior of bacterial cells at a near atomic level of detail: the rates of diffusion of proteins are matched to known experimental values , and their thermodynamic stabilities are found to be in good agreement with the few measurements that have so far been performed in vivo . While the simulation approach is certainly not free of assumptions , it offers a potentially important complement to experimental techniques and provides a vivid illustration of molecular behavior inside a biological cell that is likely to be of significant educational value .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/molecular",
"dynamics",
"biophysics/macromolecular",
"assemblies",
"and",
"machines",
"biophysics/theory",
"and",
"simulation",
"biophysics/protein",
"folding"
] |
2010
|
Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm
|
The disc-large ( DLG ) –membrane-associated guanylate kinase ( MAGUK ) family of proteins forms a central signaling hub of the glutamate receptor complex . Among this family , some proteins regulate developmental maturation of glutamatergic synapses , a process vulnerable to aberrations , which may lead to neurodevelopmental disorders . As is typical for paralogs , the DLG-MAGUK proteins postsynaptic density ( PSD ) -95 and PSD-93 share similar functional domains and were previously thought to regulate glutamatergic synapses similarly . Here , we show that they play opposing roles in glutamatergic synapse maturation . Specifically , PSD-95 promoted , whereas PSD-93 inhibited maturation of immature α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid–type glutamate receptor ( AMPAR ) –silent synapses in mouse cortex during development . Furthermore , through experience-dependent regulation of its protein levels , PSD-93 directly inhibited PSD-95’s promoting effect on silent synapse maturation in the visual cortex . The concerted function of these two paralogs governed the critical period of juvenile ocular dominance plasticity ( jODP ) , and fine-tuned visual perception during development . In contrast to the silent synapse–based mechanism of adjusting visual perception , visual acuity improved by different mechanisms . Thus , by controlling the pace of silent synapse maturation , the opposing but properly balanced actions of PSD-93 and PSD-95 are essential for fine-tuning cortical networks for receptive field integration during developmental critical periods , and imply aberrations in either direction of this process as potential causes for neurodevelopmental disorders .
The postsynaptic density ( PSD ) is a proteinaceous network that regulates and coordinates the signaling of multiple receptors and other proteins in a confined region at the synapse , including developmental changes , to reach its mature functionality . Most proteins of the glutamate receptor complex are evolutionarily diversified as paralogous proteins [1] . A common notion is that this diversification enables specific adaptations of protein functions in increasingly complex organisms [2 , 3] . In particular , paralogs either adapted for specific requirements of different cellular compartments or organs , such as the liver- or heart-specific lactate dehydrogenases , or evolved more specialized functions in the same compartment , such as the opsins for color vision in photoreceptor cells [4 , 5] . However , it remains elusive whether the multiple paralogous proteins of the PSD functionally interact within the same synapse or each individually predominates in different synapses . The importance of individual PSD paralogs in synapse maturation is highlighted by the observation that genetic variants in single genes cause neurodevelopmental disorders , including autism spectrum disorders ( ASDs ) [6] and schizophrenia [7] . Focusing on PSD-95 , a core protein of the glutamate receptor signaling complex , we recently demonstrated that PSD-95–dependent maturation of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid ( AMPA ) -type glutamate receptor ( AMPAR ) -silent synapses ends the critical period of the juvenile form of ocular dominance ( OD ) plasticity ( ODP ) in the visual cortex [8] , a classical model for experience-dependent critical period plasticity [9–11] . The experience-dependent silent synapse maturation may thus serve as a model mechanism for studying the specific role of PSD-95 and its paralogs of glutamate receptor complexes in synapse and circuit maturation during developmental critical periods . Furthermore , sensory defects are typical for neurodevelopmental disorders , including schizophrenia and autism [12–14] . Thus , given the similar cytoarchitecture of functional domains of the neocortex , mechanistic insights into sensory cortical phenotypes likely also translate to pathomechanisms of mental disorders . As a paralog of PSD-95 , PSD-93 also directly interacts with glutamate receptors and controls AMPAR synaptic trafficking [15–17] . The gene Dlg2 , coding for PSD-93 , contains six different N-terminal isoforms [18 , 19] . Allelic variants and somatic mosaicism of Dlg2 are associated with schizophrenia and other neurodevelopmental disorders [20–22] . The analysis of PSD-93 loss and gain of function in different brain regions has uncovered partly conflicting results that so far prevented a clear understanding of the physiological function of the paralogs PSD-93 and PSD-95 in the regulation of glutamatergic synapses , and what might go awry in neurodevelopmental disorders [19 , 23–25] . Here , we show that PSD-93 and PSD-95 regulate experience-dependent maturation of silent synapses in an opposing manner in that PSD-95 promotes , while PSD-93 inhibits , the maturation . Concurrently , critical period closure is impaired in PSD-95 knock-out ( KO ) mice [8] , while it closes precociously in PSD-93 KO mice . The lack of either paralog impaired the functional optimization of cortical networks and resulted in the impairment of visual perception with visual acuity remaining intact , indicating a dissociation of developmental processes for perception and vision . In PSD-93/95 double KO ( dKO ) mice—lacking both of the promoting and inhibiting effects—silent synapse maturation proceeded in terms of maturation speed more similar to but mechanistically distinct from wild-type ( WT ) mice . Consequently , visual features were compromised with impaired visual acuity . Thus , PSD-95 and PSD-93 functionally cooperate in constructive silent synapse maturation with opposing roles in the glutamate receptor complex , with either paralog promoting vision , while requiring their opposing function for fine-tuning perceptual capabilities . The opposing function of paralogs extends the repertoire of evolutionary functional specialization and provides a conceptual framework for the analysis of paralog-specific pathomechanisms in neurodevelopmental disorders .
Loss of function of PSD-95 impairs silent synapse maturation in the visual cortex [8 , 26] . Reassuring this previous finding , we observed in PSD-95 KO mice that the fraction of silent synapses in the layer 4 ( L4 ) –to–layer 2/3 pyramidal neurons of mouse visual cortex before eye opening at postnatal day ( P ) 11 was similar to that in WT mice ( F5 , 92 = 12 . 06; p < 0 . 001; P11: WT versus PSD-95 KO , p = 1 . 0; Fig 1A , 1B and 1G ) . While in WT mice , the fraction of silent synapses decreased after eye opening , the fraction did not change in PSD-95 KO mice ( WT: P11 versus P28 , p < 0 . 01; PSD-95 KO: P11 versus P28 , p = 1 . 0; Fig 1D–1E and 1G ) . In contrast , in PSD-93 KO , the fraction of silent synapses at P11 was smaller compared with that of WT and PSD-95 KO mice ( P11: WT versus PSD-93 KO , p < 0 . 05; PSD-93 KO versus PSD-95 KO , p < 0 . 05; Fig 1C and 1G ) . In PSD-93 KO mice , the fraction of silent synapses progressively decreased but was approaching 0% , instead of approximately 25% , in WT mice at P28 ( PSD-93 KO: P11 versus P28 , p < 0 . 05; Fig 1F and 1G ) . While loss of PSD-95 prevented the developmental decrease in silent synapses , the effect of loss of PSD-93 was opposite by accelerating this developmental decrease . Thus , the developmental trajectories of silent synapses were different in response to loss of function of PSD-93 versus PSD-95 ( two-factor ANOVA; genotype: F2 , 92 = 17 . 3 , p < 0 . 001; age: F1 , 92 = 13 . 2 , p < 0 . 001; interaction: F2 , 92 = 3 . 94 , p < 0 . 05 ) . To further characterize the time course of the accelerated developmental trajectory of silent synapses in PSD-93 KO mice , we assessed the fraction of silent synapses at two additional time points , after birth ( P4 ) and at the beginning of the critical period ( P20 ) . At P4 , the fraction of silent synapses was high in both WT and PSD-93 KO mice , with similar percentages ( F3 , 60 = 41 . 2 , p < 0 . 001; P4: WT versus PSD-93 KO , p = 0 . 94; Fig 1H ) . At P20 , the fraction of silent synapses in both genotypes declined , but PSD-93 KO mice exhibited much lower levels of silent synapses compared with WT mice ( P20: WT versus PSD-93 KO , p < 0 . 01; Fig 1H ) . Thus , the developmental trajectory of silent synapses of both genotypes starts at a similar value at birth but declines at an accelerated pace in PSD-93 KO mice , reaching toward 0% already during the critical period ( S1 Fig ) . In contrast , the fraction of silent synapses in PSD-95 KO mice remained constantly high and did not decline from P11 until P28 , nor further throughout the critical period into late adulthood [8] . Previous studies reported different effects of loss of PSD-93 on synaptic AMPARs and N-methyl-D-aspartate ( NMDA ) -type glutamate receptors ( NMDARs ) in different brain regions [19 , 23–25] . In the cortex of PSD-93 KO mice , the number of synaptic NMDARs is reduced [25] . In the hippocampus of PSD-93 KO mice , one study reported a reduction of synaptic AMPARs , while others did not observe this effect [19 , 23 , 24] . Therefore , we investigated whether loss of PSD-93 generally reduces the fraction of silent synapses in cortical synapses of principal neurons and whether loss of PSD-95 impairs their maturation . We assessed the fraction of silent synapses in layer 2/3 pyramidal neurons of the medial prefrontal cortex ( mPFC ) and Cornu Ammonis 1 ( CA1 ) pyramidal neurons of the hippocampus . At P30 in the mPFC of PSD-95 KO mice , the fraction of silent synapses was higher compared with that of WT mice ( t = −5 . 45; p < 0 . 001; Fig 1K ) . Conversely , at P15 in the mPFC of PSD-93 KO mice , the fraction of silent synapses was reduced compared with that of WT mice ( t = 3 . 35; p < 0 . 01; Fig 1N ) . Similarly , at P20 in the hippocampus of mice with loss of PSD-95 through a short hairpin RNA ( shRNA ) -mediated knock-down ( KD ) , or in PSD-93 KO mice , the fraction of silent synapses was increased or decreased compared with that of WT mice , respectively ( F2 , 30 = 38 . 1 , p < 0 . 01; WT versus PSD-95 KD , p < 0 . 01; WT versus PSD-93 KO , p < 0 . 05; PSD-95 KD versus PSD-93 KO , p < 0 . 01; Fig 1R ) . Together , these results reveal that in all assessed pyramidal neurons , PSD-95 is necessary for the maturation of AMPAR-silent synapses , while , without PSD-93 , their fraction is reduced . Because in PSD-93 KO mice , the fraction of silent synapses was already reduced compared with WT mice at eye opening ( about P12 ) , we tested whether the accelerated developmental decrease resulted from visual experience . In dark-reared ( DR ) WT mice , the fraction of silent synapses did not decline between P11 and P28 ( F3 , 80 = 15 . 7 , p < 0 . 001; WT: P11 versus P28 , p = 0 . 73 , Fig 2A , 2B and 2E ) , indicating that the decline of silent synapses after eye opening was visual experience dependent [27] . However , lack of experience through dark rearing did not affect the accelerated decline of silent synapses in PSD-93 KO mice ( PSD-93 KO: P11 versus P28 , p < 0 . 01; Fig 2C–2E ) , indicating that by removing PSD-93 , the developmental decrease of silent synapses became independent of visual experience and remained accelerated . Notably , at P4 , the fraction of silent synapses was about 80% and thus higher than at P11 ( about 55%; Fig 1 ) . Both DR and normal-reared ( NR ) WT mice exhibited similar fractions of silent synapses at P11 ( Figs 1 and 2 ) , indicating that before eye opening , the fraction of silent synapses declines independently of visual experience . Furthermore , because in PSD-95 KO mice , the fraction of silent synapses stays at the eye-opening level , mechanisms to decrease the fraction of silent synapses before eye opening are apparently intact , but visual experience–dependent maturation after eye opening is absent , indicating two different mechanisms for the decrease of silent synapses ( Fig 1 ) [8] . We then tested whether the accelerated maturation of silent synapses by loss of PSD-93 was cell autonomous . Using low-titer recombinant adeno-associated viral vectors ( AAV ) expressing an shRNA against PSD-93 ( sh93 ) or short hairpin luciferase ( shLC ) as a control [8 , 19] , we sparsely transduced a low fraction of primary visual cortex ( V1 ) neurons . On P28 , in AAV-sh93–expressing layer 2/3 pyramidal neurons , the fraction of silent synapses was smaller than that of AAV-shLC–expressing ones ( t = 3 . 85 , p < 0 . 01; Fig 2F–2H ) . Similar to PSD-93 KO , the fraction of silent synapses in AAV-sh93–transduced neurons was about 0% at P28 , while the values in AAV-shLC–expressing control neurons was about 25% , similar to WT mice ( Fig 2H ) . Taken together , these results reveal that the PSD-93–dependent acceleration of silent synapse maturation is cell autonomous rather than a consequence of compensatory network mechanisms . Our results so far reveal opposing functions of PSD-95 and PSD-93 on the decrease of silent synapses in the developing visual cortex . To further examine this conclusion , we measured the time course of silent synapses in PSD-93/95 dKO mice . The survival rate of newborn dKO mice was low [23] . Thus , to generate sufficient numbers of mutant mice lacking both PSD-93 and -95 in the visual cortex , we analyzed two types of mutant mice in parallel . When available , we used dKO mice from PSD-93 KO and PSD-95 heterozygous breeding and , alternatively , mice with a combination of genetic KO of PSD-93 and AAV-mediated KD of PSD-95 . For the latter approach , we injected an AAV expressing shRNA against PSD-95 ( sh95 ) into the visual cortex of P0 PSD-93 KO mice [8] . We validated this approach by comparing the fraction of silent synapses between the two manipulations . At both P11 and P28 , the fraction of silent synapses between PSD-93/95 dKO and PSD-93 KO with sh95 , respectively , was similar ( P10: dKO versus PSD-93 KO/sh95 , t = 0 . 577 , p = 0 . 58; P28: dKO versus PSD-93 KO/sh95 , t = 0 . 378 , p = 0 . 71 ) , indicating that a cell selective loss of both proteins and loss in all neurons had a similar effect . At P4 , we used the low-yield PSD-93/95 dKO mice , as P4 did not allow sufficient time for AAV-mediated expression . The fraction of silent synapses was similar to that of WT mice ( 84 . 5% ± 2 . 8% , n = 5; S1 Fig ) . Likewise , at P10 and P28 , in PSD-93/95-lacking neurons , the fraction of silent synapses was similar to that of WT mice , but higher than that of PSD-93 KO mice and smaller than that of PSD-95 KO mice ( two-way ANOVA: WT versus dKO/KD , F1 , 77 = 0 . 182 , p = 0 . 18; PSD-95 KO versus dKO/KD , F1 , 69 = 13 . 3 , p < 0 . 001; PSD-93 KO versus dKO/KD , F1 , 79 = 9 . 24 , p < 0 . 005; Fig 2I–2K ) . These results further support our hypothesis that PSD-93 and PSD-95 prevent and promote the developmental decrease of AMPA-silent synapses , respectively . However , in neurons lacking both paralogs , the time course of silent synapse decrease was similar to that of WT mice , indicating that developmental decrease of silent synapses is also achieved without these proteins . To test whether this decrease in the absence of the two paralogs is mechanistically different than that in their presence , we measured the developmental time course of silent synapses in DR mice . In DR PSD-93 KO/sh95 mice , the fraction of silent synapses at P28 was similar to that of NR PSD-93 KO/sh95 mice ( t = 0 . 64; p = 0 . 54; Fig 2L and 2M ) , indicating that in the absence of both paralogs , the decrease of silent synapses was independent of visual experience . Thus , the mechanisms of silent synapse decrease differ between WT and KOs . While in WT mice , the developmental decrease of silent synapses is experience dependent , it progresses both before eye opening and in the absence of PSD-93 or the absence of both paralogs , independently of experience . A classical test for experience-dependent cortical plasticity in mammals is ODP in V1 , which is induced by closing one eye ( monocular deprivation [MD] , an experimental model of a cataract ) [10] . In mouse V1 , neurons in the binocular region of V1 predominantly respond to sensory inputs from the contralateral eye ( contra ) and to lesser extend to the ipsilateral eye ( ipsi ) . During the critical period , mouse V1 is susceptible to activity-dependent refinement of neural circuits and establishment of critical visual functions , particularly receptive field integration , including binocular vision [10 , 28] . In standard cage-raised mice , a brief ( 4-d ) MD induces an OD shift of visually evoked responses in V1 towards the open eye [8 , 29 , 30] . This juvenile ODP ( jODP ) is mediated by a reduction of deprived eye responses in the binocular part of V1 and is temporally confined to the critical period [31 , 32] . We previously reported that PSD-95–dependent silent synapse maturation is required for the closure of the jODP in mice [8] . Using PSD-93 KO mice , we explored the potential reverse correlation that precocious silent synapse maturation leads to a precocious termination of the critical period for OD plasticity . We performed MD for 4 d at two different time points during the critical period , in mid critical period ( P24–P27 ) , when silent synapses in PSD-93 KO were still present , and in late critical period ( P28–P35 ) , when the fraction of silent synapses was approaching 0% ( Fig 2 ) . During both time periods , V1 of PSD-93 KO and WT control mice was dominated by visual inputs from the contralateral eye , and the OD index ( ODI ) was positive , measured with optical imaging of intrinsic signals ( pre-P28 , no MD ODI: WT versus KO; p = 0 . 66; post-P28 , no MD ODI: WT versus KO , p = 0 . 17; Fig 3A , 3E , 3I and 3J ) . In contrast to standard cage-raised WT mice that expressed jODP after 4 d of MD at both time points ( Fig 3B , 3C , 3I and 3J ) , PSD-93 KO mice expressed jODP only before P28 , but not later ( Fig 3F , 3G , 3I and 3J ) . Before P28 , the shift in the ODI in WT mice was mediated by a reduction of deprived ( contra ) eye responses in V1 ( no MD versus MD , p < 0 . 01; Fig 3B , 3C , 3I and 3J ) , which is characteristic for jODP during the critical period [31 , 32] , whereas ipsi-evoked responses in V1 did not change ( no MD versus MD , p = 0 . 29 ) . The change in ODI in PSD-93 KO mice before P28 was similar to that of WT mice ( ODI: WT MD versus KO MD , p = 0 . 42 , Fig 3B , 3F , 3I and 3J ) and also mediated by a reduction of deprived ( contra ) eye responses in V1 ( KO no MD versus KO MD , p < 0 . 001; Fig 3F , 3I and 3J ) . Ipsi-evoked responses did not change ( KO no MD versus KO MD , p = 0 . 31 ) . These results indicate that jODP plasticity itself does not require PSD-93 . In PSD-93 KO mice after P28 ( ≥P28 ) , a 4-d MD was unable to induce an OD shift towards the open eye ( p = 0 . 17; Fig 3G ) , whereas age-matched WT mice continued to express OD plasticity ( p < 0 . 001 ) , which was also mediated by a reduction of deprived ( contra ) eye responses in V1 , as expected in the critical period ( WT no MD versus WT MD , p < 0 . 05 ) . Thus , while jODP is expressed in PSD-93 KO mice , its critical period terminates precociously . In DR WT mice , the critical period for jODP is prolonged [33] . Because in PSD-93 KO mice , silent synapse maturation was not halted by dark rearing , we tested whether jODP would be . This was not the case . Even in the critical period , DR PSD-93 KO mice did not show jODP ( ODI: MD versus no MD , p = 0 . 5; Fig 3H and 3I ) , whereas DR WT mice exhibited jODP both during the critical period ( ODI: MD versus no MD , p < 0 . 001; Fig 3I ) and also after the critical period ( P40–P50 ) , in contrast to NR WT mice ( ODI: DR MD versus NR MD , p < 0 . 001 , Fig 3I ) . The OD shift of DR WT mice exhibited a clear trend for a reduction of deprived ( contra ) eye responses ( no MD ≥P28 versus MD ≥P28 , p = 0 . 052; Fig 3D and 3H and 3J ) , whereas ipsi-evoked responses in V1 remained unchanged ( no MD ≥P28 versus MD ≥P28 , p = 0 . 74 ) . Together , these results reveal that jODP does not require PSD-93 , because before P28 , jODP was expressed in PSD-93 KO mice , whereas the lack of jODP after P28 was correlated with the precocious decrease of silent synapses . Furthermore , dark rearing , which halts silent synapse maturation in WT mice and prolongs the critical period of jODP , neither halted the decrease in silent synapses nor prolonged the critical period of jODP in PSD-93 KO mice , revealing a strict correlation between silent synapse decrease and the closure of the critical period for jODP in the visual cortex . The basic organization of the brain is normal in PSD-93 KO mice [34] . To test whether the visual cortex of PSD-93 KO mice is similarly organized as in WT mice , we analyzed optically recorded V1 activity and retinotopic maps by stimulating the contralateral eye with either horizontally or vertically moving bars ( S2A and S2D Fig ) . V1 activation ( elevation: WT versus KO , p = 0 . 993; azimuth: WT versus KO , p = 0 . 22; S2G and S2I Fig ) and retinotopic map quality ( map scatter ) ( elevation: WT versus KO , p = 0 . 573; azimuth: WT versus KO , p = 0 . 197; S2H and S2J Fig ) were similar in PSD-93 KO and WT mice , indicating that basic visual activation of V1 is not altered in the absence of PSD-93 . PSD-93 cell-autonomously regulated silent synapse maturation ( Fig 2 ) . To test whether visual cortex–restricted deletion of PSD-93 expression was sufficient for precocious closure of the critical period for jODP , we delivered AAV-sh93 or AAV-shLC as control , into the visual cortex of WT P0–P1 mice [8] . V1 activities of non-deprived mice with AAV-sh93 or AAV-shLC were similar and dominated by visual inputs from the contralateral eye ( Fig 4A and 4C ) ; the ODI for both was also similar and positive ( V1 activation shLC: contra versus ipsi , p < 0 . 01; V1 sh93: contra versus ipsi , p < 0 . 05; ODI: shLC no MD versus sh93 no MD , p = 0 . 55; Fig 4E and 4F ) . In AAV-shLC–expressing mice , 4 d MD induced an OD shift towards the non-deprived eye in the late critical period ( ≥P28 ) so that both eyes activated V1 more evenly and the ODI was reduced . In contrast , in AAV-sh93–expressing mice , no shift was induced in the late critical period and the deprived eye continued to dominate V1 ( V1 activation after MD shLC: contra versus ipsi , p = 0 . 27; V1 after MD sh93: contra versus ipsi , p < 0 . 01; ODI after MD: shLC versus sh93 , p < 0 . 001; Fig 4B and 4D–4F ) . Similar to PSD-93 KO mice , both V1 activity and retinotopic maps were not significantly altered: V1 activation ( elevation: shLC versus sh93 , p = 0 . 73; azimuth: shLC versus sh93 , p = 0 . 79; S3 Fig ) and retinotopic map quality ( elevation: shLC versus sh93 , p = 0 . 89; azimuth: shLC versus sh93 , p = 0 . 72; S3 Fig ) were indistinguishable between shLC and sh93 KD mice . Thus , visual cortex–restricted KD of PSD-93 phenocopied the effect of PSD-93 KO on jODP timing , indicating that PSD-93 expression in the visual cortex is required to prevent precocious critical period closure . Silent synapses mature ( unsilence ) by long-term synaptic potentiation ( LTP ) -driven incorporation of AMPARs [35–38] . However , the results on the developmental time course of silent synapses , especially in the PSD-93 KO mice , did not allow us to determine whether the decrease was due to the maturation of silent synapses or their elimination , two processes that likely occur competitively during experience-dependent cortical network refinement [39] . To resolve these two possibilities , we analyzed additional synaptic parameters from the minimal stimulation assay . In the cortex , one glutamatergic axon forms on average five synapses with a target pyramidal neuron [40] . Thus , with the maturation of silent synapses and the resulting decrease of the fraction of silent synapses , the amplitude of the unitary response will increase [27] . In both PSD-93 KO and WT mice , the amplitude of the successes ( potency ) increased during development ( two-way ANOVA: F3 , 120 = 6 . 89 , p < 0 . 001; Fig 5A ) , but the time course of this developmental increase was different between the two genotypes ( two-way ANOVA: genotype , F1 , 120 = 4 . 453 , p < 0 . 05; interaction , F3 , 120 = 4 . 332 , p < 0 . 01 ) . In WT mice , synaptic potency increased from P20 to P28 ( F7 , 120 = 5 . 61 , p < 0 . 01; WT: P20 versus P28 , p < 0 . 01; Fig 5A ) . In contrast , in PSD-93 KO mice , it already reached the high level at P11 , similar to that of WT mice at P28 ( PSD-93 KO P11 versus WT P28 , p = 0 . 52; Fig 5A ) . Furthermore , the success rate of the minimal AMPAR excitatory postsynaptic currents ( EPSCs ) increased during development ( two-way ANOVA: F3 , 127 = 27 . 49 , p < 0 . 001; Fig 5B ) , and was higher in PSD-93 KO mice than in WT mice ( F1 , 127 = 4 . 054 , p < 0 . 05; Fig 5B ) . We also assessed the potency and success rate in shLC , sh93 , and dKO/KD neurons . At P28 , both the potency and success rate were similar ( potency: F2 , 38 = 0 . 22 , p = 0 . 80; success: F2 , 38 = 0 . 15 , p = 0 . 86 , Fig 5C and 5D ) . Together , these results indicate that the decrease of the fraction of silent synapses is at least partially mediated by an increase in mature synapses in all WT , PSD-93–lacking , and PSD-93/95–lacking neurons , and that in PSD-93 KO mice , maturation already starts before eye opening , while in WT mice , it is restricted to the critical period after P20 . While the minimal stimulation assay of L4 to layer 2/3 pyramidal cells allowed us to assess the maturation state of synapses from single or few stimulated axons , it did not reveal whether loss of PSD-93 affected the total number of synaptic connections onto layer 2/3 pyramidal neurons . To address this question , we measured miniature ( m ) EPSCs from layer 2/3 pyramidal neurons at P24 , a time point at which the difference of the fraction of silent synapses between WT and PSD-93 KO mice was high ( Fig 1 ) . The mEPSC amplitude distribution and its average value was similar between WT and PSD-93 KO mice ( Kolmogorov-Smirnov [KS] test: p = 0 . 84; t = 0 . 532 , p = 0 . 60; Fig 5F ) , indicating that the synaptic strength of individual AMPA receptor–positive synapses was similar . However , the mEPSC frequency was higher in PSD-93 KO mice than that in WT mice ( KS test: p < 0 . 01; t = 2 . 65 , p < 0 . 05; Fig 5G ) . Although the mEPSC amplitude revealed no changes in the average AMPA receptor function per synapse sampled over all layer 2/3 pyramidal neuron synapses in PSD-93 KO mice , we had not yet ruled out a selective change in the L4 onto layer 2/3 pyramidal cell synapses . The substitution of Sr2+ for Ca2+ in the artificial cerebrospinal fluid desynchronizes synaptic vesicle release , so that individual quantal responses can be analyzed in the stimulated synaptic pathway to specifically assess quantal size for the L4 to layer 2/3 synapses [41] . The amplitude of these evoked mEPSCs was similar in WT and PSD-93 KO mice ( KS test: p = 0 . 90; t = 0 . 29 , p = 0 . 78; S4B and S4C Fig ) , indicating no contribution of the quantal response , such as synaptic potentiation , to the increase of the unitary response in PSD-93 KO mice . Notably , the amplitude of the evoked mEPSCs was similar to that of spontaneous mEPSCs ( t = 0 . 055 , WT mEPSC versus WT evoked mEPSC , p = 0 . 96 ) , indicating that the quantal size of the L4-to-L2/3 synaptic connection is similar to that of the average connection onto L2/3 pyramidal neurons . Similar to the mEPSC inter-event intervals , the inter-event intervals of the evoked mEPSCs were smaller in PSD-93 KO compared with that of WT mice ( WT , 355 . 9 ± 46 . 1 ms; PSD-93 KO , 221 . 6 ± 28 . 7 ms; t = 2 . 47 , p < 0 . 05 ) . While mEPSC frequency is a measure for the number of AMPAR-positive synapses , it is not a specific assay and can be influenced by synaptic vesicle fusion propensity , often referred to as release probability . To test whether the increase of mEPSC frequency was solely due to the increased number of matured silent synapses , we performed additional experiments . We previously reported that the number of AMPARs and NMDARs was unaltered in the PSD fraction of adult PSD-93 KO mice [42] . It was thus unlikely that an increase in synapse number caused the increase in mEPSC frequency ( see also Fig 6 ) . To reveal whether changes in release probability contributed to the increase , we performed two additional electrophysiological assays . The paired pulse ratio of two shortly spaced synaptic responses is a sensitive measure of release probability [43 , 44] . At three different interstimulus time intervals , paired pulse ratios were similar in WT and PSD-93 KO mice ( two-way ANOVA: time interval , F2 , 59 = 9 . 29 , p < 0 . 01; genotype , F1 , 59 = 0 . 33 , p = 0 . 57; Fig 5H and 5I ) . In the second test , we performed the use-dependent NMDAR blocking assay with the open channel NMDAR blocker MK-801 . This assay is based on the progressive decrease of NMDAR-mediated EPSCs , which is directly influenced by the presynaptic release probability [45] . Consistent with the results in the paired pulse ratio measurement , the time course of NMDAR EPSC blockade in WT and PSD-93 KO mice was similar ( two-way ANOVA: genotype , F1 , 1040 = 1 . 01 , p = 0 . 32; sweep number , F49 , 1040 = 71 . 89 , p < 0 . 01; interaction , F49 , 1040 = 0 . 29 , p = 1; S4A Fig ) . Together , these additional electrophysiological results reveal that loss of PSD-93 did not significantly affect release probability , and the increase in mEPSC frequency was primarily mediated by the increased number of AMPAR-positive synapses , i . e . , a lower fraction of silent synapses . A previous study reported an increase in the ratio of AMPAR/NMDAR EPSCs in the cortex of PSD-93 KO mice [25] . These studies concluded that NMDAR function was selectively decreased in PSD-93 KO mice . We thus analyzed NMDAR EPSCs in L4 onto layer 2/3 pyramidal cell synapses in detail . The ratio of AMPAR/NMDAR EPSCs was increased in PSD-93 KO mice compared with that of WT mice ( Mann-Whitney [MW] test: p < 0 . 01; S4F and S4G Fig ) . This change could be caused by an increase in AMPAR EPSC amplitudes , e . g . , by an increase of AMPAR-positive synapses and/or a decrease in NMDAR EPSC amplitudes . To compare the size of NMDAR EPSCs between the genotypes , we measured their sizes by minimal stimulation that allows the comparison of absolute values across recordings from different slices . The NMDAR EPSC amplitudes in WT and PSD-93 KO mice were similar ( t = 0 . 15 , p = 0 . 88; Fig 5J and 5K ) , as were the success rates ( t = 1 . 56 , p = 0 . 13; Fig 5L ) . Together with the unchanged release probability , these results indicate a similar connectivity between individual L4 star pyramids and layer 2/3 pyramidal neurons in the two genotypes . In conclusion , silent synapses mature precociously in the visual cortex of PSD-93 KO mice , while synapse density and NMDAR EPSCs were not affected in the critical period . Because the mEPSC frequency provided a good estimate of the relative changes of AMPAR-positive synapses , we used this assay to test PSD-93/95 dKO mice . At P24 , the mEPSC frequency was similar to that of WT mice ( KS test: p = 0 . 24; t = 0 . 28 , p = 0 . 78; Fig 5N ) , corroborating our result from the silent synapse measurement and indicating that in the dKO mice , the number of synapses and the fraction of silent synapses was similar to that of WT mice . However , in dKO mice , the mEPSC amplitude was reduced compared with WT mice ( KS test: p < 0 . 01; t = 5 . 59 , p < 0 . 01; Fig 5M ) . Thus , maturation of silent synapses lacking both paralogs resulted in a reduced number of AMPARs in individual synapses . For comparison , we also assessed the AMPAR-positive synapses in PSD-95 KO mice . In the hippocampus of PSD-95 KO mice , the mEPSC amplitude is unchanged and the frequency reduced [24] . In visual cortical layer 2/3 pyramidal neurons , both the frequency and the amplitude were reduced compared with WT mice ( KS test: amplitude , p < 0 . 01; frequency , p < 0 . 01; S4D and S4E Fig ) , indicating that the impairment of silent synapse maturation may cause the reduction in AMPAR numbers . Collectively , these results show that silent synapses mature in the absence of both PSD-93 and PSD-95 , but PSD-95 is required for the normal mature functional state , shown by WT-like AMPAR content ( mEPSC amplitude ) in individual synapses . PSD-93 counterbalances PSD-95 to drive synapses in the mature state and thus regulates the experience-dependent component of the maturation . PSD-93 and PSD-95 are abundant proteins at glutamatergic synapses [46 , 47] . While some studies report a colocalization at single PSDs of rat brain or at about 90% synaptic puncta in primary neuron cultures [48] , other studies report a distribution of PSD-93 and PSD-95 to different synapses in the CA1 region of the hippocampus [23 , 49] . To assess the potential colocalization of PSD-93 and PSD-95 in mouse visual cortex , we decorated semi-thin slices with antibodies against PSD-93 , PSD-95 , and the presynaptic active zone marker Mammalian uncoordinated 13–1 ( Munc13-1 ) to specifically identify synaptic puncta [50] . Immunofluorescence of PSD-93 and PSD-95 was punctate and overlapped about 95% at puncta associated with a presynaptic marker ( Fig 6A–6C ) , indicating that these two paralogs are predominantly colocalized at the same synapses . The specificity of the PSD-93 or PSD-95 labeling was validated with semi-thin slices from the corresponding KO mice ( S5 Fig ) . While in PSD-95 KO mice , the signal for PSD-95 was diminished to background levels , in PSD-93 KO mice , some—but <10% of the number of puncta compared with WT or PSD-95 KO slices—synaptic puncta were still stained by the PSD-93 antibody . However , these puncta are not PSD-93 , as no residual PSD-93 is detectable in western blots of cortical protein extracts [19 , 34 , 42] . Nevertheless , a small fraction of the PSD-93–positive puncta in WT slices might originate from antibody cross-reactivity without changing our overall conclusion of colocalization of PSD-93 and PSD-95 in individual visual cortical synapses . The slice thickness of 500 nm minimizes confounding staining of synapses that may overlap in different layers in thick slices . Notably , some puncta were only labeled for Munc13-1 and thus likely represent inhibitory synapses . We assessed the density of excitatory synapses by counting the puncta with Munc13-1/PSD-95 colocalization or Munc13-1/PSD-93 colocalization . Consistent with the high degree of colocalization of PSD-93 and PSD-95 , excitatory synapse density was similar , about 0 . 4 per μm3 , for both approaches . In PSD-95 KO mice , the PSD-93–based synapse density was similar to that of WT mice ( p = 0 . 99; Fig 6D ) . Similarly , in PSD-93 KO mice , the PSD-95–based synapse density was similar to that of WT mice ( p = 0 . 095; Fig 6E ) . Thus , consistent with the electrophysiological assessments , the number of excitatory synapses was not changed in either PSD-93 or PSD-95 KO mice at P40 . To test for protein alterations in the synaptic composition of PSD-93 KO mice , we isolated crude synaptosomal fractions from the visual cortex of approximately P28 mice . The protein levels of PSD-95 , Munc-13 , and glutamate receptor subunits ( Glu ) were not significantly altered ( Fig 6F ) . However , synapse-associated protein ( SAP ) 102 levels were increased in PSD-93 KO mice ( t = −2 . 60 , p < 0 . 05; Fig 6F ) . Notably , in PSD-95 KO mice , the early expressed paralog SAP102 is also increased [8 , 51] , indicating that in both single-KO mice , SAP102 levels were increased . In the visual cortex , PSD-95 protein levels increase temporally in parallel to silent synapse maturation after eye opening [8] . To test whether PSD-93 levels are also correlated with silent synapse maturation , we measured the level of PSD-93 together with other PSD/synaptic proteins in crude synaptosomal fractions during normal standard cage rearing . Similar to PSD-95 , PSD-93 protein levels increased from low levels before eye opening to plateau already at adult levels during the critical period ( P38 , two-factor ANOVA , age: F6 , 57 = 145 , p < 0 . 001; Fig 6G and 6H ) . Similarly , SAP97 protein levels increased steeply after eye opening throughout the critical period . The developmental increase of PSD-93 was shifted to younger ages and thus increased relatively faster than PSD-95 ( two-factor ANOVA , genotype: F1 , 57 = 120 , p < 0 . 01; interaction age and genotype: F6 , 57 = 6 . 74 , p < 0 . 01; Fig 6H ) . These results reveal that before eye opening , only small amounts of PSD-93 and PSD-95 are expressed in the visual cortex , further corroborating our result that the fraction of silent synapses at P4 was similar in WT , PSD-93 KO , and PSD-93/95 dKO mice ( Figs 1 and S1 ) . The developmental profile of SAP102 was different . Its protein levels peaked after eye opening and progressively decreased during the critical period ( Fig 6G and 6H ) . A group of proteins exhibited a similar developmental pattern , including GluN2B , GluN3A , and ras-related protein Rab3B ( S6 Fig ) [8] , indicating a common role of these proteins in an immature state of synapses [42 , 52] . Another group of proteins that exhibited a similar developmental pattern as PSD-95 , particularly a preferential increase during the critical period , included the voltage-gated potassium channel subunit ( Kv ) 1 . 1 and the vesicular glutamate transporter ( vGluT ) 1 , while most other synaptic proteins increased to adult levels before the onset of the critical period ( S6 Fig ) [8] . Next , we tested whether the increase of PSD-93 and PSD-95 protein levels in the visual cortex were visual experience dependent . PSD-95 protein levels exhibited similar increases throughout development between DR and NR mice ( t = −1 . 30: p = 0 . 20; Fig 6I ) , indicating that the developmental increase of PSD-95 protein levels is independent of visual experience . In contrast , PSD-93 protein levels were higher in DR mice than in NR mice ( t = −2 . 32: p < 0 . 05; Fig 6I ) , revealing differences in the regulation of PSD-93 and PSD-95 protein levels by visual experience . Confirming previous results [53] , dark rearing changed the NMDAR subunit composition towards a decreased GluN2A/GluN2B ratio ( MW: NR , 1 . 06 ± 0 . 12 versus DR , 0 . 72 . 2 ± 0 . 051 , p < 0 . 01 ) . Other tested synaptic protein levels were similar in NR and DR mice . In DR PSD-93 KO mice , SAP102 protein levels were increased , whereas the GluN2A/GluN2B ratio was similar compared with that in NR WT mice ( SAP102: t = −2 . 36 , p < 0 . 05; GluN2A/GluN2B ratio: MW , p = 0 . 13; S6 Fig ) . As dark rearing did not affect silent synapse maturation in PSD-93 KO mice and it progressed faster ( Fig 2 ) , these results indicate that SAP102 protein levels are regulated by PSD-93 or PSD-95 , while the GluN2A/GluN2B ratio is implicated in silent synapse maturation . In summary , these results reveal that PSD-93 and PSD-95 are predominantly expressed together in individual excitatory synapses in the visual cortex . Despite the halted silent synapse maturation in the visual cortex in DR mice ( Fig 2 ) [27] , PSD-95 protein levels increased . This result seemingly disconnects the PSD-95–level increase from silent synapse maturation and hence the well-established correlation of PSD-95 protein levels and excitatory synaptic strength [23 , 54 , 55] . However , the increase of PSD-93 and its inhibiting function in silent synapse maturation offers a potential mechanism: during normal development , PSD-93 prevents PSD-95–promoted silent synapse maturation by counteracting PSD-95 . Our results are based so far on PSD-93 or PSD-95 loss of function . To test whether ( 1 ) the converse , the gain of function , also governs the time course of silent synapse maturation and ( 2 ) which of the six PSD-93 and two PSD-95 N-terminal isoforms can mediate this , we chose the following isoforms based on previous reports . In organotypic hippocampal slice cultures , PSD-95α overexpression accelerates silent synapse maturation [56] , whereas PSD-93α2 overexpression reduces AMPAR EPSCs [19] . For the in vivo manipulation , we expressed the PSD-95α and PSD-93α2 isoforms with AAVs and stereotaxically injected them at P0–P1 into the visual cortex of WT mice . Overexpression of PSD-93α2 increased the fraction of silent synapses at P28 compared with green fluorescent protein ( GFP ) -expressing pyramidal neurons ( t = 4 . 02 , p < 0 . 01; Fig 7A–7C ) , indicating that a gain of function of the α2 isoform of PSD-93 is sufficient to oppose silent synapse maturation . In contrast , overexpression of PSD-95α decreased the fraction of silent synapses at P11 compared with GFP-expressing pyramidal neurons ( t = 5 . 32 , p < 0 . 01; Fig 7D–7F ) , indicating that expressing PSD-95α before eye opening is sufficient to induce silent synapse maturation to reach to the adult level precociously . Thus , gain of PSD-93 or PSD-95 function affected silent synapse maturation in opposite directions , echoing the results from the loss of function approaches . Using a molecular replacement approach , we expressed PSD-93α2 in the background of PSD-93 KO . In PSD-93α2–expressing neurons at P28 , the fraction of silent synapses was higher compared with that in GFP-expressing neurons ( t = 4 . 84 , p < 0 . 01; Fig 7G–7I ) . Thus , the PSD-93α2 isoform alone was sufficient to prevent precocious silent synapse maturation in PSD-93 KO mice . The increase of PSD-93 protein levels in DR mice indicated that a gain of PSD-93 function opposes silent synapse maturation ( Fig 6 ) . Furthermore , PSD-93 and PSD-95 are primarily expressed in the same synapses ( Fig 6 ) . Together , these results imply that PSD-93 opposes PSD-95 during silent synapse maturation in the same functional pathway . To directly test this hypothesis , we performed an epistasis experiment , combining PSD-95 KD with PSD-93α2 overexpression . The rationale was that if both proteins function through independent mechanistic pathways , the effects of the combined interrogation should be additive . However , in sh95 + PSD-93α2–expressing neurons at P28 , the fraction of silent synapses was not further increased compared with that of PSD-95 KO mice , but was higher compared with that of WT mice ( F2 , 42 = 8 . 493 , p < 0 . 01; sh95 + PSD-93α2 versus PSD-95 KO , p = 0 . 48; sh95 + PSD-93α2 versus WT , p < 0 . 01; Fig 7J and 7K ) . A lack of increase was unlikely caused by the ceiling effect at about 50% silent synapses , considering the fraction of silent synapses at 80% in P4 mice ( Fig 1 ) . Thus , PSD-93 opposes the PSD-95–dependent maturation of silent synapses through the same mechanistic pathway . Our results so far reveal opposing functions of PSD-93 and PSD-95 in both silent synapse maturation and the timing of the critical period for jODP ( Figs 1 , 3 and 7 ) [8] . We next tested whether the balanced function of PSD-95 and PSD-93 on silent synapse maturation is required for neural network refinement during critical periods to optimize visual capabilities . We used the visual water task ( VWT ) , a visual discrimination task based on reinforcement learning [57] . In this test , mice were trained in a trapezoid basin filled with shallow water ( Fig 8A ) to distinguish a vertical sine wave grating from an isoluminant gray stimulus to measure visual acuity ( Fig 8B ) . The sinusoidal grating was randomly presented on either the left or the right monitor at the wide end of the pool and rewarded with an invisible escape platform below the water surface to enforce swimming towards the stimulus . After the mice learned to swim to the rewarded stimulus , the spatial frequency of the grating was gradually increased to test the mice’s visual acuity limit . Visual acuity is not altered in PSD-95 KO mice [8] . In WT , PSD-93 KO and PSD-93/95 dKO mice , the number of training blocks required to learn the task was similar between the three genotypes ( Mantel-Cox; χ2 ( 2 ) = 2 . 92 , p = 0 . 23; Fig 8C ) , indicating that learning of the discrimination task was not significantly compromised in these mice . PSD-93 KO mice had a similar visual acuity as WT mice ( F2 , 20 = 11 . 49 , p < 0 . 01; WT versus PSD-93 KO , p = 0 . 98; Fig 8D ) . In PSD-93/95 dKO mice , however , visual capabilities were severely compromised ( WT versus dKO , p < 0 . 01; Fig 8D ) . These results reveal that while silent synapses mature in the absence of PSD-93 and PSD-95 , the refinement to acquire and improve vision critically depends on PSD-93 or PSD-95 , a result consistent with the mechanistic difference in silent synapse maturation in the absence of the two paralogs compared with WT mice ( Figs 2 and 5 ) . Notably , this was a sensory impairment , as the dKO mice learned the task similarly to WT mice ( Fig 8C ) , indicating that learning per se was not significantly impaired in the dKO mice , but rather , their visual acuity was impaired . As for voluntary physical exercise , dKO mice exhibited greatly reduced running wheel use and rearing in the cylinder test ( run: t = 7 . 58 , p < 0 . 01; cylinder: t = 3 . 72 , p < 0 . 01; S7 Fig ) . Despite this , these dKO mice were not significantly compromised in performing the reinforced exercise in the VWT . To test whether the visual cortex of PSD-93/95 dKO mice was similarly organized to that of WT mice , we visualized V1 activity and retinotopic maps using intrinsic signal optical imaging . We stimulated the contralateral eye with either horizontally or vertically moving bars ( S8A and S8D Fig ) . V1 activation for elevation ( WT versus dKO , p = 0 . 051; S8G Fig ) and retinotopic map quality ( map scatter ) ( elevation: WT versus dKO , p = 0 . 11; azimuth: WT versus dKO , p = 0 . 39; S8H and S8J Fig ) were similar in dKO and WT mice . However , V1 activation for azimuth was slightly increased in dKO mice ( WT versus dKO , p < 0 . 05; S8I Fig ) . Collectively , these results indicate that basic visual activation of V1 was not severely altered in the absence of PSD-93 and PSD-95 . The expression of both PSD-93 and PSD-95 in the visual cortex increased during the critical period and was rather low before eye opening ( Fig 6 ) . This expression time course indicates a specific role in developmental plasticity and neural network refinement during the critical period . To test this hypothesis , we analyzed an innate behavior , the defensive response to looming visual stimuli ( Fig 8E ) [58] . Both WT and dKO mice responded similarly to the looming visual stimuli ( t = −1; p = 0 . 42; Fig 8F ) . Together , these and our previous results reveal that in both single PSD-93 KO and PSD-95 KO mice , visual acuity is similar to that of WT mice [8] . In contrast , visual acuity was severely impaired in the dKO mice , while their innate response to a looming object was similar to WT mice . Visual information is segregated into distinct features and represented in the visual cortex in feature-tuned neurons [59] . The percept of the original image is computed by feature integration through interconnected brain areas . To assess the role of silent synapse-based neural network refinement for improving visual perception , we used a variant of the VWT . We trained mice on discriminating gratings of different orientations . Initially , animals learned to discriminate vertical from horizontal gratings ( Fig 8G ) . Again , the rewarded stimulus was presented randomly on the left or right monitor to avoid location bias . After learning the task , in which WT , PSD-95 KO , and PSD-93 KO mice needed a similar number of training blocks ( Mantel-Cox; χ2 ( 2 ) = 2 . 76 , p = 0 . 25; Fig 8H ) , the orientation of the nonrewarded stimulus was successively altered in 5° steps towards the orientation of the rewarded stimulus . The orientation discrimination was reached once the success rate of the mice to make the correct choice fell below 70% , a criterion also used in the task for visual acuity . Both PSD-93 and PSD-95 KO mice exhibited impaired orientation discrimination and required a larger angular difference to perceive the rewarded stripes correctly ( F2 , 15 = 18 . 59 , p < 0 . 01; PSD-95 KO versus WT , p < 0 . 01; PSD-93 KO versus WT , p < 0 . 05; Fig 8H and 8I ) . These results reveal that both PSD-93 and PSD-95 are required to achieve optimal orientation discrimination abilities , likely by the cooperative function of these proteins to achieve the proper pace of silent synapse-based neural network refinement .
The DLG-MAGUKs , including the paralogs PSD-93 and PSD-95 , constitute signaling scaffolds of the PSD , which govern receptor signaling events in the PSD by assembling receptors with signaling enzymes and the effector proteins [2 , 60 , 61] . Similar to other paralogs , the DLG-MAGUK paralogs and their isoforms diversified from their ancestor gene Dlg in invertebrates to specialize for the complex demands in the vertebrate nervous system [3 , 19 , 54] . Among these , the palmitoylated α-isoforms regulate the number of synaptic AMPA receptors [23 , 54] . However , here , we show that PSD-93α2 inhibited PSD-95α in promoting synaptic AMPA receptor incorporation . More specifically , their cooperative function primarily regulates the unsilencing of AMPA silent synapses and not AMPAR numbers per synapse in general . These conclusions are based on the following results: the opposite phenotypes on silent synapse maturation in the respective KO mice ( Figs 1 and 3 ) [8] , the opposite phenotypes with gain of function by overexpression of PSD-95α versus PSD-93α2 ( Fig 7 ) , and loss and gain of PSD-95 or PSD-93 function affects primarily mEPSC frequency , rather than mEPSC amplitude ( Figs 5 and S4 ) [19 , 23 , 24 , 26 , 56] . Furthermore , we show that PSD-93 and PSD-95 largely colocalize in the same synapse ( Fig 6 ) , as well as , in an epistasis test by combining PSD-95 KD with PSD-93α2 overexpression , the effect of increasing the fraction of silent synapses was not additive ( Fig 7 ) . These results support a model in which PSD-95α promotes the unsilencing of immature nascent synapses during developmental critical periods . The unsilenced mature state is maintained by the continuous presence of PSD-95 , as loss of PSD-95 after maturation reinstates juvenile numbers of silent synapses [8] . PSD-93α2 inhibits this maturation by directly competing with PSD-95 in the same synapse . Notably , in DR mice , PSD-95 levels increased similarly to NR mice , but silent synapse levels remained high ( Figs 2 and 6 ) . In contrast , in DR mice , PSD-93 protein levels were increased above that in NR mice , a result supporting its active role in inhibiting PSD-95’s promoting effect on silent synapse maturation by direct competition . Similarly , PSD-93 protein levels increased earlier than PSD-95 protein levels after eye opening ( Fig 6 ) . Thus , the concerted expression of PSD-93 is critical to pace the maturation of silent synapses , and its levels are primarily adjusted during experience-dependent maturation . Are PSD-93 and PSD-95 slots for AMPARs , as earlier reports suggested [62–65] ? For PSD-93 , our results are inconsistent with such a function , as with loss of PSD-93 , synaptic AMPAR numbers in unsilenced synapses increased ( Figs 1 and 2 ) . Similarly , for PSD-95 , such a function is unlikely , based on results both from this study and previous reports . In DR mice , PSD-95 protein levels increased , while the fraction of silent synapses remained high ( Fig 6 ) . This result seems at odds with the well-established correlation of PSD-95 protein levels and excitatory synaptic strength [23 , 54] . However , this result is in agreement with our model of opposing functions of PSD-93 and PSD-95 , in which the balanced action of both proteins determines silent synapse maturation . Furthermore , in PSD-93/95 dKO mice , the fraction of silent synapses decreased with a similar pace after eye opening until late critical period , as in WT mice ( Fig 2 ) . Thus , just for the process of AMPAR synaptic incorporation , neither PSD-93 nor PSD-95 was required during this developmental time window . However , in the absence of these two paralogs , the mechanism of unsilencing differed and visual acuity was impaired , potentially by disrupting synaptic integrity or by differences in the synaptic state [42 , 63] . A previous report shows that the loss of PSD-93 and PSD-95 in the hippocampus produces additive decreases in AMPAR EPSCs [23] . We think that the apparent difference in result is primarily due to the different developmental time points studied . Given the mechanistic difference in silent synapse maturation between WT and PSD-93/95 dKO mice , our results do not exclude that the trajectories of silent synapse maturation between WT and PSD-93/95 dKO mice separate at a later developmental time point , and together with the reduced quantal size in PSD-93/95 dKO mice , result in reduced AMPAR EPSCs , as reported previously ( Fig 5 ) [23] . Finally , LTP is enhanced in PSD-95 KO mice [24 , 26 , 66] . This result appears inconsistent with a slot function of PSD-95 but is consistent with PSD-95 regulating silent synapse maturation , as in PSD-95 KO mice , more silent synapses exist , which likely serve as substrates for LTP , lower the induction threshold , and increase LTP magnitude [26 , 35–38] . Collectively , these results are inconsistent with an AMPAR slot function of the DLG-MAGUKs . It should be noted that the DLG-MAGUKs execute functions additional to AMPAR regulation , as they outnumber AMPARs in the PSD more than 3-fold , they bind to other receptors and cell adhesion proteins , and loss of their function results in severe defects of the synaptic structure [63 , 67–69] . Therefore , interpretations of the results of multiple KO mice are complex . Importantly , loss of PSD-95 resulted in a 100% penetrant phenotype of loss of experience-dependent maturation of silent synapses after eye opening ( Fig 1 ) , so the interpretation of the results here were not complicated by redundancy or compensatory mechanisms , as the other DLG-MAGUK isoform , SAP97α , with a similar function to PSD-95α , is not expressed in detectable amounts [8 , 54] . SAP102 protein levels peaked at eye opening in the visual cortex and progressively decreased , while PSD-93 and PSD-95 protein levels increased ( Fig 6 ) . In both PSD-93 and PSD-95 KO mice , SAP102 protein levels are higher ( Fig 6 ) [42 , 51] . These results are consistent with SAP102’s function as a signaling scaffold in regulating early synapse development and with SAP102 being replaced by PSD-93 and PSD-95 once synapses mature . Furthermore , because SAP102 protein levels are increased in both PSD-93 and PSD-95 KO mice , SAP102 is unlikely to account for the opposite effect on silent synapse maturation in PSD-93 and PSD-95 KO mice ( Figs 1 and 2 ) . This notion is further supported by previous reports showing a function of SAP102 in early development , primarily on NMDAR function and with limited effect on silent synapse numbers [52 , 70 , 71] . Our results support a direct competition of PSD-93 and PSD-95 in single synapses to either inhibit or promote silent synapse maturation , respectively . Both proteins are part of a big protein complex of the PSD , while SAP102 , in contrast , is part of another , smaller protein complex [72 , 73] . These two complexes might represent different functional transmission sites in the synapse . Based on the developmental profile of SAP102 versus PSD-93 and -95 ( Fig 6 ) , the abundance of either complex changes during development from relatively more SAP102 complexes to relatively more PSD-93/95 complexes . The ratio of PSD-93 and -95 will then govern the stable incorporation of AMPARs . Our results show that dark rearing increases PSD-93 but does not affect the protein levels of PSD-95 ( Fig 6 ) . Furthermore , eye opening induces the translocation of PSD-95 into dendritic compartments [74] . Thus , both proteins are regulated by visual activity: the protein levels of PSD-93 and the dendritic localization of PSD-95 . Consequently , the ratio of both proteins in the synapse might be controlled by both mechanisms . The DLG-MAGUKs act as signaling scaffolds to transduce a receptor signal through signaling proteins onto effectors [60 , 61] . To exert the opposing function , PSD-93 and -95 may interact with different receptors , signaling proteins and/or effectors . Consistent with this hypothesis , PSD-95 binds directly to the signaling proteins striatal enriched protein tyrosine phosphatase ( STEP ) 61 and Rous sarcoma virus oncogene tyrosine kinase family proteins Src , Lyn , and Yes , while PSD-93 associates preferentially with the Src family kinase Fyn [75–77] . Future studies will need to reveal whether these or other proteins contribute to the opposing function of the two signaling scaffolds PSD-93 and -95 . Critical periods are time windows of heightened neuronal plasticity , during which intrinsically connected cortical neural networks are refined by experience to optimize their functional output [9 , 10] . This plasticity is sharpened by local inhibitory circuits [78–81] and is limited by so-called plasticity brakes [82–86] . However , instructive changes in neural networks occur largely at excitatory synapses on principal neurons , which form the “memory engram” of refinements [87–89] . During early cortex maturation , silent synapses are abundant [35 , 38 , 90] and serve as synaptic substrates for experience-dependent neural network refinements [27 , 91 , 92] . We previously hypothesized a link between the instructive process of silent synapse-based cortical network refinement and the duration of critical periods [8] . In support of our hypothesis , we found that accelerated silent synapse maturation in PSD-93 KO mice precociously terminated the critical period of the juvenile form of ODP in the visual cortex ( Fig 3 ) . Together with our previous observations that lack of silent synapse maturation prevents the closure of the critical period [8] , our current results reveal both a forward and a reverse correlation between progressive silent synapse maturation and the closure of the critical period . Notably , if silent synapses are reinstated after the closure of the critical period by knocking down PSD-95 in the adult visual cortex , both juvenile numbers of silent synapses and juvenile-like ODP are reinstated [8] . Furthermore , both silent synapse maturation and the duration of the critical period are extended by dark rearing ( Figs 2 and 3 ) [27 , 93 , 94] . Further supporting our conceptual model of silent synapse–based critical period closure , dark rearing had no effect on silent synapse maturation in PSD-93 KO mice that matured precociously and concurrently terminated the critical period earlier ( Figs 2 and 3 ) . The strict correlation between the time course of silent synapse maturation and the duration of the juvenile form of ODP implies that silent synapses serve as the instructive substrates for refinement during critical periods: once they decline , critical periods end . A similarly strict correlation is not consistently detected for the proposed inhibitory tone-based ending of the critical period . First , in PSD-95 KO mice , the inhibitory tone of parvalbumin-positive interneurons in V1 developed similarly to that in WT mice [8] . Nevertheless , jODP persisted lifelong in PSD-95 KO mice , indicating that a low inhibitory tone is not a prerequisite for jODP . Second , pharmacological increase of the inhibitory tone in PSD-95 KO mice in vivo does not prevent ODP [8] . Third , some studies detect an effect of dark rearing on γ-aminobutyric acid ( GABA ) -mediated transmission [95] , while others do not [96] . Fourth , visual experience–dependent increases in brain-derived neurotrophic factor ( BDNF ) expression are prevented by dark rearing [97] . While transgenic expression of BDNF normalizes the delayed maturation of GABAergic inhibition by dark rearing and the closure of critical periods [94] , BDNF is also critical for maturing silent synapses [37] . Thus , it is not clear whether the BDNF effect on critical periods was through inhibitory neurons or silent synapses . More likely , local inhibition and silent synapses act in concert to refine excitatory synapses of principal neurons , but the decline of silent synapses marks the end of the critical period . When assessing how silent synapse maturation affects vision , we observed dissociable consequences on different aspects of vision ( Fig 8 ) . First , loss of PSD-93 or PSD-95 , resulting in either accelerated or impaired silent synapse maturation , respectively , did not impair visual acuity . However , loss of both PSD-93 and PSD-95 , resulting paradoxically in a time course of silent synapse maturation more similar to WT mice , impaired visual acuity . Second , orientation discrimination was impaired in both PSD-93 and PSD-95 KO mice , i . e . , independent of whether silent synapse maturation was accelerated or impaired . These results indicate that visual acuity is not dependent on proper silent synapse maturation , while visual perception is . Importantly , both visual acuity and orientation discrimination were tested with a similar reinforcement training ( VWT ) , indicating that the selective impairment of orientation discrimination is likely based on perceptual impairments rather than other cortical functions , including learning and decision-making . Consistent with our results , previous studies in mice show that dark rearing , and thus preventing silent synapse maturation , does not prevent the maturation of visual acuity [33 , 98] . In contrast , binocular matching of orientation tuning and binocular vision require visual experience during critical periods [28 , 99–101] . Thus far , the cellular mechanisms for developing these features remained elusive . Our results now start to unravel the mechanistic differences . Silent synapses represent synaptic opportunities , which are consolidated into mature synaptic connections between principal neurons through experience-dependent processes [27 , 91 , 92] . We extend this concept by showing that a properly paced maturation is required to refine network function for receptive field integration to perceive orientation differences properly ( Fig 8 ) . The underlying mechanism of this silent synapse–based refinement is likely to alter the synaptic connection pattern and thus only occurs during critical periods when silent synapses are abundant . Which connection matures and is consolidated is of utmost importance , as it will impact the overall visual performance . As such , the maturation process is finely regulated from multiple angles , exemplified by the balanced cooperated regulations between PSD-93 and PSD-95 . As a consequence , in both PSD-93 and PSD-95 KO mice , the regulation of silent synapse maturation is impaired , and the nonrefined synaptic connection pattern results in impaired orientation discrimination . The opposing function of PSD-93 and PSD-95 in regulating silent synapse maturation fundamentally changes the current conceptual framework of scaffolding proteins in glutamate receptor complexes . The DLG-MAGUKs , SAP90/PSD-95–associated proteins ( SAPAPs ) , and Src homology domain and multiple Ankyrin repeat domains proteins ( Shank ) form a three-layered protein network attached to glutamate receptors and cell adhesion proteins [63 , 102–106] . Mutations in the genes of several components of this complex , especially Shank3 and Neuroligin 3 and 4 , are linked to several genetic-associated neurodevelopmental disorders , in particular , ASD and schizophrenia [107–110] . PSD-95 is associated with a specific ASD-related syndrome , Williams syndrome [20 , 111] . Remarkably , in affected individuals , the developmental improvement of orientation discrimination , but not all other visual features , is halted at an immature childhood level , echoing our findings in PSD-95 KO mice [112] . Although it has long been known that components of the glutamate receptor complex and its syndrome-associated paralogs are critical for developmental glutamatergic synapse maturation [113 , 114] , its role in developmental silent synapse maturation remains elusive . So far , a role in silent synapse maturation has been reported only for SAPAP3 , Shank2 , PSD-93 , and PSD-95 ( Fig 1 ) [8 , 70 , 115 , 116] . Critical periods for language skills and social interactions coincide with the typical onset of ASD in children , and critical periods for higher cognitive functions precede the typical time of clinical manifestation of schizophrenia in young adults . Furthermore , besides the characteristic symptoms of impaired social and language skills in autism and cognitive deficits in schizophrenia , sensory defects are also typical [12–14] . Thus , it is conceivable that scaffolding components of the glutamate receptor complex more generally contribute to silent synapse maturation and/or neural network refinement , and that this process is also critical for the pathogenesis of these neurodevelopmental disorders . As such , the visual perception defect as a consequence of PSD-93 or PSD-95–based developmental impairments can serve as a proxy what may go wrong in other cortical areas , as well . Consistent with this notion , we found similar maturational defects of silent synapses in the nucleus accumbens , hippocampus , and mPFC ( Fig 1 ) [70] . Furthermore , cognitive impairments reminiscent of defects in ASD or schizophrenia were described in PSD-93 and -95 KO mice [3 , 117] . The involvement of PSD-93 in neurodevelopmental disorders might not be limited to the familial forms but also extend to idiopathic forms , because newly generated mutations causing schizophrenia are associated with dlg2 , the gene for PSD-93 , which appears to be highly susceptible for spontaneous somatic mutations [20–22] . In terms of the direction of synaptic alterations , be it precocious maturation or lack of maturation , future studies need to focus on whether PSD-93– or PSD-95–associated complexes are impaired to predict the direction of the maturational defects that cause alterations during neurodevelopment .
Experiments were approved by the Institutional Animal Care and Use Committee ( IACUC ) of the University of Pittsburgh ( #18063191 and 2 ) and the Lower Saxony State Office for Consumer Protection and Food Safety . PSD-93 KO or PSD-95 KO mice and littermate controls were generated from heterozygous breeding pairs from a mixed 129SV/C57Bl6 background [8 , 34 , 70 , 118] . PSD-93/95 dKO mice were generated from PSD-93 KO and PSD-95 heterozygous breeding pairs . Mice were group housed , two to five per standard cage ( 33 cm × 17 cm ) , under a 12-h light/dark cycle with controlled temperature and humidity and were provided food and water ad libitum . All procedures were performed during the light cycle . For dark rearing , pregnant females were transferred to standard cages placed into a light-tight Scantainer ( Scanbur Technology , Denmark ) in a completely light-tight darkroom , with food and cage changes performed under red light illumination . Mice were killed under isoflurane anesthesia by decapitation . Coronal visual cortical , medial prefrontal cortical , or hippocampal slices ( 300 μm ) of different age groups ( P4 ± 1 , P11 ± 1 , P20 ± 1 , P25 ± 4 , P28 ± 2 ) of mice of either sex were sliced with a vibratome in ice-cold sucrose ( in mM: sucrose 168 , NaCl 25 , KCl 1 . 9 , MgSO4 10 , NaHCO3 26 , NaH2PO4 1 . 2 , D-glucose 25 ) or NMDG cutting buffer ( in mM: NMDG/HCl 135 , KCl 1 , MgCl2 1 . 5 , Choline HCO3 20 , KH2PO4 1 . 2 , D-glucose 10 , CaCl2 0 . 5 ) [8] . Slices were recovered at 35°C for 20 min in standard artificial cerebrospinal fluid ( ACSF ) ( in mM: NaCl 119 , NaHCO3 26 , D-glucose 20 , KCl 2 . 5 , NaH2PO4 1 , MgSO4 1 . 3 , CaCl2 2 . 5 , saturated with carbogen , 95% O2 , and 5% CO2 ) and then stored in carbogenated ACSF at room temperature until further use ( 1–7 h ) . Standard whole-cell voltage-clamp recordings were carried out at 30 ± 2°C in a recording chamber continuously ( 2 mL/min ) perfused with ACSF . L2/3 or CA1 pyramidal neurons were visually identified with infrared-differential interference contrast microscopy . Glass pipettes ( 3–5 MΩ ) were filled with Cs-based internal solution ( in mM: CsMeSO3 133 , HEPES 10 , TEA-OH 10 , EGTA 0 . 25 , D-glucose 10 , MgCl2 2 , QX314-Cl 5 , Na-ATP 4 , Na-GTP 0 . 3 , pH 7 ) or ( in mM: Cs-gluconate 120 , HEPES 20 , EGTA 0 . 4 , NaCl 2 . 8 , TEA-Cl 5 , Mg-ATP 4 , Na-GTP 0 . 3 , pH 7 . 2 ) . The input and series resistance were monitored throughout the recording by applying a short hyperpolarizing voltage step before synaptic stimulation . Only cells with a series resistance smaller than 30 MΩ and changes of series and input resistance of less than 20% were used for analysis . Axons were stimulated in L4 with theta-glass bipolar electrodes filled with ACSF . Data were filtered at 3 kHz and collected with custom routines in Igor ( Wavemetrics ) , using an ELC-03XS amplifier ( NPI ) and digitized at 10 kHz with an ITC-18 ( HEKA ) . Glutamatergic transmission was isolated pharmacologically with 50 μM picrotoxin-supplemented ACSF and polysynaptic activity prevented in AMPA/NMDA ratio recordings with 1 μM 2-Chloroadenosine . Ten micromolar 2 , 3-dihydroxy-6-nitro-7-sulfamoyl-benzo[f]quinoxaline ( NBQX ) was supplemented to block AMPA receptors or 1 μM tetrodotoxin ( TTX ) to record mEPSCs [19] . For mEPSCs , 400 events of each cell were recorded , sorted by mEPSC amplitude size or inter-event interval time , and binned in 20 bins . Averages of bin values from different cells were plotted as cumulative probability plots . The stimulation strength was adjusted such that successes and failures of AMPA receptor responses at a holding potential ( Vh ) = −60 mV could be clearly visually identified . At a Vh = +40 mV , we then measured a composite response mediated by AMPA and NMDA receptors . Percent silent synapses were calculated using the equation 1 − ln ( F−60 ) /ln ( F+40 ) , in which F–60 was the failure rate at −60 mV and F+40 was the failure rate at +40 mV [35 , 90] . As the failure rate is additionally dependent on the release probability , the stochastic nature of synaptic vesicle release will create variability in the failure rates at either Vh , which may lead to mathematically negative fractions of silent synapses in individual trials . Whether synapses lacking NMDA receptors also contribute to the failure rate , as they exist , e . g . , in Purkinje cells , is not known [119] . To generate sh93 , we cloned the shRNA expression cassette from the lentiviral vector sh93 ms into the AAV vector AAV-sh95 to obtain pA_tn93b_CAGW [8 , 19] . An AAV with an shRNA against luciferase or with an expression cassette for EGFP only was used as a control for shRNA expression or AAV transduction , respectively [8] . To generate the AAV overexpressing PSD-95α , GFP-tagged PSD-95 was cloned from a lentiviral vector into the AAV vector under the CAG promoter to obtain pA_EB_CAGW_p95GFP [19] . To generate the AAV overexpressing PSD-93α2 , the cDNA was cloned from a lentiviral vector into a dual promotor AAV vector , a gift from Dr . Yingxi Lin ( Addgene 84474 ) [19 , 120] . The promoters were replaced by the mouse RNA polymerase II promoter to drive red fluorescent protein ( RFP ) expression and mouse CaMKIIα promoter to drive PSD-93 expression , named pA_R3A_Kp93a2 or pA_R3Ash95_Kp93a2 , with , additionally , an miR30-based shRNA targeting PSD-95 in the 3′ UTR of the RFP . AAVs were produced based on described procedures , pseudo-typed with the capsid for AAV8 , and purified by iodixanol gradient centrifugation [8] . A total of 80 nL of concentrated AAV was injected bilaterally into V1 of P0 mice , with surgical procedures as described previously [8] . The stereotactic coordinates for V1 were from lambda for P0: ±1 . 5 mm , +0 . 1 mm , and −0 . 8 mm . Subcellular fractions of a crude synaptosomal pellet were prepared as described previously [42] . Ten 1-mm-diameter punches of the visual cortex from brain slices were homogenized in 10 volumes of homogenization buffer ( 4 mM HEPES/NaOH pH 7 . 4 , 320 mM sucrose ) , and after differential centrifugation , the crude synaptosomal pellet ( P2 ) was resuspended with 1% SDS and adjusted to 1 μg/μL in SDS sample buffer . The protein extracts for the developmental profile were obtained from a previous study [8] . A total of 10–30 μg of protein was separated on Bis-Tris polyacrylamide gels and transferred on nitrocellulose membranes [42] . Protein bands were decorated with the following primary antibodies: PSD-93 ( N18/30 ) , PSD-95 ( K28/43 ) , SAP97 ( K64/15 ) , SAP102 ( N19/2 ) , Kv1 . 1 ( K20/78 ) , mortalin ( N52A/42 ) , GluA2 ( L21/32 ) , GluN2B ( N59/36 ) , vGluT1 ( N28/9 ) , GKAP ( N238/31 ) , CASK ( K56A/50; all mouse from UC Davis/NIH NeuroMab ) , endocannabinoid receptor 1 ( #258003 ) , Munc13-1 ( #126104; rabbit and guinea pig from Synaptic Systems ) , GluN2A ( #05–901 ) , GluA1 ( #ABN241 ) , GluN3A ( #07–356 ) , phospho-S295 PSD-95 ( #04–1066; rabbit from Millipore ) , GluA3 ( EP813Y ) , GluA4 ( EPR2512[2] ) , phospho-S845 GluA1 ( EPR2148; rabbit from Abcam ) , α-Synuclein , Rab3A , Rab3B , Synaptobrevin 2 , Syntaxin 1a , Synaptotagmin 1 , Synapsin 1 , and Synaptopyhsin as described previously [121] . Bands were detected by the secondary antibodies goat anti-mouse Alexa 680 ( Invitrogen ) ; goat anti-rabbit Alexa 680 ( Invitrogen ) ; goat anti-mouse IR800 ( Li-COR Biosciences ) ; and goat anti-rabbit IR800 ( Li-COR Biosciences ) , visualized , and quantified with an infrared fluorescence scanner . Band intensities for each sample and protein were normalized to the average of the control condition on the same blot to obtain a relative amount for each sample , which could be compared across different blots . Using previously described samples [8] , for the developmental profile , band intensities for each sample and protein were normalized to the intensity at P90 . One-millimeter-diameter punches from visual cortex of mouse brain slices ( about 500 μm thick ) were flash frozen in liquid nitrogen–cooled isopentane [50] . Lyophilization of the sample was typically performed in a vacuum of about 10−5 Pa for about 24 h . Samples were infiltrated with EPON resin at 22°C with degassing for 24 h . The resin-embedded samples were cured at 60°C for 24 h . After trimming the tissue block , 0 . 5-μm thin sections were cut with a diamond knife and collected on a glass slide . The EPON resin was removed by successive incubations with 30% Na-methanolate ( in methanol ) for 10 min , 50% xylol ( in methanol ) for 10 min , twice in acetone for 10 min , H2O for 10 min , and PBS for 10 min . Primary and secondary antibody incubations were performed as described [61] . Labeled samples were imaged on a Zeiss LSM 800 confocal microscope with a 63× objective . We acquired 7–10 mages ( 10 . 13 μm × 10 . 13 μm ) for each semi-thin slice and analyzed 3–5 slices per animal . Images were analyzed with Fiji , and puncta-like objects were identified automatically by intensity and size [122] . The threshold of individual object size was set at 200 pixel2 ( about 7 , 200 nm2 , pixel size: 6 nm ) . Images were then processed manually , and fused puncta were segmented manually . Synapses were defined as puncta with overlapping ( >5% ) fluorescence signals of Munc13-1 and PSD-93 or PSD-95 . We used a custom-built version of the looming test [58] to check the defensive response of PSD-93/95 dKO mice to looming visual stimuli . The setup consisted of an acrylic box ( 48 cm × 48 cm × 30 cm ) with a hut ( 20 cm × 12 cm ) in a corner . Bottom , hut , and three walls were opaque; the front wall was translucent . The ceiling LCD monitor ( HP Z22i IPS ) displayed the looming stimuli ( expanding black circles ) as soon as a mouse entered a field in the center of the arena . The visual stimulus imitates an approaching predator: a black disc of 2-degree diameter on white background expands to 20 degrees within 250 ms and persists for another 250 ms , followed by a 500-ms break . Once triggered , the stimulus loops 15 times . Infrared LEDs in the ceiling illuminate the interior of the arena , invisible to the human and rodent eye but recordable , with a camera ( Logitech , c920 HD Pro Webcam; with removed infrared filter ) positioned outside of the setup , filming through the translucent wall . A mouse was placed in the arena and given 10 min to adapt to the new environment . In the subsequent test phase , we recorded whether an animal reacted to the stimulus or not . Each animal was tested maximally three times , only once per day , with at least 1 day of break in between tests . To check basic activity of PSD-93/95 dKO mice , we transferred animals for 24 h to a standard cage ( 22 cm × 37 cm × 15 cm ) equipped with a running wheel [123] , and calculated individual running distances . Each mouse was tested three times , with at least 24 h recovery between two measures . To quantify exploratory behavior and activity , we adapted a cylinder test from the Behavioral and Functional Neuroscience Laboratory of the Stanford School of Medicine ( http://med . stanford . edu/sbfnl/services/bm/sm/CylinderTest . html ) . Briefly , a mouse was placed in a glass cylinder ( diameter = 14 cm , height = 21 cm ) . The forelimb contacts against the wall within 180 s were counted . To avoid conditioning , the test was only performed once per animal . Visual acuity of the mice was assessed using the VWT , a visual discrimination task that is based on reinforcement learning [57 , 124] . For this task , mice were initially trained to distinguish a low spatial frequency vertical sine wave grating ( 0 . 086 cycles/degree ) from equiluminant gray , and then their ability to recognize successively higher spatial frequencies was tested . The apparatus consists of a trapezoidal-shaped water-filled pool with two monitors placed side by side at one end . An escape platform that was invisible to the mice was placed below the monitor , on which the rewarded stimulus ( grating ) was projected . The position of the grating and the platform were alternated in a pseudorandom sequence over the training and test trials . Once 90% accuracy was achieved , the discrimination threshold was determined by increasing the spatial frequency of the grating until performance fell below 70% accuracy . The highest spatial frequency at which 70% accuracy was achieved was taken as the visual acuity threshold . To measure orientation discrimination in mutant mice and WT controls , we used a variation of the published VWT [57 , 124] . Initially , mice were trained to distinguish vertical from horizontal gratings of a low spatial frequency ( 0 . 086 cycles/degree ) , and then their ability to recognize increasingly smaller orientation differences was tested by decreasing the difference in orientation of the two gratings in 5° steps until accuracy fell below 70% accuracy . The smallest orientation difference at which 70% accuracy was achieved was taken as the orientation discrimination threshold . Only mice that learned the respective task ( visual acuity or orientation discrimination ) first were included in the “blocks to learn” analyses , i . e . , our quantification of learning behavior only included naïve mice with respect to VWT training . This was done because learning one of the tasks often resulted in an accelerated learning of a second task [125] . Therefore , numbers of mice for this particular quantification of learning behavior were smaller compared with the two vision tests . The right eye ( contralateral to the recorded hemisphere ) was deprived of vision for four d according to published protocols [30 , 126] . Briefly , mice were anesthetized with 2% isoflurane in 1:1 O2/N2O . Lid margins were trimmed and an antibiotic gel ( gentamicin ) was applied . The eye was closed with two mattress sutures . Mice were checked daily to make sure that the eyes remained closed . After MD , the mice were returned to their home cages . After initial anesthesia with 2% halothane in a mixture of 1:1 O2/N2O , the mice received atropine ( Franz Köhler , 0 . 1 mg/mouse , s . c . ) , dexamethasone ( Ratiopharm , 0 . 2 mg/mouse , s . c . ) and chlorprothixene ( Sigma , 0 . 2 mg/mouse , i . m . ) . In addition , lidocaine ( 2% xylocain jelly ) was applied locally to all incisions . The mice were placed in a stereotaxic frame , their body temperature was maintained at 37°C , and electrocardiograph leads were attached to monitor the heart rate throughout the experiment . Anesthesia was maintained with 0 . 6%–0 . 8% halothane in a mixture of 1:1 O2/N2O applied through a tube over the nose . We incised the skin to expose the visual cortex of the left hemisphere , and low-melting-point agarose ( 2 . 5% in 0 . 9% NaCl ) and a glass coverslip were placed over the exposed area . To avoid dehydration of the mouse during the experiment , we injected 0 . 2 mL saline ( 0 . 9% , s . c . ) . Mouse visual cortical responses were recorded through the skull using the Fourier imaging method developed by Kalatsky and Stryker and optimized for the assessment of OD plasticity by Cang and colleagues [126 , 127] . The experimenter was blinded for the genotype of the recorded mouse . Briefly , a temporally periodic stimulus was continuously presented to the animal and the cortical responses at the stimulus frequency was extracted by Fourier analysis . Optical images of intrinsic cortical signals were obtained using a Dalsa 1M30 CCD camera ( Dalsa , Waterloo , Canada ) controlled by custom software . Using a 135 mm × 50 mm tandem lens configuration ( Nikon , Melville , NY ) , we imaged a cortical area of 6 . 4 × 6 . 4 mm2 . The surface vascular pattern and intrinsic signal images were visualized with illumination wavelengths set by a green ( 550 ± 10 nm ) or red ( 610 ± 10 nm ) interference filter , respectively . After acquisition of a surface image , the camera was focused 600 μm below the cortical surface . An additional red filter was interposed between the brain and the CCD camera . Frames were acquired at a rate of 30 Hz , temporally binned to 7 . 5 Hz , and stored as 512 × 512 pixel images after spatial binning of the camera image . Visual stimuli were displayed on an LCD monitor ( Benq BL240 [LED] , 1 , 920 × 1 , 080 @ 60 Hz ) , positioned 25 cm from the eyes , with the screen center aligned to the animal’s midline . Visual stimuli consisted of drifting horizontal or horizontal bars ( 2° wide ) . For imaging ODP , stimuli were restricted to the binocular visual field of the left V1 ( −5° to +15° azimuth ) , and mice were stimulated through either the left or the right eye in alternation . For visualizing elevation and azimuth maps , we used full-field stimuli extending 94° horizontally and 62° vertically ( see Fig 4 ) and contralateral eye stimulation . Visual cortical maps were calculated from the acquired frames by Fourier analysis to extract the signal at the stimulation frequency using custom software [127] . While the phase component of the signal was used for the calculation of retinotopy , the amplitude component represented the intensity of neuronal activation ( expressed as fractional change in reflectance ×10−4 ) and was used to calculate OD ( for details , see [126 , 128] ) . To quantify OD plasticity , an OD score of each pixel in the binocularly activated region was calculated as ( C−I ) / ( C+I ) , with C and I representing the raw response magnitudes of each pixel to visual stimulation of the contralateral and ipsi , respectively . We then computed an ODI as the average of the OD scores of all responsive pixels . Consequently , ODI ranged from −1 to 1 , with negative values representing ipsi and positive values representing contralateral dominance . We calculated ODIs from blocks of four runs; typically , we obtained at least five ODIs per animal . Experiments with less than three ODIs were discarded from further analyses . All ODIs of one animal were averaged for further quantification and data display . The ODIs were color-coded in a two-dimensional map of the OD scores ( OD-map ) : cold colors represent negative values ( ipsi dominance ) and warm colors represent positive values ( contralateral eye dominance ) . One- or two-factor ANOVA test with Tukey post hoc test was used for comparisons between genotype and developmental time point or groups of more than three , respectively . Two group comparisons were performed with two-tailed t test or MW test for data without normal distribution . Distributions of mEPSC data were compared with the KS test . All intra- and inter-group comparisons of the optical imaging data were done by a two-tailed t test or one-factor ANOVA test with Bonferroni post hoc test . Learning in the VWT was analyzed with Mantel-Cox survival analyses by comparing genotypes on number of blocks required to reach ≥90% accuracy . The levels of significance were set as *p < 0 . 05 , **p < 0 . 01 , ***p < 0 . 001 . Data are represented as means ± SEM .
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During development , there are restricted time windows in which the brain’s cortex is reorganized; these changes depend on experience and serve to establish functionality . In immature brains , silent synapses—newly born synapses that are inactivated due to a lack of normal receptor-mediated signaling—are frequent . Recent studies have suggested that silent synapses help to reorganize the connection pattern between excitatory neurons . However , it remains largely unknown how functions are established during critical periods and what specific cellular processes contribute to these changes . Here , we analyze silent synapses in mice and show that two very similar proteins , postsynaptic density ( PSD ) -93 and PSD-95 , which evolved via duplication of a single ancestral gene , have opposing roles in the conversion of synapses from an immature state to an active state . While PSD-95 promotes maturation , PSD-93 acts as a brake and slows the process . Consequently , in mice lacking PSD-93 , the fraction of silent synapses declines faster , and a critical period of visual cortex development closes too early . Visual acuity is not affected in mice that lack either PSD-93 or PSD-95 , but is severely reduced if mice are deficient of both proteins . In contrast , proper orientation discrimination in adult mice required both PSD-93 and PSD-95 . These results indicate that these two proteins have related and balancing functions that are crucial for normal development of synapses and optimal brain function .
|
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2018
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An opposing function of paralogs in balancing developmental synapse maturation
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Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response . A complementary approach is virtual screening , where chemical libraries can be efficiently screened against protein target ( s ) . Here , we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis . We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer ( MTC ) characterized by a transformation network activated by oncogenic dRetM955T . Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRetM955T-induced transformation . We then combined features from our hits with those of known inhibitors to create a ‘hybrid’ molecule with improved suppression of dRetM955T transformation . Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity .
Protein kinases play a key role in cell signaling and disease networks and represent major therapeutic targets . The limited capacity to test large numbers of compounds to explore diverse chemical scaffolds , coupled with the difficulty in translating in vitro kinase inhibition into whole animal efficacy , has limited the chemical space of the known kinase inhibitors ( KIs ) . As a result , obtaining optimal KIs with clinically relevant therapeutic activity has proven challenging despite extensive academic and industry effort . To expand the number of kinase inhibitors , a variety of platforms have recently emerged as useful tools for compound screening . The fruit fly Drosophila melanogaster provides an inexpensive and efficient whole animal platform for cancer drug screening , capturing clinically relevant compounds [1–3] . For example , Drosophila was used to help validate vandetanib as a useful treatment for medullary thyroid cancer [4] ( MTC ) . As a screening platform , Drosophila offers several advantages: First , flies and humans share similar kinome and kinase-driven signaling pathways [5] , facilitating the use of flies to predict drug response in humans [1 , 6] . Second , the ease of breeding and the short ( ~10 day ) life cycle of Drosophila makes it possible to carry out efficient moderate-throughput chemical screening in a whole animal system . Third , the screening readout provides a quantitative , animal-based measurement of structure-activity relationships ( SAR ) as well as information on the therapeutic potential or toxicity of the tested compounds: measurable parameters include survival and multiple phenotypic indicators that depend on kinase activity . A key limitation of a Drosophila-based moderate-throughput screening platform is its inability to explore very large chemical libraries [7] such as the ZINC library , which has over 750 million purchasable compounds [8] . In contrast , structure-based virtual screening is a fast and inexpensive computational method that can screen large compound libraries , useful for identifying unique chemical probes [9] . If the structure of the protein is unknown , virtual screening can be performed against the homology models of the target based on experimentally determined structures . However , the automated construction of homology models—with sufficient accuracy for simultaneous virtual screening of multiple targets and the application of molecular docking to signaling networks—remains challenging in particular for highly dynamic targets such as kinases [10 , 11] and would benefit from a readily accessible whole animal platform . In this paper , we demonstrate how combining Drosophila and computational approaches provides a synergistic platform for lead compound discovery , combining the strengths of computational methods—which enable rational and rapid drug candidate selection—and a Drosophila animal model that enables fast and relevant biological readouts of tested compounds . We demonstrate the practicality of this approach using MTC as a test case . RET is a receptor tyrosine kinase associated with multiple roles in development and disease . The gain-of-function M918T mutation of RET ( analogous to Drosophila M955T ) activates multiple proliferation pathways and is directly associated with MTC pathogenesis [12 , 13] . Transgenic Drosophila expressing the analogous dRetM955T isoform show key aspects of transformation , including proliferation and aspects of metastasis [6 , 14] . Genetic modifier screens with dRetM955T flies led to the identification of multiple RET pathway genetic ‘suppressors’ and ‘enhancers’ , loci that when reduced in activity improve or worsen the dRetM955T phenotype , respectively . These functional mediators of RET-dependent transformation include members of the Ras/ERK and PI3K pathways as well as regulators of metastasis such as SRC [6 , 15] . Oral administration of the FDA-approved multi-kinase inhibitor analogs sorafenib and regorafenib—along with additional structural analogs—partially rescued dRetM955T-induced transformation in Drosophila [1 , 15] . Sorafenib-class inhibitors are classified as ‘type-II’ KIs that bind the kinase domain in their inactive state [16] , a conformational state regulated by the aspartate-phenylalanine-glycine ( DFG ) -motif ( Fig 1A ) [17 , 18] . In the inactive , ‘DFG-out’ conformational state the directions of DFG-Asp and DFG-Phe ‘flip’ , vacating the DFG-Phe pocket ( ‘DFG-pocket’ ) that modulates binding to type-II inhibitors . A key challenge of targeting kinases in the DFG-out conformation with structure-based virtual screening is that few kinase structures have been reported with the DFG-out conformation [19 , 20] . We recently developed DFGmodel [10] , a computational method for modeling kinases in DFG-out conformations . This method informed the mechanism of clinically relevant multi-kinase inhibitors that target the MTC network [15] . In this study , we report the development of an integrated platform ( Fig 2 ) that combines ( i ) computational modeling of kinases in their inactive state plus massive multi-target virtual screening with ( ii ) whole animal Drosophila assays to identify previously unappreciated chemicals that perturb RET-dependent transformation . This integrative platform combines the strengths of computational methods—including facilitating rational and rapid compound prioritization for experimental testing—and Drosophila models that provide a whole animal readout of compound efficacy . We leverage this integrated fly/computational modeling platform to create a novel ‘hybrid’ molecule with unique chemical structure and biological efficacy . Finally , we discuss the relevance of this approach to expedite the discovery of novel chemical scaffolds targeting disease networks .
In transgenic patched-GAL4;UAS-dRetM955T ( ptc>dRetM955T ) flies , the ptc promoter drives expression of an oncogenic isoform of Drosophila Ret in multiple tissues; the result is lethality prior to adult eclosion [1 , 15] . We previously used this and similar fly MTC models in genetic screens to identify ~40 kinases that mediate dRetM955T–mediated transformation [6 , 15] ( Figs 1B and 2A; S1 Fig ) . To narrow this list , we prioritized candidate kinases based on two considerations: ( i ) pharmacological relevance as known mediators of RET signaling [6 , 21]; ( ii ) structural coverage , specifically kinases with known DFG-out structures or those that can be modeled with sufficient accuracy [10] . Atypical kinases ( e . g . , mTOR and eEF2K ) and members of the RGC family were excluded as they have diverse sequence and structure features that limit our ability to generate accurate homology models . Applying these criteria to our genetic modifier list , we focused on targeting four key kinase targets: RET ( receptor tyrosine kinase ) , SRC ( cytoplasmic tyrosine kinase ) , BRAF ( tyrosine kinase-like ) , and p70-S6K ( AGC family ) . Description of the various conformations adopted by the kinases during activation and inhibition is needed for rationally designing novel , conformation-specific inhibitors . Therefore , our approach was to perform massive structure-based virtual screens of purchasable compound libraries against multiple models with DFG-out conformation; our goal was to identify generic kinase inhibitors that may target one or more prioritized kinases but , importantly , demonstrate an effect on the disease pathway in the animal model . The structure of two of the kinases identified in our dRetM955T genetic screens—BRAF and SRC—have been solved in the DFG-out conformation; the DFG-out structures of RET and p70-S6K have not been reported . We therefore generated DFG-out models using DFGmodel , a computational tool that generates homology models of kinase in DFG-out conformation through multiple-template modeling that samples a range of relevant conformations [10] . In our previous study , we tested and confirmed subsets of DFG-out models that enrich for known type-II inhibitors among a diverse set of non-type-II KIs found in the Protein Data Bank ( PDB ) with accuracy similar to or better than that obtained for experimentally determined structures [10] . For example , in a recent application of DFGmodel , models generated by this method were used in parallel with medicinal chemistry to optimize clinically relevant compounds that are based on the established kinase inhibitor sorafenib [15] . Conversely , in this study models generated by DFGmodel were used to develop compounds outside of the current kinase inhibitor chemical space . To guide the identification of a ‘generic’ kinase inhibitor that can affect a disease pathway we first compared the DFG-out models of each kinase , identifying key similarities and differences in physicochemical properties among their inhibitor-binding sites . First , we noted that the prioritized targets RET , BRAF , p70-S6K , and SRC present negative electrostatic potential on the DFG-pocket surface , while many non-targets such as ERK have positive electrostatic potential ( Fig 3A , S2 Fig ) . This difference may partially explain the partial selectivity of type-II inhibitors ( e . g . , sorafenib ) toward our prioritized targets while avoiding electrostatic positive kinases such as ERK . Second , RET and SRC have large DFG-pocket volumes ( 163 Å3 , 196 Å3 , respectively ) and p70-S6K and BRAF have moderately large pockets ( 158 Å3 , 136 Å3 ) . In contrast , ERK has a small DFG-pocket ( 113 Å3; Fig 3B ) . We used this size difference to computationally select for kinases with larger DFG-pockets ( e . g . , RET , SRC ) while excluding kinases with smaller DFG-pockets ( e . g . , ERK ) . We performed virtual screening against multiple DFG-out models of MTC targets to identify putative small molecules that modulate the disease network ( Fig 2C ) . We docked a purchasable lead-like library from the ZINC database ( 2 . 2 millions compounds; [22] ) against 10 DFG-out models for each kinase target , yielding over 88 million total docking poses . To combine the screening results , a two-step consensus approach was used . In the first step , the top scoring pose of compounds that ranked in the top 10% in 5 or more of the 10 models of each kinase were selected , resulting in approximately 2 , 000 compounds per kinase . In the second step , compounds that ranked in the top 25% in at least 3 of 4 targets were selected , resulting in 247 compounds . For comparison , sorafenib , an inhibitor that rescues ptc>dRetM955T flies , would rank eighth in this consensus docking result . From these consensus compounds , eight commercially available compounds were purchased to test their ability to rescue ptc>dRetM955T flies ( S1 Table ) . These compounds were selected based on their interactions with key elements of the “ensemble” of targets’ binding sites , with the emphasis on the conserved glutamate in αC-helix , the amide backbone of DFG-aspartate , and if present , the amide backbone of the hinge region ( S3 Fig ) . Although the compounds are not predicted to bind optimally to each one of our targets , we hypothesized that these compounds may have an additive effect on the disease pathway , which could be improved with medicinal chemistry . Transgenic ptc>dRetM955T flies express the oncogenic Drosophila dRetM955T isoform in several tissues in the developing fly , leading to aspects of transformation of dRetM955T tissues [6 , 14] . As a result , ptc>dRetM955T flies exhibited 0% adult viability when cultured at 25°C , providing a quantitative ‘rescue-from-lethality’ assay to test drug efficacy [1 , 15] . Compounds were fed at the highest accessible concentrations ( see Experimental Procedures ) . We used sorafenib as a positive control , as it previously demonstrated the highest level of rescue among FDA-approved KIs in ptc>dRetM955T flies [15] . Similar to our previous results , feeding ptc>dRetM955T larvae sorafenib ( 200 μM ) improved overall viability to 3–4% adult survival ( P < 0 . 05 ) . We used this rescue-from-lethality assay to test the efficacy of the eight compounds identified through virtual screening ( Figs 4B and 5B ) . When fed orally , two unique compounds , 1 and 2 ( S2 Table ) , rescued a small fraction of ptc>dRetM955T flies to adulthood ( Figs 4A and 5A ) . 1 and 2 did not affect the body size of ptc>dRetM955T larvae or pupae compared to wild type controls , a metric for comparing food intake . At the maximum final concentration in fly food ( 100 μM ) , 1 rescued 1% ( P < 0 . 05 ) ptc>dRetM955T flies to adulthood as compared to 3–4% rescue by sorafenib at 200 μM ( Fig 4A ) . 1 is characterized by a 3-phenyl- ( 1H ) -1 , 2 , 4-trazole moiety ( Fig 4B ) . 2 , characterized by a 1H-indole-2-carboxamide moiety , improved ptc>dRetM955T fly viability to an average of 1% ( P < 0 . 05 ) when tested at 25–400 μM ( Fig 5A and 5B ) . To validate the chemical scaffolds identified in our initial Drosophila-based chemical genetic screening , we conducted a ligand-based chemical similarity search in the updated ZINC [8] to identify analogs of 1 and 2 . For compound 1 , we retrieved five compounds that share the 3-phenyl- ( 1H ) -1 , 2 , 4-triazole feature and have docking poses similar to 1 . Our Drosophila ptc>dRetM955T viability assay confirmed two compounds as active , 1–1 and 1–2 ( S2 Table; Fig 4A and 4B ) . 1–1 slightly outperformed 1 in ptc>dRetM955T viability assays at similar concentrations ( 4%; P < 0 . 05 ) . Conversely , 1–2 was tested at higher concentrations ( 50 and 200 μM ) but did not result in improved efficacy ( P < 0 . 05 ) . The docking poses of 1–1 and 1–2 resemble the proposed docking pose of 1 ( Fig 4B ) , which has a typical DFG-out-specific , type-II KI binding pose and is predicted to occupy the DFG-pocket with its terminal phenyl moiety . The 1 , 2 , 4-triazole moiety , resembles the urea moiety found in sorafenib ( Fig 1A ) , forms favorable hydrogen bonds with the side chain of the conserved αC-helix glutamate residue and the backbone amide of the DFG-Aspartate . In addition , this series of compounds is smaller and shorter ( MW < 360 ) than the fully developed type-II KIs ( MW > 450 ) such as sorafenib , as they lack an optimized moiety that interacts with the hinge region of the ligand-binding site ( Fig 4C ) . Compound 1–2 is structurally different from 1 and 1–1 and was less effective in rescuing ptc>dRetM955T flies , even though it was tested at higher concentrations ( Fig 4A ) . While 1 and 1–1 have an ( 1H ) -1 , 2 , 4-triazole moiety , 1–2 has an 1 , 2 , 4-oxadiazol-5-amine moiety where the ( 1H ) -nitrogen is replaced by an oxygen . This modification distinguishes 1–2 from 1 and 1–1 in their interaction preference: 1–2 loses a hydrogen bond donor due to the nitrogen-to-oxygen substitution , while the electronegative oxygen introduces an unfavorable electrostatic repulsion to the carboxylate sidechain of the conserved αC-helix glutamate ( Fig 4C , 1–2 insert ) . Co-administering sorafenib with 1 and 1–1 led to synergistic improvement of ptc>dRetM955T fly viability ( Fig 4A ) . Individually , 200 μM of sorafenib and 100 μM of 1 rescued 3% and 1% of ptc>dRetM955T flies to adulthood , respectively . Co-administering the two compounds rescued 6% of ptc>dRetM955T flies to adulthood ( P < 0 . 05 ) . Similarly , co-administering sorafenib and 100 μM of 1–1 rescued 8% of ptc>dRetM955T flies ( P < 0 . 05 ) . In contrast , co-administering 200 μM of sorafenib and 200 μM of 1–2 did not improve fly viability . As 1–2 only weakly rescued ptc>dRetM955T flies and showed no synergy with sorafenib , we did not pursue this hit any further . We examined the kinase inhibition profile ( DiscoverX ) of 1 against a subset of the human protein kinome ( Table 1 ) . At 50 μM , 1 did not appreciably inhibit SRC , BRAF , or S6K1 , while it demonstrated weak activity against wild-type RET and moderate activity against the oncogenic isoform RETM918T . Of note , 1 inhibited other cancer-related targets such as FLT3 ( Table 1 ) , which activates the Ras/ERK signaling pathway [23] . 1 also showed activity against aspects of transformation and metastasis in the fly . In the mature larva , the ptc promoter is active in epithelial cells in a stripe pattern in the midline of the developing wing epithelium ( Fig 4C; wing disc ) . ptc-driven dRet activates multiple signaling pathways , promoting proliferation , epithelial-to-mesenchymal transition ( EMT ) , and invasion of dRetM955T-expressing cells beyond the ptc domain [14] ( Fig 4C ) . Similar to sorafenib , oral administration of 1 blocked the invasion of dRetM955T-expressing cells into the surrounding wing epithelium ( Fig 4B ) . At lower dosage ( 25 μM ) , compound 2 weakly rescued ptc>dRetM955T flies ( 1%; P < 0 . 05 ) ( Fig 5A ) . Unlike 1 , 2 did not act synergistically with sorafenib . This difference was confirmed by the kinase inhibition profile of 2 ( Table 2 ) , in which it has stronger inhibition of RET and RETM918T , but loses the inhibition of FLT3 , two key differences between the kinase inhibition profiles of 1 and 2 . Through a chemical similarity search of the ZINC database , we identified five compounds that share the 1H-indole-2-carboxamide moiety with docking poses similar to that of 2 ( Fig 5B and 5C; S2 Table ) , and confirmed all five analogs increased viability of ptc>dRetM955T flies ( Fig 5A ) albeit with weak efficacy ( some with a P-value above 0 . 05 ) . At low dose ( 10 μM ) , 2–1 showed improved efficacy in rescuing ptc>dRetM955T flies relative to 2 and displayed similar efficacy as sorafenib at 200 μM . However , 2–1 showed poor solubility , limiting its usefulness as a lead compound . 2–3 was also more efficacious than 2 and displayed better solubility in both DMSO and water than 2–1; it also has the N-phenylacetamide moiety as a linker group , a common linker feature found in type-II KIs such as imatinib . Compound 2–3 displayed a different inhibition profile than 1 and 2 ( Table 2 ) : it strongly inhibits FLT3 and PDGFRB , weakly inhibits RET and RETM918T , and does not inhibit SRC . Interestingly , the chemical scaffolds of our newly identified active compounds are not associated with inhibition of protein kinases based on an analysis with SEA search [24] , which relates ligand chemical similarity of ligands to protein pharmacology . Nevertheless , they provided rescue of ptc>dRetM955T flies at a level similar to sorafenib and regorafenib [15] . The docking poses of these compounds suggest a less-than-optimal interaction with the hinge region of protein kinases , a common feature of most KIs . Furthermore , the relatively low molecular weight ( ~350 g/mol ) of these lead-like compounds provides a window for conducting lead optimization with medicinal chemistry . Hence , we sought to improve the efficacy of our computationally derived leads by installing a hinge-binding moiety found in known type-II KIs such as sorafenib . To select the optimal position on our initial hit to conduct fragment exchange with known type-II kinase inhibitors ( sorafenib and AD80 [1] ) , we took into consideration ( i ) the docking poses and phenotypic results of these compounds and ( ii ) the synthetic accessibility and the novelty of the putative hybrid compounds , even if they do not dock optimally to our intended kinase targets . We focused on 2/2-3 due to: 1 ) their 1H-indole moiety docks uniquely into the DFG-pocket and with the potential to interact with the αC-helix glutamate ( Fig 5C ) ; 2 ) their 1H-indole-2-carboxamide moiety resembles the urea linker that is commonly found in type-II KIs such as sorafenib ( Fig 6A; blue box ) ; 3 ) the N-phenylcarboxamide moiety of 2–3 is a common linker between the hinge-binding and the DFG-pocket moieties of type-II KIs , e . g . imatinib ( Fig 6A; grey box ) , while the N- ( piperidin-4-yl ) carboxamide moiety of 2 is not a common linker; 4 ) the docking pose of 2/3’s 1H-indole moiety overlaps with the trifluoromethylphenyl moiety of sorafenib/AD80 . Based on these four criteria , we chose to perform a fragment exchange at the carboxamide position to combine the 1H-indole-2-carboxamide moiety of 2/2-3 with the hinge-binding moiety of sorafenib and of AD80 , a multi-kinase inhibitor that has shown promise in MTC treatment [1] , to create 3 and 4 , respectively ( Fig 6B ) . Oral administration of 3 and 4 to ptc>dRetM955T flies demonstrated that the efficacy of 4 was low with only 3% rescue , while 3 demonstrated strongly improved efficacy with 15% rescue ( Fig 6C; P < 0 . 05 ) ; this level of rescue was significantly higher than the parent compound 2/2-3 or sorafenib . Additionally , 3 suppressed the invasion/migration of dRetM955T-expressing cells in the wing epithelium ( Fig 6D ) , further confirming its efficacy against dRetM955T-mediated phenotypes . The kinase inhibition profile of 3 ( Table 3 ) resembles that of the parent compound 2–3 ( Table 2 ) with at least two notable exceptions: 3 inhibits CSF1R , PDGFRB , and FLT3 , receptor tyrosine kinases and orthologs of Drosophila Pvr that activate the Ras/ERK signaling pathway [25] and play key roles in SRC activation and tumor progression; 3 inhibits Aurora kinases AURKB and AURKC ( Drosophila ortholog aurA or aurB ) . Of note , although 4 did not improve the viability of ptc>dRetM955T flies , it shares chemical similarity to several known type-I½ kinase inhibitors that have the common adenine moiety and a related indole moiety . This group of inhibitors was shown to inhibit other related kinases , increasing our confidence in the relevance of this chemical space for kinase pathway modulation [26] .
This study demonstrates the utility of an integrated platform that combines Drosophila genetics , computational structural biology , and synthetic chemistry to enrich for the discovery of useful chemical tools in an established Drosophila MTC model ( Fig 2 ) . We have previously shown that Drosophila can provide a unique entry point for drug development by capturing subtle structural changes in lead compounds that are often missed by cellular or biochemical assays [15] . Here we refine this approach by iteratively combining experimental testing with computational modeling . A key strength of the integrated approach is its ability to rapidly derive candidates from a large , purchasable chemical library via virtual screening to test chemically unique compounds with our fly models in a cost-effective manner . This platform allowed us to quickly confirm the in situ relevance of active chemotypes through iterations of computational modeling , synthetic chemistry , and phenotypic testing in the fly . We expect this integrated pipeline is generally applicable to kinase networks associated with other diseases [7] . DFGmodel is a recently developed computational tool that generates models of kinases in their inactive , DFG-out conformation for rational design of type-II KIs [10] . In a recent study , models generated by DFGmodel were used to guide the optimization of the drug sorafenib to better target a new disease space [15] . Here , we demonstrate a successful application of DFGmodel to explore compounds that are not appreciated as kinase inhibitors . For each kinase target , DFGmodel uses multiple experimentally determined structures as modeling templates and generates multiple homology models . Thus , this method samples a large fraction of the DFG-out conformational space during the model construction , which enables us to account for the flexibility of the binding site during virtual screening [27] . Notably , DFG-out models capture key features that are important for protein-ligand interactions in multiple kinases simultaneously , providing a framework for rationalizing activity of known inhibitors and developing unique KIs that target a signaling pathway . For example , our results suggest that the electrostatic potential within the DFG-pocket is a key feature for inhibitor selectivity: ERK has an inverse electrostatic potential in the DFG-pocket than that of our target kinases RET and BRAF ( Fig 3 ) , which may explain the insensitivity of ERK toward inhibitors such as sorafenib . Although used in the clinics for MTC , sorafenib ( and its close analog regorafenib ) show limited efficacy in MTC patients; this poor activity is mirrored in the ptc>dRetM955T fly model , which was rescued 3–4% at 200 μM [15] . Despite considerable academic and industry effort , the known chemical space of kinase inhibitors is limited [7] . For example , sorafenib and regorafenib differ in only one non-hydrogen atom . Through structure-based virtual screening against multiple kinase targets in the MTC pathway , we discovered chemically unique compounds ( S2 Table ) with an ability to rescue ptc>dRetM955T viability that is similar to sorafenib , an FDA-approved KI ( Figs 4 and 5 ) . Importantly , our data indicates that these compounds act through key cancer networks . For example , compounds 1 , 2 , 2–3 and 3 all have shown the ability to suppress invasion of ptc>dRetM955T cells in the wing epithelium . Previous work demonstrated that dRetM955T-mediated wing cell invasion is controlled by SRC [15 , 28] , which acts by controlling E-cadherin and Matrix Metalloproteases ( MMPs ) . Of note , 1 , 2 , 2–3 and 3 each demonstrated significant activity against orthologs of Drosophila Pvr , a key regulator of Src: all show significant activity against human FLT3 , while 3 shows additional activity against Pvr orthologs CSF1R and PDGFRB . In addition to being orthologs of Pvr , FLT3 , CSF1R , and PDGFRB similarly can activate SRC [29] . We propose that this activity against regulators of SRC account for the ability of 1 , 2 , 2–3 and 3 to suppress invasion , a key first step in tumor metastasis . Other activities , for example , 3’s inhibition of Aurora kinases—required for proliferation during tumor progression [30]—likely also contributes . Indeed , AURK inhibitors are known to be active against MTC [31 , 32] and synergy between AURKs and FLT3 is currently being explored clinically through a number of dual-AURKB/FLT3 inhibitors [33 , 34] . Although the new tool compounds 1 and 2 are not themselves sufficiently potent to serve as therapeutic leads , they reveal diverse fragment-like pharmacophores that serve as starting points for an exploration of new chemical space . These pharmacophores can be further optimized by combining with well-developed chemotypes that are known to interact with kinase binding sites ( e . g . , the hinge binding region ) to form more efficacious chemical probes [35]; this provides a key second step towards building effective compounds . For example , 2 and 2–3 include an 1H-indole moiety capable of occupying the DFG-pocket of protein kinases from different families and a carboxamide group commonly found in type-II KIs ( Fig 6A ) . Guided by the docking poses of these compounds , the 1H-indole-2-carboxamide group was combined with an optimized hinge-binding moiety from sorafenib , to form a significantly more efficacious compound ( i . e . , 3 ) . As indicated in the kinase inhibition profile of 3 ( Table 3 ) , it shares part of the target set of its constituents 2 and 2–3 . Despite its promise , our approach has several limitations . The computational modeling does not take into account conformational changes modulated by inter- or intra-molecular interactions between the kinase domain and binding partners ( e . g . , SH2/SH3 domains , scaffolding proteins ) , as well as the differential propensity among kinase domains to adopt DFG-out states [36] . This weakness is partly mitigated through careful template selection for model building as well as docking of the small molecules to multiple models representing different conformers . Second , although kinases are closely conserved between humans and Drosophila , fly models have some differences with human disease networks including lacking an adaptive immune system . They lack some relevant target organs ( e . g . , thyroid , breast , prostate , pancreas ) ; in these cases we focused on developing eye and wing epithelia , which provide ‘generic’ polarized epithelia that can give biological and pharmacological results in Drosophila that have proven translatable to mammals as we previously reported [1 , 15 , 37–39] . Despite these limitations , flies offer a useful genetic and pharmacological toolkit which can facilitate drug development for cancer as we show in this study . Future studies will include testing the compounds discovered in this study on mammalian models including human MTC cell lines and mouse xenografts [15] . In summary , we demonstrate the potential of combining chemical modeling with Drosophila genetics to rapidly and efficiently explore novel chemical space . This provides an accessible and cost-effective platform that can be applied to a broad palette of diseases that can be modeled in Drosophila . Combining the strengths of these two high-throughput approaches opens the opportunity to develop novel tool and lead compounds that are effective in the context of the whole animal .
Models of kinase targets ( human RET , SRC , BRAF , S6K1 ) in the DFG-out conformation were generated using DFGmodel [10] . Briefly , the method takes a DFG-in structure or the sequence of the protein kinase as input . DFG-model relies on a manually curated alignment between the target kinase and multiple structures representing unique DFG-out conformations . It calls on the structure-based sequence alignment function of T_COFFEE/Expresso [40] v11 . 00 . 8 to perform sequence alignment of the kinase catalytic domain to the templates , followed by the multi-template function of MODELLER [41] v9 . 14 to generate 50 homology models covering a range of conformations . For each kinase 10 DFG-out models with largest binding site volume , as calculated by POVME [42] v2 . 0 , were used for further study . These ensembles of DFG-out models of our targets BRAF , p70-S6K , RET , and SRC have been evaluated and confirmed to enrich known type-II inhibitors over non-ligands derived from kinase-inhibitor complexes found in PDB in our previous study [10] . The area-under-curve ( AUC ) of virtual screening performance of our targets BRAF , RET , and SRC DFG-out models are 87 . 7 , 82 . 8 , and 76 . 8 , respectively , which correspond to at least 4- to 5-fold increase in the enrichment accuracy over randomly selected ligands in a known sample set [43–45] . This ensemble of models provides an approximation of the binding site flexibility , as well as optimizes the binding site for protein-ligand complementarity and structure-based virtual screening [11 , 27 , 43] . Initial virtual screening utilized the ZINC12 [22] “available now” lead-like chemical library ( downloaded in 2013 , 2 . 2 million compounds ) . A maximum of 300 conformers for each compound were generated with OMEGA . Each conformer was docked with FRED using the default settings [40][41] . The top scoring pose was used for further analysis . For each of the four targeted kinases ( RET , BRAF , SRC , p70-S6K ) , the ensemble of 10 DFG-out models was used for screening . Each compound was docked against 10 models of each kinase target , resulting in 10 docking poses per each kinase-compound pair . The results were filtered by selecting compounds that rank in the top 10% in at least five models per kinase-compound pair , represented by the best scoring pose . This filtering process was done for all four targets . Next , compounds that scored in the top 25% in at least 3 of 4 the kinase ensemble models were collated into a final set of 247 compounds . These consensus best-scoring ligands , representing 0 . 0112% of the library , were visually inspected to remove molecules with energetically unfavorable or strained conformations , or with reactive functional groups that may interfere with assays [46] , which are commonly observed in large virtual screenings . Eight compounds , all top-100 ranking among 247 candidates ( S1 Table ) , were selected based on their interactions with the models ( DFG-pocket occupancy , hydrogen-bond to conserved amino acids , etc ) and chemical novelty and were purchased for Drosophila viability screening . Analogs 1–1 , 1–2 , 2–1 to 2–5 , and others ( S2 Table ) were identified based on the structure of compounds 1 and 2 through the chemical similarity search function available in ZINC15 [8] and SciFinder using the default setting and Tanimoto coefficient above 70% similarity . These compounds are commercially available through vendors such as ChemBridge and Enamine . For synthetic procedures and characterization data related to compounds 1 , 3 , and 4 , please see supplementary materials . Kinase inhibition profile of the compounds was assessed at 50 μM through commercially available kinase profiling services ( DiscoverX ) . Human orthologs of Drosophila genes were predicted by DIOPT ( http://www . flyrnai . org/cgi-bin/DRSC_orthologs . pl ) . The multiple endocrine neoplasia ( MEN ) type 2B mutant form of Drosophila Ret carries the M955T mutation ( dRetM955T ) , which corresponds to the M918T mutation found in human MTC patients . The ptc-gal4 , UAS-GFP; UAS-dRetM955T/SM5 ( tub-gal80 ) -TM6B transgenic flies were prepared according to standard protocols [15] . In these flies , the tubulin promoter drives GAL80 , a suppressor of GAL4 , to repress dRetM955T expression . We crossed them with w- flies to obtain ptc>dRetM955T flies that lost GAL80 allele , which derepressed dRetM955T expression ( S1A Fig ) . Transgenic ptc>RetM955T flies were calibrated to have 0% survival when raised at 25°C , which allowed for drug screening ( S1B Fig ) . We employed dominant modifier screening [15] using the ptc-gal4 , UAS-GFP; UAS-dRetM955T/SM5 ( tub-gal80 ) -TM6B to screen for fly kinase genes that affected the dRetM955T-induced lethality in flies when heterozygous ( ptc>RetM955T;kinase-/+ ) . Genes that improved or reduced survival of ptc>dRetM955T flies when heterozygous were designated as genetic ‘suppressors’ or ‘enhancers’ , respectively ( Fig 1B ) . Suppressors are candidate therapeutic targets that when inhibited may reduce tumor progression . Stock solutions of the test compounds were created by dissolving the compound in DMSO at the maximum concentration . The stock solutions were diluted by 1 , 000-fold or more and mixed with semi-defined fly medium ( Bloomington Drosophila Stock Center ) to make drug-infused food ( 0 . 1% final DMSO concentration; maximum tolerable dose in flies ) . Approximately 100 ptc>dRetM955T embryos alongside with wild-type ( +;+/SM5tubgal80-TM6B ) flies were raised until adulthood on drug-infused food for 13 days at 25°C . The numbers of empty pupal cases ( P in S1B Fig ) and that of surviving adults ( A ) were used to determine percentage of viability , while their body size , which is affected by food intake , temperature , and humidity , were compared to vehicle-treated groups to standardize the experimental conditions . For toxicity test , the viability of the wild-type flies raised alongside with the ptc>dRetM955T flies in the same vials was examined . None of the tested compounds show detrimental effect in the control flies , showing > 90% viability at any doses tested for the compounds equivalent to vehicle . Third-instar ptc>dRetM955T larvae were dissected , and developing wing discs were collected , fixed with 4% paraformaldehyde in PBS , and whole-mounted . At least 10 wing discs were analyzed for each treatment . Invasive GFP-labeled dRetM955T-expressing cells were visualized by their green pseudocolor under a confocal microscope . The apical and the virtual z-series views of the wing disc were examined to identify abnormal tissue growth and dRetM955T-expressing cells migrating beyond the ptc domain boundary .
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Effective and safe treatment of multigenic diseases often involves drugs that address multiple points along disease networks , i . e . , polypharmacology . Polypharmacology is increasingly appreciated as a potentially desirable property of kinase drugs . However , most known drugs that interact with multiple targets have been identified as such by chance and most polypharmacological compounds are not chemically unique , resembling structures of known kinase inhibitors . The fruit fly Drosophila provides an inexpensive , rapid , quantitative , whole animal screening platform that has the potential to complement computational approaches . We present a chemical genetics approach that efficiently combines Drosophila with structural prediction and virtual screening , creating a unique discovery platform . We demonstrate the utility of our approach by developing useful small molecules targeting a kinase network in a Drosophila model of Medullary Thyroid Cancer ( MTC ) driven by oncogenic dRetM955T .
|
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2019
|
Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network
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The composition and structure of microbial communities associated with mosquitoes remain poorly understood despite their important role in host biology and potential to be harnessed as novel strategies for mosquito-borne disease control . We employed MiSeq sequencing of the 16S rRNA gene amplicons to characterize the bacterial flora of field-collected populations of Aedes mcintoshi and Aedes ochraceus , the primary vectors of Rift Valley fever ( RVF ) virus in Kenya . Proteobacteria ( 53 . 5% ) , Firmicutes ( 22 . 0% ) and Actinobacteria ( 10 . 0% ) were the most abundant bacterial phyla accounting for 85 . 5% of the total sequences . Non-metric multi-dimensional scaling plots based on Bray-Curtis dissimilarities revealed a clear grouping of the samples by mosquito species , indicating that the two mosquito species harbored distinct microbial communities . Microbial diversity , richness and composition was strongly influenced by the site of mosquito collection and overall , Ae . ochraceus had significantly higher microbial diversity and richness than Ae . mcintoshi . Our findings suggest that host species and site of collection are important determinants of bacterial community composition and diversity in RVF virus vectors and these differences likely contribute to the spatio-temporal transmission dynamics of RVF virus .
Rift Valley fever ( RVF ) is a mosquito-borne zoonotic disease caused by RVF virus ( Bunyaviridae: Phlebovirus ) . Over the last several decades , the virus has caused periodic epizootics and epidemics in Africa , which have been associated with severe economic and nutritional impacts [1] . Due to its devastating effects and significant potential for international spread , RVFV is listed as a select agent with bioterrorism potential [2] . Efforts to prevent RVF are mainly devoted towards development of vaccines . Currently , there are no licensed RVF vaccines for use in humans [3] and those that are commercially available for use in livestock face many challenges that affect their effective utilization . These challenges include safety concerns , induction of abortion , fetal malformation in pregnant ewes , stillbirths , need for annual revaccination or application of multiple doses and the inability to differentiate naturally infected animals from vaccinated animals [3–5] . Moreover , the highly susceptible RVF hosts in greater need of vaccination such as sheep and goats have high population turn-over rates , limiting the maintenance of herd immunity especially in pastoralist areas [6] . The lack of safe and effective vaccines for medical and veterinary use underscores the urgent need for alternative strategies for RVF control particularly those targeting the vectors . Mosquitoes serve as hosts for diverse microscopic life-forms including viruses , bacteria , fungi , and protozoa that are collectively referred to as microbiota . Bacteria are the best-studied component of mosquito microbiota , with some members known to inhibit transmission of mosquito-borne pathogens and to contribute essential functions in mosquito survival , development , and reproduction [7–13] . In addition , certain bacterial symbionts can be genetically transformed to secrete anti-pathogen molecules within the vector [14 , 15] . These findings have stimulated interest in the development of a novel vector-borne disease control strategy based on the application of microbial symbionts to reduce vector competence and suppress vector populations [16] . Although significant progress has been made towards the application of microbes such as Wolbachia for mosquito-borne disease control , we still lack the basic understanding of the natural microbial communities associated with diverse mosquito species of medical and veterinary significance . For example , while a great wealth of knowledge is available on microbial communities of mosquito vectors of malaria , dengue , Zika , Yellow fever , and West Nile virus [17–20] , the microbiota of RVFV vectors remain poorly understood . The lack of this knowledge has limited our ability to understand the influence of mosquito microbiota on RVF transmission and their potential application in RVF disease prevention and control . The objective of this study was to characterize the microbial communities of Aedes mcintoshi and Aedes ochraceus , the primary vectors of RVFV in Kenya [21 , 22] . Aedes mcintoshi and Ae . ochraceus are flood water mosquito species belonging to the subgenus Neomelaniconion and Aedimorphus , respectively . Large populations of the two mosquito species occur in major hot spots for RVF in Kenya [23] and RVFV has been isolated in pools of both mosquito species during outbreak periods [24] . Inter-epidemic maintenance of RVFV through transovarial transmission has also been documented in Ae . mcintoshi [25] . As primary vectors , these mosquito species have been reported to exhibit distinct genetic population structure and demographic patterns in relation to variable occurrence and outbreak patterns of RVF in different ecologies of Kenya [22] . To accomplish our objectives , mosquitoes were collected from four study sites and their microbial composition characterized through MiSeq sequencing of the 16S rRNA gene . Our results reveal that the two mosquito species have distinct microbial communities and that Ae . ochraceus has significantly higher microbial diversity and richness than Ae . mcintoshi . These results provide critical insights into the composition and structure of microbial communities of important disease vectors and may guide the identification of bacterial species that could be harnessed for symbiotic control of RVF virus .
Adult females Ae . mcintoshi and Ae . ochraceus were sampled using CDC miniature light traps ( John W . Hock Company , Model 512 ) baited with dry ice . Collections were made during the rainy season between November 2015 and June 2016 from four sites ( Korisa , Masalani and Fafi in northeastern Kenya and Ahero in western Kenya ) where the two mosquito species are known to occur [22 , 26] ( Fig 1 ) . The sites were selected as part of an on-going project monitoring the inter-epidemic circulation of RVF in these communities . Collections were conducted for two consecutive nights per site with traps operated between 1800 hours and 0600 hours . Trapped mosquitoes were anesthetized for 2 min using triethylamine before sorting and transporting in liquid nitrogen to the laboratory of the International Centre of Insect Physiology and Ecology ( icipe ) , Duduville Campus , for storage at -80°C until further processing . Identification of Ae . mcintoshi and Ae . ochraceus was established with the aid of published taxonomic keys [27 , 28] . The number of samples processed per site are shown in Table 1 . Adult females that were not visibly blood-engorged were processed for microbial analysis . Prior to genomic DNA extraction , adult female mosquitoes were surface sterilized by rinsing them in 2% bleach solution for 1 min , followed by 70% ethanol for 5 min and then three rinses in sterile phosphate-buffered saline solution each lasting 10 sec . Genomic DNA was extracted from individual mosquitoes using the Qiagen DNeasy Blood and Tissue Kit ( Qiagen , GmbH Hilden , Germany ) following the manufacturer’s recommendations and stored at -20°C until further processing . In total , 153 mosquito samples from two mosquito species , Ae . mcintoshi ( n = 70 ) and Ae . ochraceus ( n = 83 ) were processed ( Table 1 ) . A similar sample size has been used in previous studies on mosquito microbiota [18 , 29] . All DNA samples were shipped to the W . M Keck Center for Comparative and Functional Genomics at the University of Illinois for sequencing using Illumina MiSeq Bulk V3 platform . The PCR amplification and sequencing procedures targeting the V3-V5 hypervariable region of bacterial 16S rRNA gene were performed as described in our previous reports [19 , 20 , 30] . In brief , all DNA samples were measured on a Qubit ( Life Technologies ) using High Sensitivity DNA kit and diluted to 2 ng/μl . PCR master mix was prepared using the Roche High Fidelity Start Kit and 20x Access Array loading reagent and aliquoted into 48 well PCR plates along with 1 μl DNA sample and 1 μl Fluidigm Illumina linkers ( V3-V5-F357: ACACTGACGACATGGTTCTACA and V3-V5-R926:TACGGTAGCAGAGACTTG-GTCT ) with unique barcode . In a separate plate , 20x solutions of the following primer pairs: forward 5`-CCTACGGGAGGCAGCAG-3`and reverse 5`-CCGTCAATTCMTTTRAGT-3`were prepared by adding 2 μl of forward and reverse primer , 5 μl of 20x Access Array Loading Reagent and water to a final volume of 100 μl . A 4 μl aliquot of sample was loaded in the sample inlets and 4 μl of primer ( 20x ) loaded in primer inlets of a previously primed Fluidigm 48 . 48 Access Array IFC , and samples on the array were then amplified on the Fluidigm Biomark HD PCR machine using the following Access Array cycling program without imaging: 50°C for 2 min ( 1 cycle ) , 70°C for 20 min ( 1 cycle ) , 95°C for 10 min ( 1 cycle ) , followed by 10 cycles at 95°C for 15 sec , 60°C for 30 sec , and 72°C for 1 min , 2 cycles at 95°C for 15 sec , 80°C for 30 sec , 60°C for 30 seconds , and 72°C for 1 min , 8 cycles at 95°C for 15 sec , 60°C for 30 sec , and 72° for 1 min , 2 cycles at 95°C for 15 sec , 80°C for 30 sec , 60°C for 30 sec , and 72°C for 1 min , 8 cycles at 95°C for 15 sec , 60°C for 30 sec , and 72°C for 1 min , and 5 cycles at 95°C for 15 sec , 80°C for 30 sec , 60°C for 30 sec , and 72°C for 1 min . The PCR product was transferred to a new 96 well plate , quantified on a Qubit fluorimeter ( Thermo-Fisher ) and stored at -20°C . All samples were run on a Fragment Analyzer ( Advanced Analytics , Ames , IA ) and amplicon regions and expected sizes confirmed . Samples were then pooled in equal amounts according to product concentration . The pooled products were size selected on a 2% agarose E-gel ( Life Technologies ) and extracted from the isolated gel slice with QIAquick gel extraction kit ( QIAGEN ) . Cleaned size selected products were run on an Agilent Bioanalyzer to confirm appropriate profile and determination of average size . The final library pool was spiked with 10% non-indexed PhiX control library ( Illumina ) and sequenced using Illumina MiSeq V3 Bulk system . The libraries were sequenced from both ends of the molecules to a total read length of 300nt from each end . Cluster density was 964k/mm2 with 85 . 9% of clusters passing filter . De-multiplexed FASTQ-formatted files obtained from the sequencing facility were processed using IM-TORNADO 2 . 0 . 3 . 2 platform which is specifically designed to process non-overlapping reads [31] . A detailed description of how raw reads were processed prior to OTUs assignment is provided in our previous report [20] . In brief , forward and reverse primers were removed prior to quality trimming , discarding reads with less than 150 base pairs . R1 and R2 reads were joined by the IM-TORNADO workflow . Sequences were clustered at 97% sequence similarity to generate operational taxonomic units ( OTUs ) , and then representative sequences were classified taxonomically using the Ribosomal Database Project ( RDP ) version 10 as the reference set and mothur v 1 . 28 [32] , with a threshold of 60% bootstrap confidence [31 , 33 , 34] . All data were analyzed using R version 3 . 3 . 2 , and PAST 3 . 20 statistical packages . There were marked variations in the number of bacterial sequences between mosquito samples ( Mean ± SE = 27 , 796 . 01 ± 2 , 229 . 73 per mosquito , minimum = 5 , maximum = 164 , 228 ) . Only samples with at least 900 sequences ( n = 143 ) were used for downstream analysis in R . Rarefaction curves for entire dataset were generated using “phyloseq” [35] . For additional analysis , OTUs accounting for less than 0 . 005% of the total sequences were discarded . Venn diagrams to visualize the OTUs that were shared between mosquito species and study sites were created using “limma” [36] . The difference in Ae . mcintoshi and Ae . ochraceus OTUs that were shared across study sites were compared using Fisher’s exact test implemented in the package RVAideMemoire [37] . Alpha diversity metrics for each sample including Shannon diversity index , observed OTUs ( richness ) and Chao1 were computed using vegan package after rarefying all samples to an even depth of 900 sequences [38] . We compared the relative OTU abundances across sites for each species using chi square good-ness-of-fit test implemented in the package RVAideMemoire [37] . Multiple pairwise comparisons across sites was performed using adjusted P after false discovery rate ( fdr ) correction [39] . Non-parametric Scheirer-Ray-Hare test was used to test for statistical significance and Wilcoxon rank sum test with Bonferroni correction for multiple comparisons was used to establish treatments that were significantly different . For beta diversity , we used unrarefied data as suggested by [35] . To minimize sampling bias , we transformed the raw dataset into proportion representing relative contribution of each OTU . This simple normalization provides a simple representation of the count data as a relative abundance measure [40] . Non-metric multidimensional scaling ( NMDS ) with Bray-Curtis dissimilarity metric was then performed in R package “phyloseq” [35] to visualize the effect of site and mosquito species on bacterial communities [41] . Results of NMDS were confirmed using analysis of similarities test ( ANOSIM ) with 9 , 999 permutations using PAST . Indicator species analysis was conducted using the R package ‘labdsv’ [42] to identify the bacterial OTUs that characterized each mosquito species based on fidelity and specificity [43] . Only OTUs with an indicator value ≥ 60% were considered significant . Consent was sought from community elders or chiefs to set up traps away from homesteads on community land .
To survey the microbial communities of field-collected populations of Ae . mcintoshi and Ae . ochraceus , the V3-V5 hypervariable region of bacterial 16S rRNA was PCR amplified and sequenced using Illumina MiSeq platform . Rarefaction curves generated using all mosquito samples ( n = 153 ) demonstrated that bacterial OTU richness for both mosquito species varied markedly across study sites ( Fig 2 ) . OTU richness was highest in Ae . ochraceus from Fafi and lowest in Ae . ochraceus from Ahero . Rarefaction curves indicated that sequencing coverage of the bacterial communities was adequate for some but not all samples . Of the 358 OTUs that were detected in the two mosquito species , 85 and 78 OTUs were unique to Ae . mcintoshi and Ae . ochraceus , respectively and 195 were shared between the two mosquito species ( Fig 3 ) . For Ae . mcintoshi , 35 , 15 , 39 , and 30 OTUs were unique to Ahero , Fafi , Korisa and Masalani , respectively , with only 37 ( 13% ) common across the sites . For Ae . ochraceus , 8 , 59 , 8 , and 48 OTUs were unique to Ahero , Fafi , Korisa and Masalani , respectively . There was a significant difference in unique OTUs between Ae . mcintoshi and Ae . ochraceus across the study sites ( Fisher’s exact test , P <0 . 0001 ) . Significant differences were observed between Ahero and Korisa ( P <0 . 0001 ) , Ahero and Masalani ( P <0 . 0001 ) , Fafi and Korisa ( P <0 . 0001 ) and Fafi and Masalani ( P <0 . 0001 ) . Only 10% of the OTUs ( 28 out of 273 ) were shared across the study sites in Ae . ochraceus . Overall , the proportion of shared OTUs among sites for both species were comparable ( Ae . mcintoshi: 13% , 37/280; Ae . ochraceus: 10%; 28/273; Fisher’s exact test , P = 0 . 5082 ) . The proportion of OTUs unique to specific study sites was also comparable between the two mosquito species ( Ae . mcintoshi vs Ae . ochraceus: 119/280 vs 123/273; Fisher’s exact test , P = 0 . 5498 ) . For Ae . mcintoshi , the number of OTUs exclusively shared between any pair of sites was lowest between Ahero and Fafi ( 4 ) and highest between Korisa and Masalani ( 28 ) . For Ae . ochraceus , the lowest number of OTUs that were shared between any pair of sites was lowest in Ahero and Korisa ( 0 ) and highest between Fafi and Masalani ( 37 ) . Shannon diversity index , observed OTU and Chao1 but not evenness were significantly influenced by host species and site of collection but not their interaction ( Table 2 ) . Ae . ochraceus had significantly higher microbial richness and diversity compared to Ae . mcintoshi ( Fig 4 ) . For the site effect , Shannon diversity index , observed OTUs , and Chao1 values were significantly lower in Ahero compared to the other sites . Chao1 values were also significantly lower in Fafi compared to Masalani . A total of 10 bacterial phyla were detected in the two mosquito species ( Fig 5a ) . The five most abundant phyla were Proteobacteria ( 53 . 5% ) , Firmicutes ( 22 . 0% ) , Actinobacteria ( 10 . 0% ) , Bacteroidetes ( 5 . 9% ) , and unclassified bacteria ( 5 . 6% ) which collectively accounted for 97% of the total microbiota . Other bacterial phyla included Chloroflexi , Cyanobacteria , Fusobacteria , Planctomycetes and Tenericutes . Among the proteobacteria , Gammaproteobacteria ( 37 . 1% ) and Alphaproteobacteria ( 11 . 7% ) were the most dominant groups followed by Betaproteobacteria ( 4 . 6% ) . Gammaproteobacteria and Firmicutes were abundant in both mosquito species and their relative abundance varied markedly by study site ( S1 Table ) . Actinobacteria occurred in both mosquito species but was more abundant in mosquito samples from Ahero especially Ae . micintoshi . The most abundant OTUs were from the families Enterobacteriaceae ( 27 . 9% ) , Moraxellaceae ( 7 . 5% ) , Propionibacteriaceae ( 7 . 3% ) , Bacillaceae ( 6 . 2% ) , Acetobacteraceae ( 6 . 0% ) , unclassified bacteria ( 5 . 6% ) , Staphylococcaceae ( 5 . 3% ) , Flavobacteriaceae ( 4 . 6% ) , Streptococcaceae ( 3 . 6% ) and Sphingomonadaceae ( 2 . 8% ) ( Fig 5b ) . The relative abundance of the most common bacterial families varied markedly by mosquito species and study site ( S2 Table ) . For example , Propionibacteriaceae were more abundant in Ae . mcintoshi from Ahero ( 35 . 5% ) while Enterobacteriaceae were more abundant in Ae . mcintoshi from Korisa ( 33 . 8% ) and Masalani ( 44 . 4% ) and Ae . ochraceus from Fafi ( 27 . 8% ) , Korisa ( 60 . 6% ) and Masalani ( 27% ) . Flavobacteriaceae were more abundant in mosquito samples from Ahero ( 17 . 9% ) . A summary of the comparisons in relative abundance of Ae . mcintoshi and Ae . ochraceus OTUs across the study sites are presented in S1 Table . Similar variations in relative abundance of bacterial communities by species and study site were observed at genus level ( Fig 5c ) . For example , Propionibacterium spp . and Achromobacter spp . , respectively , were more abundant in Ae . mcintoshi ( 6 . 6% ) and Ae . ochraceus from Ahero ( 16 . 6% ) compared to other sites . Unclassified genera from family Flavobacteriaceae was more abundant in Ae . ochraceus from Ahero ( 16 . 2% ) while Lactococcus spp . was more abundant in Ae . ochraceus from Masalani ( 13 . 6% ) . Staphylococcus spp . was more abundant in both mosquito species from Masalani ( 17–17 . 4% ) . NMDS plots based on Bray-Curtis dissimilarities revealed a clear grouping of the samples by mosquito species with some degree of overlap ( Fig 6a ) . These results were confirmed by ANOSIM test ( R = 0 . 19 , P = 0 . 0001 ) . Additional analysis revealed a significant effect of study site on microbiota of the two mosquito species ( Fig 6b , 6c and 6d ) . A follow up ANOSIM test showed that 20 out of the 28 pairwise comparisons were significantly different after Bonferroni correction for multiple comparisons ( R = 0 . 37 , P <0 . 0001 ) . One pairwise comparison ( OC-MA vs MC-FA ) had an R value >0 . 75 indicating substantial differences in microbial community structure . Nineteen pairwise comparisons had R values ranging from 0 . 27–0 . 68 indicating varying degree of overlap but generally different community structure ( Table 2 ) . The remaining 8 pairwise comparisons had R values ≤0 . 24 indicating little separation . Indicator species analysis revealed 7 bacterial genera that were strongly associated with either Ae . mcintoshi or Ae . ochraceus ( Table 3 ) . Propionibacterium , Anoxybacillus , Chryseobacterium , and Roseomonas were significantly associated with Ae . mcintoshi while Enterobacter , Acinetobacter , and unclassified bacteria were significantly associated with Ae . ochraceus . However , only the unclassified bacteria had the cutoff indicator value ≥60% .
We used amplicon-based MiSeq sequencing to characterize the bacterial communities of Ae . mcintoshi and Ae . ochraceus , the primary vectors of RVFV in Kenya . Our findings reveal that the two mosquito species harbor distinct microbial communities and that Ae . ochraceus has a more diverse microbial community compared to Ae . mcintoshi . The microbial communities of the two mosquito species were also strongly influenced by the site of collection . Proteobacteria ( 53 . 5% ) , Firmicutes ( 22 . 0% ) and Actinobacteria ( 10 . 0% ) were the most abundant phyla which is consistent with previous findings on microbiota of field-collected mosquitoes [17–20] . Variation in microbial composition between the two mosquito species is intriguing given that they were collected from the same study sites and are known to utilize similar aquatic habitats commonly referred to as dambos for oviposition and larval development [44] . They also exploit livestock hosts as the primary source of blood meals [45 , 46] . The findings may relate to differences in their biology and exploitation of resources as adults . Both species may have different physiologies that dictates which microbial species can colonize and thrive in their bodies . In addition , blood feeding and sugar feeding are common in these mosquito species [47] , and both traits are known to influence the composition and diversity of microbial communities in mosquitoes [48] . Delineating the microbiota contribution of different blood meal and nectar feeding sources , would require additional research . For each species , OTU richness varied by the site of mosquito collection suggesting an effect of local environment on microbial richness . Our findings are consistent with previous findings that the local environment is a key determinant of microbial communities in wild-caught mosquitoes [17 , 29 , 49] . This effect may partly be mediated by microclimatic differences among the sites such as temperature and humidity . These variables have been found to influence the abundance of microbial communities such as Wolbachia in Culex mosquitoes [50] . Also , plants serve as a food resource for adult mosquitoes and variation in the composition and diversity of plant communities between the study sites may influence bacterial composition , richness , and diversity in mosquitoes [49] . Contamination of aquatic habitats with pollutants such as pesticides may also alter the microbial communities in the aquatic habitats which in turn may influence the microbial communities that mosquitoes acquire from the larval environment [30] . However , we neither quantified the chemical contaminants that the mosquitoes were exposed to nor the plant communities that were present in our study sites . Future studies should include these aspects . There is evidence that the two mosquito species differ in vector competence for RVFV as well as in their contribution to RVFV transmission in nature based on infection rates during the RVF outbreak of 2006/07 [24] . OTUs belonging to the bacterial genera Propionibacterium , Anoxybacillus , Chryseobacterium , Roseomonas were mostly associated with Ae . mcintoshi , while the genera Enterobacter , Acinetobacter and unclassified bacteria were mostly associated with Ae . ochraceus ( Table 3 ) . Bacterial species in some of these genera have been known to influence vector susceptibility to pathogens . For instance , Enterobacter spp . isolated from Ae . albopictus was shown to directly inhibit La Crosse virus in vitro assays [51] . Similarly , Acinetobacter spp . inhibited P . falciparum infection in mosquitoes [52] . Removal of midgut bacteria including Chryseobacterium via antibiotic treatment increased the susceptibility of Anopheles gambiae to Plasmodium infection [53 , 54] . Conversely , Plasmodium infection success in An . gambiae was associated with higher abundance of Enterobacteriaceae [17] . While bacteria in the genera Propionibacterium , Anoxybacillus and Roseomonas have been previously reported in other mosquitoes [20 , 55 , 56] , little is known about their effects on pathogen transmission . Whether differences in the contribution of the two mosquito species to RVFV transmission in nature are related to their microbiota profiles remains unclear . Thus , assessing the impact of the identified bacteria on vector competence for RVFV should be the focus of future studies . Some of the bacterial genera that were differentially abundant among study sites or between mosquito species included Tatumella , Gluconobacter , Acinetobacter , Anoxybacillus , Lactococcus , Achromobacter , Staphylococcus , Proprionibacteria among others . Acinetobacter , an acetic acid bacterium largely associated with floral nectar is among the core microbiota of many mosquito species and is likely acquired through sugar-feeding or contact with flowering plants [57 , 58] . Gluconobacter and Roseomonas are also acetic acid bacteria that are adapted to various sugar-rich and ethanol-rich environments [59] and are commonly found in insects that depend on sugar-based diets including mosquitoes [60] . Achromobacter and Anoxybacillus are commonly found in soil and water and adult mosquitoes may acquire them through contact with soil and water or through transstadial transmission [61] . Tatumella spp . has previously been described in other adult mosquito species [20] . Members of this genus along with those of genus Lactobacillus are involved in fermentation [62] . Propionibacterium include common bacteria of human skin and other animals and may be acquired when the mosquito lands on a blood-meal host . In summary , we provide the first comprehensive report of the microbial communities of field-caught populations of the primary vectors of RVFV in Kenya . We show that the two mosquito species have distinct microbial communities whose diversity and richness is heavily influenced by the site of collection . Because vector susceptibility to pathogens may be influenced by certain bacterial species , further studies are warranted to investigate the functional role of identified microbiota on the biology of the mosquito species including influence on susceptibility to RVFV . These studies may propel identification of bacterial taxa that may be harnessed for symbiotic control of RVF virus .
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Knowledge of the microbial communities associated with disease vectors can be exploited for symbiotic control of vector-borne diseases . Here , we characterized and compared the bacterial communities of field-caught populations of Aedes mcintoshi and Aedes ochraceus , the primary vectors of Rift Valley fever ( RVF ) virus in Kenya . We show that the two mosquito species harbor distinct microbial communities whose diversity and richness are heavily influenced by the site of collection . Because some bacterial species are known to influence vector susceptibility to pathogens , differences in bacterial communities between the two mosquito species is likely one of the primary factors accounting for the spatial and temporal variation in transmission dynamics of RVF virus .
|
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2019
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Host species and site of collection shape the microbiota of Rift Valley fever vectors in Kenya
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South America has a complex demographic history shaped by multiple migration and admixture events in pre- and post-colonial times . Settled over 14 , 000 years ago by Native Americans , South America has experienced migrations of European and African individuals , similar to other regions in the Americas . However , the timing and magnitude of these events resulted in markedly different patterns of admixture throughout Latin America . We use genome-wide SNP data for 437 admixed individuals from 5 countries ( Colombia , Ecuador , Peru , Chile , and Argentina ) to explore the population structure and demographic history of South American Latinos . We combined these data with population reference panels from Africa , Asia , Europe and the Americas to perform global ancestry analysis and infer the subcontinental origin of the European and Native American ancestry components of the admixed individuals . By applying ancestry-specific PCA analyses we find that most of the European ancestry in South American Latinos is from the Iberian Peninsula; however , many individuals trace their ancestry back to Italy , especially within Argentina . We find a strong gradient in the Native American ancestry component of South American Latinos associated with country of origin and the geography of local indigenous populations . For example , Native American genomic segments in Peruvians show greater affinities with Andean indigenous peoples like Quechua and Aymara , whereas Native American haplotypes from Colombians tend to cluster with Amazonian and coastal tribes from northern South America . Using ancestry tract length analysis we modeled post-colonial South American migration history as the youngest in Latin America during European colonization ( 9–14 generations ago ) , with an additional strong pulse of European migration occurring between 3 and 9 generations ago . These genetic footprints can impact our understanding of population-level differences in biomedical traits and , thus , inform future medical genetic studies in the region .
Our understanding of fine-scale patterns of population structure in humans has dramatically increased with the advent and deployment of fast , inexpensive , and accurate genome-wide technologies for assaying variation [1–3] . However , our understanding of regional patterns of genomic variation is quite poor in many parts of the world particularly in populations that are currently underrepresented in GWAS studies , including those in Latin America [4] . Understanding patterns of genomic variation is especially important for populations throughout the Americas , which have undergone multiple recent admixture events , making the reconstruction of their evolutionary past and the design of multi-ethnic medical genetic studies challenging . Recently , studies in Mexico , the Caribbean , and throughout the Americas have shed light on the complex demographic processes that occurred in those regions and have illuminated how differences in the pre- and post-colonial history have shaped differences in genomic variation that ultimately impact variation in complex biomedical traits [5–7] . The South American landmass features unique geographic , archaeological , and historical records that are distinct from other regions of the Americas [8] . The contributions of these events to patterns of genomic variation remains to be laid out to a greater extent . For example , in contrast to North America , South America’s indigenous population history derives from a single migration wave that rapidly expanded southwards throughout the Andean highlands and eastwards into the Amazon basin [9] . Previous analyses of native South American variation based on microsatellites have reported a west-to-east difference in genetic diversity between Andean and eastern Brazilian tribes as one of the strongest signals of sub-continental genetic differentiation [10–12] . The largest human settlements in South America , however , occurred throughout the Andean region and likely represent a major source of Native American variation in present day South American Latinos . Characterizing the extent of substructure and differential contribution of these ancestral components is therefore crucial to understanding the genetic heterogeneity of the South American population . Previous studies on South American Latino populations have either used a limited number of genetic markers to evaluate continental-level patterns of population structure or focused on particular geographic regions [13–18] . Many of these studies and others have demonstrated a large amount of genetic diversity in Native American and mestizo populations , especially between different geographic regions [11–13 , 19] . Wang et al . analyzed multiple mestizo populations throughout South America using 678 microsatellite markers [13] and found evidence of correlations between ancestry components and geography . The Galanter et . al . and Ruiz-Linares et al . studies [15 , 16] used a limited set of ancestry informative markers to analyze the global ancestry proportions throughout Latin America . However , due to the smaller numbers of markers , these studies were unable to perform analyses that rely upon dense genetic information such as local ancestry inference , ancestry specific principal components analysis , and demographic modeling based upon ancestry tract length . Recent work in Brazil using dense genomic information has demonstrated that individuals differ markedly in ancestry proportions both within and between populations in metropolitan regions of South America [20] . They also demonstrate significant variation within the European and African ancestry components . Here , we expand upon previous work by focusing on admixed populations from five countries in Spanish speaking South America ( Argentina , Chile , Colombia , Ecuador , and Peru ) , spanning much of the Andean region of the continent . Similar to other areas in Latin America , South America has experienced multiple migration and admixture events , including Native American settlement , European colonization , and the African slave trade . However , the timing and magnitude of migration waves from a myriad of continental and subcontinental ancestral groups varies dramatically throughout the continent and affects the population genetic profile of the region at a local scale . The earliest settlements in South America date back over 14 , 000 years ago [8] . Native Americans developed multiple civilizations throughout the continent , including settlements in the Andes , the Amazon , and along the coasts . In the 16th century , European colonization and conquest led to a dramatic population bottleneck in the Native American population as well as an increasing influx of European migrants , quickly followed by admixture with West Africans brought to the Americas through the slave trade . During the following centuries , there was continuous admixture between European , Native Americans , and African individuals . Early European migration into the Spanish South American colonies came mainly from the Iberian Peninsula . Spanish conquistadors in the early 16th century conquered many of the indigenous populations in the Andean region of South America , establishing South American colonies throughout the continent [21] . All five of the countries studied here were originally part of the Spanish viceroyalty of Peru . These Spanish colonies followed separate but related developmental paths , eventually splitting into the viceroyalties of Peru , New Granada , and Rio de la Plata . The Peruvian colony was a major source of silver for the Spanish Empire , while the colonies in Rio de la Plata ( including present day Argentina ) and New Granada ( including Colombia and Ecuador ) became important commercial centers [21 , 22] . The Spanish colonies in South America continued to receive immigration from Europe concurrent with admixture with Native American populations . In the 19th and 20th centuries , there is evidence of increased migration from many regions of Southern Europe , especially in Argentina [23 , 24] . To explore the impact of this complex demographic history upon the current genetic structure of South American Latino populations , we analyze single nucleotide polymorphism ( SNP ) genotyping data from 436 unrelated admixed samples , including 175 Argentinians , 119 Peruvians , 27 Chileans , 19 Ecuadorians , and 96 Colombians . We combined these data with reference panels of European and Native American populations , and applied admixture deconvolution methods to trace back the origin of each ancestry component within Europe and the Americas . We also analyzed the length distribution of ancestral segments in admixed individuals to test hypotheses about past migration patterns and examine whether different countries have experienced different genetic histories .
To characterize the ancestral components of South American Latino individuals from Colombia , Ecuador , Peru , Chile , and Argentina , we applied unsupervised clustering models and principal components analysis to genotype data from ancestral and admixed populations ( Fig 1 ) ( see Methods ) . This data set contains 436 admixed South American individuals together with 204 European individuals from the POPRES study [1] , 50 Yoruban and 50 Han Chinese from the 1000 Genomes Project [3] , and 493 unmasked Native American individuals from Reich et al . 2012 [9] . The South American individuals showed varying proportions of European , Native American and , to a lesser extent , West African ancestry in PCA space , supporting the notion of a broad range of global ancestry patterns throughout South America . We observed some dispersion of Native American individuals away from the main ancestral cluster due to the presence of European admixture . We then ran clustering models for K = 2 through K = 15 ancestral populations with ADMIXTURE [25] on a total of 1 , 233 individuals . Cross validation errors for the ADMIXTURE analysis are shown in S2 Fig . The minimum CV error was observed at K = 13 . When clustering is performed assuming K = 4 ancestral populations ( Fig 1C ) , the algorithm separates the individuals into four major continental clusters . Average continental ancestry proportions for each of the admixed populations are shown in Table 1 . As expected from historical records [21 , 22] and previous results from other Latino populations in the Caribbean [6] and Mexico [5] , South American Latino individuals show a mixture of European , Native American , and African ancestry . However , some populations , especially those in Peru , Chile , and Argentina , tend to have a smaller proportion of African ancestry than seen in Latino populations in the Caribbean ( p < 2 . 2 x 10−16 , Wilcoxon test , S6 Fig ) , also observed in previous analyses [13 , 16–18] . We find significant differences in global ancestry proportions between countries within South America . The Peruvian individuals tend to have a higher proportion of Native American ancestry than individuals from any of the other South American populations ( Tukey HSD Test , p < 0 . 001 vs . Argentina , Chile , Colombia , Ecuador; S3 Fig ) . We observed multiple Peruvian individuals with a >25% proportion of East Asian ancestry , which is not surprising given that there were large Asian migrations to Peru especially during the 19th and early 20th century where laborers from Guandong ( formerly Canton ) province in China were brought to the country [26] . Peru opened its borders to Asian immigration in 1849 , and it is estimated that over 87 , 000 Chinese individuals entered Peru between 1859 and 1874 [22] . This East Asian ancestry component is also seen in the Northern Amerindian individuals . These individuals are from Eskimo , Aleut , and Na-Dene populations and the observed clustering is consistent with the hypothesis of multiple waves of gene flow from Asia to America suggested by a previous study [9] . At higher values of K in ADMIXTURE , these individuals are assigned to their own ADMIXTURE component , indicating a unique ancestry component that is separate from the East Asian cluster ( Fig 1 and S5 Fig ) . The Argentinian population has a significantly higher proportion of European ancestry than the Peruvian , Chilean , and Ecuadorian populations ( Tukey HSD Test , p = 0 . 018 vs . Chile , p = 0 . 129 vs . Colombia , p<0 . 001 vs . Peru and Ecuador ) with some individuals having close to 100% European ancestry ( S3 Fig ) . Even so , there is a large range of ancestry proportions within individuals from Argentina , consistent with previous results based on a small number of ancestry informative markers and blood group antigens [17 , 27 , 28] . This variance is most likely a result of the contrasting histories of different Argentinean regions . For example , the original Spanish settlers of Argentina came through the Pacific/Andean region [21] . However , as Argentina developed , individuals from Spain and Southern Europe settled throughout the coastal regions on the Atlantic [23] . We also observed a small number of Argentinian individuals with relatively high amounts of African ancestry , whereas the rest of the individuals have a very low African ancestry component . This diversity is reflected in the large range in ancestry proportions seen within Argentina and is consistent with previous studies [17 , 28 , 29] . At higher order Ks ( K = 13 in Fig 1 ) , we observed significant substructure in both the Native American and European populations . The North-South gradient among European populations is strongly correlated with the latitude of each country’s capital ( p < 2 . 2 X 10−16 , linear regression ) , with a southern European component ( light blue ) most prominent in Spain , Portugal , Italy , and Greece . Most of the admixed Latino individuals in the sample have a high proportion of this southern European component , suggesting that the Europeans involved in admixture events in South America are from the Iberian Peninsula and Mediterranean Europe . This observation is consistent with historical migration patterns and maintained cultural influence [19] . On the other hand , the primary cluster of Native ancestry is reflective of the local indigenous diversity . We find that a component of the Native American ancestry in the Peruvian samples is shared with local Andean native groups , such as Quechua and Aymara , and that of Colombians is more closely shared with the Southern and Central Amerindian groups ( Fig 1 , K = 13 ) . In contrast , we see that the Native American component in Argentina and Chile is shared between components from Central/Southern Native American and Andean Native American groups , showing a wider range of ancestral origins that we explore below in further analyses ( Fig 1C , K = 13 ) . Sex biased ancestry is an important feature of many Latin American populations , and has been observed and described thoroughly in many previous research articles [6 , 18] . European migrants to the Americas were mainly male , especially during the earlier years of colonization . This has resulted in increased Amerindian ancestry on the X-chromosome when compared to the autosomes . After excluding admixed males from the analysis , we had admixed individuals from only four populations: Argentina , Chile , Colombia , and Peru . We compared ADMIXTURE estimates at K = 3 of autosomal and X-chromosomal ancestry ( S7 Fig ) . We find an increase in Native American ancestry on the X-chromosome compared to the autosomes ( S8 Fig , Wilcoxon p < 0 . 001 ) . This is suggestive of the fact that there was an overabundance of European males and Amerindian females that participated in the admixture process . To identify the admixed individuals’ subcontinental lineages rooted within Europe and South America , we performed ancestry-specific PCA analysis . ASPCA is a technique developed to perform principal components analysis on the fraction of an individual’s ancestry from a specific continental origin . In contrast to PCA , which is performed on individual ( unphased ) diploid genotype calls , ASPCA is performed on phased haploid genomes conditional on ancestry calls ( see Methods for details ) . To explore their European origins , we combined our admixed individuals with the POPRES European data set [1] and performed ASPCA on the merged data set ( Fig 2 ) . Due to the limited overlap between the POPRES data set and our samples , we performed ASPCA on the Argentinian , Chilean , and Peruvian haplotypes separately from the Colombian and Ecuadorian haplotypes . The European reference samples cluster according to geography [30] . We find that the majority of the European haplotypes of the admixed samples cluster with Iberian and Southern Europeans , consistent with historical records and previous reports [6 , 31] . However , we observed interesting differences between countries in South America . For example , Argentina showed the highest number of European haplotypes that cluster in the Italian peninsula . This is consistent with recent migration events from Italy to Argentina in the late 19th and early 20th centuries [22] . Between 1880 and 1930 , 2 . 3 million of the 4 . 7 million migrants to Argentina had Italian nationality [24] . We also find that Argentina has the largest range in the European ancestry components and even includes two haploid genomes that cluster near individuals from Germany , Poland , and Hungary in the top right of the ASPCA plot ( Fig 2 ) . No other South American population showed samples with such distant ancestry from the Iberian cluster , nor other Latino samples from previous studies in the Caribbean and Mexico [5 , 6] . To further our investigation of the European component beyond the Spanish ancestry found in the Iberian Peninsula , we combined masked samples from the Canary Islands with South American individuals from Colombia and Ecuador . The Canary Islands were colonized by the Spanish in the early 15th century and became a stopping point for Spanish on their way to the Americas . Here , we find undifferentiated patterns of ancestry between the European component of these three populations ( S9 Fig ) , suggesting that the European ancestry of these groups either originated from a similar source on the Iberian peninsula or that methods of increased resolution are needed to untangle more subtle differences . To investigate the Native American component of the South American individuals’ ancestry , we combined our samples with those from 49 Native American populations previously genotyped [9] . We removed Native American samples that appeared as outliers in ASPCA space and that were geographically distant from South America ( see Methods and S10 Fig ) . We also excluded Native American individuals with greater than 10% estimated European ancestry , as we found these individuals were biasing the principal components analysis towards a European/Native American axis ( S11 , S12 and S13 Figs ) . For visualization purposes , Native American populations were grouped corresponding to the labels used in Reich et al . [9] and are referenced geographically ( see S1 Table for mapping ) . We find that the Native American component of the South American haplotypes clusters along a gradient between the Andean Amerindian populations and the Southern Amerindian populations along ASPC1 and ASPC2 ( Fig 3 ) . Notably , the Native American ancestry in the admixed South American individuals is drastically different from the genetic components observed among Central and Northern Native American groups , such as Kaqchikel in Guatemala and Zapotec or Tepehuano in Mexico . None of these groups showed close affinities with Latino-derived South American haplotypes , supporting the notion of a highly substructured architecture of the Native American component among Latinos from different regions across Latin America . Our ASPCA analysis revealed that South American native haplotypes cluster primarily into two groups: one represented by central Andean individuals , such as Quechua and Aymara , and another group that includes most of the remaining native populations from South America . The differentiation between the Andean Amerindians and other South Amerindians is consistent with previous results using Y chromosome and mtDNA analyses [11 , 12] , and suggests that the mountain range of the Andes acted as a major geographic barrier to gene flow during Native American evolution . This created further population structure among South Native American groups separating populations in the Amazon and east coastal regions from highland populations in the Andes . Interestingly , a number of the populations classified as Andean such as the Hulliche , Inga , and Yahgan clustered close to the Southern/Amazonian Native Americans and far from the other Andean Native Americans such as the Quechua and Aymara . Reich et al . in 2012 [9] suggested that , based on linguistic affinities , these populations would be expected to cluster with the Aymara and Quechua populations . Indeed , among the samples from the main Amazonian cluster in Fig 3 , these are the only ones spreading towards the Quechua/Aymara cluster , supporting the idea of pre-Columbian admixture events giving rise to populations like the Inga , Huilliche , and Yahgan along the Andes . The separation between the Andean and other South American populations is consistent with the hypotheses of either multiple migration routes into South America , with an early split soon after crossing the Isthmus of Panama , or restricted levels of gene flow shortly after establishment of Native American settlements in the continent [8 , 11 , 12 , 32] . Likewise , the clustering of northern Argentinian Wichi and Paraguayan Guarani and Toba with lowland groups from Brazil and Colombia , suggests an Amazonian origin of Native American migration into the Gran Chaco and Pampas areas rather than strong evidence of a trans-Andean route . The branching pattern of these ancestral migrations have directly impacted the genetic profile of present day South American Latino populations , even between neighboring countries such as Argentina and Chile . We detail these patterns in what follows . The clustering of the masked haploid genomes from the admixed individuals tended to be population specific ( Fig 3 ) . We find that the Peruvian individuals cluster more closely with the Andean Native American individuals than with any other Native American group , suggesting that the Native American component of the Peruvian population is mainly from the Andean region . While the Andean Native Americans and Peruvian individuals cluster closely , many of them do not overlap . Both the Quechua and Aymara individuals are from the Central Andes , while the admixed individuals are from Lima . Mitochondrial and Y-chromosomal studies of Andean ancestry have indicated that there is relatively low geographically-correlated genetic diversity in the Andean region , likely due to the historically higher gene flow and population size in the Andean region [11 , 12] . While there seems to be less geographic correlation in ancestry in the Andean Native Americans than in other Native American populations , some geographic stratification may be detected through high-density genotyping that was not detected using mitochondrial or Y-chromosomal analysis . In other populations with lower levels of genetic differentiation , such as Europeans , high density genotyping data revealed correlations between geography and ancestry [30] . Also , our reference panel has little representation from coastal Peruvian Native Americans , and these groups may also have contributed to the admixture process in cosmopolitan areas . Argentinian individuals show a broader range of indigenous ancestry: some cluster closer to the Southern/Amazonian Native Americans , while others cluster with the Latino Peruvians and the Andean group , reflecting a rich diversity of pre-Columbian roots in Argentina , whose geography spans the breadth of the continent from the Andes to the Atlantic , thus absorbing haplotypes from both major streams of Native American migration . We find only a marginal relationship between clustering and sampling location within Argentina . We find that sampling latitude is marginally associated with ASPC1 in a linear regression ( p = 0 . 025 , S14 Fig ) , and not significantly associated with ASPC2 ( p = 0 . 3387 , S15 Fig ) . We find no significant linear relationship between longitude and ASPCs ( p = 0 . 322 vs . ASPC1 and p = 0 . 844 vs . ASPC2; S16 and S17 Figs ) . However , we do not expect the sampling locations in our current sample to be indicative of an individual’s history to this degree of resolution . Most individuals were sampled at hospitals in major cities throughout Argentina , with the largest number of individuals sampled in Buenos Aires . Because of recent major migrations of individuals , especially from rural to urban areas , current location may not be indicative of the location of an individual’s ancestors . There has also been recent intraregional migration throughout South America , especially in urban regions such as Buenos Aires [33 , 34] . This could be contributing to the genetic diversity we observe within the Argentinian individuals’ Native American ancestry . A sampling scheme based upon the “four grandparent” ancestry principle , such as the one used in the European POPRES [1] study , along with more representation for different regions throughout Argentina may better elucidate finer scale structure in the country , although this is also known to be imperfect [17] . In contrast , the Colombian and Ecuadorian Latino haplotypes tend to cluster with geographically nearby Southern Native Americans , such as the Wayuu , Piapoco , and Ticuna from Colombia . The Ecuadorian individuals cluster farther away from this ancestral group than the Colombians , which could be due to the lack of Ecuadorian Native American groups in the reference panel or be the result of admixture with Andean Native American lineages . The Chilean individuals cluster towards the middle of the admixed group , between the Andean cluster and the Chilean Huilliche and Yaghan samples . The Native American reference panel used here does not include many Native Americans from Southern Chile . Only two haploid genomes from one Hulliche individual are in the subset of the reference panel used for analysis due to the high proportion of European ancestry in the remaining Hulliche individuals . The lack of representation of Hulliche and other Chilean Native Americans could explain why we do not see a strong differentiation of the admixed Chilean haploid genomes . A deeper sampling effort is needed to assess fine-scale genetic patterns within Chile . We find that the Native American ancestry of admixed Latinos is associated with population of origin ( ANOVA p < 2 x 10−16 for both ASPC1 and ASPC2 ) . This is consistent with many previous results in population history analysis , which have also shown strong correlation between geographic features and ancestry . To further investigate the differences between the European and Native American ancestry components of South American individuals , we used GERMLINE [35] to identify genomic regions of identity by descent ( IBD ) in the admixed individuals and compared the patterns of IBD matching within and among populations to the local ancestry calls inferred throughout each IBD match . We find 12 , 348 segments of IBD shared within populations compared with only 4 , 941 segments of IBD shared between populations . On average , the individuals from Colombia share the most IBD within the population ( 15 . 2 cM ) , followed by Chile ( 3 . 42 cM ) , Ecuador ( 2 . 58 cM ) , Peru ( 2 . 06 cM ) , and Argentina ( 0 . 84 cM ) . We find that segments shared between populations are shorter than those shared within populations ( Wilcox p < 2 . 2e-16 ) . For IBD segments that could be identified using a haploid comparison , we calculated for each segment the proportion of European , Native American , and African local ancestry . We find that in both within and between populations , longer IBD segments have a higher proportion of European ancestry ( linear regression , p = 4 . 04 x 10−16 ) . The effect size based upon linear regression is greater in IBD segments shared between populations ( β = 0 . 050 ± 0 . 0084 s . e . , p = 3 . 7 x 10−9 ) than in IBD segments shared within populations ( β = 0 . 0076 ± 0 . 0011 s . e . , p = 4 . 4 x 10−11 ) ( S18 , S19 and S20 Figs ) . To test if a single admixed population primarily drove the observed effect , we also analyzed the shared IBD segments within each admixed South American population on its own . We observed a significant association in a linear regression model between IBD segment length and ancestry in the Peruvian and Argentinian populations ( Peru p = 6 . 01 x 10−8 , Argentina p = 0 . 0088 , S21 Fig ) , a suggestive association in the Colombian populations ( p = 0 . 0955 , S21 Fig ) , and found no evidence for the association in Chilean or Ecuadorian populations ( Chile p = 0 . 132 , Ecuador p = 0 . 903 , S21 Fig ) . However , the difference in significance may be a function of the differential power to detect this effect within populations due to sample sizes differences , since we have the lowest number of admixed individuals from Chile and Ecuador . We also find that the majority of the long IBD segments ( greater than 10 cM ) shared between populations are of European ancestry ( 20 European vs . 2 Native American ) . IBD tracts shared between populations tend to consist of only a single continental ancestry , while those shared within populations often contain multiple ancestry switch points . These patterns are consistent with previous studies of IBD sharing in Latino populations [36] and suggest that the most recent common ancestors shared between Latino populations in South America are much more likely to be European than Native American , and that these trace back to a reduced source of founders . These results are also supported by the ASPCA analyses , where we find remarkable substructure among the different Native American components of each Latino population , but similar patterns of Southern European ancestry across Latino populations in ASPCA space . To further confirm the results of the ASPCA analysis , we looked for diploid IBD matches between admixed and reference populations ( S22 Fig and S23 Fig ) . We find that consistent with the ASPCA , the most IBD sharing with European populations occurs between admixed populations and Iberian populations . The Peruvian individuals also share a much higher amount of IBD with the Andean Native Americans such as the Quechua and Aymara , as suggested by the ASPCA plots . Taken together , the ASPCA and IBD results suggest that admixture between Europeans and Native Americans occurred in multiple locations throughout the colonization of the Americas and involved many different Native American populations . Individuals in our study were collected from major metropolitan regions in South American countries ( Argentina , Peru , Chile , Colombia ) or are recent US immigrants ( Ecuador , Colombia ) . Our sample is thus an important reflection of the admixture patterns in cosmopolitan areas of these South American countries . While not fully representing the breadth of diversity across South America , the unique signatures of each of these populations gives deeper insight into regions that have historically been understudied . Important insights into population structure have been discovered using similar sampling schemes in previous work [6 , 18] . A more comprehensive sampling scheme throughout South American countries will help to reveal finer population structure patterns [17] . Analyzing the length of contiguous tracts of the same ancestry in an admixed population can help to determine the timing of admixture events and subsequent migrations . We used the program Tracts [37] to fit multiple models of admixture to the observed data . Tracts uses an optimization function to fit the ancestry tract length distribution under a given population history model to the observed tract length distribution . It also estimates the timing of major admixture and migration events within the model . Multiple models can be compared based upon their observed likelihoods given the data . To infer the genetic migration history of South American Latinos , we compared three potential hypotheses ( S24 Fig ) . First , we fit a model with a single admixture event between Europeans and Native Americans followed by a migration of Africans . We considered this model our base model , reflecting the initial colonial-era contact followed by slave importation . Next , we fit a second model with an additional European migration event . A third model consisted of the base model with an additional African migration event . We compared the fits of the models using the observed likelihoods . The tracts model makes an assumption about independence of the tract length distributions that may not necessarily be true and can cause likelihood based methods to falsely reject a model in favor of a model with more ancestry pulses [38] . In order to ensure we were correctly rejecting the single pulse model , we compared our observed changes in likelihood to the changes in likelihood seen in 1000 simulations of the single pulse model ( see Methods ) . We find that for all of the admixed populations studied , the model with the extra pulse of European migration was preferred ( P < 0 . 001 for Argentina and Colombia , P = 0 . 002 for Peru , P = 0 . 003 for Ecuador , and P = 0 . 027 for Chile , S28 Fig ) . The best fitting models for each population are shown in Fig 4 . This result suggests a demographic history of continued European migration to Latin South America . This is in contrast to many countries within the Caribbean , where Tracts models estimated multiple pulses of African migration [6] . This contrast reflects historical differences in the slave trade among regions of the Americas and its differential impact in present day Latino populations . While African slavery was extensive throughout colonial Spanish America , it is estimated that only 12 . 9% of the total number of slaves disembarked in colonial Spanish America [39] . Of the estimated 1 . 5 million slaves imported into Spanish colonies , over 80% of them disembarked in Central America , Cuba , and Puerto Rico [39] . While Rio de la Plata was a major slave destination , only about 83 , 000 of the estimated 1 . 5 million slaves sent to Spanish Colonial America disembarked at this port [39] . While disembarkation location is not necessarily indicative of a slave’s final destination , it gives a broad picture of the regional differences in the slave trade . Some of the difference in ancestry proportions and demographic models between the Caribbean and South America could be related to these regional differences . A unique feature of South American migration history is the timing of European contact in colonial times . According to the best-fitting Tracts models , we can infer a rough estimate of the number of generations that have passed since the initial admixture and the subsequent migrations in each South American population . The estimates of these parameters are shown in S3 Table and in Fig 4 we have plotted the tract length distributions of the most likely models ( as determined by the simulated likelihood comparison ) for each South American population . The maximum-likelihood time of initial admixture between Native Americans and Europeans ranges from 9 to 14 generations ago among the studied populations , representing the youngest estimate in mainland Latin America . Previous studies of mestizo populations throughout South America have shown estimates of mean time to admixture between 6 and 14 generations [13 , 16] , a range that our models agree with . The models return the maximum likelihood estimate for the time of onset of admixture in the entire population ( once additional pulses have been taken into account ) . This , however , does not necessarily equate to an estimate for the earliest possible time that admixture may have taken place . The discrepancy between the recorded initial time of colonization and the onset of admixture described in our and previous work is likely due to many factors , including the fact that the admixture process occurred over time in the colonies and that further immigration occurred throughout the 16th , 17th , and 18th centuries [22] . Our models argue for a more recent European migration into South America compared to that in the Caribbean and Mexico [6 , 40] , consistent with the colonial history in the region . Strong pulses of European migration occurred between 3 and 9 generations ago in Colombia , Ecuador , Peru , Chile , and Argentina ( Fig 4A ) . This more recent European migration into southern South American countries is consistent with historical records of European migration from Iberia and Southern Europe throughout the 19th and 20th centuries [22] . For example , it is estimated that over 4 . 7 million individuals immigrated into Argentina between 1880 and 1930 [24] , and that the majority of these immigrants were of European origin . Our models suggest a strong pulse of European immigration between 3 and 4 generations ago in Argentina , which is consistent with the recorded history . The Tracts model for Argentina ( Fig 4 ) , however , is not able to properly fit the tail of the distribution , where longer Native American tracts were observed . This is likely due to the high variance in ancestry proportions in Argentina . Because of this , the admixture time estimates for individuals in Argentina may be strongly dependent upon subpopulation . This result argues for the need to study these populations at an even finer scale to help discriminate the complex local patterns of ancestry . Previous work has performed ASPCA ancestry analyses using both trio [6] and population [5] phased data . Here , we show that the results of these analyses between trio-phasing and pop-phasing samples are similar . Trio phasing generally produces more accurate haplotypes than population phasing . This could affect the results of ancestry deconvolution methods that rely upon long range phasing information , such as ASPCA and Tracts . However , we find no significant difference between trio and population phasing results when using RFMix’s phase correction feature . In the paper describing the RFMix algorithm [41] , Maples et . al . demonstrate that the RFMix phase correction feature produces highly accurate long range haplotypes in admixed populations even when population phasing was performed . To assess the differences between trio-phased and population phased data , we compared ASPCA and Tracts results from the 1000 Genomes Peruvian and Colombian individuals between the different phasing approaches . For ASPCA , we find that the population and trio-based methods return similar results for both the Native American and European ancestry ( S31 Fig ) . To assess the effect of phasing on the IBD analysis , we compared the results of IBD tract length analysis of trio-phased and pop-phased samples . The trio-phased IBD analysis finds an increased number of IBD tracts , however , the proportion of European , Native American , and African tracts is very similar ( S32 Fig ) and the length distribution of the tracts is similar . We find the IBD tracts of each population have a Spearman correlation of 0 . 995 for the European tracts , 0 . 996 for the Native American IBD tracts , and 0 . 952 for African IBD tracts . Thus , we find no evidence of systematic bias in the IBD analysis due to the population phasing . For Tracts analysis , the trio-phased Tracts result has an earlier onset of admixture than the population-phased samples in the both populations . Specifically , we estimate 10 generations to the onset of admixture in the population-phased 1000 Genomes Peruvians vs . 9 generations for the same individuals when trio-phased . For the 1000 Genomes Colombians , we estimate 12 generations for both the population-phased and trio-phased data . Therefore , the admixture onset times calculated here may be slightly biased towards overestimating the initial onset of admixture . These tests indicate that using population-phased samples in combination with RFMix’s phase correction abilities in our ancestry analysis pipeline introduces little bias to the results . South America has experienced major demographic shifts caused by multiple Native American migration routes into the region , European colonization and the African slave trade and , more recently , by continued inter-continental and local migration . A number of genetic differences distinguish these populations from other Latino groups . A highly structured pre-Columbian population transmitted local patterns of variation that cluster by country and are not observed outside South America . The Native American ancestral component differs significantly throughout the populations in South America , with an especially striking difference between Andean and non-Andean populations . The later contact of Europeans and recent migrations from Southern Europe translates into a different European gene pool contributing to South American admixture compared to that involved in the initial pulses of migration into the Americas , particularly in Chile and Argentina . In particular , strong pulses of European migration identified in Argentina correspond to historical records of strong Southern European immigration to the region . These findings not only shed light on the reconstruction of demographic events associated with population admixture in South America , but also on population-specific genetic profiles defining particular Latino groups , which have important implications for the expected relative proportion of deleterious mutations that can be detected via association studies in the region . The extensive structure observed in subcontinental ancestry between different populations also suggests that medically relevant genetic variation may vary between populations , demonstrating the need to ensure representation of diverse populations in future genetic association studies .
Newly collected samples were obtained with individual written consents provided by voluntary participants recruited under IRB approval of the Oklahoma Medical Research Foundation ( number 09–23 ) , and the local IRB of each participating institution at recruitment sites in South America . The populations included in this study combine newly collected samples and publicly available data from relevant samples . Participants from Argentina , Peru , and Chile were recruited as part of a larger study aimed at understanding the genetic basis of Lupus in Latinos [42] . The complete GWAS cohort was genotyped using Illumina OMNI1 arrays and only healthy controls from the three aforementioned countries of origin were considered for inclusion in the present population study ( n = 266 ) . Markers with less than 99% call rate were filtered and a total of 694 , 834 SNPs remained for intersection with additional data sets . Individuals from Argentina , Peru , and Chile were recruited at hospitals within major metropolitan regions of each country , including Lima ( Peru ) , Santiago ( Chile ) , Cordoba ( Argentina ) , Mar del Plata ( Argentina ) , Rosario ( Argentina ) , Santa Fe ( Argentina ) , Mendoza ( Argentina ) , and multiple sites in Buenos Aires ( Argentina ) . Most individuals were sampled from public hospitals . Only 6 of the 175 individuals from Argentina were sampled from private hospitals . The number of individuals sampled from each hospital is reported in S5 Table . Individual genotype data for the 266 newly genotyped individuals will be made available through dbGaP under the Susceptibility Genes for SLE of Amerindian Origin in Hispanics study . Individuals from Ecuador and Colombia were sampled in the New York City area as described previously in Bryc et al [18] and were genotyped using Illumina 650K SNP arrays and filtered as described therein . We also included genotype data from unrelated Peruvian and Colombia individuals from the 1000 Genomes Project , who were sampled in Lima ( Peru ) and Medellin ( Colombia ) . We then removed admixed individuals with an estimated PLINK kinship score of greater than 0 . 25 . The final data set of unrelated admixed individuals consisted of 175 Argentinian samples , 119 Peruvian samples , 27 Chilean samples , 19 Ecuadorian samples , and 96 Colombian samples . Different intersections between data sets resulted in varying SNP densities for each of the analyses as described below and are summarized in S4 Table . Statistical analysis and plotting were performed in R version 3 . 1 . 2 [43] and using ggplot2 [44] . We performed global ancestry analysis by combing the admixed South American individuals with reference panels representing each continent . For West Africa , we used genotypes from 50 Yoruba individuals in 1000 Genomes [3] . For Asia , we used 50 of the Han Chinese from Beijing ( CHB ) individuals . A large proportion of the admixed ancestry for the South American individuals was expected to be from European and Native American populations of diverse origin . Therefore , we used larger panels for these groups in the global ancestry analysis . For European populations , we used a subset of 204 individuals from the POPRES sample that capture the North-South gradient of genetic diversity [1] . For Native American samples in the global analysis , we used the individuals previously genotyped by Reich et al . [9] . This data set comprises 49 Native American populations from throughout the Americas with genotype data available for 364 , 470 SNPs . The combined data set had a total intersection of 24 , 592 SNPs . For most of the analyses described , we have grouped these Native American populations by sampling location and thus will refer to them as “Northern Amerindian” , “Central Amerindian” , “Southern Amerindian” , and “Andean Amerindian” ( S1 Table ) . Principal Components Analysis ( PCA ) was performed on the combined dataset using the EIGENSOFT method implemented in the Plink software package [45 , 46] . We used the unmasked version of the Reich et al . data set , since standard PCA is highly sensitive to missing data . We next performed ADMIXTURE [25] analysis on the combined data set . However , some of the individuals in the Reich et al . data set have significant European ancestry , and this is reflected in the global PCA results . ADMIXTURE is less affected by this additional European admixture component or by missing data in the Native American samples than PCA . We performed ADMIXTURE with the unmasked Native American samples . ADMIXTURE models were explored at varying number of K clusters from K = 2 through K = 15 . We observed the lowest CV error at K = 13 . Higher order Ks resulted in within-population clusters rather than population-level signals and were thus not considered . To compare global ancestry results to previous analyses , we combined our data with previously published individuals from the Caribbean [6] and performed ADMIXTURE at K = 4 . We compared values of African ancestry using the Wilcoxon signed-rank test . To assess sex-biased ancestry in admixed female individuals , we combined our data with POPRES Europeans , 1000 Genomes Africans , and Native Americans from South America [6 , 47] . After excluding admixed males from the analysis , we had admixed individuals from only four populations: Argentina , Chile , Colombia , and Peru . We compared the ADMIXTURE estimates at K = 3 of X-chromosomal and autosomal ancestry from admixed females . For local ancestry analysis , we used continental reference panels to identify each of the three continental-level ancestry components along each admixed chromosome . To maximize phasing accuracy , we chose representative populations for which trio samples were publicly available . To represent Africa , we used the YRI population from 1000 Genomes [3] . For Europe , we used the CEU population from the 1000 Genomes Project . For Native American ancestry , we used a combined set of Maya and Tepehuano individuals from Mexico [5] . In previous studies we have demonstrated that for continental-level inferences , the population chosen as reference plays a minor role in the accurate identification of highly diverged components of African , European and Native American ancestry [6 , 36 , 40] . Phasing was performed independently on each of the three reference panels and on the admixed individuals using SHAPEIT [48] with default parameter settings . From the phased data , we used a discriminative modeling approach implemented in RFMix to perform local ancestry inference [41] . RFMix is a local ancestry inference method that uses random forests to infer the local ancestry of chromosomal segments . Input to RFMix consists of phased genotype data from a set of reference panels for each population and phased genotype data from admixed individuals . Using the genotype data from the reference panels , RFMix builds a local ancestry model for each of the phased admixed chromosomes by training a random forest classifier . It also performs phase correction to improve the accuracy of haplotypes in the admixed individuals . Finally , RFMix uses an EM algorithm to iteratively improve local ancestry calls . We ran RFMix on our data set with the phase correction feature enabled and performed two rounds of the EM algorithm to improve local ancestry calls . We used the population phased version of the RFMix program , which assumes population phasing for the admixed individuals . We used the default window size of 0 . 2 cM , the default number of trees ( 100 ) and the default RFMix model of admixture occurring 8 generations previously . We used the three continental reference panels ( African , European , and Native American ) in RFMix to identify the genomic regions in admixed individuals that are likely to have originated from each continent . RFMix generated a local ancestry call at each site for each haploid genome . We used the program PCAmask to perform ancestry specific PCA analysis ( ASPCA ) [6] . The input to the program consists of admixed individuals with local ancestry calls and a subcontinental reference panel of the ancestry under investigation . PCAmask masks the loci in the haplotypes of the admixed individuals that have local ancestry that does not correspond to the given continental population . Because of the high amount of missing data this masking generates , PCAmask uses a modified version of subspace PCA [49] to implement an ancestry specific PCA . The analysis combines both the admixed individuals and the subcontinental ancestral population into the same PCA space for analysis . To run ASPCA on the European component of the admixed individuals ancestry , we combined our admixed individuals with the POPRES European data set [1] . Because both of our Latino data sets were genotyped using different Illumina SNP arrays , and the POPRES European reference panel [1] was genotyped in a different platform ( Affymetrix 500K ) , the intersection of all three data sets would lead to a substantially reduced marker overlap . Therefore , we performed the ASPCA analysis of the European component separately for the individuals from Ecuador and Colombia ( genotyped on Illumina 650K arrays ) and the individuals from Peru , Chile , and Argentina ( genotyped on Illumina OMNI1 arrays ) . The intersection of the POPRES samples and the Colombians and Ecuadorians contained 21 , 570 SNP markers while the intersection of POPRES and the Argentinian , Peruvian , and Chilean samples contained 35 , 070 SNPs . The intersection of the three data sets contained too few SNPs for analysis . To further assess the European ancestry of the Colombians and Ecuadorians , we combined these individuals with previously published genotype data from Canary Islanders [50] . We used SHAPEIT and RFMix as above to mask the Canary Islanders using a European and a North African reference panel [51] . We performed ASPCA analysis with the POPRES individuals , Canary Islanders , Colombians , and Ecuadorians . We masked all non-European regions of the genomes of the Canary Islanders , Colombians and Ecuadorians . For the ASPCA analysis of the Native American component , we used a combined data set including the admixed Latino individuals from all 5 South American countries investigated and the unmasked Native American reference panel from Reich et al . [9] . We excluded admixed individuals with less than 25% Native American ancestry inferred through local ancestry inference . In addition to masking regions of the admixed genomes that corresponded to non-Native American ancestry , we also masked loci where RFMix reported a less than 95% posterior probability of the inferred ancestry . After these filters , we considered 182 Argentinian , 236 Peruvian , 51 Chilean , 114 Colombian , and 38 Ecuadorian haploid genomes for analysis . The final intersection of these data sets contained 142 , 161 polymorphic sites . The first comparison with the Native American reference panel identified many of the extremely homogenous Native American groups as outliers ( S7 Fig ) . This included the Brazilian Surui and the Costa Rican Cabecar individuals , among other populations . We also removed North American , Na-Dene and Aleut groups from downstream analyses , as they are geographically distant from South America and further analyses indicate these individuals were unlikely to be involved in South American Latino admixture . After excluding these individuals , we re-ran ASPCA and found that there was a strong gradient of dispersion within the Native American reference panel ( S8 Fig ) . This gradient correlated strongly with ADMIXTURE estimated European ancestry components in linear regression ( p < 2 x 10−16 for ASPC1 and ASPC2 , S9 Fig and S10 Fig ) . We hypothesize , therefore , that this gradient is an artifact of recent European admixture in Native American populations . We therefore excluded any individuals from the Native American reference panel with greater than 10% European ancestry . After this filter , we re-ran ASPCA . This data set included 108 Andean , 132 Central Native American , 118 Northern Native American , 122 Southern Native American haplotypes . A table of the specific numbers of haplotypes from each Native American population is provided in the Supplement ( S2 Table ) . ASPCA was performed with this reduced Native American reference panel of 480 haploid genomes in combination with the 182 Argentinian , 236 Peruvian , 51 Chilean , 114 Colombian , and 38 Ecuadorian masked haploid genomes . To further investigate the relationships between admixed individuals throughout South America , we performed an identity by descent analysis using the program GERMLINE [35] . For the IBD analyses , we used the default settings of GERMLINE , with settings of bits = 128 and allowing a maximum of two homozygous marker mismatches per IBD slice ( -err_hom ) and a maximum of 0 heterozygous marker mismatches per IBD slice ( -err_het ) . For IBD analyses involving combining the IBD tract information with local ancestry assignments , we used the haploid mode of GERMLINE to look for IBD matches between individual haplotypes inferred through Shapeit and RFMix . The haploid mode of GERMLINE is more conservative than the diploid mode , as a phasing switch error will interrupt an IBD match . The minimum length for IBD segment detection was 3 cM . For IBD within admixed individuals , we used the rephased alleles output from RFMix . We then compared the locations of IBD matches to the inferred local ancestry calls throughout each IBD tract . To better understand the IBD relationships between admixed populations and subcontinental reference populations , we compared estimated IBD tract lengths between our admixed individuals , the Native American ASPCA subset of the Reich et . al . data set , and the European 1000 Genomes populations . The European 1000 Genomes individuals were used in place of the POPRES data set for IBD detection due to the low marker overlap with the POPRES individuals . We calculated the sum of the total length of IBD sharing between the admixed populations and reference populations . This value was normalized by the product of the numbers of individuals in each respective admixed and reference population . Standard error was calculated using chromosome weighted jackknife sampling [52] . To infer migration times and admixture events , we used the program Tracts [37] . Tracts fits a migration model to the local ancestry tract length distribution within a population . In doing so , it also infers the number of generations since the admixture events in each model . We ran the Tracts program on multiple demographic models for each of the countries in South America . We excluded individuals with estimated ancestry proportions of greater than 95% of one continental population ( European , African , or Native American ) as these individuals represent either non-admixed individuals or extremely recent migrants . After this filter , we had a total of 154 Argentinians , 117 Peruvians , 95 Colombians , 27 Chileans , and 19 Ecuadorians included in the Tracts analysis . We compared the likelihoods of each ancestry model . The Tracts model makes an assumption about the independence of the distribution of ancestry tracts that may not necessarily true , this is discussed further in Liang and Nielsen [38] . This assumption may skew the reported likelihood of the model , causing the model to , in certain situations , be biased towards favoring the inclusion of additional ancestry pulses . To ensure our results were not affected by this bias , we used forward simulation to generate a tract length distribution given the estimated parameters from the single pulse model for each population . We then for the simulated data calculate the change in likelihood between the single pulse and two pulse models . We compare our observed change in likelihood against 1000 iterations of the simulated changes in likelihood when the true model is the single pulse model . If our observed likelihood change is of greater magnitude than 95% of the simulated likelihood changes , we rejected the model with the single pulse of migration . We tested three models ( S24 Fig ) based upon our hypotheses for the recent history of South America . The first model was a base model with two parameters and included a single admixture event between Native American and European populations followed by a pulse of migration of African individuals . The parameters for this model were time of original admixture and time of African migration . The second model had an additional pulse of European migration , while the third model had an additional pulse of African migration . Each of these two models had two additional parameters , one corresponding to the magnitude and one corresponding to the time of the subsequent migration . In order to assess the possible effects of using population-phased samples instead of trio-phased samples in this analysis , we compared results from the 1000 Genomes PEL and CLM cohorts . We generated one set of haplotypes and local ancestry calls through population phasing using SHAPEIT including only the parents . We generated another set of haplotypes and local ancestry calls by performing a trio-based phasing through SHAPEIT with the PEL and CLM analysis . We performed a comparison of the ASPCA , IBD , and Tracts analysis between these two data sets . For the ASPCA analysis , we used the same reference panels of Native Americans and Europeans as in previous analyses . We compared the correlation between tract lengths of different ancestries in the haploid version of the GERMLINE IBD program . We compared the estimated migration times between the trio-phased and pop-phased ancestry tract length analysis . Throughout the manuscript , multiple combinations of admixed individuals and data sets are used . This is due to the different requirements of each of the analyses in terms of SNP densities and reference populations . The major difference is that admixed individuals , continental reference panels , and the Native American reference panel were genotyped on various Illumina genotyping platforms while the POPRES data set was genotyped on the Affymetrix GeneChip 500k . For local ancestry inference , the combination of admixed individuals and reference panels contained approximately 190 , 000 SNPs . These data sets were used for the local ancestry estimation and tract length analysis . For combinations of the admixed individuals and the Reich et . al . data set of Native Americans , the average SNP density was approximately 140 , 000 . These include the Native American ASPCA and IBD analysis . For combinations with the POPRES data set , which was genotyped on the Affymetrix GeneChip 500k , combined data sets had a much lower SNP density , approximately 30 , 000 SNPs . These data sets were used in the global admixture analysis and European ASPCA . The data sets used for each of the analyses are summarized in S4 Table .
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South America is home to over 400 million people who share a rich demographic history , including settlement by Native Americans , European colonization , and the African slave trade . We use genomic data to infer which populations from Europe and the Americas contributed to these admixture events . We provide evidence for multiple origins of the Native American ancestry of admixed South American Latinos . The Native American ancestral component correlates strongly with geography , indicating that admixture occurred between European colonists and local Native American populations throughout South America . We also show that the European ancestry of South American Latinos comes mainly from the Iberian peninsula , however , a significant number of Argentinians have European ancestry from other Southern European regions . The genetic signal of European admixture in South American populations is younger than the signal observed in Mexico and the Caribbean . We find evidence for a second pulse of European migration to many regions of South America subsequent to the original colonization . These results demonstrate the heterogeneous nature of the Latino population in South America and help elucidate the complex genetic and admixture events that shaped the population structure of the region .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Genomic Insights into the Ancestry and Demographic History of South America
|
Type VI secretion ( T6S ) is a cell-to-cell injection system that can be used as a microbial weapon . T6S kills vulnerable cells , and is present in close to 25% of sequenced Gram-negative bacteria . To examine the ecological role of T6S among bacteria , we competed self-immune T6S+ cells and T6S-sensitive cells in simulated range expansions . As killing takes place only at the interface between sensitive and T6S+ strains , while growth takes place everywhere , sufficiently large domains of sensitive cells can achieve net growth in the face of attack . Indeed T6S-sensitive cells can often outgrow their T6S+ competitors . We validated these findings through in vivo competition experiments between T6S+ Vibrio cholerae and T6S-sensitive Escherichia coli . We found that E . coli can survive and even dominate so long as they have an adequate opportunity to form microcolonies at the outset of the competition . Finally , in simulated competitions between two equivalent and mutually sensitive T6S+ strains , the more numerous strain has an advantage that increases with the T6S attack rate . We conclude that sufficiently large domains of T6S-sensitive individuals can survive attack and potentially outcompete self-immune T6S+ bacteria .
Microbes employ a staggering range of extracellular tools to engineer their immediate environment [1–6] . Very often , that environment is defined by the multitude of other cells in close proximity . These neighbors pose both a threat and an opportunity , and represent an important target for manipulation [7–10] . The Type VI secretion system ( T6SS ) is a mechanism for direct cell-to-cell manipulation through the translocation of effector proteins . The T6SS consists of a helical sheath , surrounding an inner tube with associated effectors , and a baseplate attached to the bacterial cell wall ( Fig 1a ) [11 , 12] . The T6SS is functionally close to the contractile phage tail , with which it shares evolutionary origins [13–17] . When triggered , the sheath contracts rapidly , pushing the effector through a specialized pore and into a neighboring cell [18–22] . Specialized T6SSs can directly damage both prokaryotic and eukaryotic target cells through the translocation of toxic proteins directly into the target cell . T6SSs are observed to cause death via numerous mechanisms in both bacteria and eukaryotes ( Fig 1b; S1 Video ) [13 , 18 , 23–28] . In fact , many species have developed multiple , specialized T6SSs [26]; for example , Burkholderia thailandensis has five separate T6SSs , which allow it to attack both prokaryotic and eukaryotic cells [29] . T6SSs are present in approximately 25% of the Gram-negative genomes studied by Boyer and colleagues [30] . Antibacterial T6SSs appear to be found with cognate immunity proteins in every case [26] . Given this tactical advantage , one might expect T6S to be even more widespread . The lack of universality of the T6SS suggests that there are limits to its utility relative to its costs . To address the question of T6S’s utility , we focused on the case of cell-to-cell killing between bacteria . We explored this scenario through the use of individual-based models ( IBMs; also called “agent-based models” ) . IBMs simulate the behavior of many , possibly different individuals each of which obeys rules that dictate the individual’s behavior as a function of its immediate environment . IBMs are a common tool in ecology , and have been widely used in the study of spatially explicit biological processes . Examples at the multicellular scale include the evolution of cancer , the spread of disease , and the dispersal of plants [31–38]; IBMs are also used to study dynamics at the subcellular scale [39] . More generally , IBMs have been used to address a wide range of questions concerning cooperation and conflict , of which T6S strategy can be viewed as an example [40–45] . In this study , we develop a series of IBMs . The first competes self-immune T6S+ and sensitive individuals in a range expansion , analogous to a surface colony ( 2D ) or a biofilm ( 3D ) . We find that cell growth from the inside of a sufficiently large ( or “established” ) domain can offset cell death at the interface between a T6S-sensitive strain and a self-immune T6S attacker . Consequently , given a sufficiently large domain , T6S-sensitive strains can survive T6S attack . The sensitive strain does not require a growth advantage to survive; in fact , the sensitive strain can resist elimination even with a slower growth rate . Given even a small growth advantage , the T6S-sensitive strain can outcompete a self-immune T6S+ competitor . In a variant on the original model , we also find that moderate nutrient limitation has a negligible effect on competition outcomes . We validated these findings through in vivo competition experiments between T6S+ Vibrio cholerae and T6S-sensitive Escherichia coli . In these 2D plate assays , E . coli can form microcolonies that survive , provided the initial local density of V . cholerae is not too high . Along similar lines , simulated competitions between mutually sensitive T6S+ strains ( strains that are self-immune but sensitive to one another ) reveal that the initially more numerous strain benefits most from higher attack rates . We conclude with a discussion of the ecological impact of T6SSs .
Escherichia coli MG1655 GentR ( LacZ+ ) was competed against Vibrio cholerae str . 2740–80 ( LacZ- ) , similarly to what was described previously [19] . E . coli and V . cholerae were each grown from frozen stocks in Luria-Bertani broth ( LB ) , supplemented with the appropriate antibiotic , shaking overnight at 37°C and 200 rpm . The cells were washed twice with LB before being diluted to an OD600nm of 0 . 5 . To confirm that the initial number of viable cells were comparable among the competition assays , the colony forming units ( CFUs ) were determined by serially diluting the washed and diluted V . cholerae and E . coli cultures 10-fold in 96-well plates in triplicate . Thereafter , 5 μL of each dilution were spotted on an LB agar plate ( LA ) . For the competition assays , the cultures were mixed in a 1:1 ratio , which was then serially diluted 3-fold in a 96-well plate . For selected dilutions 5 μL were spotted on a LA/IPTG 100 μM/X-Gal 40 μg/mL plate in duplicate . The competition plates were incubated at 37°C overnight . To determine the E . coli to V . cholerae ratios resulting from the competition assays , the CFUs of both strains were determined for each spot . This was achieved by excising the spots from the competition assay plates and resuspendig the bacteria in 1 mL LB by vigorously vortexing for at least 15 sec . These suspensions were serially diluted 10-fold in 96-well plates and 5 μL of each dilution were spotted on LA plates supplemented with the appropriate antibiotic . The CFU plates where either incubated at 37°C overnight or at lower temperatures until colonies were visible . Images of the plates were taken on a white light transilluminator . Timelapse movies of the competition assay were obtained by preparing the competition assay plates and the pre-competition CFU plates as described before , except that the competition mixtures were only spotted once . The competition assay plate was incubated at 37°C on a white light transilluminator while taking an image every 10 min over 24 h using a Nikon D5200 . The contrast , brightness and white balance of the images were adjusted using Adobe Photoshop CS5 . The same settings were applied to all timelapse images . Thereafter the images were further processed and converted to a video using Fiji [46] . The growth rate determination was carried out under the same conditions as the killing assay . The same cultures ( OD600nm = 0 . 5 ) were individually spotted on LA plates and incubated at 37°C . Every hour the CFU was determined from a spot of each strain , as described for the endpoint killing assay . The growth rate was then derived from the parameters of the fit of an exponential curve . For the E . coli MG1655 GentR overnight cultures and selective CFU plates the growth medium was supplemented with 15 μg /mL Gentamicin , whereas for V . cholerae str . 2740–80 50 μg /mL Streptomycin was added . Imaging of a competition between E . coli and V . cholerae VipA-msfGFP strains was performed under conditions similar to those used previously for imaging of T6SS activity in V . cholerae [17] . Strains were grown to OD600nm ≈ 1 and mixed at a 1:1 ratio on an LB 1% agarose pad . Imaging started after 10–20 min and was performed at 37°C for the indicated number of frames and at the indicated frame rate . Computer models were implemented using Nanoverse 0 . x , a prototype of our freely available individual-based modeling platform [47] . In Nanoverse , individual agents ( e . g . cells ) occupy spaces on a regular lattice . In every step of a simulation , one or more individuals perform a series of behaviors; if multiple individuals act simultaneously , the events are resolved in random order . Two types of individual cells are included in the simulations ( Fig 2a and 2b ) : self-immune T6S+ ( “T6S+” ) cells , shown in red , and sensitive T6S- ( “sensitive” ) cells , shown in blue . ( Self-sensitive T6S+ strains “self-destruct” rapidly in simulations , and indeed have not been observed in nature . ) Every cell has an associated probability of cell division per step of the simulation . The T6S+ division rate αt is taken as the ( inverse ) time unit of the system and is set equal to 1 . The sensitive division rate αs is generally set higher than αt , as only T6S+ cells pay the cost of maintaing the T6S . Upon cell division , a copy of the dividing cell is placed in a vacant space adjacent to the dividing cell ( Fig 2a ) . If no vacancies exist adjacent to the dividing cell , nearby cells are pushed out of the way to make room ( S1 Text ) . Each T6S+ cell has a fixed rate γ of initiating an attack ( Fig 2b ) . The attack is then resolved according to an individual-based rule: attack exactly one randomly chosen nearest neighbor if there is one; otherwise do nothing . If the attack targets a sensitive cell or a cell of a different T6S+ strain , the target dies and its lattice site becomes unoccupied; T6S+ cells are immune to attack by cells of the same T6S+ strain , as observed experimentally [26] . The overall rate of events is controlled by the simulation timestep multiplier , λ ( S1 Text ) .
To determine the effect of T6S on multi-species population dynamics , we simulated a competition between T6S+ and sensitive strains during a range expansion . The simulations begin with a well-mixed , fully occupied circular inoculum of approximately 500 individuals ( S1 Text ) . For 2D simulations on a triangular lattice , the starting population is 469 individuals ( i . e . inoculum radius r0 = 12 ) . The T6S+ division rate is chosen as the unit of time , αt = 1 . The three other parameters are the sensitive strain growth rate αs , the initial sensitive strain fraction , and the attack rate γ . ( In simulations in which there are no T6S+ cells , the unit of time is αs = 1 . ) The attack rate γ and the sensitive strain growth rate αs are found to offset one another as discussed below . The parameter space was extensively explored . Fig 2 shows parameters chosen to emphasize the effect of varying the attack rate γ and the initial sensitive strain fraction . Specifically , we fixed the sensitive strain growth rate as αs = 4 and varied γ and the sensitive fraction . When the attack rate is low ( γ = 5 ) , sensitive cells can ultimately dominate even when the sensitive strain fraction starts as only a 10% minority ( Fig 2c , S2 Video ) . Initially , the sensitive population declines as isolated individuals are attacked and killed . Eventually , only a small number of surviving sensitive domains remain , concentrated along the periphery of the colony . However , because sensitive cells grow faster than T6S+ cells , these domains begin to outgrow the T6S+ strain , eventually leading to a majority sensitive population . By contrast , at high attack rate ( γ = 15 ) and an initial 10% sensitive strain fraction all sensitive individuals are rapidly eliminated ( Fig 2d ) . When the initial sensitive strain fraction is increased to 50% , a larger number of sensitive cells begin near to one another , accelerating the formation of sensitive domains; the early formation of these domains helps the sensitive strain to survive and eventually dominate the T6S+ strain , even under a high rate of attack ( Fig 2e and 2f ) . An analysis of multiple , independent simulations ( Fig 2g ) shows that sensitive populations decline and then recover when both the attack rate and initial sensitive strain fraction are low ( upper left ) , or when both are high ( lower right ) . During the period of decline , isolated sensitive cells are eliminated while clusters of sensitive cells enjoy a degree of protection from attack . The monotonic increase of the sensitive population fraction in the most favorable conditions—high initial sensitive strain fraction , low attack rate ( lower left ) —results from the early formation of sensitive domains , whereas adverse conditions—low initial sensitive strain fraction , high attack rate ( upper right ) —preclude sensitive domain formation and lead to elimination of the sensitive strain . Since T6S-mediated killing can take place only at the interface between T6S+ and sensitive strains , we hypothesized that the net growth rate of the sensitive strain depends on the difference between the area or volume of a sensitive domain and the extent of the interface between the strains . To identify the dependence of this relationship on attack rate and relative growth rates , we studied a simple sensitive domain model ( Fig 3a and 3b ) . The 2D simulations begin with a fully-occupied , homogeneous circular sensitive inoculum . As in the competition model , all individuals are capable of cell division . As before , the model assumes that interior cells can push other cells toward the surface of the colony to make room for their daughter cells ( S1 Text ) . To simulate attack , individuals at the outer periphery are subject to being killed at a rate γ ˜ , essentially equivalent to embedding the sensitive domain in a larger T6S+ domain . To explore the transition from sensitive strain collapse to sensitive strain growth observed in Fig 2 , we varied the sensitive strain domain radius while holding constant the “attack” rate γ ˜ = 8 , retaining the αs = 4 growth rate from the earlier competitions . Most sensitive strain domains with starting radius r0 ≤ 5 shrank toward zero , while larger domains survived ( S3 Video ) . We then varied the sensitive strain growth rate , allowing it to fall below αs = 1 . Strikingly , the minimum sensitive strain domain radius required for survival depends inversely on the relative sensitive strain growth rate , implying that a sufficiently large sensitive strain domain can resist displacement by even a faster-growing T6S+ attacker ( Fig 3c ) . We can readily estimate the critical population size n* above which a sensitive strain domain is expected to enjoy a net positive growth rate . Above this value , a sensitive domain would not shrink as a result of T6S+ competition , although it could , depending on conditions , represent an increasingly small fraction of total population . Eq 1 represents a theoretical “worst-case” scenario for a domain of sensitive cells , in which they are completely surrounded by an infinite domain of T6S+ cells . The key observation is that the rate of killing is proportional to the length of the interface between strains , while the rate of sensitive strain population growth is proportional to the sensitive population . For a population size n in 2D , the size of the interface is simply the circumference of the circle . Hence , d n d t = α s n - 2 γ ˜ ( π n ) 12 . ( 1 ) Solving Eq 1 for n at dn/dt = 0 , i . e . at the unstable fixed point between increasing and decreasing n , we find that n * = 4 γ ˜ 2 π α s 2 , ( 2 ) which is shown as a dotted line on Fig 3c . The slight divergence at high radius between the predicted and simulated values is the result of accumulated simulation error ( S1 Text ) . The finding suggests that , even at this theoretical limit of maximal contact with T6S+ competitors , a sensitive domain can persist for long times . Fig 3d shows simulation results for dn/dt plotted against the prediction from Eq 1 . The rate of change of sensitive strain population was measured periodically in simulations with initial domain radii from r0 = 3 to r0 = 12 . Attack rates ranged from γ ˜ = 0 to γ ˜ = 14; sensitive strain growth rates ranged from αs = 1 to αs = 4 . The simulations show excellent agreement with the predicted dynamics ( R2 > . 98 ) , despite deviations of the sensitive domain from a pure circle arising both from the lattice structure and from the stochasticity of the simulations . Similar results are obtained for a corresponding relationship in 1D and 3D ( S2 Text ) . The simulations described so far assume an unlimited supply of nutrients . To determine the effect of nutrient depletion on T6S population growth and competition , we developed a variant of the IBM that incorporates local depletion of nutrients . Even very limited nutrient concentrations still lead to exponential growth during range expansions , resulting in growth and competition dynamics that are nearly identical to those of the unlimited-nutrient case ( S3 Text ) . To validate our simulation results , we inoculated 2 . 5 μL each of of LacZ- T6S+ V . cholerae and LacZ+ T6S- E . coli onto X-Gal plates at various dilutions ( see “Materials and Methods” ) . We compared the outcomes of these experiments with simulations for which the growth rates of sensitive and T6S+ cells were matched to those of E . coli and V . cholerae , respectively . In a preliminary estimate , E . coli was observed to grow slightly faster than V . cholerae ( 2 . 19 h−1 vs 2 . 05 h−1 ) , so this difference was also used in the simulations . The simulation attack rate was set to γ = 5 , which yielded a rough parallel with the experimental images . These simulations were run until the colony had doubled in radius . Fig 4a–4d compare the experimental and simulated competitions , with initial inoculum concentrations decreasing 9-fold with each successive panel . As the inoculum becomes more dilute , single-species domains become larger . Simultaneously , E . coli become more numerous ( Fig 4f; S4 Video ) . In a micrograph of the experimental competition , large domains of E . coli are observed to grow , while smaller domains undergo proportionately higher cell death ( Fig 4e ) . S5 Video suggests that these E . coli domains persist stably after 24h . In the simulations , the final sensitive population is seen to increase as initial inoculum density decreases . This is due to the formation of large sensitive domains prior to initial T6S+ encounter , leading to increased sensitive strain survival . Interestingly , in the low-resolution images , a darkened region is observed along the interspecies interfaces , but not at same-species microcolony interfaces . We infer that the darkened zones represent an accumulation of E . coli lysates , due to the continual renewal of the interspecies front by cell division within the bulk . So far , we have considered competition between T6S+ and sensitive bacteria . We next investigated whether being T6S+ could help in the case of invasion by a T6S+ competitor . To answer this question , we simulated a competition between two T6S+ strains during a range expansion . Each strain can kill the other , but is immune to self-attack . Each strain has the same attack rate γ and cell division rate αt = 1 . Fig 5 shows two T6S+ strains ( yellow and red ) that were allowed to compete during a range expansion from n0 = 469 ( r0 = 12 ) to a final population of nf = 4690 . The relative success of the invasion was measured by comparing the initial yellow ( minority ) fraction to the final yellow fraction . In the presence of attack , the minority population is quickly eliminated ( Fig 5a ) . By contrast , in the absence of attack the minority fraction remains roughly constant throughout the course of the range expansion ( Fig 5b , S6 Video ) . As the attack rate increases , the initial minority fraction needed for survival asymptotically approaches 50% ( Fig 5c ) . Note that for equal initial numbers of red and yellow cells , attack leads to spontaneous segregation from a well-mixed inoculum , with higher attack rates leading to faster and more thorough sectoring ( S6 Video ) . Equivalent competitions in 1D and 3D led to analogous results ( S4 and S5 Figs ) . These results imply that T6S+ is useful for defending established populations against invasion .
Gram-negative bacteria can employ T6S to kill competitors , yet the system is not found universally among these bacteria . To better understand the conditions favoring T6S , we modeled a competition between T6S+ and sensitive strains . In a range expansion from a well-mixed inoculum , we found that the sensitive cells can survive in the presence of T6S+ competitors by forming compact domains that protect interior cells from attack . To test these results , we competed T6S+ V . cholerae and T6S- , sensitive E . coli in an analogous range expansion . We observed that E . coli outcompeted V . cholerae , so long as the E . coli had the opportunity to form compact domains . Finally , we found that in a model competition between two equivalent T6S+ strains the more numerous strain always drove the minority to extinction . It is informative to compare the current model to related model systems . For example , in a Lotka-Volterra model , a prey species grows in the absence of predation , and a predator grows faster in the presence of prey [48]; such systems have also been generalized to lattices [49] . By contrast , T6S+ does not grow faster as a result of killing , but potentially occupies more of the habitat . In this sense , the current model is more closely analogous to colicin dynamics in E . coli [50 , 51] . Chao and Levin [52] observed that a colicin-producing strain of E . coli dominated a sensitive strain on soft agar by creating a zone of inhibition around itself , preventing the sensitive cells from exploiting the habitat . Colicin dynamics have also been studied using an IBM based on contact-mediated killing [53] . The colicin IBM differs from our T6S model in two respects: in [53] , agents can only divide into adjacent vacancies , and sensitive cells have a strict growth advantage . The colicin model predicts that either species can dominate , with dominance depending primarily on parameter choices . By contrast , in the current study , initial colony size determines the survivorship of sensitive cells at all parameter values . The difference comes from the fact that in our model for T6S-mediated competition , interior sensitive cells are protected from killing by the outermost layer of cells . Such a “refuge” effect has previously been studied in the context of predator-prey dynamics , where density-driven sheltering is observed to destabilize predator-prey ratios relative to a well mixed model [54] . Our model employs a number of simplifying assumptions . Most importantly , cells are represented as agents on a regular lattice , and cells divide stochastically . While cell shape can affect the details of colony morphology during range expansions , it does not seem to affect the qualitative population dynamics [55]; indeed , lattice population dynamics have been shown to be consistent with the dynamics of real microbial populations [56] . The similarity of our observations in 1D , 2D , and 3D further suggests that our results are not sensitive to cellular geometry . Similarly only overall growth rates , rather than the detailed timing of cell divisions , are important for long-term population dynamics [55] . It has been hypothesized that nutrient depletion may introduce a substantial advantage for T6S+ strains . In practice , cells at the interior of a natural community face nutrient and oxygen depletion [57] . Does this limitation result in a different competitive outcome ? In a simple model of nutrient depletion , we found that a moderately nutrient-limited environment leads to dynamics extremely similar to those in the absence of limitation ( S3 Text ) . This is because exponential growth ensures that only a very small fraction of the population occupies a fully depleted zone ( S7 Fig ) . Thus , our preliminary results suggest that the effects of nutrient depletion on cell growth do not qualitatively alter the population dynamics arising from T6S-mediated competitions . Under special circumstances , such as burrowing invasions of a nutrient-depleted biofilm , T6S-mediated cell lysis could provide a significant nutrient benefit beyond the direct benefit of killing competitor cells . Typically , this effect would be limited , as the nutrient benefit would be divided among both T6S+ species and their prey . In an entirely nutrient-depleted environment , though , actively growing invaders would have an early growth advantage over previously quiescent resident cells . In determining the ecological role of T6S , the costs of maintaining a T6SS must be taken into consideration . The T6SS requires the expression of 13 core genes , the assembly and disassembly of the baseplate structure and sheath , and the production of the secreted effectors [19 , 27 , 30] . Immunity to T6S requires the maintenance of a complementary immunity protein , and may require additional modifications to the attacker’s peptidoglycan [26] . Selective use of T6S can mitigate these costs by reducing the frequency of wasteful attacks . To this end , bacteria have evolved a variety of T6SS regulatory schemes , including quorum-sensing and retaliation . Quorum sensing can reduce wasteful attacks by repressing T6S until it is likely to provide a benefit [21 , 58] . For example , QS regulates expression of T6SS in V . cholerae [59] . Interestingly , expression of T6SS and natural competence is induced by high cell density and growth on chitinous surfaces , which suggests a role of T6SS in horizontal gene transfer [60] . In addition , the V . cholerae QS signal integrates both species-specific and multigeneric signals [61] , which means that the presence of competitors could also activate V . cholerae’s T6SS . However , reflecting the diversity of T6S roles , T6S is not always upregulated in response to high density . In P . aeruginosa , there are three T6SSs; species-specific QS signals LasR and MvfR activate two of these T6SSs , but repress the third [62] . Like quorum sensing , “retaliatory” T6S attack can prevent attack until a hostile cell is encountered . For example , P . aeruginosa is observed to engage in retaliatory T6S attack [27 , 63 , 64] . This ‘tit-for-tat’ strategy could limit wasteful T6S+ interactions within clonal populations , as well as facilitating coexistence within productive consortia . Notably , P . aeruginosa also attacks its target repeatedly; by eliminating wasteful attacks , retaliators are also free to employ a more concerted ( and damaging ) series of attacks [27] . In considering the ecological role of T6S , it is instructive to consider an analogous system found in marine invertebrates . Members of the phylum Cnidaria , which includes corals , hydrae , and jellyfish , possess an explosive cell called a nematoycte containing a harpoon-like projectile [65] . Upon detonation , the effector is propelled with extreme force ( up to 40 , 000g ) into a target , leading to paralysis and death [66] . Among corals , nematocytes are used interspecifically to compete for habitat access . High attack rates are most commonly observed among slower-growing species , where nematocytes are used to defend against encroachment [67] . Our results suggest that , like nematocytes , T6S can also offset a growth rate disadvantage . The full breadth of its ecological role , however , is only beginning to come into focus .
|
Type VI secretion ( T6S ) is a cell-to-cell injection system that can be used as a microbial weapon . T6S kills vulnerable cells , and is present in a significant fraction of bacteria . Given the tactical advantage conferred by T6S , the system’s lack of universality suggests limits to its effectiveness relative to its costs . In our study , we use theory and experiments to identify the limits of T6S as a cell-to-cell weapon . We find that cell birth inside an existing colony can offset cell death due to T6S killing at the colony’s edge , helping sufficiently large ( “established” ) groups of sensitive cells to survive . T6S has been extensively studied because of its implications in both disease and inter-microbial competition . The present study is the first to identify the practical limits of T6S as a killing mechanism .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Established Microbial Colonies Can Survive Type VI Secretion Assault
|
Publication of accurate and detailed descriptions of methods in research articles involving animals is essential for health scientists to accurately interpret published data , evaluate results and replicate findings . Inadequate reporting of key aspects of experimental design may reduce the impact of studies and could act as a barrier to translation of research findings . Reporting of animal use must be as comprehensive as possible in order to take advantage of every study and every animal used . Animal models are essential to understanding and assessing new chemotherapy candidates for Chagas disease pathology , a widespread parasitic disease with few treatment options currently available . A systematic review was carried out to compare ARRIVE guidelines recommendations with information provided in publications of preclinical studies for new anti-Trypanosoma cruzi compounds . A total of 83 publications were reviewed . Before ARRIVE guidelines , 69% of publications failed to report any macroenvironment information , compared to 57% after ARRIVE publication . Similar proportions were observed when evaluating reporting of microenvironmental information ( 56% vs . 61% ) . Also , before ARRIVE guidelines publication , only 13% of papers described animal gender , only 18% specified microbiological status and 13% reported randomized treatment assignment , among other essential information missing or incomplete . Unfortunately , publication of ARRIVE guidelines did not seem to enhance reporting quality , compared to papers appeared before ARRIVE publication . Our results suggest that there is a strong need for the scientific community to improve animal use description , animal models employed , transparent reporting and experiment design to facilitate its transfer and application to the affected human population . Full compliance with ARRIVE guidelines , or similar animal research reporting guidelines , would be an excellent start in this direction .
Chagas disease ( also known as American Trypanosomiasis ) is a widespread condition , caused by the hemoprotozoa parasite Trypanosoma cruzi , affecting approximately 8 million people worldwide[1] . Formerly considered an endemic illness in South America , it recently became recognized as a global public health concern due to migratory movements [2] . Available drugs for Chagas disease , Nifurtimox ( NFX ) and Benznidazole ( BZ ) , were developed more than 30 years ago . Although their efficacy in the acute phase of the infection is well documented , clinical outcomes in chronic stages are more variables [2] , and occurrence of adverse events is common , especially in adults . Therefore , there is a considerable need for new compounds to improve Chagas disease chemotherapy [3] . Animals models have commonly been employed to study mechanisms involved in pathogenesis , immunological response , and to estimate efficacy of new chemotherapies and vaccines for Chagas disease , among others [4] . Variability of animal models for Chagas disease , and the heterogeneity in readout methods used to define drug response ( e . g . parasitemia , PCR in blood , PCR in blood and in tissues ) have led to highly variable results when evaluating new drug candidates . This wide gap between results in preclinical research and high failure rate in clinical trials may be explained , in part , by the scarce information contained in most experiments that employ laboratory animals , in which crucial information related to species , strains , genetic background , microbiological status , husbandry conditions and procedures are not properly described or even missed in some occasions . Inaccurate description of materials and methods and failure to report results appropriately has significant scientific , ethical and economic implications both for the research community and the public opinion . Furthermore , detailed reporting of animal use in scientific papers has a direct connection with the “3Rs’ Principles” of humane use of animals in scientific research ( i . e . Replacement , Reduction and Refinement ) since a complete and systematic description of what was done and what was found in the experiments may avoid unnecessary repetition [5] , facilitate systematic revisions before new essays involving animals are carried out , [6] and simplify comparisons and data integration from different studies [7] . The Animals in Research: Reporting in vivo Experiments ( ARRIVE ) Guidelines were published in June 2010 . The main objectives of the ARRIVE guidelines are to improve the quality of animal use reporting in scientific publications to maximize the availability and utility of information gained from every animal in every experiment , preventing unnecessary animal use , and to allow an accurate critical review of animal experiments , making results easier to compare among different research groups to validate and contextualize results to promote translational research to patients’ benefit [8] . The ARRIVE Guidelines consist in a checklist describing the minimum information that all scientific publications using animals should include , such as number and specific characteristics of animals employed; details of housing , husbandry and procedures; experimental design , statistical and analytical methods [8] . The main objective of this systematic review was to evaluate the degree of compliance with ARRIVE guidelines of scientific publications assessing efficacy of new chemotherapy for T . cruzi in animal models . The secondary objective was to compare these results to information presented in similar papers published before the ARRIVE guidelines were made available .
A systematic search was carried out in PubMed database ( National Library of Medicine , USA ) to identify potentially relevant scientific papers reporting original research on efficacy of new drugs for Chagas disease using animal models . A modified filter suggested by Hooijmans et al . was used to find all studies in PubMed reporting animal experiments to evaluate drugs for Chagas disease [6] . Modifications consisted in restricting the search only to studies including mammals . The following MeSH ( Medical Subject Headings ) terms and connectors were used: Chagas disease OR trypanosoma cruzi AND chagas disease/drug therapy AND animal model . In order to compare information present in papers published before and after ARRIVE guidelines became widely available the search was performed between 2008/06/30 to 2011/06/30 ( i . e before ARRIVE publication ) and between 2011/07/01 until 2014/06/30 ( i . e . after publication ) , respectively . The dates for the search period after ARRIVE guidelines publication were set one year after ARRIVE guidelines actual publication to allow scientific community ( researchers , reviewers , editors and journals ) to adopt them ( Fig 1 ) . Abstracts were reviewed manually and the ones which did not meet inclusion criteria were discarded . Supplementary Material ( S2 Fig ) illustrates search filters used . Relevant publications fulfilling inclusion criteria were randomly assigned to independent reviewers , ensuring that revision was done blindly until the final compilation of results . The ARRIVE guidelines were used to analyze the papers focusing on the “Material and methods” section to evaluate the degree of compliance of the publicatons with the guidelines . Issues addressing animal model information , husbandry conditions , ethics and strategies implemented to follow 3R’s Principles were compared to the checklist . Refined approaches established to avoid or minimize pain or stress such as days acclimation before the study starts , refined oral administration with minimum volume and / or oral gavage replacement with a pipette tip instead of oral gavage were considered . Also , anticipated end points determined by parasitaemia peak or severe adverse drug effects were included as refinement strategies . Reported information rates before and after the ARRIVE guidelines publication were compared using Chi-square test . P values < 0 . 05 were considered statistically significant in all cases . Statistical calculations were performed in R version 3 . 1 ( The R Foundation for Statistical Computing ISBN 3-900051-07-0 ) .
Before ARRIVE guidelines publication , animal models for Chagas disease were more diverse . Even though Mouse ( Mus musculus ) was the most popular species used ( 34 / 39 ) , some papers reported studies on Rat ( Rattus norvegicus ) and Dog ( Canis lupus familiaris ) . When Mouse models were used , inbred strains were used slightly more than outbred stocks ( 18 / 32 vs . 14 / 32 ) . After ARRIVE guidelines were published , the only animal species reportedly employed to assess in vivo efficacy of new compounds for Chagas disease was the Mouse . Half of the publications used inbred strains ( 22 / 44 ) . There was a considerable predominance of BALB/c strains and Swiss mice stocks in the inbred and outbred experiments , respectively . Table 1 shows in detail all animal models and strains employed . Before ARRIVE guidelines appeared , animal gender was not reported in 13% of papers ( 5 / 39 ) while females were more used than males ( 20 / 39 vs . 12 / 39 ) and both sexes were used in two papers . After ARRIVE guidelines , male and female animals were employed almost in the same proportion ( 16 / 44 vs . 20 / 44 ) and only one paper used both sexes in the same experiment . Within this period , sex information was not reported in 16% of papers ( 7 / 44 ) ( Table 2 ) . Basic details about animal age , weight and microbiological status and source ( Tables 3 and 4 ) were not provided in almost half of the reviewed publications , both pre- or post publication of ARRIVE guidelines . Macroenvironmental information such as room temperature was detailed in nearly the same proportion before and after ARRIVE guidelines publication ( 10 / 39 vs . 16 / 44 ) , but reporting of light/dark cycle increased from 23% ( 9 / 39 ) to 43% ( 19 / 44 ) respectively , although not statistically significant . In addition , more than half of the analyzed studies in both periods failed to mention any macroenvironmental parameters or included ambiguous information . However , the percentage decreased from 69% ( 27 / 39 ) to 57% ( 25 / 44 ) after publication of ARRIVE guidelines ( Table 5 ) , but this change was not enough to reach statistical significance . Concerning microenvironmental conditions , access to food and water ( mostly ad libitum ) , was reported in same proportion ( 17 / 39 vs . 21 / 44 ) in papers published before or after ARRIVE guidelines appeared . Similarly to macroenvironmental details , more than half of the analyzed studies ( 22 / 39 and 27 / 44 ) failed to provide any microenvironmental information for both periods of time ( Table 6 ) . Before ARRIVE guidelines became widely available , approximately 51% of papers on animal models evaluating drugs for Chagas disease included a statement about compliance with local or international guidelines for experimentation with animals . This percentage increased to 66% ( p = 0 . 26 ) after ARRIVE guidelines were published . Reporting of protocol revision and approval by an Institutional Animal Care and Use Committee ( IACUC ) increased from 26% ( 10 / 39 ) to 41% ( 18 / 44 ) after ARRIVE guidelines publication ( p = 0 . 22 ) ( Table 7 ) . The number of papers that mentioned refined strategies and procedures increased from 15% ( 6 / 39 ) to 23% ( 10 / 44 ) after ARRIVE guidelines publication . Ten publications mentioned proceedings which would require anesthesia/analgesia ( such as terminal bleeding . bioluminescence imaging techniques ) ; seven of these reports ( 70% ) described the procedures only as “under anesthesia” or “with anesthetized mouse” without further details , while two of them specifically reported isofluorane use . The vast majority of the reviewed papers failed to mention euthanasia methods . Some papers published before ARRIVE guidelines applied methods not accepted nowadays; only three reported carbon dioxide use , all of them published after the ARRIVE guidelines ( Table 8 ) . The ARRIVE guidelines checklist includes statements about reporting of sample size calculations and statistical methods used . More than 60% ( 27 / 39 and 27 / 44 ) of the reviewed papers , all published after ARRIVE guidelines , reported the statistical tests used to analyze results , but only 11% ( 5 / 44 ) gave information about data distribution , Regarding experimental design , a similar proportion of papers from both periods ( 5 / 39 and 7 / 44 ) declared treatment randomization or any effort to minimize subjective bias ( e . g . randomized block design ) . None of the publications in any studied period substantiated the sample size employed ( Table 9 ) . Tables 10–16 show different characteristics of reported animal models of T . cruzi infection . To assess efficacy of new compounds , acute infection was the preferred phase to start treatment , both before and after ARRIVE guidelines publication . In only 1 ( 3% ) and nine papers ( 20% ) , before and after ARRIVE respectively , drugs were tested in both acute and chronic stage in separated essays . No rationale was provided for studying parasiticidal effects of drugs in chronic animal models . Most used T . cruzi strains were Y strain and Tulahuen , in 38 and 27% ( before ARRIVE ) and 15 and 18% papers ( after ARRIVE ) , respectively . Furthermore , thirteen publications in total reported assessing compound efficacy on more than one T . cruzi strain . A wide range of inoculum sizes were reported , from less than 1 , 000 to more than 100 , 000 trypomastigotes per animal . Inoculation was intraperitoneal in the vast majority of papers ( 87 and 95% , before and after ARRIVE respectively ) , whilst 3 to 5% of papers did not specify such information . Before ARRIVE guidelines publication , treatment was administered most commonly by oral ( 13 / 39 ) or intraperitoneal routes ( 14 / 39 ) . After ARRIVE treatment was reportedly administered by the oral route in half of the reviewed papers , while eleven ( 25% ) preferred the intraperitoneal route . Treatment initiation , schemes and duration were reported with large variations .
Research involving animal studies is essential to many disciplines in the biomedical sciences . Detailed descriptions in publications of experimental methods and results enable researchers to interpret data , evaluate results accurately , replicate findings and move science forward [9] . The “Materials and methods” section of research papers is intended to provide basic information about how the research was performed . Comprehensive reporting is essential to correctly understand how investigations were undertaken , to properly interpret findings [10] and to compare and integrate results obtained from previous experiments . Consistent reporting of animal use is directly related to scientific quality . Employed animals should not be unnecessarily stressed and should be kept under appropriately controlled conditions: poor animal welfare is likely to result in poor science [11] . Moreover , experiments involving animals have also ethical requirements and are increasingly scrutinized by the public opinion . Minimum information guidelines seek to promote transparency in experimental reporting , enhance accessibility to data and support effective quality assessment , which increases the general value of data , and therefore , of scientific evidence [7] . In this sense , some initiatives such as the Guidance for the Description of Animal Research in Scientific Publications by the National Research Council ( NRC ) , the Gold Standard Publication Checklist ( GSPC ) [12] and the Animals in Research: Reporting in vivo Experiments ( ARRIVE ) [8] , have been published with the aim to be adopted as a requirement for publication . For this review , ARRIVE guidelines were used as a benchmark to measure quality in animal use reporting use because of its wide acceptance , and its useful checklist to easily identify key information . Chagas disease is one of the seventeen neglected disease prioritized by World Health Organization and a secure and effective treatment is urgently needed . Despite high throughput screening systems and growing capacity to identify anti-T . cruzi compounds , both from pharmaceutical companies’ libraries and the public domain , many lead compounds with promissory results in animals models of infection have unfortunately failed in clinical trials [3 , 13] . To evaluate the quality of information reported in articles referred to new compounds for Chagas disease , we contrasted descriptions of animal use and care with those descriptions suggested by the ARRIVE Guidelines , using the guidelines checklist . In order to compare the quality of report before and after the ARRIVE guidelines publication , the date period for the search was selected from 2008/06/30 to 2014/06/30 , a year after ARRIVE guidelines first appearance in print . We observed that before publication of the ARRIVE guidelines , animal species used as models for experimental infection with T . cruzi seemed more diverse , even though mice were the most commonly employed . After ARRIVE publication , Mus musculus was the only species used to assess efficacy of new chemotherapies for Chagas disease in the papers published . This election of animal model may be explained by the historical use of mice to evaluate new compounds [14 , 15] and by the conclusions reached at the Experimental Models in Drug Screening and Development for Chagas Disease workshop , held in Rio de Janeiro , Brazil in 2008 , which suggested the use of the Mouse model [16] . However , no justifications for the chosen animal models were provided in any of the papers reviewed . There is possibly no ideal animal model to test drugs for Chagas disease ( i . e . an exclusively Human disease ) , but some models may better mimic particular aspects of the disease [17] . Given that only animal species used after publication of ARRIVE guidelines , ( i . e . the Mouse ) , may not resemble all Chagas disease stages and their complexity in the human host , other animal species with pathogenesis more similar to the Human ( eg . Guinea pigs ( Cavia porcellus ) ) [18] could be employed , depending on the aim of the research or whether encouraging results are obtained in a murine model of infection . Among mice , we observed that inbred and outbred strains were employed in same proportion . Strain selection is a crucial decision due differences and variability in response . Inbred strains are genetically defined and frequently stable , homogenous , and more often lead to repeatable outcomes than ‘‘genetically undefined” outbred stocks . Experiments with Mouse inbred strains may be more powerful with more accurate dose-response relationships and fewer false negative results than those carried out using outbred stocks [19] . Outcomes in animal models of T . cruzi infection are dependent on many factors , including animal species , strain , age , sex , T . cruzi strain , inoculum size and route of infection , among others [20] . Since several variables contribute to establish an in vivo model , reported information must be as detailed as possible . In the papers reviewed from the period after ARRIVE publication , information on animal gender was not provided in seven publications ( 16% ) , a proportion similar to that observed before ARRIVE guidelines publication ( 13% ) . In a similar previous survey , Kilkenny et al . revealed that in 24 of 72 reviewed papers ( 33% ) sex was not reported [21] . This observed lack of detail in reporting undermines repeatability and robustness of studies , since some research suggests that response to infection in male mice is different from females which are , apparently , more resistant to T . cruzi infection [22 , 23] . Interestingly , a few of the reviewed papers used both sexes , but did not analyze results with a factorial design missing the opportunity to test for interactions between factors ( sex ) and drug response or disease progression [23–25] . Regrettably , other key information such as age and weight range at time of infection was missing in more than the half of the publications both before and after the ARRIVE guidelines publication . This omission prevents further testing of covariates , if necessary , as these variables may modify the final outcome of certain models . Animal source is not reported in more than 50% of papers , irrespective of period studied; This , added to the lack of information on animal microbiological status , goes clearly in detriment of quality standards of any preclinical studies . Besides , results can be distorted and misunderstood by concurrent infections , as reported previously in a biological characterization of a T . cruzi strain [26] . Macro and microenvironment are essential variables which influence animal well-being and , accordingly , repeatability and reproducibility of the results . This information was incomplete or totally absent in more than half of the papers from both periods reviewed . ARRIVE guidelines do not seem to have had an impact on reporting of these variables since there were no significant differences between information provided in articles published before or after ARRIVE release . For instance , exposure to wide extremes temperature may result in behavioral , physiologic , and morphologic changes , which might negatively affect animal well-being and research performance as well as outcomes of research protocols . [27] . The standard temperature range for mice and other rodents is 23 ± 3°C to prevent triggering compensatory thermoregulatory mechanisms that can affect animal health , and alter experimental results . A good management program provides environment , housing , and care that minimizes variations that can affect research [28] . Unfortunately , more than half of the papers evaluated from both periods did not report any information about macroenvironmental conditions . Similarly , more than 50% of publications did not report any information relative to microenvironment variables . These factors can potentially influence experimental results and are therefore scientifically important so it is unclear why omission of these essential details is so prevalent . Two papers admitted to housing animals in individual cages , “for better management” . This can make workload easier for personnel , but when it comes to animal welfare , individual caging can be more harmful since solitary confinement may increase alter immunological responses , produces changes in body and organ weights and alterations in blood cell counts , among others , potentially affecting drug response [28] . Adding to this , there is a growing concern in public opinion about animal laboratory testing and a well-detail husbandry conditions may contribute to proper understanding even to lay public . What is written in those reports , and how it is written , may thus be crucial to the public perception of animal experiments [11] . Investigators conducting research with animal subjects have an ethical and legal responsibility to ensure they are treated humanely . Scientists are required to conduct their studies in compliance with a framework of federal , state , local , and institutional rules and regulations [29] . It is widely accepted that applying 3R’s Principles to experiments using animals is in consonance with good scientific practice [21] . Since there is no validated replacement method yet to assess efficacy and safety compounds for Chagas disease treatment in humans , animals models are expected to fill the gap between in vitro testing and clinical trials . Therefore , strategies to refine procedures and reduce pain and distress are desired . Typically , parasitaemia values and mortality were the principal outcomes used to assess trypanocidal activity in the papers reviewed . Times have changed and it is currently necessary to establish and validate anticipated endpoints ( i . e . endpoints that can predict death and can be used to avoid unnecessary suffering or distress in the experimental animals ) , which carries benefit for both researchers ( e . g . they do not lose samples for histopathology , sera , etc ) and animals ( e . g . avoiding stressing death as result of sickness behavior and septic shock ) [30] . At any rate , only 14% ( 5 / 36 ) and 23% ( 10 / 44 ) of papers ( before and after ARRIVE guidelines publications , respectively ) applied any refinement strategy , including anticipated endpoints at parasitaemia peak or with severe adverse effects , with a clear , but unjustifiably modest , increment in papers appeared after ARRIVE guidelines arise . A comprehensive analysis of experimental design and statistical methods is beyond the scope of this review , but many topics recommended in the ARRIVE guidelines are missing or incomplete from analyzed publications , . Treatment randomization , an essential step to avoid experimental bias , was declared only in 16% ( 7 / 44 ) publications . Sample size was not justified in any of the papers , suggesting that there was no previous sample size calculation , and that animal numbers were more a matter of habit than a statistical decision . One may argue that is difficult to predict parasitaemia levels in this models , and that dispersion is very large ( due in part to direct counting methods in Neubauer chamber or between glass side and cover slip ) which would make determining accurate sample size difficult . Nevertheless , strategies exists to justify number of animals employed such as conducting previous pilot studies , or applying Mead’s resource equation , suitable in cases where there is no information about standard deviation and/or it is difficult to specify an effect size [31 , 32] . These results agree with a previous quality of reporting survey which observed that only 5 in 72 ( 7% ) of studies using mice informed sample size calculations or treatment randomization [21] . Statistical methods were declared and detailed in nearly 2/3 of publications , but since there was no information about data distribution , a proper analysis of the correct application of these methods cannot be established . As result from a workshop held in 2008 [16] , guiding principles for drug testing in animal models of Chagas disease were put forward , in which certain experimental variables were agreed upon in order to perform similar research in different groups , allowing to screen candidate compounds and discard or move forward rapidly compounds to further testing . Although original standardized protocols could be modified and updated , the initiative was very promissory but only partially accepted by the Chagas scientific community given the variety of existing animal models using different mice and parasite strains , inoculums sizes , treatment schedules and others differing factors . Unfortunately , it seems that standardization of animal models is no easy in the field , possibly due to difficulties in accessing animal and/or parasite strains from different those already in use by each group . The fact that parasite strains in particular , are not easy ( or cheap ) to transfer across country borders , among other issues , should be kept in mind when judging this difficulties . Finally , regarding suggestions put forward by Romanha et al . , only 50% of the publications described the use of oral treatment ( as suggested ) , and 63% of the studies started treatment with patent parasitaemia . Also , less than one-fourth ( 22% ) of studies performed a treatment for at least 20 consecutive days , indicating an incomplete and partial adherence to the suggested guidelines for in vivo drug screening for Chagas disease [16] . Our results illustrate a general lack of compliance with ARRIVE guidelines in research involving animals for testing of efficacy of new compounds for Chagas disease treatment . Other fields in preclinical research are not exempt from these problems , according to conclusions obtained in a survey conducted in experimental autoimmune encephalomyelitis and multiple sclerosis two years before ARRIVE publication [33] . Unfortunately , we observed that publication of clear guidelines such as ARRIVE was not sufficient to improve reporting of animal studies , at least in the field of Chagas disease drug research . In conclusion , a systematic review has been carried out to measure adherence degree to ARRIVE guidelines in animal models for new chemotherapy for Chagas disease treatment . There is vast key information missed or incomplete which difficult proper evaluation and comprehension of obtained results . Ensuring animal well-being and responsible use while meeting scientific aims must be emphasize to allow translational research to contribute to resolve affected population problems . This review does not want to cast doubt the results obtained in the evaluated papers , and is not its matter examine their scientific merits . On the contrary , it attempts to warn about the weak reporting quality in search of new chemotherapy compounds for Chagas disease and has a teaching intention to encourage scientific community to adopt ARRIVE guidelines to correctly report their preclinical trial results and to unify animal models in order to maximize obtained information and to be more transparent inside and outside the academic field . We did not observe an improvement in publication quality after ARRIVE guidelines publication , compared to the previous period . There is a clear need to improve design and reporting of animal research studies in Chagas disease . Full compliance with ARRIVE guidelines would be a welcome starting point .
|
There is a growing concern about animal use for scientific purposes . In order to maximize impact of results obtained and avoid unnecessary experiment repetition , certain strategies have been implemented such as publication of guidelines to encourage detailed reporting of animal studies . We contrasted information reported in publications assessing new compounds for Chagas disease in animal models with the suggestions contained in the ARRIVE guidelines . A significant lack of compliance with the guidelines was observed even years after ARRIVE publication , including lack of minimally expected information . These results illustrate the importance of promoting inclusion of detailed information on animal use , in Chagas disease research in particular , but also in all research reporting in general .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Quality of Reporting and Adherence to ARRIVE Guidelines in Animal Studies for Chagas Disease Preclinical Drug Research: A Systematic Review
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β-Catenin , the core element of the Wnt/β-catenin pathway , is a multifunctional and evolutionarily conserved protein which performs essential roles in a variety of developmental and homeostatic processes . Despite its crucial roles , the mechanisms that control its context-specific functions in time and space remain largely unknown . The Wnt/β-catenin pathway has been extensively studied in planarians , flatworms with the ability to regenerate and remodel the whole body , providing a ‘whole animal’ developmental framework to approach this question . Here we identify a C-terminally truncated β-catenin ( β-catenin4 ) , generated by gene duplication , that is required for planarian photoreceptor cell specification . Our results indicate that the role of β-catenin4 is to modulate the activity of β-catenin1 , the planarian β-catenin involved in Wnt signal transduction in the nucleus , mediated by the transcription factor TCF-2 . This inhibitory form of β-catenin , expressed in specific cell types , would provide a novel mechanism to modulate nuclear β-catenin signaling levels . Genomic searches and in vitro analysis suggest that the existence of a C-terminally truncated form of β-catenin could be an evolutionarily conserved mechanism to achieve a fine-tuned regulation of Wnt/β-catenin signaling in specific cellular contexts .
The Wnt/β-catenin pathway is an evolutionarily conserved intercellular signaling pathway with essential roles in virtually every developmental process [1–3] and links to a wide range of human diseases [3–7] . Given its multiple , context-dependent roles , the pathway must be extensively regulated . A key element of this pathway is β-catenin , a bi-functional protein first discovered as a component of adherens junctions [8 , 9] . β-catenin transduces the Wnt signal to the nucleus [5 , 10] and is primarily regulated at the level of nuclearization . Binding of Wnts to their receptors ( Frizzleds and LRP5/6 ) uncouples the β-catenin destruction complex ( mainly composed of APC [adenomatous polyposis coli] , Axin , CK-1 and GSK-3 ) and promotes β-catenin stabilization and its nuclear translocation [5 , 11–14] . Inhibition of the ligand-receptor interaction through secreted inhibitory molecules ( WIF , sFRP , DKK ) also represents a common level of Wnt/β-catenin signal regulation [15–18] . Since β-catenin does not have a DNA binding domain , once it reaches the nucleus it must interact with a member of the DNA‑binding T cell factor/lymphoid enhancer factor ( TCF/LEF ) family to regulate its downstream targets [19 , 20] . Several β-catenin/TCF partners have been identified , which mainly target the C-terminal part of β-catenin , and which confer master regulatory properties to β-catenin since they are mainly involved in regulating chromatin structure and RNA polymerase II [21 , 22] . Thus , the final activity of β-catenin relies not only on its nuclearization but also on its ability to bind to TCFs and their nuclear co-factors . Although an increasing number of factors have been reported to modulate the transcriptional activity of the β-catenin/TCF complex ( e . g . ICAT , Groucho and Chibby ) [20 , 22–27] , the regulation of β-catenin activity once it reaches the nucleus remains poorly understood . Planarians , flatworms with an almost unlimited ability to regenerate and remodel their tissues during their whole life span [28–30] , have become a robust model to study the function of Wnt/β-catenin signaling in different developmental contexts [31–39] . Although most organisms have a single bi-functional β-catenin protein , gene duplication and functional specialization have led to the generation of two β-catenins in planarians: Smed-β-catenin1 ( β-cat1 ) and Smed-β-catenin2 ( β-cat2 ) [40] . β-cat1 is the intracellular effector of Wnt/β-catenin signaling but exerts no role in cell adhesion , whereas β-cat2 is exclusively found in cell-cell junctions [40] . This functional diversification provides an ideal scenario in which to study the Wnt signaling properties of β-catenin . A functional specialization of β -catenins has also been found in the nematode C . elegans . However , their specific role in nuclear signaling appears extremely complex and , apparently , divergent [41 , 42] . Thus , although genetic tools to generate cells that exclusively lack canonical Wnt pathway activity have been reported in mouse [43] , planarians represent an excellent scenario in which to study the signaling properties of β-catenin in vivo without interference of the cell adhesion properties . Functional analysis of β-cat1 and the main elements of the Wnt/β-catenin signaling pathway demonstrate an essential role for this pathway in the specification of the antero-posterior ( A-P ) axis during planarian regeneration and homeostatic cell turnover [31–34 , 44] . β-cat1 silencing generates a range of anteriorized phenotypes , from “tailless” to “radial-like hypercephalized” planarians [32 , 45] . Recently , novel functions for β-cat1 have been reported in planarian brain and eye regeneration , and in gonad development [34 , 36 , 46] . Importantly , analysis of β-cat1 protein localization reveals that it is present in the nucleus of posterior cells , according to its role in A-P axial identity specification , and also in the main planarian tissues [45] . Thus , given that it has both activity- and context-dependent effects , nuclearization alone cannot account for its regulation . This makes planarians an excellent model to further understand how the transcriptional activity of β-catenin might be regulated once it is in the nucleus . Here we report the existence of two new planarian β-catenins , β-catenin3 ( β-cat3 ) and β-catenin4 ( β-cat4 ) , which have a truncated C-terminal transactivation domain and are expressed primarily in the nervous system . β-cat3 and β-cat4 can bind to TCF but do not activate the Wnt signal in vitro . Functional analysis in planarians indicates that β-cat4 acts as a negative regulator of nuclear β-cat1 in planarian eye photoreceptors probably by competing for binding to TCF-2 , a new TCF found in planarian photoreceptor cells . We provide evidence to suggest that this novel mechanism for the regulation of nuclear β-catenin activity could be conserved across animal evolution .
A search for β-catenin family members in the Schmidtea mediterranea transcriptomes revealed two new genes with protein sequences indicating that they were β-catenin homologs ( S1 Fig ) . We named them Smed-β-catenin3 ( β-cat3 ) and Smed-β-catenin4 ( β-cat4 ) , since two β-catenin paralogs had been already reported in this species [31–33 , 40] . β-cat3 and 4 proteins conserve the GSK3 phosphorylation sites in the N-terminal region , and their armadillo repeats contain the interacting amino acids for multiple β-catenin-binding proteins , including APC , Axin , TCF and E-cadherin ( Fig 1A and S1 Fig ) . The α-catenin binding sites are conserved in β-cat4 but not in β-cat3 ( Fig 1A and S1 Fig ) . Importantly , the C-terminal transactivation domain , which interacts with crucial chromatin‑dependent factors , is lost in both β-catenins ( Fig 1A and S1 Fig ) . The finding that the Wnt signaling domains but not the transactivation domain are conserved suggests that β-cat3 and 4 could function as dominant-negative forms of β-cat1 , which is the β-catenin homolog involved in signaling to the nucleus in planarians [37 , 40] . Since β-cat3 and 4 contain several conserved domains or residues involved in Wnt signaling and cell adhesion , we further tested the interaction of β-cat3 and 4 with the main components involved in these two processes in mammalian cell lines . Co-immunoprecipitation experiments indicated that β-cat4 strongly interacts with the cell-cell adhesion elements E-cadherin and α-catenin ( S2A Fig ) , whereas β-cat3 showed interaction with E-cadherin but not α-catenin ( S2B Fig ) . This observation is consistent with the conservation of their functional protein domains ( Fig 1A and S1 Fig ) . In this experiment , β-cat1 was used as a control , since it retains the essential residues for the interaction with E-cadherin , but not the conserved α-catenin binding domain [40] . Interestingly , both β-cat3 and 4 were able to interact with the elements involved in Wnt signaling , β-Trcp and TCF ( Fig 1B ) . In this experiment , planarian β-cat2 was used as a control and , according to its reported role in cell adhesion but not in the Wnt cascade [40] , it showed no binding to β-Trcp or TCF . Moreover , immunofluorescence assays revealed co-localization of β-cat3 and 4 with Axin , the core element of the β-catenin destruction complex , which is also consistent with their protein sequence analysis ( Fig 1C ) . Thus , these results indicate that β-cat3 and 4 are under the control of the β-catenin destruction complex , and have the potential to bind to their nuclear co-factor TCF . Considering the loss of their C-terminal transactivation domain , our results further support the hypothesis that β-cat3 and 4 could act as dominant negative forms of β-cat1 . To test this hypothesis , we used the Super-TOPflash reporter system in HEK293T cells [47] to analyze the potential of β-cat3 and 4 to activate Wnt/β-catenin signaling . Whereas β-cat1 activated the reporter significantly , consistent with its reported role in Wnt signal transduction [40] , β-cat3 and 4 had no effect on the reporter , even after increasing the dosages ( Fig 1D ) . Consistent with its specific role in cell adhesion [40] , the β-cat2 paralog was also not able to activate the Super-TOPflash reporter . Importantly , when β-cat1 was co-transfected together with β-cat3 or 4 , the levels of reporter activity decreased in a dose-dependent manner ( Fig 1E ) . The same result was obtained when analyzing the axial induction capability of planarian β-catenins in Xenopus embryos ( S2C Fig ) . To further test whether β-cat3/4 could act as competitors of β-cat1 for the binding to TCF , we performed a binding competition assay . Following co-transfection of HEK293T cells with β-cat1 , β-cat4 and TCF , quantitative analysis indicated that β-cat1 and β-cat4 disrupt each other’s binding to TCF ( Fig 1F ) . The specificity of this competition is supported by the finding that co-transfection of β-cat2 does not alter the binding of β-cat1 or 4 to TCF ( Fig 1F ) . These results demonstrate that β-cat3 and 4 do not show any transactivation properties and that their expression inhibits β-cat1 activity ‘in vitro’ or in a heterologous system . Furthermore , both β-cat4 and β-cat1 are able to bind to TCF . These results are consistent with a role of β-cat3 and 4 as competitive inhibitors of β-cat1 . Since β-cat3 and 4 act through inhibition of β-cat1 , they could be essential in any of the processes in which β-cat1 is involved , such as posterior identity specification or organogenesis [31–34 , 36 , 45 , 46 , 48] . Whole-mount in situ hybridization ( WISH ) in intact and regenerating animals showed that β-cat3 and 4 are expressed in the parenchyma and in the central nervous system of intact animals , as well as in the new regenerating brain ( S3A and S3B Fig ) . Remarkably , β-cat4 is also highly expressed in the eyes ( S3A and S3B Fig ) , specifically in photoreceptors , since fluorescent in situ hybridization ( FISH ) analysis demonstrated that it is exclusively expressed in opsin+ cells ( Fig 2A ) [49] . No apparent defects were observed in regenerating β-cat3 ( RNAi ) planarians ( S3C Fig ) . In contrast , compared to control animals , β-cat4 ( RNAi ) animals regenerated smaller eyes , with smaller pigmented spots and missing the periglobular unpigmented epidermis , which corresponds to the photoreceptor area ( Fig 2B ) . Importantly , posterior identity specification , which is disrupted in β-cat1 ( RNAi ) animals [31–33] , was not affected after β-cat4 ( RNAi ) ( S3C and S3D Fig ) . The efficiency and specificity of the RNAi inhibition was assessed by qPCR , showing that β-cat4 RNAi animals show highly reduced levels of β-cat4 but not of β-cat1 , 2 and 3 mRNA ( S3E Fig ) . Thus , we focused on the study of β-cat4 function specifically in the eye . Planarian eyes are simple structures comprising two main , well-characterized cell types: photoreceptors and pigment cells [50 , 51] . The number of each cell type was quantified during regeneration of β-cat4 ( RNAi ) planarians by analyzing the expression of opsin and tph , specific markers of photoreceptor and pigment cells , respectively [52] ( Fig 2B and S4 Fig ) . Remarkably , RNAi of β-cat4 resulted in reduced photoreceptor cells early in regeneration ( R4d ) , whereas pigment cells did not show significant differences at this stage ( Fig 2B and S4 Fig ) . As regeneration progressed , β-cat4 ( RNAi ) animals showed a significantly reduced number of pigment cells compared to control ( Fig 2B and S4 Fig ) , possibly due to a non-autonomous effect [53] . Consistent with the effects on photoreceptor cells , β-cat4 ( RNAi ) animals did not show the proper negative phototaxis behavior ( Fig 2C ) . When exposed to a light gradient , all control animals moved away from the light and remained in the darkest zone ( zone 3 ) . Conversely , although β-cat4 ( RNAi ) organisms seemed to move normally , most of them remained in the clearest zone and did not reach the darkest zone in the same time period ( Fig 2C and S1 and S2 Movies ) . These data show that β-cat4 silencing causes a reduction of photoreceptor cells , followed by a reduction of pigment cells , and influences their normal behavioral responses to light , suggesting its role in photoreceptor specification . Since planarians continuously remodel their tissues [28] , we analyzed whether β-cat4 is also required for eye cell maintenance during normal planarian homeostasis . Injection of β-cat4 dsRNA over a period of 5 weeks produced a decrease in eye size and in the photoreceptor area ( S5A Fig ) . Quantification of photoreceptor and pigment cells through opsin and tph FISH over the 5 weeks of the experiment revealed that β-cat4 ( RNAi ) animals always have fewer photoreceptor cells ( S5A Fig ) . The gradual reduction of photoreceptor cell number observed in control animals ( S5A Fig ) is due to shrink age of the animals , which remained starved over the 5-week period . According to the phenotype , β-cat4 ( RNAi ) animals showed a defective negative phototaxis response that got worse as the experiment progressed ( S5B Fig ) . Thus , these data demonstrate that β-cat4 is required for photoreceptor maintenance during homeostasis . Planarian photoreceptor and pigment cells differentiate from progenitor cells that are located as a trail of cells extending caudally from the eye and express the pan-eye marker ovo [54] . A small number of eye progenitor cells co-express ovo with stem-cell specific markers ( h2b ) and correspond to the specialized eye stem-cells [54] . Eye stem cells acquire the expression of specific determinants that direct their final fate to photoreceptor or pigment cells [52 , 55] . In order to understand the mechanism by which β-cat4 influences eye regeneration , β-cat4 expression was further studied with FISH . Besides its expression in eye photoreceptor cells , β-cat4 was found to be expressed in the trail of eye precursors posterior to the eye both in intact animals and during regeneration ( Fig 2D and S5C Fig ) . Double FISH analysis of β-cat4 with ovo and sp6-9 , a pigment cell determinant , revealed that β-cat4 is exclusively expressed in photoreceptor but not pigment-cell progenitors , since it was always co-expressed with the eye marker ovo but never with the pigment-specific marker sp6-9 ( Fig 2E and S5D Fig ) . In addition , a few isolated β-cat4+ cells were also found to be h2b+ ( Fig 2F ) , indicating that β-cat4 is already expressed in the eye stem cell . To test whether β-cat4 is required for specification of photoreceptor cells from the common eye stem cell precursor , we quantified the number of photoreceptor and pigment-cell progenitors in the eye trail of β-cat4 ( RNAi ) animals using the specific markers otxA and sp6-9 , which label differentiating photoreceptor and pigment cells , respectively [52 , 54 , 55] . As expected , β-cat4 ( RNAi ) animals had a reduced number of otxA+ cells in the eye from early regeneration stage and a later decrease in sp6-9+ cells ( S6 Fig ) . Importantly , the same result was observed when quantifying otxA+ and sp6-9+ cells in the trail; β-cat4 ( RNAi ) resulted in failure of photoreceptor progenitor-cell specification from the early regeneration stage ( R3d ) , whereas pigmented cells appeared reduced at a later stage ( R7d ) ( Fig 2G and S6 Fig ) . Thus , β-cat4 is required for photoreceptor progenitor-cell specification from the common eye stem cell ( Fig 2H ) . Considering that β-cat4 inhibits TCF-mediated β-cat1 activity in cell cultures and that it has lost the C-terminal transactivation domain , β-cat4 could inhibit Wnt signaling in photoreceptors by competing with β-cat1 for TCF binding in the nucleus . To test whether this molecular mechanism could be functional in planarians , we first analyzed β-cat1 and β-cat4 expression in the planarian eye field . FISH for β-cat1 followed by immunostaining of VC-1 , which labels the rhabdomeres of photoreceptor cells [52 , 56 , 57] , showed that β-cat1 was expressed in regenerating photoreceptor cells ( S7A Fig ) . Moreover , double FISH for both β-cat1 and β-cat4 mRNA revealed that both are found in photoreceptor cells ( S7A Fig ) . Using a specific antibody generated in this study ( S7B Fig ) we could demonstrate that β-cat4 protein is localized in the nucleus of photoreceptors ( Fig 3A ) . Immunostaining with a β-cat1-specific antibody [45] , revealed that β-cat1 is also localized in the nucleus of photoreceptor cells in S . polychroa , the sister species of S . mediterranea ( Fig 3A ) . Thus , our results show that both β-cat1 and β-cat4 are localized in the nucleus of photoreceptor cells , which is consistent with their nuclear interaction . Next , we performed RNAi experiments to analyze the functional relationship between the two β-catenins . Planarians were decapitated after β-cat1 dsRNA injection and allowed to regenerate . The efficiency of the inhibition was tested by qPCR ( S7C Fig ) . Newly formed heads showed “slanted eyes” with a very thin and elongated periglobular unpigmented epidermis and pigmented cup ( Fig 3B ) . The observed phenotype is very similar to a previously reported β-cat1 ( RNAi ) [38] . FISH with eye-specific markers confirmed that β-cat1 ( RNAi ) led to a disordered eye structure , in which photoreceptor and pigment cells formed larger eyes and in which ectopic eye cells appeared ( Fig 3B , S7D Fig and S3 and S4 Movies ) . Quantification of opsin+ and tph+ cells present in the eye structure showed an increase with respect to control animals ( Fig 3B and S7D Fig ) . This result is the opposite of that found in β-cat4 ( RNAi ) planarians , which had a decrease in the number of photoreceptor cells , thus supporting the opposing role of these β-catenins . Since techniques for overexpression are currently unavailable in planarians , we took an indirect approach to up-regulate β-cat1 through silencing APC-1 , the APC homolog in planarians [31 , 34] . APC-1 ( RNAi ) leads to β-cat1 up-regulation and nuclear accumulation [45] , resulting in the regeneration of a tail at anterior wounds [31 , 34 , 45] . In order to analyze the eye field in APC-1-knockdown planarians , we performed RNAi experiments in intact animals , since it is known that APC-1 silencing during two weeks does not lead to tail-head transformation [45] . We injected dsRNA for APC-1 , β-cat4 , and APC-1;β-cat4 for 2 weeks . The efficiency of the inhibition was analyzed by qPCR ( S7E Fig ) . As expected , β-cat4 ( RNAi ) caused smaller eyes with a decrease in both photoreceptor and pigment cell numbers compared to controls ( Fig 3C ) . Importantly , APC-1 ( RNAi ) generated the same phenotype ( smaller eyes with fewer photoreceptor and pigment cells ) ( Fig 3C ) . Co-silencing APC-1 and β-cat4 caused an even more severe decrease in the number of photoreceptor cells ( Fig 3C ) . This last result is consistent with the hypothesis that β-cat4 competes with β-cat1 in the nucleus inhibiting its transcriptional activity . However , it should also be considered that , since both β-catenins have the potential to be regulated by the destruction complex , in APC RNAi animals not only β-cat1 but also β-cat4 could be stabilized . β-cat1 gain of function through APC-1 ( RNAi ) results in the same phenotype of diminished eye cell numbers as β-cat4 ( RNAi ) , whereas β-cat1 loss of function causes the opposite effect . Thus , β-cat1 , as a key downstream transcriptional co-activator in Wnt signaling , plays a negative role in planarian photoreceptor development . To further understand the functional relationship of β-cat1 and β-cat4 in photoreceptor cells , the phenotype of the double RNAi was analyzed . The efficiency of the inhibition was analyzed by qPCR ( S7F Fig ) . The result shows that co-silencing β-cat1 and β-cat4 causes extremely disorganized eyes , with abundant delocalized eye cells , which show a reduction in the number of photoreceptor and pigment cells ( Fig 3D ) . This result does not support the hypothesis of β-cat4 directly acting as a dominant-negative form of β-cat1 , but suggests alternative competition models ( see discussion ) . Furthermore , the appearance of ectopic eye cells in β-cat1 RNAi animals , and the severe disorganization of the eyes of β-cat1/β-cat4 RNAi planarians also suggest that β-cat1 could exert additional autonomous roles , in pigment or neuronal cells , which influence the localization of photoreceptor cells . Overall , our results indicate that the activity of β-cat1 in the eyes is not only controlled by the elements of the β-catenin destruction complex , like APC-1 , but also by β-cat4 , which exerts a negative regulatory effect on a process that is β-cat1 and APC dependent . To gain further insight into the mechanism through which planarian β-catenins specify photoreceptor differentiation , we searched for the TCF transcription factor that acts as a target . Although most invertebrate genomes contain a single TCF/LEF ortholog , we identified three TCF orthologs ( Smed-TCF-1 to -3 ) in the S . mediterranea transcriptome database Planmine [58] ( S8A Fig ) . The corresponding homologs were found in five more planarian species in the same database ( S8A Fig ) . The phylogenetic analysis suggests that the duplications found in planarians arise from Platyhelmintes and are independent of the vertebrate TCF expansion ( S8A Fig ) . Protein sequence analysis of the three S . mediterranea TCFs demonstrated that TCF-2 is the only S . mediterranea TCF that conserves all functional domains required to bind to β-catenin , Groucho and DNA ( Fig 4A and S9 Fig ) [59] . TCF-1 has lost the β-catenin binding domain and TCF-3 does not conserve the Groucho binding sites ( S9 Fig ) . Analysis of their expression pattern in planarians showed that TCF-1 was expressed specifically in the planarian brain , as previously reported [60] ( S8B Fig ) . TCF-2 and -3 are also mainly expressed in the CNS and , importantly , TCF-2 is found in photoreceptors ( Fig 4B ) , strongly resembling the β-cat4 expression pattern . Thus , TCF-2 is a candidate to function as a β-cat1 and β-cat4 target during photoreceptor development . The eyes of TCF-2 RNAi animals were analyzed to understand its possible function . The efficiency of the inhibition was analyzed by qPCR ( S8C Fig ) . Analysis of TCF-2 RNAi regenerating animals revealed that their eyes were bigger than in controls ( Fig 4C and S8D Fig ) . The brain of TCF-2 RNAi animals appeared normal ( S8E Fig ) , which suggests that the larger eye phenotype is eye specific . Quantification of the different eye populations demonstrated that TCF-2 silencing results in an increased number of photoreceptor cells ( Fig 4C and S8D Fig ) . The number of pigment cells also increased ( Fig 4C and S8D Fig ) , probably due to the cellular relationship between the two compartments , as shown earlier in β-cat4 RNAi planarians . The TCF-2 ( RNAi ) phenotype in the eyes phenocopies the β-cat1 ( RNAi ) phenotype with respect to the number of photoreceptor cells and is consistent with the hypothesis that Wnt/β-cat1 signal inhibition is required for correct planarian photoreceptor specification . To note , TCF-2 RNAi animals showed bigger eyes but not the appearance of ectopic eye cells , suggesting that this defect is caused in a TCF-2 independent manner . To analyze whether β-cat4 function in photoreceptor specification also depends on TCF-2 , we performed a double RNAi assay to inhibit β-cat4 and TCF-2 simultaneously during planarian regeneration . The efficiency of the inhibition was tested by qPCR ( S8F Fig ) . As expected , β-cat4 ( RNAi ) planarians had smaller eyes with a reduced number of photoreceptor cells , whereas TCF-2 ( RNAi ) resulted in larger eyes with an increased number of photoreceptor cells . Remarkably , the size of the eyes in double β-cat4 and TCF-2 RNAi animals , and the number of photoreceptor and pigment cells , resembled the phenotype observed with TCF-2 ( RNAi ) alone ( Fig 4D ) . This observation is consistent with a role for TCF-2 as the transcription factor downstream of β-cat4 . Above all , the data suggests that β-cat4 , which lacks the C-terminal transactivation domain , modulates the β-cat1/TCF-2-mediated signal for the correct differentiation of photoreceptor cells ( Fig 5 ) . To determine whether the existence of inhibitory β-catenins could be an evolutionary conserved mechanism for the regulation of Wnt signaling , we investigated the existence of C-terminally truncated β-catenins in other organisms . The existence of β-catenin paralogs is not exclusive to planarians . The β-catenin family has undergone a vertebrate-specific subphylum duplication ( β-catenin and plakoglobin ) [61] , two nematode-specific phylum duplications ( 4 β-catenins in C . elegans ) [41] , and multiple species-specific duplications in the Arthropoda phylum [61] ( S10 Fig ) . Phylogenetic analysis of the β-catenin family members of different Lophotrocozoa species shows the existence of a unique bi-functional β-catenin in all of them except for Platyhelmintes ( S10 Fig ) . The analysis suggests that β-catenin underwent two phylum-specific duplications in Platyhelmintes to generate β-cat1 , 2 and 3/4 classes , and that in Triclads a third duplication produced the β-catenin3 and 4 orthologs ( S10 Fig ) . Furthermore , a genus-specific duplication occurred in the β-cat3 class ( S10 Fig ) . Thus , a β-cat3/4 ortholog that retains the signaling domains but not the C-terminal transactivation domain ( S11 Fig ) exists in all Plathyhelminthes . Taking into account the number of β-catenin duplications found across evolution , we hypothesized that the existence of an inhibitory β-catenin to regulate β-catenin-dependent Wnt signaling could be a common mechanism throughout evolution as a result of convergent evolution . A protein sequence analysis of different β-catenin orthologs found across the animal phyla shows the shortening of the C-terminal end in several cases , for instance in one of the two β-catenins found in the sponge A . quensatlantica or the vertebrate β-catenin duplication Plakoglobin ( S11 Fig ) . Although Plakoglobin shows high protein sequence conservation with β-catenin in the central armadillo repeats , and shares the binding domains for α-catenin , Cadherins and TCF , it has very low amino acid sequence conservation in the C-terminal transcriptional transactivation domain ( 15% ) ( Fig 6A ) [62] . Accordingly , it has been shown that Plakoglobin has limited transactivation ability compared to β-catenin [63] . Furthermore , although a unique β-catenin is found in the genome of D . melanogaster ( Armadillo , Arm ) , a C-terminally truncated Arm named Neural Armadillo ( NArm ) has been reported to occur through alternative splicing [64] ( Fig 6B and S11 Fig ) . It is known that NArm is expressed in the brain from larval stage [64] but no functional studies have been reported . To analyze whether vertebrate Plakoglobin or Drosophila NArm could have an inhibitory function , we designed in vitro Super-TOPflash experiments . Results showed that while β-catenin ( S37A ) , a stabilized β-catenin that cannot be captured by the cytoplasmic destruction complex , could highly activate Wnt signaling , Plakoglobin activity was very low ( Fig 6A ) . Interestingly , Plakoglobin co-transfection with β-catenin ( S37A ) decreased the reporter signal in a dose-dependent manner ( Fig 6A ) . Similarly , activation of Wnt signaling by NArm was very weak compared to that induced by Arm . Importantly , co-transfection of both plasmids showed that NArm suppresses the Arm induced TCF-reporter activity in dosage dependent manner ( Fig 6B ) . Our results demonstrate that Plakoglobin and NArm inhibit the Wnt signal activated by β-catenin/Arm through a TCF transcription factor . This result , together with the presence of β-catenin paralogs in several species , indicates that the existence of an inhibitory β-catenin could be a conserved mechanism to fine tune β-catenin-dependent Wnt signaling . The phylogenetic relationship between the β-catenin family members and the existence of splice variants indicates that the inhibitory form of β-catenin would not have evolved from a common ancestor but appeared during evolution as a product of unrelated events ( species- or phylum-specific genome duplications or alternative splicing ) , thus representing an example of convergent evolution .
The multiple roles and complexity of the Wnt/β-catenin signaling necessitate regulation at different levels . Extracellularly , secreted proteins interact with Wnts or their receptors ( e . g . DKK1 , SFRP , WIF1 , notum ) to inhibit the ligand-receptor interaction [65 , 66] , and in the cytoplasm , regulation of the β-catenin destruction complex determines the amount of β-catenin that will escape phosphorylation and degradation and reach the nucleus [5] . However , the modulation of β-catenin activity once it reaches the nucleus remains poorly understood . We used planarians to approach this question since , although β-cat1 exerts multiple functions , it is primarily localized to the nucleus [31–33 , 36 , 44–46 , 48] , suggesting that mechanisms must be available to modulate β-cat1 nuclear activity . Here we found two new β-catenins ( β-cat3 and 4 ) in planarians , after the two β-catenins ( β-cat1 and 2 ) already described [40] , which 1 ) showed a C-terminal truncated transactivation domain , while conserving the TCF binding amino acid residues , and 2 ) were mainly expressed in the nervous tissues . Those findings lead to hypothesize that β-cat3 and 4 could be acting as inhibitors of the canonical β-cat1 in the nucleus and in a tissue-specific manner . Indeed , we could demonstrate that β-cat4 is required for normal specification of photoreceptors from the common eye stem cell and that its function relies on the inhibition of β-cat1 activity . Importantly , β-cat4 has no role in A-P axial polarity , in contrast to β-cat1 . Our RNAi experiments show that inhibition of β-cat4 or APC , which in planarians is demonstrated to increase β-cat1 activity [31 , 34 , 45] , produces a decrease in the number of photoreceptor cells , whereas inhibition of β-cat1 produces the opposite phenotype , indicating that β-cat4 exerts a negative regulatory effect on a β-cat1-dependent transcriptional activity . The finding that inhibition of TCF-2 , the TCF factor identified in the present study , leads to an increase in the number of photoreceptor cells , as observed after β-cat1 RNAi , and that RNAi inhibition of β-cat4 together with TCF-2 leads to the same phenotype as TCF-2 RNAi alone , indicates that TCF-2 is the downstream effector of the β-cat1 and β-cat4 action . Although we cannot rule out the possibility that β-cat1 and β-cat4 could interact in the cytoplasm , since β-cat4 conserves the essential domains to interact with cytosolic β-catenin destruction elements , our results support the regulative interaction between β-cat1 and β-cat4 at nuclear level . Thus , both β-cat1 and β-cat4 are found in the nucleus of photoreceptor cells ( although β-cat1 expression analysis was performed in a S . mediterranea sister species due to technical limitations [45] ) . Furthermore , although we have not directly demonstrated the binding of β-cat1 or β-cat4 to TCF-2 , we demonstrate that both planarian β-catenins conserve the TCF binding sites and bind to xTCF-1 in co-immunoprecipitation experiments . Overall , our results in planaria indicate that β-cat1 and β-cat4 regulate in an opposite manner the transcription of genes required for photoreceptor specification , and that this action depends on the TCF-2 transcription factor . According to the presented data , three main scenarios could be considered ( Fig 7 ) . In the first one , β-cat4 could act as a dominant negative form of β-catenin , which is able to bind to TCF-2 but not to activate transcription , due to the missing C-terminal domain ( Fig 7A ) . The RNAi phenotypes of β-cat1 , β-cat4 , and TCF-2 , agree with this possibility . However , the finding that the double β-cat1/ β-cat4 inhibition produces a decrease in the number of photoreceptors cannot be directly explained under this supposal . In the second scenario , β-cat4 could be able to directly repress transcriptional targets through binding to TCF-2 ( Fig 7B ) . In Drosophila , it has been shown that Arm and TCF can directly repress transcriptional targets and that this repressive activity of Arm does not require the C-terminal domain [67 , 68] . Thus , the balance between β-cat1/TCF-2 transcriptional activation and β-cat4/TCF-2 transcriptional repression would determine the amount of photoreceptor cells . The finding that double inhibition of β-cat1 and β-cat4 does not phenocopy the β-cat1 RNAi phenotype , favor this hypothesis . However , under this scenario the β-cat1/β-cat4 RNAi should produce a similar phenotype to the one produced after TCF-2 inhibition , which is not the observed . A third possibility would be that the proper transcriptional regulation of photoreceptor targets is achieved by the combinatory action of both β-cat1/TCF-2 and β-cat4/TCF-2 complexes when bind to different TCF responsive elements ( Fig 7C ) . In the future , the analysis of specific downstream targets of β-cat1 , β-cat4 and TCF-2 , and the possibility of performing tissue specific RNAi and rescue experiments would help to clarify the specific mechanism of β-cat1 and β-cat4 activity . Altogether , our data supports the existence in planarians of a novel level of Wnt/β-catenin signaling modulation in the nucleus through the action of β-catenins with different transactivation capacity . The regulatory interaction between those different β-catenins and TCFs would result in the modulation of the transcriptional rates of downstream Wnt target genes and Wnt-responsive activities . Regulation of β-catenin/TCF transcriptional activity is achieved through nuclear factors that regulate β-catenin binding properties ( e . g . Chibby and ICAT ) [20 , 22–27] , or regulation of TCF transcriptional and binding properties [20 , 22–27] . Accordingly , the existence of TCFs that lack the β-catenin-binding domain and act as endogenous dominant negative forms [59] , and also post-translational modifications of TCFs that modulate their activity have been extensively reported [69–74] . The C-terminal truncated inhibitory β-cat4 found in this study represents a novel mechanism to modulate β-catenin/TCF activity that also targets to TCF . As reported for the endogenous dominant negative forms of TCFs , in which alternative splicing and alternative promoters leads to the expansion of the isoforms [59] , this level of regulation would allow the refinement of Wnt signaling in time and space . Here we have found a new planarian β-catenin ( β-cat4 ) which represents a novel mechanism to fine tune the activity of nuclear β-catenin in a context-specific manner . Not surprisingly , this mechanism acts in neural tissues , where β-cat3 and 4 are mainly expressed , the complexity of which requires a more sophisticated regulation . Phylogenetic analysis of the β-catenin family in platyhelminthes supports the hypothesis that a β-cat1 , β-cat2 and β-cat3/4 ortholog was present in their common ancestor . Although the expression pattern and functional analysis of the β-cat3/4 ortholog in other platyhelminth species should be done , our data predicts that the β-cat3/4 ortholog would modulate β-cat1 activity in Platyhelminthes . Importantly , although the presence of a unique β-catenin with dual adhesion and signaling functions has been proposed to be present in the last common animal ancestor [75] , genomic duplications in the β-catenin family are more common than previously thought . In vertebrates , plakoglobin is a genomic duplication of β-catenin that has suffered a functional specialization , since it is predominantly involved in desmosomes [76 , 77] . However , Plakoglobin shows limited capacity to activate Wnt signaling [63 , 78] and , importantly , its nuclear accumulation down-regulates β-catenin activity in a dose-dependent manner , as demonstrated by our TOPflash reporter assays ( Fig 6A ) . Thus , Plakoglobin could be acting as a negative regulator of β-catenin in vertebrates . Since Plakoglobin shows much lower transactivation properties than β-catenin , its presence results in a decrease of the transcriptional activation promoted by β-catenin . The real existence and the importance of this regulatory mechanism is further supported by recent studies demonstrating that an increase of Plakoglobin nuclear translocation is associated with diseases such as arrhythmogenic right ventricular cardiomyopathy or head neck cancer and leads to a suppression of the β-catenin mediated TCF/LEF transcriptional activity [79 , 80] . Taking into account that the C-terminal part of Plakoglobin is the one showing less conservation with respect to β-catenin ( Fig 6A ) , the inhibitory function could be associated with the lack of conservation of the transactivation domain , as described in the present study for planarian β-cat4 . In invertebrates , the β-catenin family has undergone several species- and phylum-specific duplications [81] . The nematode C . elegans has four β-catenins with different roles in cell adhesion and nuclear signaling [42] , and several insects have two β-catenins , which have also undergone partial subfunctionalization between the cell adhesion and the centrosome separation functions [81] . Importantly , although in Drosophila a unique β-catenin is found ( Arm ) , an alternative splicing which occurs mainly in neural cells has been reported , which is known as Neural Armadillo ( NArm ) [81] . Interestingly , this alternative splicing deletes exon 6 and results in a C-terminal truncated Arm isoform that , according to our TOPflash reporter assay , exerts an inhibitory role of Arm in a dose dependent manner , as described for planarian β-cat4 towards β-cat1 . Although the role of NArm as modulator of Arm should be demonstrated ‘in vivo’ , it should be stressed that the presence of the alternative splicing isoform deleting exon 6 is conserved across all insect homologs [81] , supporting the evolutionarily pressure to maintain it and thus its biological implications . Overall , the present data supports the hypothesis that modulation of nuclear β-catenin transcriptional activity through the action of a C-terminally truncated β-catenin able to bind to TCF could be a conserved mechanism to regulate the Wnt/β-catenin pathway . Interestingly , the C-terminally truncated β-catenin of different species did not arise from any homolog of a common ancestor but originated in different animal groups from different mechanisms of gene diversification , as gene duplication or alternative splicing . Thus , the presence of a β-catenin with limited transactivation activity , which could compete with the canonical β-catenin , in different species represents an example of convergent evolution and supports its importance as a regulatory mechanism . The existence of inhibitory β-catenins and its expression in specific cell types provides a new answer to the important question of how the same transcription factor elicits so distinct responses . It is assumed that the different transcriptomic/proteomic context of each cell could result in differential activation of β-catenin target genes . However , few tissue-specific Wnt/β-catenin regulators have been found , leading to the idea that not only Wnt/β-catenin regulators but the interplay with the other conserved signaling pathways as BMP would be essential for their complex regulation [82] . Our results provide evidences that the existence of inhibitory β-catenins is not only a new mechanism to regulate Wnt signaling but to modulate it in specific cell types , for instances in specific subsets of neuronal types . Our results demonstrate that β-cat4 is a new essential element for photoreceptor cell specification in planarians . Planarian eyes are true cerebral eyes and their simplicity makes them an excellent system to study eye development . They are composed by two cell types: rhabdomeric photoreceptor neurons , which express evolutionarily conserved photoreceptor genes such as otxA or opsin [49 , 55] , and pigment cells , which express Tryptophan hydroxylase ( tph ) , an enzyme involved in the production of melanin , the pigment found in planarian eyes . Several studies demonstrate that both cell types arise from a common eye stem cell that express stem cell markers ( h2b ) and eye determinants as the transcription factors ovo , eya and six1/2 [54 , 55 , 83] . Among them ovo is the only factor exclusively found in the eye cell and specifically essential for eye regeneration and maintenance [54] . The expression of sp6-9 and dlx in the common eye progenitor determines the pigment fate , while otxA specifies photoreceptors [52 , 55] . We demonstrate that β-cat4 is a new factor that specifies the photoreceptor fate and that it is expressed in the stem cell precursors ( h2b+/ovo+ ) , a fact that has not been yet demonstrated for otxA . Inhibition of β-cat4 results in a decrease in number of photoreceptor progenitor and differentiated cells from the earliest time points analyzed ( 3–4 days of regeneration ) , while the number of progenitors and differentiated cells of the pigment lineage only decrease several days after . This observation agrees with the already described mutual dependence of both cell populations in planarians [53] . Thus , although β-cat4 is specifically required for photoreceptor determination , according to its expression in the photoreceptor lineage , the reduced number of photoreceptor cells would lead to the subsequent decrease of the pigment lineage . Whether the origin of this mutual dependence is at the level of the common stem cell progenitors , or at the level of the differentiated cells in the optic cup , remains to be studied . Although not much is known about the mutual dependence between photoreceptor and pigment cells in vertebrates , some studies suggest that it also exists [84] . To date , the main focus on the role of the Wnt/β-catenin signaling pathway in planarians has been in its essential role for axial patterning and posterior identity specification [31–33] . Our results highlight the importance of the regulation of the Wnt/β-catenin pathway in a different context , namely during planarian photoreceptors regeneration and maintenance . The finding is not surprising , since appropriate Wnt/β-catenin signaling levels are required for retina progenitor differentiation in several animal models [85–90] . For example , during chicken embryo development , β-catenin overexpression in the central neural retina inhibits the differentiation of retinal neurons , while loss of β-catenin leads to neural retina enlargement [86] . In zebrafish the ectopic expression of the Wnt antagonists DKK1 or SFRP1 causes expansion of the embryonic retina [91 , 92] , whereas activation of the Wnt/β-catenin pathway leads to lack of expression of eye markers [93] . Here we show that in planarians β-cat1 inhibition results in larger eyes , while APC-1 silencing causes a reduction of photoreceptor cell number . The simplicity and approachability of planarian eyes has allowed the identification of a new mechanism to fine regulate Wnt/β-catenin pathway activity in the eyes . Thus , the truncated β-cat4 that competes with the canonical β-cat1 for binding to TCF-2 allows the required fine-tuning of the Wnt/β-catenin pathway in planarian photoreceptor cells . Since the in vitro experiments showed that β-cat4 could bind to E-cadherin and α-catenin , a role of β-cat4 in cell adhesion should be considered . However , β-cat4 protein was only found in the nucleus of photoreceptors ( Fig 3A ) , and β-cat4 RNAi eyes appeared properly patterned with no apparent defects in cell adhesion . The new described mechanism to fine-tune the Wnt/β-catenin signal in specific cell types implies the existence of Wnt elements specifically expressed in photoreceptor and not in pigment cells . The finding that the phenotype of β-cat1 RNAi animals , which show ectopic differentiation of eye cells , differs from the TCF-2 RNAi , which show bigger eyes but properly patterned , indicates that β-cat1 could exert a TCF-2 independent role during eye regeneration . The cause of this defect could be a non-autonomous role of β-cat1 , which is broadly expressed in planarians [45] , or a role in pigment cells . This finding emphasizes the requirement of a cell-specific regulation of the Wnt/β-catenin signal during development . It will be interesting to study whether in other animal species eye-population specific Wnt elements ( or isoforms ) are found and to investigate if this novel mechanism of β-catenin regulation also takes place during eye development .
Our finding that planarian β-cat4 functions as a modulator of β-cat1 activity in the nucleus by regulating TCF-2 transcriptional activity has two deep implications: 1 ) considering its specific role in modulating β-cat1 activity during planarian photoreceptor specification , and not in other Wnt/β-catenin dependent processes such as axial patterning , it appears as a tissue-specific manner to fine-tune nuclear β-catenin activity; and 2 ) the finding of C-terminal truncated/non-conserved β-catenin forms in other species ( NArm or Plakoglobin ) , which inhibit β-catenin activity in TOPflash experiments , suggests that an inhibitory β-catenin could be an evolutionarily conserved mechanism to regulate the Wnt/β-catenin signaling . The identification of a novel mechanism of Wnt/β-catenin signaling regulation has important implications in view of the complex and essential roles of this pathway in development and diseases . Thus , the present research represents a starting point to design further studies: 1 ) to demonstrate whether the C-terminal truncated/non-conserved β-catenins found in Drosophila ( NArm ) and vertebrates ( Plakoglobin ) can compete for Arm or β-catenin binding , respectively , to the TCF co-factor ‘in vivo’ and assess whether this mechanism provides a tissue-specific manner to modulate β-catenin/Arm once reaches the nucleus; 2 ) to study the existence and function of possible β-catenin isoforms present in other animal species , as a product of gene duplication or alternative splicing , and understand their real contribution to the regulation of the Wnt/β-catenin signal; and 3 ) to test whether new drugs mimicking the inhibitory action of β-cat4 could be used to fine tune the activity of nuclear β-catenin in human diseases .
The planarians used in this study belong to an asexual clonal strain of S . mediterranea BCN-10 biotype . The animals were maintained at 20°C in PAM ( 1X ) water [94] . Animals were fed with organic veal liver and starved for at least a week before all experiments . A sexual strain of S . polychroa collected from Sant Celoni ( Barcelona , Spain ) was used in Fig 3A . β-Catenin sequences from Planarian species were identified in the Planmine database [58] . β-Catenin sequences from Echinococcus multilocularis and Kronborgia cf . amphipodicola were found in the available databases [95 , 96] . The rest of the sequences were found in the NCBI . β-Catenin protein sequences from different species were aligned using the MAFFT server ( http://mafft . cbrc . jp/alignment/server/ ) . Neighbor joining distance-based analyses were conducted using MEGA version 6 [97] , and the support given by bootstrap percentiles of 1000 replicates . BioEdit was used to edit the protein alignments . HEK293T cells were cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal calf serum . To obtain pCS2+-6Myc-Smed-β-catenin3 and 4 , the full length Smed-β-catenin3 and 4 was amplified by PCR and inserted into pCS2+-6Myc vector at StuI/XbaI sites . HEK293T cells were seeded in 96-well plates and transfected in triplicates with pCS2+-6Myc-Smed-β-catenin1/2/3/4 plasmids , together with Super-TOPflash and pRL-TK as the internal control . Firefly and Renilla luciferase activities were measured 36h after transfection using the Dual-Luciferase assay kit ( Promega ) . TOPflash luciferase activity was normalized to that of Renilla . 15 ng of Super-TOPflash and 0 . 5 ng of pRL-TK reporter plasmids were added per well . pCS2+ empty vector was used to adjust the total DNA amount to 150 ng/well . All experiments were repeated at least three times . HeLa cells were cultured in DMEM with 10% fetal calf serum and grown on glass coverslips . Thirty-six hours post transfection of pCS2+-6Myc-Smed-β-catenin3/4 and pCS2+-HA-mAxin , cells were fixed with4% paraformaldehyde/PBS for 20 min , permeabilized with 0 . 2% Triton X-100/PBS for 10 min and then blocked with 3% BSA/PBS for 30 min before primary antibodies were applied . A PBS rinse for 5 min between each step was performed . The primary antibodies anti-Myc ( mouse , Santa Cruz ) and anti-HA ( rabbit , Santa Cruz ) were incubated for 1h at RT diluted in 1%BSA , 0 . 1%Tween 20/PBS at 1:100 . After washing 5min X 3 times in 3%BSA , 1%TritonX-100/PBS , the fluorophore-conjugated secondary antibodies diluted at 1:400 in 3%BSA/PBS were incubated for 1h . Donkey anti mouse–Alexa Fluor 568 ( Molecular Probes ) was used to visualize β-catenin3/4 , and goat anti rabbit–Alexa Fluor 488 ( Molecular Probes ) to visualize Axin . Cell nuclei were visualized with DAPI staining . Cells were mounted in Prolong Gold antifade reagent ( Thermo Fisher Scientific ) and stored at 4°C before imaging . Images were recorded using a Zeiss LSM710 confocal microscope . HEK293T cells were seeded into 6-well plates and the following plasmids transfected the following day: pCS2+-6Myc-Smed-β-catenin1/2/3/4 , pCS2+-flag-xTCF1 , pcDNA3 . 1-flag-β-Trcp , pcDNA3 . 1-flag-E-cadherin and pcDNA3 . 1-flag-α-catenin . Forty-eight hours after transfection , cells were lysed and sonicated in 400 μl of lysis buffer/well ( 50 mM Tris-HCl , pH 7 . 4 , 300 mM NaCl , 1 mM EDTA , pH 8 . 0 , 1% NP-40 ) containing protease inhibitor mixture ( Roche Applied Science ) at 4°C . After centrifugation at 14000 rpm , 4°C for 15min , 40 μl of supernatant was mixed with 10 μl 5x SDS-loading buffer and treated at 95°C for 5min . The remaining supernatant for each well was incubated with 10 μl of FLAG-M2 beads ( Sigma ) at 4°C for 6h . The beads were then washed three times with lysis buffer at 4°C for 10min each , and bound proteins were eluted with 40 μl of 2x SDS loading buffer at 95°C for 5min . Immunoprecipitates and total lysates were separated by SDS-PAGE and analyzed by immunoblot with anti-Myc or anti-Flag specific antibodies . Xenopus embryos were cultured under standard conditions . mRNA was synthesized using a mMESSAGE mMACHINE SP6 kit ( Ambion , Austin , TX ) according to the manufacturer’s instructions . Synthetic mRNAs were microinjected into embryos cultured in 2% Ficoll 400 in 0 . 3× MMR ( 1× MMR: 100 mM NaCl , 2 mM KCl , 2 mM CaCl2 , 1 mM MgCl2 ) at 8-cell stage , and fixed at stage 20 . Colorimetric whole-mount in situ hybridization ( WISH ) and fluorescent in situ hybridization ( FISH ) were performed as elsewhere described [98 , 99] . The following DIG- ( Roche ) , FITC- ( Roche ) , or DNP- ( Perkin Elmer ) labeled riboprobes were synthesized using an in vitro transcription kit ( Roche ) : Smed-β-catenin1/3/4 , Smed-TCF1/2/3; Smed-opsin , Smed-otxA , Smed-sp6/9 [52]; Smed-tph [100]; Smed-ovo [54]; Smed-h2b [101]; Smed-th ( tyrosine hydroxylase ) , Smed-tbh ( tryptophan hydroxylase ) [102] , Smed-pc2 ( prohormone convertase 2 ) [103] . Primers used for their synthesis are indicated ( S1 Table ) . Riboprobes were finally diluted to 250 ng/μL in pre-hybridization solution , stored at -20°C , and were used at 1:500 in hybridization solution , except for sp6/9 ( 1:200 ) . Samples were observed through Leica MZ16F ( Leica Microsystems , Mannhiem , BW , Germany ) , Zeiss Stemi SV6 stereomicroscopes and a Zeiss Axiophot microscope ( Zeiss , Jena , TH , Germany ) ; images were captured with a ProgRes C3 camera from Jenoptik ( Jena , TH , Germany ) , sCMEX 3 . 0 camera ( Euromex , Arnhem , The Netherlands ) and Leica DFC300FX camera ( Leica Microsystems , Heerbrugg , CH , Switzerland ) . Confocal laser scanning microscopy was performed with a Leica TCS-SP2 ( Leica Lasertchnik , Heidelberg , BW , Germany ) adapted for an inverted microscope . After FISH , samples were rinsed with TNTx 5min , 50%PBSTx , 50%TNTx ( 0 . 1M Tris•HCl pH7 . 5 , 0 . 15M NaCl , 0 . 3%TritonX-100 ) 10min , then PBSTx 10min . 1% BSA or 10% Goat serum were used as blocking reagent for 2 hours , followed by anti-β-cat4 ( 1:200 , diluted in 10% goat serum ) or anti-VC-1 ( 1:15000 , diluted in 1% BSA , kindly provided by Hidefumi Orii , Himeji Institute of Technology , Hyogo , Japan ) antibody incubation . PBSTx ( PBS with 3% TritonX-100 ) wash for 15min x3 . Goat-anti-mouse-488 conjugated antibody ( 1:400 , Molecular Probes ) and Goat-anti-rabbit-HRP conjugated antibody ( 1:500 , Pierce ) were used as secondary antibody . HRP signal was developed with a tyramide signal amplification kit following manufacturer’s recommendations ( Perkin Elmer ) . anti-β-cat1 immunohistochemistry was performed as previously described [45] . Nuclei were counterstained with DAPI ( Sigma , 1:5000 ) . The polyclonal anti-β-cat4 antibody was generated against 34 amino acids of the N-terminal part of the β-cat4 protein ( indicated in S1 Fig ) ( GeneCust , Luxembourg ) . Double-stranded RNAs ( dsRNA ) for Smed-β-catenin1/3/4 , Smed-TCF2 and Smed-APC-1 were synthesized and delivered as described elsewhere [49] . Primers used for their synthesis are indicated ( S1 Table ) . Control animals were injected with dsRNA for GFP [53] . dsRNA was diluted to 1 μg/μl in water . Microinjections were performed as described elsewhere [49] following the standard protocol of a 32 nl injection of dsRNA on three consecutive days . On the next day planarians were amputated pre- and post-pharyngeally , and the head , trunk , and tail pieces were allowed to regenerate . When injecting Smed-β-catenin3/4 the same procedure was performed during 2 weeks to improve the penetrance of the phenotype . For experiments in intact planarians , animals were injected 3 consecutive days per week for 5 weeks . Total RNA was extracted from a pool of 4 planarians each for every RNAi condition . Quantitative real-time PCR was performed as previously described [104] , and data was normalized based on the expression of URA4 as internal control . All the experiments were performed using three biological replicates . The primers for qPCR are indicated in S1 Table . Statistical significance was measured by Student's T test by comparing values from each sample to their respective control sample . Phototactic assay was carried out as described previously [53] . The behavior analysis software SMART v . 2 . 5 . 21 was used to quantify the numbers of worms in each of the three virtual subdivisions of a 60x30x10 mm transparent container filled with 10 ml planarian water after 2 minutes of positioning the worms at the indicated beginning point in the light zone .
|
The Wnt signaling pathway is essential for proper intercellular communication in every developmental process since it controls basic cellular events as cell fate or proliferation . The key element of the Wnt signaling is β-catenin , which controls the transcription of multiple genes in the Wnt receiving cell . A main level of regulation of the Wnt/β-catenin signaling occurs in the cytoplasm , where β-catenin protein levels depend on the activity of the β-catenin destruction complex . However , once it reaches the nucleus , β-catenin transcriptional activity requires a fine-tuned regulation to enable the multiple context-specific responses that it performs . These nuclear mechanisms that regulate the Wnt/β-catenin signaling remain poorly understood . Here we report the existence of C-terminal truncated forms of β-catenin in planarians ( β-cat3 and 4 ) , which , in vitro , do not show transactivation activity and compete with the canonical planarian β-catenin ( β-cat1 ) , thus acting as competitor inhibitors . Functional analyses in planarians indicate that β-cat4 acts as a negative regulator of β-cat1 during planarian eye photoreceptor specification . We provide evidence to suggest that this novel mechanism for the regulation of nuclear β-catenin activity could be conserved across animal evolution .
|
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2017
|
A C-terminally truncated form of β-catenin acts as a novel regulator of Wnt/β-catenin signaling in planarians
|
Toll-like receptor ( TLR ) -mediated signaling are critical for host defense against pathogen invasion . However , excessive responses would cause harmful damages to the host . Here we show that deficiency of the E3 ubiquitin ligase TRIM32 increases poly ( I:C ) - and LPS-induced transcription of downstream genes such as type I interferons ( IFNs ) and proinflammatory cytokines in both primary mouse immune cells and in mice . Trim32-/- mice produced higher levels of serum inflammatory cytokines and were more sensitive to loss of body weight and inflammatory death upon Salmonella typhimurium infection . TRIM32 interacts with and mediates the degradation of TRIF , a critical adaptor protein for TLR3/4 , in an E3 activity-independent manner . TRIM32-mediated as well as poly ( I:C ) - and LPS-induced degradation of TRIF is inhibited by deficiency of TAX1BP1 , a receptor for selective autophagy . Furthermore , TRIM32 links TRIF and TAX1BP1 through distinct domains . These findings suggest that TRIM32 negatively regulates TLR3/4-mediated immune responses by targeting TRIF to TAX1BP1-mediated selective autophagic degradation .
The innate immune system is the first line of host defense against pathogen invasion . After detection of structurally conserved components of the invading pathogens by so-called pathogen recognition receptors ( PRRs ) , the host cells initiate a series of signaling cascades which ultimately induce the transcription of downstream antiviral genes , such as type I interferons ( IFNs ) and inflammatory cytokines , to induce innate immune and inflammatory responses as well as facilitate adaptive immunity [1 , 2 , 3 , 4] . However , excessive immune and inflammatory responses cause tissue damages and serious diseases such as septic shock [5] . Toll-like receptors ( TLRs ) are evolutionarily conserved PRRs that play critical roles in host defense against various pathogens . TLRs contain an extracellular domain , a transmembrane domain , and a conserved cytoplasmic toll/IL-1 receptor ( TIR ) domain . Upon ligand stimulation , the TIR domains of TLRs mediate their homo- or hetero-dimerization [6] , and act as platforms to recruit downstream TIR domain-containing adaptor proteins and other signaling molecules , leading to the activation of transcription factors such as IRF3 and NF-κB . These transcription factors collaborate to induce the transcription of a series of downstream antiviral genes [7] . Most TLRs except TLR3 and TLR4 signal through the TIR-containing adaptor MyD88 . TLR3 , which recognizes viral dsRNA and plays important roles in innate antiviral responses , signals through the TIR-containing adaptor TRIF but not MyD88 [8] . TLR4 , which recognizes LPS of bacteria and is essential for innate and inflammatory responses to infected bacteria , signals through MyD88 to activate NF-κB and TRIF to activate NF-κB and IRF3 [9] . Double knockout of TRIF and MyD88 results in completely abolishment of LPS-induced activation of NF-κB , whereas TRIF-deficiency results in abolishment of LPS-induced activation of IRF3 [9] . Protein degradation is one of the main strategies which have been employed by host cells to inactivate proteins in biological processes . Autophagy is an essential homeostatic process by which damaged organelles , protein aggregates , and invading cytoplasmic microbes are sequestered in double-membraned autophagosomes and delivered to the lysosome for degradation [10] . There are growing evidences that autophagy can be highly selective [11] . Selective autophagy depends on the cargo receptors , including p62 , TAX1BP1 , NDP52 and so on , which are able to bind to special cargoes and dock onto the forming phagophores [11] . Certain selective autophagy receptors have been reported to be involved in regulation of immune responses . For examples , the cytosolic DNA viral sensor cGAS and intracellular Salmonella typhimurium can be degraded via p62- and TAX1BP1-dependent selective autophagy respectively [12 , 13] . Whether selective autophagy is involved in the regulation of other immune processes are unknown . The tripartite motif-containing proteins ( TRIMs ) of E3 ubiquitin ligase families have been demonstrated to play critical regulatory roles in regulation of immune responses [14 , 15] . TRIM32 has been reported to mediate K63-linked polyubiquitination of MITA/STING and regulates innate immune responses to RNA and DNA viruses in human cell lines [7] . In this study , we generated TRIM32-deficient cells and Trim32 knockout mice , and found that TRIM32 negatively regulated TLR3/4-mediated innate immune and inflammatory responses . Biochemical and cellular analysis revealed that TRIM32 mediated selective autophagic degradation of TRIF through TAX1BP1 . Our findings suggest that TRIM32-TAX1BP1-dependent selective autophagic degradation of TRIF is an important negative regulatory mechanism of TLR3/4-mediated innate immune and inflammatory responses .
Previously , it has been demonstrated that TRIM32 mediates K63-linked polyubiquitination of MITA/STING and regulates virus-triggered induction of downstream antiviral genes in human cell lines [7] . To investigate the functions of TRIM32 , we utilized TRIM32 gene knockout mice ( S1A & S1B Fig ) . We found that TRIM32-deficiency had no marked effects on the mRNA levels of downstream antiviral genes Ifnb1 and Isg56 as well as inflammatory cytokine genes Tnfa and Il6 induced by Sendi virus ( SeV ) or herpes simplex virus 1 ( HSV-1 ) in mouse embryonic fibroblasts ( MEFs ) , bone marrow-derived macrophages ( BMDMs ) and dendritic cells ( BMDCs ) ( S1C Fig ) , suggesting that TRIM32 does not regulate virus-triggered signaling in primary mouse cells . However , we found that TRIM32-deficiency potentiated poly ( I:C ) ( a synthetic dsRNA ligand for TLR3 ) - and LPS ( a ligand for TLR4 ) - but not PGN ( a ligand for TLR2 ) - or R848 ( a ligand for TLR7 ) -induced transcription of downstream genes Ifnb1 , Isg56 , Tnfa and Il6 in BMDMs , BMDCs and mouse lung fibroblasts ( MLFs ) ( Fig 1A ) . Consistently , poly ( I:C ) - and LPS-induced phosphorylation of IRF3 and IκBα ( hallmarks for IRF3 and NF-κB activation respectively ) was dramatically increased in Trim32-/- MLFs in comparison to their wild-type counterparts ( Fig 1B ) . LPS has been reported to induce both MyD88- and TRIF-dependent signaling , which usually results in serious harmful inflammation in vivo . Monophosphoryl lipid A ( MPLA ) , a derivate of LPS , mainly induces TRIF- but not MyD88-dependent signaling , leading to some protective immune responses in vivo instead [16 , 17 , 18] . Interestingly , TRIM32-deficiency also increased MPLA-induced transcriptions of these genes in BMDMs ( Fig 1C ) . These results suggest that TRIM32 negatively regulates TLR3/4- but not TLR2/7-mediated signaling in primary mouse cells . Consistently , overexpression of TRIM32 markedly inhibited poly ( I:C ) - and LPS-induced activation of the IFN-β promoter , ISRE and NF-κB in human HEK293-TLR3 and HEK293-TLR4 cells respectively in reporter assays ( Fig 1D ) . In similar experiments , TRIM32 did not inhibit IFNγ-induced activation of the IRF1 promoter ( Fig 1E ) . To investigate the role of TRIM32 in TLR3/4-mediated innate immune and inflammatory responses in vivo , age- and sex-matched Trim32+/+ and Trim32−/− mice were intraperitoneally injected with poly ( I:C ) plus D-galactosamine or LPS . D-galactosamine is an agent usually used to enlarge inflammatory damage of liver , since poly ( I:C ) alone is insufficient to cause inflammatory death of mice . As shown in Fig 2A , poly ( I:C ) - and LPS-induced production of IFN-β , TNFα , and IL-6 was significantly increased in the sera of Trim32−/− compared to Trim32+/+ mice . Consistently , more serious inflammation was observed in the lungs of Trim32−/− mice injected with poly ( I:C ) plus D-galactosamine or LPS ( Fig 2B ) . Trim32−/− mice showed an early death onset and a significantly higher percentage of lethality within 40 hours in comparison with their wild-type counterparts after injection of poly ( I:C ) plus D-galactosamine ( Fig 2C ) or LPS ( Fig 2D ) . It has been reported that poly ( I:C ) and LPS are able to induce TRIF-dependent cell death which might contribute to poly ( I:C ) - and LPS-induced death of mice [19 , 20] . Therefore , we also explored whether TRIM32 is involved in TLR3/4-mediated and TRIF-dependent cell necrosis . The results showed that TRIM32-deficiency had no marked effects on poly ( I:C ) - and LPS-induced cell death in cell viability assays ( S2 Fig ) . We have also explored the role of TRIM32 in MPLA-induced TRIF-dependent protective immune response in mice . Unlike LPS , MPLA does not cause much inflammatory response , and MPLA alone is insufficient to cause inflammatory death of mice . Therefore , we used D-galactosamine to enlarge the inflammatory response induced by MPLA . Interestingly , though MPLA-induced increased transcriptions of type I IFNs and inflammatory genes in Trim32-/- cells ( Fig 1C ) , MPLA plus D-galactosamine-induced serum inflammatory cytokine levels and inflammatory death were markedly decreased in Trim32-/- mice ( Fig 2E & 2F ) , suggesting that TRIM32 plays an important role in MPLA-induced TRIF-dependent protective immune response in vivo . We have also explored the role of TRIM32 in immune and inflammatory responses to Salmonella typhimurium infection . As shown in Fig 3A , Trim32-/- mice carried less Salmonella typhimurium in their livers and spleens compared with that of their wild-type littermates at 8 days post oral administration of Salmonella typhimurium , suggesting that Trim32-/- mice exhibited more efficient clearance of invaded Salmonella typhimurium than the wild-type mice . Consistently , a larger number of viable immune cells existed in the spleens of Trim32-/- mice ( Fig 3B ) . Trim32-/- mice produced much higher levels of inflammatory cytokines including TNF-α and IL-6 ( Fig 3C ) and showed much more serious inflammatory damage of their small intestinal villus ( Fig 3D ) after oral adiminstration of Salmonella typhimurium , which led to a higher sensitivity to Salmonella typhimurium-induced loss of body weight and inflammatory death of Trim32-/- mice ( Fig 3E & 3F ) . These results suggest that TRIM32 negatively regulates TLR3/4-mediated innate immune and inflammatory responses in vivo . We next investigated the molecular mechanisms of TRIM32 in the regulation of TLR3/4-mediated signaling . Reporter assays showed that TRIM32 inhibited TRIF- , but not TBK1- and IRF3-mediated activation of ISRE ( Fig 4A ) . Furthermore , both overexpression and endogenous coimmunoprecipitation experiments indicated that TRIM32 interacted with TRIF ( Fig 4B and 4C ) . In addition , we routinely found that TRIM32 dramatically destabilized TRIF but not TRAF3 , TBK1 or IRF3 in our co-transfection experiments ( Fig 4D ) . Endogenous experiments indicated that TRIM32-deficiency markedly attenuated poly ( I:C ) -induced degradation of TRIF in MLFs ( Fig 4E ) . These results suggest that TRIM32 mediates the down-regulation of TRIF , which is an adaptor protein specifically utilized by TLR3/4 but not other TLRs . Since TRIM32 is an E3 ubiquitin ligase , we examined whether TRIM32 destabilizes TRIF via the ubiquitin-proteasomal pathway . Unexpectedly , the E3 enzyme-inactive mutants of TRIM32 , TRIM32 ( C40S ) and TRIM32 ( ΔRING ) , destabilized TRIF as efficient as the wild-type TRIM32 ( Fig 4F ) . Furthermore , TRIM32 failed to catalyze polyubiquitination of TRIF ( Fig 4G ) . Consistently , reconstitution of either wild-type TRIM32 or TRIM32 ( ΔRING ) in Trim32-/- cells could inhibit poly ( I:C ) - and LPS-induced transcription of Ifnb1 , Tnfa and Il6 genes to similar levels ( Fig 4H ) . These data suggest that TRIM32 mediates the down-regulation of TRIF independent of its E3 ligase activity . Protein degradation is one of the main strategies involved in inactivating proteins in biological processes . Two major systems exist for protein degradation , including the ubiquitin-proteasome and autophagy-lysosome pathways . We found that TRIM32-mediated degradation of TRIF could be inhibited by the lysosomal inhibitor NH4Cl and the autophagic inhibitor 3MA but not the proteasomal inhibitor MG132 ( Fig 5A ) , suggesting that TRIM32 probably mediates degradation of TRIF via an autophagic pathway . Confocal microscopy experiments showed that poly ( I:C ) stimulation caused aggregation of GFP-LC3 ( a marker of autophagy ) in HEK293-TLR3 cells ( Fig 5B ) . Poly ( I:C ) stimulation also caused colocalization of TRIF with GFP-LC3 in HEK293-TLR3 cells ( Fig 5C ) . To further confirm that autophagic degradation pathway is involved in TRIF degradation upon stimulation , we used MG132 and 3MA to pre-treat Trim32+/+ and Trim32-/- BMDMs for 2 hours before poly ( I:C ) stimulation . The results showed that 3MA pre-treatment had no marked effects on TRIF level in both un-stimulated Trim32+/+ and Trim32-/- cells , but attenuated poly ( I:C ) -induced degradation of TRIF in Trim32+/+ but not Trim32-/- cells ( Fig 5D ) . MG132 pre-treatment markedly increased TRIF level in un-stimulated cells and also attenuated poly ( I:C ) -induced degradation of TRIF in both Trim32+/+ and Trim32-/- cells ( Fig 5D ) . TRIF has been reported to be degraded by TRIM38 via the ubiquitin-proteasome dependent pathway , and TRIM38-deficiency increases TRIF level in un-stimulated cells and attenuates poly ( I:C ) -induced degradation of TRIF [21] . Consistently , knockdown of TRIM38 increased TRIF level in un-stimulated Trim32-/- cells , and also attenuated poly ( I:C ) -induced degradation of TRIF to a larger extent in these cells ( Fig 5E ) . Taken together , these results suggest that TRIM32 is involved in the autophagic degradation of TRIF induced by poly ( I:C ) stimulation . Additional experiments showed that knockdown of LC3B , which is an important marker of autophagy and is required for fusion to the lysosomes , markedly inhibited TRIM32-mediated degradation of TRIF ( Fig 5F ) , whereas overexpression of TRIM32 dramatically enhanced the interaction of TRIF with LC3B-II ( Fig 5G ) , which is a basic membrane component of autophagesomes and derived from LC3B-I during autophagy [22] . In similar experiments , overexpression of TRIM32 did not cause the conversion of LC3B-I to LC3B-II ( Fig 5H ) . Moreover , deficiency of ATG7 , which is an essential E1 ligase for LC3B-II formation , inhibited poly ( I:C ) - and LPS-induced degradation of TRIF ( Fig 5I ) . These results suggest that TRIM32 mediates degradation of TRIF through the autophagy-lysosome pathway . Consistently , pre-treatment of cells with balifomycin , an inhibitor for fusion of autophagosomes and lysosomes , markedly attenuated poly ( I:C ) -induced down-regulation of TRIF ( Fig 5J ) . Endogenous TRIM32 constitutively associated with TRIF in un-stimulated cells , and their association slowly decreased following poly ( I:C ) stimulation ( Fig 5J ) , suggesting that TRIM32 disassociates from TRIF-containing autophagosomes at the later stage of stimulation . The autophagic pathways can be distinguished as the canonical or the selective autophagic pathway . The selective autophagy receptors deliver cargoes to the autophagosomes for selective degradation [23] . It has been shown that ULK1/2 , FIP200 and ATG13 are critical for initiation of the classical autophagic pathway [23] . We found that deficiency of ULK1/2 , FIP200 or ATG13 had no marked effects on the degradation of TRIF induced by poly ( I:C ) or LPS treatment ( Fig 6A–6C ) , suggesting that poly ( I:C ) - and LPS-induced degradation of TRIF is not via the canonical autophagic pathway . It has been shown that NDP52 serves as a selective receptor for TRIF and TRAF6 for their selective autophagic degradation [24] . However , in our experiments , we observed that NDP52 failed to interact with and promote degradation of TRIF ( Fig 6D & 6E ) . In similar experiments , NDP52 promoted the degradation of TRAF6 ( Fig 6D ) . Instead , we found that TRIF interacted with other selective receptors including TAX1BP1 , OPTN and p62 ( Fig 6E ) , but only overexpression of TAX1BP1 , but not OPTN or p62 down-regulated the level of TRIF ( Fig 6F ) . Consistently , knockdown of TAX1BP1 inhibited TRIM32-mediated degradation of TRIF ( Fig 6G ) . In addition , knockdown of TAX1BP1 but not NDP52 attenuated poly ( I:C ) -induced degradation of TRIF ( Fig 6H ) . Furthermore , knockdown of TAX1BP1 markedly impaired endogenous association of TRIF with LC3 induced by poly ( I:C ) stimulation ( Fig 6I ) . These results suggest that TAX1BP1 but not NDP52 mediates the selective autophagic degradation of TRIF . Consistent with the biochemical results , qPCR experiments showed that knockdown of TAX1BP1 potentiated poly ( I:C ) -induced transcription of Ifnb1 , Cxcl10 and Il6 genes ( Fig 6J ) . To explore the mechanism of TRIM32- and TAX1BP1-mediated autophagic degradation of TRIF , we tested a straightforward hypothesis that TRIM32 acts as a bridge protein for TRIF-TAX1BP1 interaction . Confocal microscopy indicated that overexperssion of TRIM32 promoted colocolization of TRIF and TAX1BP1 in certain aggregates that were positive for the autophagosome marker GFP-LC3 ( Fig 7A ) or lysosome marker GFP-LAMP1 ( Fig 7B ) . Endogenous coimmunoprecipitation experiments indicated that TRIM32-deficiency abolished poly ( I:C ) -induced association of TRIF with TAX1BP1 as well as attenuated poly ( I:C ) -induced degradation of TRIF ( Fig 7C ) . These results suggest that TRIM32 acts as a bridge protein for TRIF-TAX1BP1 interaction following poly ( I:C ) stimulation . Domain mapping experiments indicated that the NHL ( aa360-655 ) and BBOX ( aa66-139 ) domains of TRIM32 are required for its interaction with TAX1BP1 and TRIF respectively ( Fig 7D ) . Similar experiments indicated that TRIM32 interacted with the middle TIR domain ( aa386-475 ) of TRIF ( Fig 7E ) , whereas TAX1BP1 interacted most strongely with the C-terminal domain ( aa476-732 ) of TRIF ( Fig 7E ) . These results suggest that TRIM32 links TAX1BP1 and TRIF through distinct domains ( Fig 7F ) . Additionally , we have also explored whether TRIM32 and TAX1BP1 are recruited to lipid rafts of membrane where TLR3/4 recruits TRIF for signaling . Cellular fractionation experiments indicated that membrane-associated TRIF was increased at 0 . 5 hour after poly ( I:C ) treatment and then decreased at 1 hour probably because of the degradation of TRIF ( S3 Fig ) . TRIM32 constitutively existed in both cytosol and membrane franction , and poly ( I:C ) treatment had no marked effects on its distribution . Interestingly , TAX1BP1 only existed in the cytosol either before or after poly ( I:C ) treatment . Furthermore , TRIF-deficiency had no marked effects on the subcellular location of TRIM32 and TAX1BP1 either before or after poly ( I:C ) treatment ( S3 Fig ) . These results suggest that TAX1BP1 is not recruited to lipid rafts of membrane , and TRIM32-TAX1BP1-TRIF association occurs in the cytosol after TRIF is dis-associated from the TLR3/4 receptor complexes on the membrane .
In this study , we investigated the role of TRIM32 in TLR3/4-mediated signaling in mouse primary immune cells and in vivo by genetic and biochemical studies . TRIM32-deficiency potentiated poly ( I:C ) - and LPS- but not R848- or PGN-induced transcription of downstream genes Ifnb1 , Isg56 , Tnfa and Il6 in BMDMs , BMDCs and MLFs . TRIM32-deficiency also elevated the serum cytokine levels induced by poly ( I:C ) and LPS , and renders the mice more susceptible to death triggered by administration of poly ( I:C ) and LPS or Salmonella typhimurium infection . These findings suggest that TRIM32 negatively regulates TLR3/4-mediated innate immune and inflammatory responses . It has been shown that TRIM32 is an E3 ubiquitin ligase which regulates both DNA- and RNA viruses-triggered induction of type I IFNs in several human cell lines [7] . The current study indicates that TRIM32 is not required for induction of downstream antiviral genes induced by both DNA and RNA viruses in primary mouse cells or in mice . It is possible that TRIM32 functions in different cellular processes between human and mouse cells . TRIM proteins belong to the largest E3 ubiquitin ligase family in mammals , and it has been previously shown that some TRIM family members have distinct functions between human and mouse [25] . In contrast with the observations that the E3 ligase activity of TRIM32 is required for its roles in virus-triggered signaling in human cell lines [7] , several results from the current study suggest that the E3 ligase activity of murine TRIM32 is not required for its negative regulatory roles in TLR3/4-mediated signaling . Firstly , the E3 enzyme-inactive mutants of TRIM32 destabilized TRIF as efficiently as the wild-type protein . Second , reconstitution of both the wild-type and E3 enzyme-inactive TRIM32 into Trim32-/- cells inhibited the transcription of downstream genes induced by poly ( I:C ) and LPS . TRIF is a critical adaptor protein for TLR3/4-mediated innate immune and inflammatory responses . Poly ( I:C ) or LPS stimulation causes a rapid and dramatic degradation of TRIF to avoid sustained activation of TRIF and expression of type I IFNs and inflammatory cytokines . Previous studies demonstrate that the E3 ubiquitin ligases WWP2 and TRIM38 target TRIF for degradation and inhibit TLR3/4-mediated innate immune responses [21 , 26] . Both WWP2 and TRIM38 catalyze K48-linked polyubiquitination of TRIF and promote TRIF degradation via the well-established ubiquitin-proteasome system . Instead , TRIM32 promotes TRIF degradation via the autophagic pathway , since the autophagy inhibitor 3MA and lysosome inhibitor NH4Cl but not the ubiquitin-proteasome inhibitor MG132 impaired TRIM32-mediated degradation of TRIF . In addition , TRIM32 promoted the interaction of TRIF with LC3B-II , which is the critical component for autophagosome formation . Interestingly , TRIM32 promotes TRIF degradation via the selective instead of the classical autophagic pathway , since deficiency of components of the selective but not classical autophagic pathway inhibited TRIM32-mediated , as well as poly ( I:C ) - and LPS-induced degradation of TRIF . Our results also indicated that TRIM38 but not TRIM32 down-regulated TRIF level in un-stimulated cells , whereas TRIM32 contributed to ligand-induced degradation of TRIF . Therefore , TRIM38 and TRIM32 regulate TRIF-mediated signaling through distinct mechanisms . Several experiments suggest that the selective autophagic receptor TAX1BP1 but not NDP52 is involved in TRIM32-mediated autophagic degradation of TRIF . TAX1BP1 but not NDP52 interacted with TRIF . Overexpression of TAX1BP1 but not NDP52 promoted degradation of TRIF , whereas knockdown of TAX1BP1 but not NDP52 impaired TRIM32-mediated as well as poly ( I:C ) -induced degradation of TRIF . Furthermore , knockdown of TAX1BP1 markedly impaired poly ( I:C ) -induced endogenous association of TRIF with LC3 . Our experiments suggest that TRIM32 acts as a link for TRIF and TAX1BP1 . Confocal microscopy showed that TRIM32 promoted colocalization of TRIF and TAX1BP1 in certain aggregates which are positive for the autophagosome marker GFP-LC3 or the lysosome marker GFP-LAMP1 , while TRIM32-deficiency abolished endogenous association of TRIF with TAX1BP1 induced by poly ( I:C ) . Domain mapping experiments indicated that the BBOX and NHL domains of TRIM32 were required for its interaction with the TIR domain of TRIF and TAX1BP1 respectively , whereas TAX1BP1 interacted with the C-terminal domain of TRIF . These results suggest that TRIM32 links TRIF to the TAX1BP1 autophagosomes through distinct domains . Based on our data , we propose a working model on the regulatory role of TRIM32 in TLR3/4-mediated innate immune responses ( Fig 7G ) . Ligand binding to TLR3/4 leads to the recruitment of the critical adaptor protein TRIF . TRIF in turn recruits downstream components , leading to activation of several transcription factors and ultimate induction of downstream innate immune and inflammatory genes . Upon activation , TRIF is recruited by TRIM32 to TAX1BP1-containing and LC3-associated autophagosomes for degradation , contributing to termination of TLR3/4-mediated innate immune and inflammatory responses . Our findings suggest that selective autophagic degradation is an important regulatory mechanism for timely termination of innate immune and inflammatory responses mediated by TLR3/4 .
All animal experiments were performed in accordance with the Wuhan University animal care and use committee guidelines . Mouse monoclonal antibodies against Flag ( Sigma ) , HA ( Origene ) , β-actin ( Sigma ) , p-IκBα ( CST ) , p-IRF3 ( CST ) and p-TBK1 ( Abcam ) ; poly ( I:C ) ( Invivogen ) , LPS ( Sigma ) , R848 ( Invivogen ) , PGN ( Invivogen ) , human IFN-γ ( PeproTech ) , Bafilomycin ( Sigma ) , Monophosphoryl lipid A ( Sigma ) , Z-KAD-FMK ( MCE ) were purchased from the indicated companies . Luminescent cell viability assay kit ( G7570 ) was purchased from Promega . Mouse antisera to TRIM32 and TRIF were raised against recombinant human TRIM32 and murine TRIF ( 1–475 ) respectively . Rabbit antisera to TAX1BP1 and NDP52 were raised against recombinant murine TAX1BP1 and NDP52 ( 1–160 ) respectively . Mammalian expression plasmids for Flag- or HA-tagged murine TRIM32 and its mutants , TRIF and its mutants , TRAF6 , TRAF3 , TBK1 , IRF3 , TAX1BP1 and NDP52 were constructed by standard molecular biology techniques . Trim32 gene knockout mice with a CL7/B6 background were provided by Dr . Hong-Liang Li [27] . Genotyping by PCR was performed using the following two pairs of primers: WT-1: GGAGAGACACTATTTCCTAAGTCA;WT-2: GTTCAGGTGAGAAGCTGCTGCA; MT: GGGACAGGATAAGTATGACATCA . Amplification of the wild-type allele with primers WT-1 and WT-2 results in a 250-bp fragment , whereas amplification of the disrupted allele with primers WT-1 and MT results in a 300-bp fragment . BMDMs and BMDCs were generated as described [28] . The bone marrow cells ( 1×107 ) were cultured in RPMI medium 1640 containing 10% FBS and 10 ng/mL recombinant murine M-CSF ( Peprotech ) or GM-CSF-containing conditional medium in a 100-mm dish for 5 or 9 days for generation of BMDMs or BMDCs respectively . Primary lung fibroblasts were generated as described [29] . Primary lung fibroblasts were isolated from approximately 4- to 6-week-old mice . Lungs were minced and digested in calcium and magnesium free HBSS containing 10 μg/ml type II collagenase ( Worthington ) and 20 μg/ml DNase I ( Sigma-Aldrich ) for 3 hours at 37°C with shaking . Cell suspensions were filtered through progressively smaller cell strainers ( 100 and 40 μm ) and then centrifuged at 1500 rpm for 4 min . The cells were then plated in culture medium ( 1:1 [v/v] DMEM/Ham’s F-12 containing 10% FBS , 15 mM HEPES , 2 mM L-glutamine , 50 U/ml penicillin , and 50 μg/ml streptomycin ) . After 1 hour , adherent fibroblasts were rinsed with HBSS and cultured in media . Age- and sex-matched Trim32+/+ and Trim32-/- mice were injected intraperitoneally with poly ( I:C ) ( 5 μg/g body weight ) plus D-galactosamine ( 1 mg/g body weight ) or with LPS ( 10 μg/g body weight ) . The survival of the injected mice was monitored every 2 hours . Age- and sex-matched Trim32+/+ and Trim32-/- mice were orally administrated with Salmonella typhimurium ( 1×107 pfu per mouse ) . The body weight and survival of the infected mice were monitored every day . Blood from mice injected with poly ( I:C ) plus D-galactosamine or LPS was collected at the indicated times and the serum concentration of TNFα ( Biolegend ) , IL-6 ( Biolegend ) , and IFN-β ( PBL ) were measured by ELISA kits from the indicated manufactures . Transfection and reporter assays were performed as previous described [30 , 31 , 32 , 33] . HEK293 cells were seeded on 24-well plates and transfected on the following day by standard calcium phosphate precipitation . Where necessary , empty control plasmid was added to ensure that each transfection receives the same amount of total DNA . To normalize for transfection efficiency , pRL-TK ( Renilla luciferase ) reporter plasmid ( 0 . 01 μg ) was added to each transfection . Luciferase assays were performed using a dual-specific luciferase assay kit ( Promega , Madison , WI ) . Firefly luciferase activities were normalized based on Renilla luciferase activities . Coimmunoprecipitation , immunoblotting and ubiquitination assays were performed as previous described [34 , 35 , 36] . For ubiquitination assays , the immunoprecipitates were re-extracted in lysis buffer containing 1% SDS and denatured by heating for 5 min . The supernatants were diluted with regular lysis buffer until the concentration of SDS was decreased to 0 . 1% , followed by re-immunoprecipitation with the indicated antibodies . The immunoprecipitates were analyzed by immunoblotting with the ubiquitin antibody . qPCR assays were performed as previously describe [37 , 38 , 39 , 40] . Total RNA from mouse or human cells was isolated using the Trizol reagent ( Invitrogen ) . After reverse-transcription with oligo ( dT ) primer using a RevertAidTM First Strand cDNA Synthesis Kit ( Fermentas ) , aliquots of products were subjected to qPCR analysis to measure mRNA levels of the tested genes . Gapdh was used as a reference gene . Gene-specific primer sequences were previously described [33 , 41] . Lungs or intestines from mice were fixed in formalin and embedded into paraffin blocks . The paraffin blocks were sectioned ( 5 μm ) for H&E staining . The immunohistochemistry analysis was performed on the 5-μm sections . The sections were placed on polylysinecoated slides , deparaffinized in xylene , rehydrated through graded ethanol , quenched for endogenous peroxidase activity in 3% hydrogen peroxide , and processed for antigen retrieval by microwave heating for 7 min in 10 mM citrate buffer ( pH 6 . 0 ) . Sections were counterstained with hematoxylin ( Zymed Laboratories ) for 5 min and coverslipped . Pictures were acquired using a HistoFAXS system . TRIF+/+ and TRIF-/- HEK293-TLR3 cells ( 5×107 ) were treated with poly ( I:C ) for the indicated times , and then cells were harvested and lysed by douncing for 20 times in 2 ml homogenization buffer ( 10 mM Tris-HCl [pH 7 . 4] , 2 mM MgCl2 , 10 mM KCl , and 250 mM sucrose ) . The homogenate was centrifuged at 500 g for 10 min for removal of the crude nuclei . The supernatant ( S5 ) was centrifuged at 100 , 000 g for 2 hours for cytosol ( S100K ) and membrane ( P100K ) generation . Differences between averages were analyzed by Student’s t-test . P value of less than 0 . 05 was considered significant .
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TLR3/4-mediated signaling needs to be effectively terminated to avoid excessive immune responses and harmful damages to the host . In this study , we provide genetic evidence to show that the E3 ubiquitin ligase TRIM32 negatively regulates TLR3/4-mediated innate immune and inflammatory responses . Trim32-/- mice are more sensitive to the inflammatory death upon Salmonella typhimurium infection . We found that TRIM32-TAX1BP1-dependent selective autophagic degradation of the adaptor protein TRIF effectively turned off TLR3/4-mediated innate immune and inflammatory responses . Our findings reveal a novel mechanism for terminating innate immune and inflammatory responses mediated by TLR3/4 .
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2017
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TRIM32-TAX1BP1-dependent selective autophagic degradation of TRIF negatively regulates TLR3/4-mediated innate immune responses
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Pseudomonas aeruginosa is a nearly ubiquitous human pathogen , and infections can be lethal to patients with impaired respiratory and immune systems . Prior studies have established that strong loss-of-function mutations in the egl-9 gene protect the nematode C . elegans from P . aeruginosa PAO1 fast killing . EGL-9 inhibits the HIF-1 transcription factor via two pathways . First , EGL-9 is the enzyme that targets HIF-1 for oxygen-dependent degradation via the VHL-1 E3 ligase . Second , EGL-9 inhibits HIF-1-mediated gene expression through a VHL-1-independent mechanism . Here , we show that a loss-of-function mutation in hif-1 suppresses P . aeruginosa PAO1 resistance in egl-9 mutants . Importantly , we find stabilization of HIF-1 protein is not sufficient to protect C . elegans from P . aeruginosa PAO1 fast killing . However , mutations that inhibit both EGL-9 pathways result in higher levels of HIF-1 activity and confer resistance to the pathogen . Using forward genetic screens , we identify additional mutations that confer resistance to P . aeruginosa . In genetic backgrounds that stabilize C . elegans HIF-1 protein , loss-of-function mutations in swan-1 increase the expression of hypoxia response genes and protect C . elegans from P . aeruginosa fast killing . SWAN-1 is an evolutionarily conserved WD-repeat protein belonging to the AN11 family . Yeast two-hybrid and co-immunoprecipitation assays show that EGL-9 forms a complex with SWAN-1 . Additionally , we present genetic evidence that the DYRK kinase MBK-1 acts downstream of SWAN-1 to promote HIF-1-mediated transcription and to increase resistance to P . aeruginosa . These data support a model in which SWAN-1 , MBK-1 and EGL-9 regulate HIF-1 transcriptional activity and modulate resistance to P . aeruginosa PAO1 fast killing .
Pseudomonas aeruginosa is a ubiquitous bacterial pathogen that can infect a wide range of animals and plants , and hospital-acquired P . aeruginosa infections are often lethal to patients with respiratory ailments or immune system dysfunction [1] , [2] . The cyanide produced by P . aeruginosa is thought to contribute to the potentially devastating effects of P . aeruginosa respiratory infections in cystic fibrosis patients [3] . Antibiotic-resistant strains of P . aeruginosa are becoming more prevalent , and it is increasingly important to understand the pathogenicity of this microbe and the mechanisms that enable resistance [4] , [5] . During infection and inflammation , multicellular tissues must adapt to changing levels of oxygen . The hypoxia-inducible factor ( HIF ) transcription complex mediates most of the transcriptional responses to hypoxia ( low oxygen ) [6] , [7] . While HIF transcription complexes have been shown to play key roles in mammalian innate immunity , the mechanisms by which HIF regulatory networks influence pathogenicity and disease progression are not yet fully understood [8] , [9] , [10] , [11] , [12] . In recent years , the nematode Caenorhabditis elegans has emerged as a powerful genetic system to study innate immunity and resistance to bacterial pathogens [13] , [14] , [15] , [16] , [17] , [18] . Many of the genes that contribute to C . elegans pathogen resistance are evolutionarily conserved [19] , [20] , [21] . Interestingly , there is a strong correlation between C . elegans genes that mediate resistance to bacterial pathogens and genes that protect C . elegans from stresses and extend lifespan [22] , [23] , [24] , [25] , [26] . Loss-of-function mutations in the C . elegans egl-9 gene enable the animals to survive fast killing by P . aeruginosa PAO1 [27] , [28] . While some Pseudomonas strains ( such as PA14 on NGM growth media ) kill C . elegans slowly through colonization in the gut , logarithmically growing P . aeruginosa PAO1 emits cyanide and kills C . elegans within hours [14] , [27] , [28] , [29] . C . elegans egl-9 mutants are also resistant to Crystal or Vibrio cholerae pore-forming toxins [30] . The egl-9 gene encodes a 2-oxoglutarate-dependent dioxygenase that hydroxylates the HIF-1 transcription factor . Once HIF-1 is hydroxylated , it interacts with the VHL-1 E3 ligase and is targeted for proteasomal degradation [31] . EGL-9 has also been shown to inhibit HIF-1 transcriptional activity via a vhl-1-independent pathway that has little or no requirement for EGL-9 hydroxylase activity [32] , [33] . Moderate over-expression of HIF-1 has been shown to increase resistance to heat and to increase adult longevity in C . elegans [34] , [35] , [36] , [37] . In this study , we directly test the hypothesis that increased expression and activation of the HIF-1 transcription factor in egl-9 mutants protect C . elegans from P . aeruginosa PAO1 fast killing . We show that resistance to P . aeruginosa fast killing requires both stabilization of HIF-1 protein and derepression of HIF-1-mediated gene expression . Using forward genetic screens , we identify additional mutations that confer resistance to P . aeruginosa PAO1 fast killing . This leads to the discovery that SWAN-1 inhibits HIF-1 transcriptional activity and modulates resistance to P . earuginosa PAO1 fast killing . SWAN-1 is an evolutionarily conserved protein with WD40 repeats [38] . Further , we demonstrate that SWAN-1 interacts with EGL-9 protein in yeast two-hybrid and co-immunoprecipitation studies .
The egl-9 ( sa307 ) strong loss-of-function mutation has been shown to protect C . elegans from P . aeruginosa PAO1 fast killing [27] , [28] As shown in Figure 1A , wild-type animals are paralyzed when placed on P . aeruginosa PAO1 , while egl-9 mutant animals remain motile for several hours . We tested the hypothesis that egl-9-mediated resistance to fast killing required hif-1 function . As shown in Figure 1A , the hif-1 ( ia04 ) loss-of-function allele totally suppressed the egl-9-mediated resistance phenotype . The rate at which the egl-9 , hif-1 double mutant was killed by the pathogen was very similar to the killing curves for wild-type or hif-1 ( ia04 ) animals ( Figure 1A , Text S1 ) . A prior study had shown that the fast killing of C . elegans by P . aeruginosa required cyanide synthesis [27] . Consistent with this , we found that while P . aeruginosa PAO1 killed wild-type or hif-1-deficient C . elegans within 2 hours , the hydrogen cyanide synthase mutant P . aeruginosa MP507 did not kill C . elegans in this time interval ( Text S1 ) . We next investigated which EGL-9 functions were most critical to the P . aeruginosa PAO1 fast killing phenotype . EGL-9 regulates HIF-1 via at least two pathways: EGL-9 is the oxygen-sensitive enzyme that targets HIF-1 protein for degradation through the VHL-1 pathway , and EGL-9 inhibits HIF-1-mediated transcriptional activity by a vhl-1-independent mechanism [32] , [33] , [39] . We first tested the hypothesis that stabilization of HIF-1 protein was sufficient to increase resistance to P . aeruginosa PAO1 fast killing . The HIF-1 ( P621G ) mutation precludes hydroxylation of HIF-1 by EGL-9 and stabilizes HIF-1 protein [31] , [32] , [34] . We assayed four transgenic strains , each expressing either wild-type HIF-1 or the HIF-1 ( P621G ) stabilized protein . Remarkably , none of the transgenic strains were resistant to P . aeruginosa PAO1 , as they died at rates similar to wild-type animals ( Figure 1B , Text S1 ) . Consistent with this result , the vhl-1 ( ok161 ) mutation did not protect C . elegans from fast killing ( Text S1 ) . The results thus far suggested that resistance to fast killing required multiple EGL-9 functions . To gain insight to the mechanisms by which EGL-9 repressed HIF-1 transcriptional activity and to better understand P . aeruginosa PAO1 pathogenicity , we conducted forward genetic screens . Using chemical or transposon-mediated mutagenesis , we screened for mutations in C . elegans that caused dramatic over-expression of HIF-1 target genes . As a primary screen , we assayed for the increased expression of Pnhr-57::GFP , a reporter that is expressed at very low levels in wild-type animals and is expressed at high levels in egl-9 mutants [32] , [33] . These screens identified novel loss-of-function mutations in rhy-1 ( Text S1 ) . Prior studies had shown that rhy-1 encoded a multipass transmembrane protein , and loss-of-function mutations in rhy-1 had been shown to elevate hif-1 mRNA levels slightly and to increase HIF-1 transcriptional activity [33] . As shown in Table 1 , animals that lacked rhy-1 function were resistant to P . aeruginosa PAO1 fast killing , and rhy-1-mediated resistance was completely suppressed by the hif-1 ( ia04 ) strong loss-of-function mutation . Reasoning that the effects of some mutations that derepressed HIF-1 activity might only be evident if HIF-1 protein were stable , we crossed a vhl-1 loss-of-function mutation into the parental strain and screened for mutations that increased expression of the reporter . One such screen identified ia50 , a mutation that enhanced expression of the Pnhr-57::GFP reporter in vhl-1 ( ok161 ) mutants ( compare Figure 2C to 2B ) . While vhl-1 ( ok161 ) animals over-expressed the reporter in the intestine , the ia50 mutation expanded expression of Pnhr-57::GFP to other tissues , including the hypodermis and the excretory cell ( Figure 2C ) . The enhanced GFP expression phenotype of ia50 was completely recessive . The ia50 , vhl-1 ( ok161 ) double mutants had morphological defects that were similar to those seen in egl-9 loss-of-function mutants , including egg-laying defects ( data not shown ) . Additionally , both strains exhibited reduced fertility ( Figure 2G ) . Genetic mapping with single-nucleotide polymorphisms placed ia50 near +5 . 67 map units on chromosome five of the C . elegans genome ( Figure 3A ) . Cosmid rescue experiments further delimited a genomic region that could restore a wild-type Pnhr-57::GFP expression pattern to ia50 mutant animals ( Figure 3A ) . We sequenced the genes in this region and found that ia50 mutants carried a single nucleotide mutation in the splice acceptor site for swan-1 ( F53C11 . 8 ) exon 3 ( Figure 3A , 3B ) . Full-length cDNA sequencing confirmed that ia50 mutants did not splice intron 2 from the swan-1 mRNA , and this introduced an early stop codon . The gene name swan-1 means “seven WD repeats , AN11 family” , and this family of genes includes Petunia AN11 , Arabidopsis TTG1 , zebrafish Wdr68 , and human HAN11 [40] , [41] , [42] , [43] . WD repeat proteins have beta propeller tertiary structures and often serve as platforms for the assembly of larger protein complexes . The swan-1 ( ia50 ) mutant allele is predicted to encode a truncated protein including only 2 of the WD repeats , and this suggests that it is a loss-of-function mutation . To further test the hypothesis that the ia50 mutant phenotype was due to defects in the swan-1 gene , we used bacterially mediated RNAi to deplete swan-1 mRNA . swan-1 RNAi increased the expression of Pnhr-57::GFP , as assayed by protein blots . In control experiments , RNAi for a neighboring gene , swan-2 , did not change the expression of the reporter ( compare to the empty vector control in Figure 3C ) . Prior studies had characterized the swan-1 ( ok267 ) deletion mutation as a strong loss-of-function allele [38] ( illustrated in Figure 3A ) . When HIF-1 protein was stabilized by the vhl-1 ( ok161 ) mutation , the swan-1 ( ok267 ) allele increased expression of the Pnhr-57::GFP reporter ( Figure 3D ) . This phenotype was suppressed by a loss-of-function mutation in hif-1 . The swan-1 deletion allele also reduced fertility in a vhl-1 mutant background ( Figure 2G ) . These similarities between the swan-1 ( ia50 ) and swan-1 ( ok267 ) phenotypes provided additional support for the conclusion that ia50 was a loss-of-function mutation in the swan-1 gene . For more in-depth analyses of swan-1 function , we used the swan-1 ( ok267 ) deletion allele , as it had been characterized in prior studies [38] . Having established that swan-1 negatively regulated Pnhr-57::GFP expression , we next asked whether a strong loss-of-function mutation in swan-1 also increased the expression of other HIF-1 target genes . Prior studies had demonstrated that K10H10 . 2 and F22B5 . 4 were induced by hypoxia in a hif-1-dependent manner and that they were over-expressed in vhl-1 , egl-9 , or rhy-1 loss-of-function mutants [31] , [32] , [33] , [39] . As shown in Figure 4A and 4B , mRNA levels for both K10H10 . 2 and F22B5 . 4 increased in swan-1 ( ok267 ) , vhl-1 ( ok161 ) double mutants , relative to the vhl-1 ( ok161 ) single mutants . These data represent at least three biological replicates of realtime RT-PCR experiments . As shown in Figure 4C , the swan-1 deletion mutation did not have a significant effect on HIF-1 protein levels . We hypothesized that the combination of HIF-1 stabilization and deletion of swan-1 might result in a P . aeruginosa resistance phenotype similar to that of egl-9 or rhy-1 mutants . Stabilization of HIF-1 , through either the stabilizing P621G mutation in HIF-1 transgenes or by mutation of the vhl-1 E3 ligase , was not sufficient to protect C . elegans from P . aeruginosa PAO1 fast killing ( Figure 1B and 5A ) . However , swan-1 ( ok267 ) , vhl-1 ( ok161 ) double mutants were much more resistant than wild-type animals ( >40% survived after two hours ) ( Figure 5A and Text S1 ) . As expected , this resistance was suppressed by a hif-1 loss-of-function mutation ( Text S1 ) . Similarly , in transgenic animals expressing HIF-1 ( P621G ) , the swan-1 ( ok267 ) mutation enabled almost 100% survival after two hours on a P . aeruginosa PAO1 lawn ( Figure 5B ) . Similar results were obtained using an independent hif-1 ( P621G ) transgenic line ( Text S1 ) . Since the genetic data suggested that SWAN-1 and EGL-9 acted in concert to inhibit HIF-1 activity , we next asked whether the two proteins interacted directly . To address this , we performed yeast-two-hybrid assays . In these assays , the EGL-9 catalytic domain was fused to the GAL4 DNA binding domain . In control experiments , this protein fusion by itself did not activate expression of reporter genes that were positively regulated by GAL4 upstream activating sequences . When the EGL-9 protein fusion was combined with a protein containing SWAN-1 fused to the GAL4 activation domain , the two proteins interacted to allow yeast growth on nutrient deficient plates ( -Ade/-His/-Leu/-Trp ) and to activate α-galactosidase expression ( Figure 6A ) . To further define the regions of SWAN-1 that interacted with EGL-9 , we assayed five SWAN-1 deletions , and these results are summarized in Figure 6B . A construct containing only the first three WD repeats of SWAN-1 was able to interact strongly with EGL-9 in yeast two-hybrid assays , whereas a construct that lacked the first four WD repeats did not interact with EGL-9 . To further test the hypothesis that EGL-9 and SWAN-1 could interact in a common complex , we conducted co-immunoprecipitation studies . In these experiments , the EGL-9 catalytic domain was fused to maltose binding protein and expressed in E . coli . A SWAN-1::GFP fusion protein was expressed in C . elegans and purified with a GFP-specific monoclonal antibody coupled to Sepharose beads . In control experiments , GFP alone was purified from worms . To assess interactions , the SWAN-1::GFP and MBP::EGL-9 proteins were co-incubated . Then , GFP-interacting proteins were isolated , and unbound proteins were washed away . As shown in Figure 6C , MBP::EGL-9 was coimmunoprecipitated with SWAN-1::GFP . Prior studies had demonstrated that SWAN-1 interacted with Rac GTPases and the Rac effector UNC-115 , and swan-1 had been shown to repress Rac GTPase activity in neurons [38] . Thus , we considered models in which swan-1 repressed HIF-1 activity by inhibiting Rac GTPases . However , depletion of unc-115 , rac-2 , ced-10 or mig-2 by mutation or RNAi did not abolish the induction of Pnhr-57::GFP expression in swan-1 ( ok267 ) , vhl-1 ( ok161 ) animals ( Text S1 ) . This suggested that SWAN-1 had at least two functions: it interacted with Rac GTPases in neurons to regulate cell migration , and it inhibited HIF-1 transcriptional activity , probably through interaction with EGL-9 . Homologs of SWAN-1 have been shown to interact with DYRK dual-specificity tyrosine-phosphorylation regulated kinases in yeast , zebrafish , and mammalian systems [42] , [44] . We hypothesized that swan-1 could interact with a DYRK homolog to regulate HIF-1 transcriptional activity . To test this , we used bacterially-mediated RNAi to knock down the expression of mbk-1 , hpk-1 and E02H4 . 3 , three C . elegans genes homologous to mammalian DYRK genes . As shown in Figure 7A , mbk-1 RNAi suppressed Pnhr-57::GFP expression in swan-1 ( ok267 ) , vhl-1 ( ok161 ) double mutants , while the other two RNAi treatments did not . Interestingly , mbk-1 RNAi did not inhibit expression of the reporter in egl-9 mutant animals ( Figure 7B ) . We obtained similar results using the mbk-1 ( pk1389 ) loss-of-function mutation ( Figure 7C ) . The mbk-1 deletion allele also inhibited Pnhr-57::GFP expression in swan-1 ( ok267 ) animals expressing the hif-1 ( P621G ) transgene ( Figure 7D ) . We next asked whether mbk-1 contributed to swan-1-mediated P . aeruginosa PAO1 resistance . As shown in Figure 7E and Text S1 , the mbk-1 ( pk1389 ) mutation completely suppressed the PAO1 fast killing resistance phenotype in swan-1 ( ok267 ) , vhl-1 ( ok161 ) double mutant animals . Further , mbk-1 ( pk1389 ) reduced PAO1 resistance in the swan-1 ( ok267 ) , hif-1 ( P621G ) genetic background , as assayed in two independently isolated hif-1 ( P621G ) transgenic lines ( Figure 7F and Text S1 ) . Interestingly , egl-9 mbk-1 double mutants are highly resistant to fast killing ( Text S1 ) . Thus , in assays of Pnhr-57::GFP or P . aeruginosa PAO1 fast killing , the mbk-1 mutation suppresses the swan-1 vhl-1 double mutant phenotype , but not the egl-9 loss-of-function phenotype .
Mutations , alone or in combination , that dramatically increase HIF-1-mediated gene expression can protect C . elegans from P . aeruginosa PAO1 fast killing . Prior studies had discovered that loss-of-function mutations in egl-9 conferred resistance to P . aeruginosa PAO1 fast killing and to cyanide poisoning , but the role of hif-1 had not been investigated [27] , [28] . Here , we establish that the resistance of egl-9 mutants to this pathogen is dependent upon hif-1 function ( Figure 1A ) . EGL-9 is a bifunctional protein , and it regulates both HIF-1 protein stability and HIF-1 transcriptional activity [32] , [33] ( Figure 8 ) . Interestingly , we find that stabilization of HIF-1 protein is not sufficient to protect C . elegans from P . aeruginosa fast killing , but mutations that disable both EGL-9 pathways confer resistance . We propose that over-expression of HIF-1 targets beyond a threshold level protects C . elegans from the cyanide produced by P . aeruginosa PAO1 . It is also possible that mutation of egl-9 allows HIF-1-mediated transcription in specific cells or tissues that are especially important to this resistance phenotype . Hydrogen cyanide is an inhibitor of cytochrome c oxidase , and it is a potent toxin . The cyanide produced by P . aeruginosa in cystic fibrosis patients is recognized as a clinically important virulence factor [3] . Cyanide inhibits cytochrome c oxidase , severely disabling ATP synthesis through oxidative phosphorylation . Interestingly , egl-9 mutant animals are also resistant to hydrogen sulfide , and H2S is also a cytochrome c oxidase inhibitor [45] , [46] . A parsimonious explanation is that persistent over-expression of HIF-1 targets protects C . elegans from cyanide or hydrogen sulfide treatments that disable oxidative phosphorylation . These findings introduce an important question: which HIF-1 target genes protect C . elegans from P . aeruginosa PAO1 fast killing , cyanide exposure , and/or hydrogen sulfide ? Prior studies have investigated the genes induced by short-term moderate hypoxia at 0 . 1% oxygen for 4 hours at room temperature [6] . Future studies will examine the changes in gene expression that are common to mutants or mutant combinations that activate HIF-1 and confer resistance to P . aeruginosa . Increased expression of HIF-1 targets has been shown to protect C . elegans from diverse pathogens or stresses [30] , [36] , [37] , [45] , [47] . Bellier et al . isolated a loss-of-function mutation in egl-9 in a screen for mutations that protected C . elegans from pore-forming toxins [30] . egl-9 mutants have also been shown to be resistant to enteropathogenic E . coli E2348/69 [47] . Additionally , mutations that stabilize HIF-1 or increase expression of HIF-1 targets have been shown to increase C . elegans resistance to polyglutamine or beta-amyloid toxicity and heat stress [30] , [34] , [36] , [37] . It is not yet known whether the same HIF-1 targets mediate all of these resistance phenotypes , but we anticipate that each of these functions may require multiple direct and indirect HIF-1 targets . SWAN-1 is an evolutionarily conserved WD-repeat protein of the AN11 family [38] . The data presented here show that SWAN-1 represses HIF-1-mediated gene expression , but does not control HIF-1 protein levels . While swan-1 RNAi does increase expression of the Pnhr-57:GFP reporter in an otherwise wild-type background ( Figure 3C ) , loss of swan-1 function alone is not sufficient to confer resistance to cyanide released by P . aeruginosa PAO1 ( Figures 5A and 5B ) . Resistance to fast killing requires a second mutation that protects HIF-1 protein from oxygen-dependent degradation . Double mutants that carry a loss-of-function mutation in swan-1 and a mutation that stabilizes HIF-1 protein are phenotypically similar to egl-9 loss-of-function mutants , as assayed by fertility , egg laying defects , over-expression of HIF-1 targets , and resistance to P . aeruginosa fast killing ( Figures 1A , 2G , 4A , 4B , 5A ) . swan-1 , vhl-1 double mutants also exhibit increased resistance to hydrogen cyanide ( unpublished data ) . Importantly , we show that SWAN-1 forms a complex with EGL-9 ( Figure 6A , 6C ) . Collectively , these data suggest that SWAN-1 and EGL-9 interact directly to repress HIF-1 transcriptional activity . The AN11 family is evolutionarily conserved , and comparative studies may provide important insights to the roles of these proteins in stress resistance and transcriptional regulation . Petunia AN11 interacts with a MYB family transcription factor , and the human HAN11 gene has been shown to partially rescue the Petunia an11 mutant phenotype [43] . In human cells , zebrafish , and in yeast , AN11 homologs have been shown to form complexes with DYRK kinases [42] , [44] , [48] . There are five DYRK members in mammals: DYRK1A , DYRK1B , DYRK2 , DYRK3 and DYRK4 [49] . Of these , DYRK1A has been characterized most extensively , and it is associated with Down Syndrome [50] , [51] , [52] . DYRK1A is a multifunctional protein and has more than two dozen targets or interacting proteins , including GLI1 , STAT3 , and eIF2Bε [49] . HAN11 was shown to decrease DYRK1A-mediated phosphorylation of GLI1 in a HEK293T cell line [53] . The genetic analyses presented here suggest that in the absence of SWAN-1 , MBK-1/DYRK activates HIF-1 ( illustrated in Figure 8 ) . Specifically , a loss-of-function mutation in mbk-1 suppresses the swan-1 , vhl-1 double mutant phenotypes , as assayed by expression of Pnhr-57::GFP expression and by P . aeruginosa PAO1 fast killing ( Figure 7 ) . These genetic data suggest that mbk-1 acts downstream of swan-1 . Interestingly , mutation of mbk-1 does not suppress the egl-9 mutant phenotype ( Text S1 ) . There are at least two models that could explain these findings . First , EGL-9-mediated repression of HIF-1 transcriptional activity may be modulated by SWAN-1 and MBK-1 without being totally dependent upon these regulators . An alternative model is that MBK-1 and SWAN-1 act in parallel to EGL-9 to repress HIF-1 activity . While we favor the first model , we recognize both possibilities in Figure 8 . A goal for future studies will be to identify the targets of MBK-1/DYRK to better understand how MBK-1 promotes HIF-1 activity . A long-term goal will be to understand how hypoxia-induced gene expression influences the progression of P . aeruginosa infections . P . aeruginosa can survive in anaerobic environments , and the formation of biofilms likely restricts oxygen availability to infected tissues in human patients . Our findings in C . elegans suggest that pharmacological inhibitors of the HIF prolyl hydroxylases might contribute to combinatorial therapies to protect cells from the cyanide produced by P . aeruginosa PAO1 .
C . elegans were grown at 20°C using standard methods , unless other culture conditions are specified [54] . The loss-of-function alleles and transgenic lines used in this study are listed in Text S1 . The swan-1 ( ok267 ) mutant allele was backcrossed to wild-type animals three times prior to phenotypic analyses [38] . The EMS forward genetic screen was performed as described previously [33] . Briefly , the parental strain carrying Pnhr-57::GFP and vhl-1 ( ok161 ) was mutagenized with EMS , and the F2 progeny were screened for increased expression of the Pnhr-57::GFP reporter using fluorescent stereomicroscopy . We generated the rhy-1 loss-of-function alleles ia59 , ia62 , ia63 , ia64 in a screen for Mos1 transposon-mediated mutations that caused Pnhr-57::GFP overexpression . The methods for Mos1 mobilization have been described previously [32] , [55] . The ia50 mutant allele was out-crossed twice to the parental strain prior to any further mapping or characterization . Chromosome and interval mapping were performed as described previously using single-nucleotide polymorphisms ( SNPs ) between the Bristol N2 and Hawaiian strains [56] . Briefly , the iaIs07 ( Pnhr-57::GFP ) transgene and the vhl-1 ( ok161 ) mutation were crossed extensively into the Hawaiian genetic background . The resulting males were crossed to ia50 vhl-1 ( ok161 ) double mutants carrying the iaIs07 ( Pnhr-57::GFP ) marker . Fifty F2 animals exhibiting the vhl-1 , ia50 double mutant phenotype and fifty animals exhibiting the vhl-1 ( ok161 ) single mutant phenotype ( intestinal GFP expression ) were picked into separate tubes , and genomic DNA was prepared . Analyses of the divergent SNPs showed enrichment of Bristol bands in mutant lanes and an enrichment of Hawaiian bands in non-mutant lanes for SNPs lying between -5 and +13 mu on chromosome V . For interval mapping , individual self-progeny of F1 hermaphrodites ( described above ) with the double mutant phenotype were picked into a 96-well plate to prepare the genomic DNA , and four SNPs were analyzed ( −5 , +1 , +6 , +13 ) [56] . We then used two SNPs , R10D6 ( +5 . 83 mu ) and pkP5086 ( +6 . 42 mu ) , to do three point mapping of the ia50 mutation . RNAi was performed as previously described [34] . Bacterial strains containing RNAi constructs were purchased from Geneservice Ltd . , and inserts were validated by sequencing . Individual L4-stage worms were placed on NGM plates with fresh OP50 bacterial food . The worms were transferred onto fresh plates every 12 hours , and the total progeny laid on each plate was counted and recorded at each time point . This procedure was continued until the worms reached the end of their reproductive capacity . To build pSZ18 , the bacterial expression vector for EGL-9::MBP , egl-9 cDNA was amplified with two primers: 5′GGTGGATCCAAACCAACGGTATCCAGAAC and 5′GGGATCCGATGTAATACTCTGGGTTTGTGG . The PCR products were cut with BamHI and PstI and ligated into the pMal-p2x vector ( from New England Biolabs ) . The plasmid expressing EGL-9 fused to maltose binding protein was transformed into BL21 ( DE3 ) bacteria , and a single colony was inoculated into liquid media and cultured for 16 hours at 37°C . The bacterial culture was diluted by 1:200 to 50 ml and cultured for 2 . 5 hours . IPTG was added to a final concentration of 1 mM . After 4 hours , the bacteria were pelleted and frozen in liquid nitrogen . The bacteria were frozen at −80°C overnight before adding bacterial lysis buffer ( 50 mM Tris PH 7 . 5 , NaCl 150 mM , NP-40 0 . 1% ) , 0 . 25 mg/ml lysozyme , 0 . 01 mg/ml DNase , and 1× protease inhibitor cocktail ( from Roche ) . After four hours at 4°C , the lysate was centrifuged at 12000 g for 20 min and the supernatant was kept for further use . The fast killing assay follows the approach described previously [27] , [28] . Briefly , a single P . aeruginosa PAO1 colony was inoculated and cultured at 37°C for 16 hours in 3–5 ml brain heart infusion ( BHI ) broth . The culture was then diluted 100-fold . 300 µl of diluted bacteria was evenly spread on 60 mm diameter Petri dishes with 7 ml BHI agar . To minimize the possibility of animals escaping from the bacterial lawn , bacteria were spread to cover the whole plate . The plates were incubated at 37°C for 24 hours and then cooled to room temperature for 30 minutes . Thirty to fifty developmentally synchronized L4-stage C . elegans were put on the lawn and incubated at room temperature ( 22°C ) . Lids to the petri plates remained closed during this time to keep hydrogen cyanide from evaporating . C . elegans were scored as paralyzed or dead if they did not move when the plate was tapped against the microscope stage . Each data point represents at least three independent replicate experiments . The methods for protein blots have been described previously [32] . At least three biological replicates were analyzed for each experiment . Transgenic strains expressing epitope-tagged HIF-1 were used to assay HIF-1 protein levels and each lane of a protein gel included lysate from 40–100 L4-stage animals . For the Pnhr-57::GFP reporter , we used 5–50 L4 animals . The statistical significance of differences was assessed by two-sample paired t-tests or one way ANOVA with Bonferroni post test . The methods for real time PCR were as described previously [32] . At least three biological replicates were analyzed for each experiment , and each PCR reaction was performed in duplicate . The statistical significance of differences was assessed by two-sample paired t-tests or one way ANOVA with Bonferroni post test . For yeast two-hybrid experiments , C . elegans cDNA sequences were inserted in to the pGBKT7 and pGADT7 vectors from Clonetech . The sequences encoding the EGL-9 catalytic domain were amplified with two primers , egl-9-F: CCGGAATTCGGTCTCGCACTAAGCATTCACC and egl-9-R: CGCGGATCCCCGTGGT CTCAAAAGTGATCCAAT , and cloned into pGBKT7 ( GAL4 DNA-binding domain vector ) . This construct did not result in detectable autoactivation . Dr . Erik Lundquist kindly provided the plasmid which expressed SWAN-1 fused to the GAL4 transcriptional activation domain . The deletion vectors ( shown in Figure 6B ) were amplified from this swan-1 prey plasmid [38] . The primer sets are listed in Text S1 . The AH109 yeast strain was used . Transformations of plasmids into yeast were performed as described previously [57] , [58] . The negative controls included the co-transformation of pGADT7-swan-1 plasmids with pGBKT7 ( empty vector ) ; the co-transformation of pGBKT7-egl-9 and pGADT7; and the co-transformation of pGBKT and pGADT7 . To test for interactions , yeast colonies carrying both plasmids ( selected on Leu− , Trp− plates ) were cultured in 2 ml Leu−/Trp− liquid SD-medium for 24 hours at 30°C . Twenty individual colonies were then assayed for growth on Ade−/His−/Leu−/Trp− X-α-gal SD-medium plates . Additionally , six Leu+/Trp+ colonies were cultured for quantification of the β-galactosidase activity using O-nitrophenyl-B-D-galactopyranoside as a substrate . The C . elegans strain expressing SWAN-1::GFP [transgenic array lqEx19 ( Pswan-1:: swan-1::gfp ) ] was generously provided by Erik Lundquist . C . elegans were grown on 100 mm enriched media plates with NA22 bacteria , and 0 . 4 ml mixed-stage worms were harvested . Animals were washed with cold M9 buffer three times and were washed once with worm lysis buffer ( 50 mM Tris-HCl PH 7 . 4 , 150 mM NaCl , 0 . 5% Triton-X-100 , 10% glycerol , 1 mM DTT , and 1X protease inhibitor cocktail from Roche ) . The worm pellet was resuspended in 1 . 2 ml worm lysis buffer , and the animals were lysed in a french press ( Thermo Electron Corporation ) three times at 1000 psi . To perform immunoprecipitations , 80 µl G-Sepharose beads were washed with worm lysis buffer three times for 10 minutes each , and they were then incubated with 80 µl 0 . 4 mg/ml GFP antibody ( Roche ) overnight . Beads were washed with worm lysis buffer three times for 5 minutes and divided into two 40 µl aliquots , each of which was incubated with 600 µl of SWAN-1::GFP or GFP worm lysate at 4°C for 4 hours . Beads with either control GFP [Pnhr-57::GFP] or SWAN-1::GFP were washed ( three times 10 minutes in cold lysis buffer ) and incubated with MBP::EGL-9 . After four hours incubation at 4°C , beads were washed with worm lysis buffer and boiled in 40 µl 1xSDS buffer . The proteins were fractionated by 10% SDS polyacrylamide gel electrophoresis , and blots were probed with maltose binding protein rabbit antiserum ( from NEB at 1∶3000 dilution ) or mouse GFP monoclonal antibody ( clones 7 . 1 and 13 . 1 from Roche at 1∶1000 dilution ) .
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Pseudomonas aeruginosa is a common bacterial pathogen that can infect a wide range of animals . In some conditions , P . aeruginosa produces cyanide , a toxin that limits cellular capacity to metabolize oxygen and produce energy . The nematode Caenorhabditis elegans is a powerful genetic model system for understanding the mechanisms of stress response and pathogen resistance . Here , we show that HIF-1 , a DNA-binding transcription factor that mediates cellular responses to low oxygen , can protect C . elegans from P . aeruginosa fast killing . Additionally , we identify swan-1 as a gene that functions to inhibit HIF-1 activity and suppress P . aeruginosa resistance . The SWAN-1 protein binds directly to the oxygen-sensing EGL-9 enzyme that controls HIF-1 stability and activity . This study advances understanding of HIF-1 regulatory networks , defines connections between hypoxia response and P . aeruginosa fast killing , and provides new insights into mechanisms by which animals can resist this bacterial pathogen .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/gene",
"expression",
"cell",
"biology/cellular",
"death",
"and",
"stress",
"responses",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"genetics",
"and",
"genomics/disease",
"models",
"infectious",
"diseases/bacterial",
"infections",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
] |
2010
|
C. elegans SWAN-1 Binds to EGL-9 and Regulates HIF-1-Mediated Resistance to the Bacterial Pathogen Pseudomonas aeruginosa PAO1
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Several major human pathogens , including the filoviruses , paramyxoviruses , and rhabdoviruses , package their single-stranded RNA genomes within helical nucleocapsids , which bud through the plasma membrane of the infected cell to release enveloped virions . The virions are often heterogeneous in shape , which makes it difficult to study their structure and assembly mechanisms . We have applied cryo-electron tomography and sub-tomogram averaging methods to derive structures of Marburg virus , a highly pathogenic filovirus , both after release and during assembly within infected cells . The data demonstrate the potential of cryo-electron tomography methods to derive detailed structural information for intermediate steps in biological pathways within intact cells . We describe the location and arrangement of the viral proteins within the virion . We show that the N-terminal domain of the nucleoprotein contains the minimal assembly determinants for a helical nucleocapsid with variable number of proteins per turn . Lobes protruding from alternate interfaces between each nucleoprotein are formed by the C-terminal domain of the nucleoprotein , together with viral proteins VP24 and VP35 . Each nucleoprotein packages six RNA bases . The nucleocapsid interacts in an unusual , flexible “Velcro-like” manner with the viral matrix protein VP40 . Determination of the structures of assembly intermediates showed that the nucleocapsid has a defined orientation during transport and budding . Together the data show striking architectural homology between the nucleocapsid helix of rhabdoviruses and filoviruses , but unexpected , fundamental differences in the mechanisms by which the nucleocapsids are then assembled together with matrix proteins and initiate membrane envelopment to release infectious virions , suggesting that the viruses have evolved different solutions to these conserved assembly steps .
Members of the virus order Mononegavirales represent a critical challenge to human health . The order contains major human pathogens including paramyxoviruses such as measles virus ( MeV ) , mumps virus , and respiratory syncytial virus ( RSV ) ; rhabdoviruses such as rabies virus ( RABV ) ; and filoviruses such as Ebola virus ( EBOV ) and Marburg virus ( MARV ) [1] . Members of the order are characterized by a single-stranded , negative-sense , non-segmented RNA genome , which is replicated from a nucleoprotein ( NP ) -RNA complex by the viral L protein , an RNA-dependent-RNA-polymerase , and a polymerase cofactor ( the phosphoprotein P in rhabdoviruses and paramyxoviruses ) [2] . The newly synthesized RNA is bound by NP and assembles together with other viral proteins to form a helical nucleocapsid ( NC ) . The NC , matrix protein , and other components assemble at and bud through the plasma membrane of an infected cell to form virions enveloped by a host-cell-derived membrane . The core region of Mononegavirales NPs is made up of two primarily alpha-helical domains , with the RNA bound at the interface between the two domains [3] . The NPs assemble into helical NCs where the two domains of each NP monomer protrude at an angle to the helical axis to give the NCs a strong structural polarity with characteristic “pointed” and “barbed” ends by analogy with actin [4]–[6] . These consistent features are balanced by substantial differences . The diameters of the NCs vary among the Mononegavirales , indicating different numbers of NP monomers per turn of the helix; there can even be variability in the number of NPs per turn within a single virion [7]–[9] . The number of RNA bases bound per NP also varies , from six or seven in the paramyxoviruses to nine in the rhabdoviruses , reflecting differences in genome replication mechanisms . Most strikingly , the NCs are enveloped and released into virions , which show major differences in structure and morphology . Rhabdoviruses like vesicular stomatitis virus ( VSV ) and RABV form bullet-shaped particles with a defined diameter and length [10] . Other families in the order form more heterogeneous particles . Paramyxoviruses like MeV , Sendai virus , ( SeV ) and RSV form mostly round-shaped particles of variable size , but filamentous forms may also be observed [11] , [12] . Filoviruses adopt a range of morphologies from filamentous , through “six-shaped” to round particles [13] , [14] . Only for one member of the order , the prototypical rhabdovirus VSV , is a detailed structural description of the virion available . VSV assembles regularly shaped particles , and this regularity permits application of single-particle cryo-electron microscopy ( cryoEM ) analysis , a method that cannot be applied to heterogeneous virions . The resulting 3-D structure shows that the helical NC makes direct , regular interactions with matrix , potentially allowing the NC helix to define the shape of the virion through direct contact [5] . The NC has a bullet-shape , with a dome-like tip . Interaction of the M protein with the dome-like NC tip has been suggested to mediate envelopment of viral NC to form a protruding , bullet-shaped particle that is then pinched off [15] . It is tempting to speculate that this may be a conserved mechanism also adopted by other members of Mononegavirales including the filoviruses . It has been shown that the filovirus NC associates laterally with the membrane , before envelopment initiates from an end of the NC , leading to a perpendicularly protruding intermediate that pinches off from the infected cell [14] , [16] . It is not known whether one end of the NC specifically initiates envelopment or whether it is initiated randomly from either end . It is also not clear if the filovirus NC has a structure comparable to the dome-like tip of the VSV NC . To understand the extent to which the knowledge obtained from the VSV structure can be applied to other Mononegavirales members requires that structural information is obtained for other , more heterogeneously shaped families within the order . Here we have addressed this need by describing the structure and assembly of MARV , a filovirus . The family Filoviridae contains two genera: MARV and EBOV . Both viruses are highly pathogenic , causing hemorrhagic fever in infected humans , with high mortality rates . They are classified as highest priority bioterrorism agents by the Centers for Disease Control and Prevention ( USA ) . Filovirus research is complicated by the need to perform experiments under biosafety level 4 ( BSL-4 ) conditions . Nevertheless , biochemical , structural , and functional studies have explored the properties of the viral proteins and their role in assembly and budding [17] . As a biological system , a filovirus is remarkably simple . The 19 kb RNA genome and the viral proteins NP , VP30 , VP35 ( the polymerase cofactor ) , VP24 , VP40 ( the matrix protein ) , and L are enveloped by a lipid membrane containing trimers of the glycoprotein ( GP ) [18] . VP30 , VP35 , and L have been assigned as components of the NC based upon salt dissociation and biochemical analysis of isolated MARV , and on the same basis VP24 and VP40 were described as the minor and major matrix proteins , respectively [19]–[21] . For EBOV , it has been shown that NP , VP24 , and VP35 are necessary and sufficient to form NC-like helical structures [22] , [23] . EBOV VP40 and MARV VP40 can assemble and bud enveloped filamentous virus-like particles ( VLPs ) in the absence of other viral proteins [21] , [24] . VP40 VLPs have been reported to be slightly narrower in the absence of a NC , suggesting that an interaction is present between the two [25] . More precise information about the location and arrangement of the proteins within virions is not available . No crystal structure of the NP or low-resolution structure of the NC of filoviruses is available . The structural relationship between the matrix protein VP40 and NP in filoviruses , and potential importance of this relationship in defining virion structure , therefore remains undescribed . A structural understanding of filoviruses has previously been complicated by the need to prepare viruses under BSL-4 containment conditions , and by difficulties inherent in applying the methods for 3-D structural studies to heterogeneous virions . Single-particle cryoEM methods used to derive the structure of VSV cannot be applied to the heterogeneous particles of MARV . Here we have applied multiple electron microscopy ( EM ) approaches including cryoEM , cryo-electron tomography ( cryoET ) combined with sub-tomogram averaging , and immuno-electron microscopy ( IEM ) to describe the structure of purified MARV in 3-D . The data permit determination of the location and arrangement of viral proteins within MARV , the architecture of the NC , minimal assembly determinant , RNA packaging stoichiometry , and a flexible relationship between the NC and viral matrix protein . This study provides the first detailed 3-D description of the structure of a BSL-4 pathogen , revealing principles of structure and assembly , which are likely to extend to other heterogeneously shaped Mononegavirales . CryoET can also be used to study the structure of assembly components within cells [26]–[29] . We have therefore applied cryoET to also visualize MARV budding from the filopodia of infected cells in 3-D . By combining cryo-ET with sub-tomogram averaging we were able to resolve the 3-D structure of the NC in situ , during membrane envelopment , showing that the NC has a defined orientation during transport and budding and implying a mechanism of virus assembly , which is fundamentally different than that in the rhabdoviruses .
MARV particles were harvested from the supernatant of infected Vero cells 3 d post-infection , purified by centrifugation , and fixed in paraformaldehyde ( PFA ) prior to release from the BSL-4 laboratory ( Materials and Methods ) . The purified preparation was imaged using cryoEM . Three morphological forms of the virus were observed in the samples: filamentous ( 30% , n = 128 ) , six-shaped ( 37% ) , and round particles ( 33% ) ( Figure 1 ) . All of these had a membrane with spike-like protrusions approximately 10 nm in length . This is slightly longer than the size described for the non-glycosylated EBOV GP [30] , since the MARV GP on virions should be fully glycosylated . The filamentous particles and the straight sections of the six-shaped viruses contained NCs along most of their length and had approximately hemispherical tips . In round particles , and in the curved sections of six-shaped virions , it was possible to detect short sections of NC close to the viral membrane , in agreement with previous studies of NC morphology within virions [14] . A striated density located between the NC and the viral membrane was observed in the filamentous and six-shaped particles . The filamentous particles had a mean length of 892 nm ( SD 63 nm ) and a mean diameter of 91 nm ( SD 6 nm ) , slightly longer than earlier studies , since cryo-EM avoids the compression and shrinkage effects associated with sectioning and imaging plastic embedded samples [14] , [31] . To describe in detail the structural arrangement of the viral proteins within the virion , we collected cryoET data on free virions and produced 3-D tomograms from them ( Figure 2A ) . From these tomograms , subtomograms ( or sub-volumes ) were extracted along the length of filamentous particles and averaged to generate a radial density profile of the virion ( Text S1 ) , from which it was possible to discern distinct layers of density ( Figure 2B ) . The innermost layer of density in the radial-density profile corresponds to the NC visible in our cryo-micrographs , and the outermost to the lipid membrane . As expected , there was no density layer corresponding to GP because GP is sparsely distributed on the surface of the particle rather than forming a continuous layer . In order to assign the observed layers to individual proteins , we performed thin section IEM on the purified virus sample [32] . Sections were immunolabelled using antibodies against either VP24 , VP35 , VP40 , GP , or NP followed by protein-A gold [33] and examined by EM . The distances of the gold beads from the centre of the radial cross-section were measured , and used to calculate the radial distribution of each of the epitopes ( Figures S1 and 2B and Table S1 ) . This experiment allowed us to assign the innermost density to NP , and the adjacent density band to VP24 and VP35 ( Figure 2B ) . The VP40 epitope is positioned at the innermost of two layers of density immediately underneath the membrane . A comparison with GP-VP40 VLPs confirms that the outer of these two layers is also contributed by VP40 ( see below ) . The GP signal is seen slightly outside the outer membrane , as expected . After assignment of the NC density , we wanted to explore its structure . Established 2-D helical reconstruction techniques are not directly applicable to the NC helix within the virion , because the surrounding proteins and membrane confound the image and prevent determination of the helical parameters . Instead we applied novel reference-free subtomogram averaging approaches to generate 3-D reconstructions of NCs from within 30 individual virions ( Materials and Methods ) . These reconstructions showed that the NC has an inner density forming a strong and continuous left-handed , single-start helix inclined to the filament axis and roughly “boomerang”-shaped protrusions extending outwards from this inner density . The pitch of the NC helix was 7 . 5 nm±0 . 2 nm . Twenty-one of the analyzed NCs had a symmetry of 14 . 8 protrusions per turn , seven NCs had 15 . 8 protrusions per turn , and two NCs had 13 . 8 protrusions per turn . This indicated that the individual subunits of the helical NC have sufficient flexibility to adopt slightly different overall symmetries . One case was observed in which there was a change in the symmetry from 14 . 8 to 15 . 8 protrusions per turn within a virion ( Figure S2 ) . The NCs with 15 . 8 protrusions per turn were combined into a single 3-D reconstruction with a resolution of 4 nm ( unpublished data ) ; those with 14 . 8 protrusions per turn were combined into a single 3-D reconstruction with a resolution of 3 . 4 nm ( Figure 3A–B ) . Both reconstructions show the same features . The subtomogram averaging analysis therefore allowed us to determine the pitch of the NC helix and the number of subunits per turn . We could then use this information to apply helical reconstruction techniques to derive a higher resolution 3-D structure of the NC from 2-D micrographs of purified virions [34] . We sorted the 2-D data according to symmetry ( Text S1 ) and obtained 3-D reconstructions for the different symmetries of the NC . The reconstruction of the predominant 14 . 8 protrusion per turn symmetry had 2 . 5 nm resolution ( Figure 3C–D ) . The resolution of the reconstruction is highest at a radius coincident with the inner layer of the NC , and falls off gradually outwards towards the membrane ( Figure S3 ) , indicating that the innermost NC layer is thus the most rigidly ordered part of the structure , and variability and flexibility increases with increasing distance from the centre of the helix . At this resolution , the innermost helical layer of the NC is resolved into 30 separate lobes of density per turn ( compare Figure 3C right with 3A right ) , with two lobes of density per boomerang-shaped protrusion . The protrusions emanate from each alternate interface between the inner lobes . The inclination of the lobes of density in the inner helix relative to the helical axis defines the “pointed end” and the “barbed end” of the NC ( Figure 3B , 3D , and 3E ) , as seen in the NCs of other Mononegavirales . Comparison of the 3-D reconstructions with our IEM data ( Figure 2B ) suggests that the protrusions correspond to the location of VP24 and VP35 , and that the innermost density of the MARV NC reconstruction corresponds to NP . To confirm the assignment of the NP density , we imaged purified , recombinant MARV NP by cryoEM ( Figure 4A ) . The oligomeric NP appears as loose coils with an approximate diameter of 30 nm as seen previously [35] . Based on bioinformatics analysis of the NP sequence ( Figure S4 ) , we expressed a truncated form of the MARV NP , containing only the core-conserved 390 N-terminal residues , which formed short rigid helices in dense clusters ( Figure 4B ) . CryoET and subtomogram averaging indicated that these helices had the same diameter , hand , and pitch as the innermost density of the viral NC reconstruction , with a closely matching structure ( Figure 4C–D ) , demonstrating that this layer is formed by the core-conserved N-terminal 390 residues of NP . Furthermore , it demonstrates that the core region of NP can define the helical parameters of the viral NC and assemble in the absence of any other viral proteins . We hypothesized that each lobe in the inner layer corresponded to one copy of NP . No crystal structure of MARV NP is available , but the core-conserved region is expected to have structural homology to other Mononegavirales NPs . We therefore performed rigid-body fitting of four adjacent monomers , extracted together from the pseudo-atomic structure of the VSV NP helix ( PDB ID 2WYY ) [5] , [36] into the innermost layer of the full MARV NC density we had reconstructed from within virions ( Figure 4E–F and Movie S1 , Text S1 ) . The curvature of the NC helix and the characteristic inclination of the NP from the helix axis constrain the structure so that it can only be fitted as a rigid body in one position . The inclination of VSV NP from the helix axis matches the inclination seen in the 3-D reconstructions of MARV NC . One VSV NP fits into each lobe in the inner helix . High-resolution features such as the position of the RNA may differ between MARV and VSV , but the fit suggests striking architectural homology between the two NCs . Together , the above observations allow us to calculate the number of RNA bases bound per NP . The average symmetry of the MARV NC helix is 14 . 96 protrusions per turn; therefore , it has on average 29 . 92 copies of the NP per turn . A virion of mean length 892±63 nm contains an NC of approximately 804 nm in length . The helix has a pitch of 7 . 52±0 . 19 nm per turn , meaning that an NC of mean length has approximately 107 turns , corresponding to approximately 3 , 170 copies of NP . Since the genome has a length of 19 kb , each NP monomer packages 6 . 0±0 . 2 RNA bases . During the 2-D helical reconstruction of the NC , an orientation is assigned to each NC segment within each image . We mapped this directionality information back onto the original electron micrographs ( Figure 5A ) . Out of 40 six-shaped virions where the image quality was appropriate for direction assignment , 33 had the “pointed” end of the NC towards the tip ( the top ) of the six and seven had the “barbed” end towards the tip of the six , indicating a preference of the pointed end of the NC to be towards the tip of the “six . ” Seven virions were identified that were more than twice the length of the average MARV particle , and therefore probably contained more than one RNA genome inside . In these virions , the NC did not change direction throughout their length ( Figure S5 ) , implying that both genomes were packaged with the same directionality . To obtain a clearer view of the virion tips , 248 tips were computationally extracted from micrographs . An image of a virion tip is shown in Figure 5B . Striations are seen on the underside of the viral membrane , also in some cases in the curved part of the membrane at the tip . These striations are also seen in the GP-VP40 VLPs and correspond to VP40 [37] . All the extracted tips were then aligned and averaged with each other . The average of all viral tips is shown in Figure 5C . Based on our assignment of direction , we then averaged the pointed and barbed tips separately . These averages are shown in Figure 5D and 5E , respectively . The averages show that in neither case does the NC extend to the viral membrane , but rather ends approximately 44 nm before the inner side of the membrane . No dome structure capping the NC , equivalent to that described for VSV , was seen . The barbed tips were found to be less regular than the pointed tips ( Figure S5B–D ) . VP40 was not resolved in the NC reconstructions , indicating that , in contrast to the situation in VSV [5] , the matrix layer does not follow the helical symmetry of the NC in MARV . A reconstruction of the VP40 lattice could not be generated by reference-free subtomogram averaging methods , likely due to variability and flexibility in the lattice . Instead , we applied 3-D Fourier analysis methods to measure local regularity within the VP40 lattice ( Text S1 ) . These methods revealed the presence of regularly spaced features within both inner and outer VP40 layers ( Figure S6 ) . The expression of only VP40 and GP in cells leads to the assembly and release of filamentous membrane-bound VLPs [38] . We purified GP-VP40 VLPs and subjected them to the same cryo-ET and Fourier analysis . The particles were narrower ( 71 nm , SD 7 nm ) than the virions . They also exhibited inner and outer layers of VP40 density with regularly spaced features generally similar to that in the virion but showing some clear differences ( Text S1 ) . Most strikingly , only within the virion does the VP40 layer contain features that repeat regularly around the circumference of the virion . These observations indicate a significant change in VP40 conformation occurs in the presence of the NC . Having built up an in-depth picture of the structure of the NC in the free virion , we wanted to investigate the structural changes occurring during membrane association and envelopment . Infected HUH-7 cells ( Materials and Methods ) were fixed , vitrified by plunge-freezing , and examined using cryo-ET . The cells appeared to be well preserved and , apart from some small regions of the membrane that appeared moth-eaten ( as previously described for filoviruses [39] ) , showed no adverse effects from the fixation process . In thin areas , instances of microtubules , early endosomes , clathrin coated pits , and the cortical actin network could be discerned . Many cells displayed a large number of thin , filopodia-like extensions ( Figure 6A and 6B ) , which previous studies have identified as the budding site of MARV [14] , [29] . We observed numerous NCs within filopodia and a smaller number of NCs at the plasma membrane of the cell body . Single filopodia frequently contained multiple NCs within their length ( Figure 6C , 6D , and 6E ) . The NCs in the filopodia were almost always associated with the membrane on at least one side , and many partially enveloped NCs were seen extending out from the filopodia , apparently in the process of budding . Repeating features corresponding to the NC helix could be seen in some of the reconstructions ( Figure 6F ) . The NCs were divided into three classes , as follows . NCs which had begun envelopment by the membrane , but which had not yet pinched off from the cell ( such as those in Figure 6C ) were defined as class I . For NCs that had not yet initiated envelopment , but were associated with the membrane along only one side , the side of the NCs that was associated with the membrane was defined as class II ( green in Figure 6E ) , and the opposite , cytoplasmic side of the NC , was defined as class III ( red in figure 6E ) . We were then able to use subtomogram averaging methods to generate independent 3-D reconstructions of each of these classes , representing different stages in NC envelopment , at resolutions of between 3 . 6 and 3 . 8 nm ( Figure 7 , Materials and Methods ) . No major differences were seen between the reconstructions up to a resolution of 4 nm . The boomerang-shaped protrusions assigned to VP24 and VP35 were resolved in all cases , as they are in the free virion . These observations suggest that these proteins are already present on the NC prior to association with the viral membrane . Furthermore , no substantial structural changes occur to the NC as a result of membrane association , envelopment , and budding . The 3-D reconstructions allowed us to identify the pointed and barbed ends of the NCs within filopodia or budding viruses . Twenty-one NCs that were in the process of budding could have their directionality determined , and all of them were found to be budding with the pointed end of the NC first . This indicates that despite the absence of any dome-like NC tip , factors that induce initial envelopment are specifically associated with the pointed end of the helix: the opposite end to that in rhabdoviruses . Of the NCs that were found within the filopodia-like extensions , but which had not yet been enveloped by the membrane , it was possible to assign the direction of the NC relative to the filopodia for 26 of them . All of these were found to have the pointed end facing away from the cell , which strongly suggests that there is already directionality inherent in the process by which NCs are transported into the filopodia . Eight viruses were found in early stages of budding where both the direction relative to the filopodia and the direction of budding could be assigned . One of these NCs was oriented with the barbed end facing away from the cell , but was commencing envelopment from the pointed end ( Figure S7 ) , suggesting that the directionality of the budding process is independent of the transport process .
The inherent morphological variability of Mononegavirales particles has hampered development of a structural understanding of the viruses and their assembly pathways . The bullet-shaped rhabdoviruses appear to be the most structurally regular family within the Mononegavirales , and a recent single-particle cryoEM study of VSV [5] provided the first detailed structural view of a member of the order . This study revealed an intimate and regular interaction between the NC with its dome-shaped tip and the matrix layer , providing a potential mechanism for driving assembly and initiation of virus budding . Such single-particle reconstruction methods are not applicable to the other Mononegavirales families , such as the filoviruses and the paramyxoviruses , which are much more heterogeneously shaped . Here we have used cryoET to generate low-resolution 3-D structural information of irregularly shaped viruses and have combined it with subtomogram averaging methods to derive higher resolution structures of repeating features . Such methods have a unique advantage in being applicable both to released virions , and potentially also to viruses assembling within infected cells . Previously , the structures of purified or reconstituted NCs from Mononegavirales including MeV and SeV have been solved directly using established image processing methods for the generation of 3-D reconstructions from 2-D electron micrographs of helical objects [6] , [7] . These methods are not directly applicable to solve the NC structure from within intact virions because the surrounding matrix layer of the virion is superimposed onto the image of the NC , and this , combined with inherent flexibility in the helix structure , prevents accurate measurement of the helical symmetry parameters . Here we instead used a reference-free subtomogram averaging method to resolve the structure of the NC from within intact virions . This approach allowed the membrane and matrix to be excluded from the analysis , made no prior assumptions about helical parameters , and defined the handedness of the reconstruction . It allowed us to measure helical symmetry parameters accurately , which could then be used to apply established image processing techniques to process a larger dataset and generate a higher resolution reconstruction . Sub-tomogram averaging therefore provides an effective , reference-free method for determining helical parameters and handedness for subsequent 2-D reconstruction in situ that will be applicable to other samples . The same cryoET and subtomogram averaging techniques that were applied to the free virion were also applied to infected human cells to study viral budding . In this way we were able to generate separate 3-D reconstructions of three different stages of budding in situ . These reconstructions represent uniquely detailed 3-D reconstructions of biological objects within intact cells . CryoET in combination with subtomogram averaging therefore provides a powerful method to derive detailed 3-D structural information for different intermediate steps in biological pathways within intact cells . CryoEM and cryoET of MARV virions showed the characteristic range of morphologies and dimensions that have been previously described . The preservation of 3-D structure in the absence of staining permitted the radial density distribution of the particles to be defined , and compared to a radial distribution of proteins obtained using IEM . VP24 and VP35 epitopes were located in the NC region immediately proximal to NP , assigning VP24 as a component of the NC rather as a minor matrix protein as previously suggested [20] . Though unexpected , these data support previous results that Ebola virus VP24 plays a role in formation of a functional NC [40] . It is interesting that VP24 , although located at a similar radius as VP35 , is released before VP35 upon detergent treatment , suggesting that its interaction with NP [19] is less stable than binding of NP to VP35 . The 3-D reconstructions of MARV NC within intact virions show that MARV possesses a left-handed helical NC , as seen for other members of the order ( only RSV has been suggested to have a right-handed NC , but the hand has not yet been experimentally determined [8] ) . The well-defined inner helix is formed from density lobes that show a clear inclination to the helix axis and that are located at the same radius as the NP epitope localized by our IEM analysis . VP24 , VP35 , and the C-terminus of NP form a boomerang-shaped protrusion that extends outwards from the inner helix . The VSV NP helix can be directly fitted as a rigid body into the inner helix , demonstrating the presence of a strong architectural homology between the NP helices of the filoviruses and the rhabdoviruses . A minimal construct NP ( 1–390 ) corresponding to the core-conserved region of NP assembled to form a helix with the same hand , pitch , diameter , and inclination as the inner helix of the NC within the virion , showing that the assembly determinants that define the helical structure are contained within the core-conserved domains of NP . In contrast , full-length NP assembled to form loose coils , suggesting that within the virion the disordered C-terminal region may be involved in interaction with other viral proteins , in the absence of which it disrupts NC assembly . A similar effect has been observed previously for MeV and EBOV [7] , [41] . We found that although the VP40 layer does contain local ordering , the lattice is not arranged in a defined manner relative to the helical NC . This contrasts with the situation in rhabdoviruses , where there is a fixed and close interaction between NC and matrix layers . Filamentous VLPs that are produced by the expression of VP40 and GP in the absence of an NC also contain a locally regular VP40 lattice . However , this lattice shows differences from that in NC-containing viruses . Together these data imply that formation of a flexible interaction between VP40 and NC induces conformation change in the VP40 lattice . The NP-RNA complex acts as the template for genome replication , and there is therefore an expected relationship between RNA binding and replication . Broadly , members of Mononegavirales may be divided into two groups based on genome replication mechanisms . Paramyxoviruses like SeV and MeV have a bipartite replication promoter [42] , [43] , carry out mRNA editing during transcription of the phosphoprotein gene , and have a total number of nucleotides in the genome that is a multiple of six . Intriguingly , for paramyxoviruses , the number of nucleotides bound to each NP monomer is also divisible by six ( the “rule of six” ) [44] , [45] . On the other hand , rhabdoviruses and pneumoviruses have a monopartite replication promoter [46] , [47] , do not carry out mRNA editing , and their genome lengths are not multiples of any specific non-unit integer . The number of RNA bases bound per NP monomer range from seven in RSV to nine in VSV and RABV [8] , [9] , [36] . Here we show that filoviruses package six RNA bases per NP , suggesting that the mechanism of RNA synthesis in filoviruses may be similar to that in paramyxovirinae ( SeV and MeV ) . This suggestion is consistent with the observation that the L protein sequences of filoviruses are more similar to those from paramyxovirinae than to either RSV or rhabdoviruses . Furthermore , as in SeV and MeV , EBOV undertakes mRNA editing [48] , and the replication promoter is bipartite [49] . In EBOV , total genome lengths are not multiples of six , but the length of the spacer region between the two promoter elements needs to be divisible by six for efficient EBOV replication . In paramyxovirinae the two promoter elements are located in close proximity to one another on the same face of the helix and it has been postulated for SeV that this allows the viral polymerase to interact simultaneously with both elements [50] . The two promoters and the region between them in MARV cover 75 bases . Here we show that each turn of the MARV NC contains about 180 bases; therefore , the promoter region covers almost half a turn of the first helix . It is possible that during genome replication initiation in the cell , the NC helix adopts a different conformation to bring the two promoter elements closer in space . By solving the NC structure in situ at multiple budding stages , we were able to show that both sides of the NC are in a structurally mature form at least as soon as one side of the NC is associated with the membrane . Within the mature NC , alternate NPs within the NC are not equivalent; instead , the boomerang-like protrusion is found between every other NP . How is this non-equivalence introduced ? The simplest scenario is that it is introduced at one end of the NC , concomitant with synthesis and assembly of the NC , and propagates along the helix . Alternate NPs in the helix could become non-equivalent through dimerisation of their disordered C-termini of the NP , or dimerisation of 1∶1 stoichiometrically bound associated protein ( VP24 or VP35 ) . Alternatively , VP24 or VP35 binding to NP could sterically , or through induction of a conformation change , make the adjacent NP monomer inaccessible for binding . NP and VP40 are able to assemble independently of one another to form oligomeric assemblies , but must come together during envelopment . Because a precise stoichiometry of interaction , such as that between matrix and NC in VSV , requires perfect alignment of the two assemblies , it can only be achieved by concomitant assembly of the two assemblies , or if one assembles using the other as a template . We found that in MARV association of the NC with VP40 takes place through flexible interactions that induce a rearrangement of VP40 , confine the radial position of the VP40 layer and associated membrane , but do not precisely define the lateral position of VP40 . Such flexible interactions can also mediate envelopment of a preformed NC by a preformed VP40 lattice through a Velcro-like interaction . This mechanism of interaction may be more robust and would permit significant heterogeneity in the final virion structure , as observed in the filoviruses and in other members of Mononegavirales . The dome-shaped tip of the VSV NC and the tight interaction of the tip with the matrix protein suggest that in VSV interactions between matrix and NC could structurally induce membrane curvature to initiate envelopment of the virus at the tip of the bullet . In MARV , VP40 was seen to coat the inner side of the membrane at the tip of some virions , but we found no dome-like tip structure on either end of the NC and the NC did not extend into the hemispherical tip of the virion . Filoviruses do , however , initiate envelopment at one end of the NC and bud via a protruding intermediate [14] . We therefore asked whether envelopment in filoviruses is initiated specifically from one end of the NC or occurs stochastically at either end . The directionality of the NC within the budding site is visible in our 3-D reconstructions of individual NCs . We found that the directionality of the NC in all budding sites was the same , indicating that envelopment is indeed initiated specifically at one end of the NC . Unexpectedly , whereas the barbed end of the NC forms the conical tip and buds out first in VSV [5] , [51] , all MARV NCs were oriented within buds in the opposite direction: with their pointed end outwards . We found that the barbed tips of filamentous virions were less regular than the pointed tips . Furthermore , the directionality of the NC within virions showed that the bulges of six-shaped virions are predominantly at the rear of the budding direction . These observations are consistent with a previously proposed hypothesis that release of six-shaped and irregular virions can be driven by dynamic membrane invagination prior to complete envelopment of the NC , during late stages of infection [14] . Despite the striking architectural homology of the filovirus NC with that of the rhabdoviruses , together our data show that in contrast to the rhabdoviruses , the filovirus NC has no dome-like NC tip , the NC interacts in a flexible way with the matrix protein , and it buds with the opposite directionality . The mechanism by which filoviruses initiate envelopment is therefore likely to be fundamentally different to that of the rhabdoviruses , but it is likely that at the pointed end of the NC a viral or cellular component is present that can induce structural arrangement of VP40 to form a hemispherical cap . Intriguingly whereas in VSV the 3′ end of the genome is located at the barbed end of the NC , and buds first [5] , in RSV the RNA is bound on the opposite side of the NC [8] . If the filoviruses were to assume the same packing mode as RSV , then although the NC buds in the opposite direction to VSV , the packaged RNA would still bud with the 3′ end first . Such a model is attractive: it implies that the first base of the genome to be synthesized is the first to bud , and would allow the absolute directionality of budding to result from the absolute directionality of the RNA . Surprisingly , there is also a strong ( but not absolute ) preferential orientation of the NC during transport into filopodia . This directionality is the same as the directionality of budding , suggesting that the same end of the NC is also directing transport into filopodia . In the one case where an NC was oriented within the filopodium in the opposite direction , the direction of budding was still preserved . How MARV NCs are transported into filopodia remains unknown . We used cryoET in combination with subtomogram averaging as a powerful method to derive detailed 3-D structural information on filovirus particles , and of the process of virus assembly and budding within intact infected cells . All members of Mononegavirales package their RNA genome in helical NCs . They must all transport and recruit the NC to the surface of the cell where it must be enveloped by , and bud through , the plasma membrane to generate infectious particles . The structural data reveal clear architectural homology between filovirus NCs and those of the rhabdoviruses . In contrast , we find that envelopment and budding , a conserved step in the lifecycle , are carried out by quite different mechanisms . We expect the principles of filovirus assembly to be relevant to the other heterogeneously shaped members of Mononegavirales .
Particles of MARV that were released from infected Vero cells were collected 3 d post-infection , purified by centrifugation through a 20% sucrose cushion , resuspended in PBS , and fixed with PFA prior to release from BSL-4 conditions . For investigation of budding viruses , HUH-7 cells were grown on EM grids , infected with MARV under BSL-4 conditions , and fixed with PFA 22 h post-infection . HEK 293 cells were transfected with plasmids encoding either full-length MARV NP or its ( 1–390 ) truncation mutant . Cells were lysed 3 d after transfection , and NCs were purified by CsCl gradient centrifugation . GP-VP40 VLPs were made and purified as described previously [38] . Virus pellets were fixed , processed for IEM by gelatin embedding ( “the Tokuyasu method” ) , thin-sectioned , and immunolabelled with antibodies against MARV proteins as described elsewhere [33] . EM was performed and immunolabelled radial virion cross-sections were identified . The distances between the centers of viral cross-sections and gold beads were measured for 100–500 gold beads per antibody , and the observed distributions corrected to account for the non-Gaussian nature of this distance measurement . For cryoEM studies , vitrified samples were imaged under standard low-dose conditions in a FEI CM120 Biotwin microscope , or for tomography in an FEI TF30 Polara TEM ( 300 kV ) with energy filter . Tomographic tilt ranges were typically from +60° to −60° with a total dose of 6 , 000–10 , 000 e−/nm2 . Tomograms were reconstructed using the IMOD software suite [52] . Subtomograms were extracted along the length of NCs , and iteratively aligned in six dimensions , taking into account the missing wedge . Subtomogram processing was carried out using scripts derived from the AV3 software package [53] within Matlab . Fourier analysis was carried out using Matlab . Helical reconstruction was carried out using the iterative helical real space reconstruction technique [34] .
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Marburg virus and Ebola virus cause severe hemorrhagic fever in humans and non human primates . They are members of the family of Filoviridae , and part of the order Mononegavirales , which includes other important human pathogens: rabies virus , respiratory syncytial virus , and measles virus . All of these viruses contain a single-stranded RNA genome enclosed within a helical protein assembly called the nucleocapsid . In this study , we used electron microscopy to image individual Marburg virus particles , allowing us to determine the architecture of the nucleocapsid and the positions of the viral proteins . We found that the nucleocapsid structure contained a “barbed” and a “pointed” end , and was similar to that of the related family of bullet-shaped Rhabdoviridae , which includes rabies virus . When we generated 3-D images of virus particles frozen in the act of assembling and budding from infected cells , however , we found that despite their structural similarities , Marburg virus particles are released from infected cells with the pointed end of the nucleocapsid facing out , whereas rabies virus is released with its barbed end facing out , suggesting different budding mechanisms . This study illustrates not only how electron microscopy of frozen samples can resolve structural information in vivo , but it provides knowledge of the structure of a filovirus and its assembly pathway , generated by direct 3-D imaging of infected cells , revealing similarities and differences in the assembly and budding mechanisms of members of Mononegavirales .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"transmission",
"and",
"infection",
"macromolecular",
"assemblies",
"microbiology",
"host-pathogen",
"interaction",
"viral",
"structure",
"viruslike",
"particles",
"protein",
"structure",
"membranes",
"and",
"sorting",
"biology",
"biophysics",
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"nucleocapsid",
"macromolecular",
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"analysis"
] |
2011
|
Cryo-Electron Tomography of Marburg Virus Particles and Their Morphogenesis within Infected Cells
|
The radically distinct morphologies of arthropod and tetrapod legs argue that these appendages do not share a common evolutionary origin . Yet , despite dramatic differences in morphology , it has been known for some time that transcription factors encoded by the Distalless ( Dll ) /Dlx gene family play a critical role in the development of both structures . Here we show that a second transcription factor family encoded by the Sp8 gene family , previously implicated in vertebrate limb development , also plays an early and fundamental role in arthropod leg development . By simultaneously removing the function of two Sp8 orthologs , buttonhead ( btd ) and Sp1 , during Drosophila embryogenesis , we find that adult leg development is completely abolished . Remarkably , in the absence of these factors , transformations from ventral to dorsal appendage identities are observed , suggesting that adult dorsal fates become derepressed when ventral fates are eliminated . Further , we show that Sp1 plays a much more important role in ventral appendage specification than btd and that Sp1 lies genetically upstream of Dll . In addition to these selector-like gene functions , Sp1 and btd are also required during larval stages for the growth of the leg . Vertebrate Sp8 can rescue many of the functions of the Drosophila genes , arguing that these activities have been conserved , despite more than 500 million years of independent evolution . These observations suggest that an ancient Sp8/Dlx gene cassette was used in an early metazoan for primitive limb-like outgrowths and that this cassette was co-opted multiple times for appendage formation in multiple animal phyla .
During Drosophila embryogenesis , the cells that will give rise to both the dorsal ( wing and haltere ) and ventral ( leg ) appendages are allocated from a ventral region of each thoracic hemisegment [1] , [2] . About a quarter of the way through embryogenesis ( stage 11 ) , these cells can be recognized by the expression of the homeobox gene Distalless ( Dll ) [3] . Initially , ∼30 ventral cells activate Dll in response to receiving positive input from Wingless ( Wg ) and negative inputs from the Decapentaplegic ( Dpp ) and Epidermal Growth Factor ( EGF ) pathways [1] , [4] , [5] . At this early stage , these Dll-expressing cells can contribute to any part of the leg or to any part of the wing and haltere; the distinction between ventral and dorsal appendage identities has not yet occurred [2] , [6] . A few hours later ( by stage 14 ) , the cells that will give rise to the wing and haltere no longer express Dll , and the Dll-expressing cells will only contribute to the portion of the leg that is distal to the coxa , the telopodite [2] , [7] . Thus , within a few hours , the Dll-expressing cells in the thorax have dramatically changed their presumptive fates . This refinement of developmental potential mirrors a change in the cis-regulatory elements used to control Dll expression . At stage 11 , when the Dll-expressing cells are multi-potent , Dll is activated by the Dll-304 enhancer [2] , [8] . At stage 14 , when the fate of Dll-expressing cells is limited to the telopodite , Dll-304 is no longer active and a different regulatory element , Dll-LT , is used [2] . Loss-of-function experiments demonstrate that in the absence of Dll the leg telopodite fails to develop [1] , [9] . Conversely , mis-expression of Dll in dorsal appendages can lead to the ectopic development of distal leg segments , most typically , tarsal segments [10] . The necessity and in some contexts sufficiency of Dll to generate leg fates has been interpreted to suggest that Dll is a selector gene for the ventral appendage . However , although Dll is transiently expressed in cells that will give rise to the coxa and dorsal appendages , it is not required to generate these fates , nor is it required to generate any other ventral structure besides the telopodite [1] , [9] , [10] . Most strikingly , when transplanted to wild type hosts , ventral tissue dissected from Dll null embryos retains the capacity to generate proximal leg fates , demonstrating that ventral appendage specification and , in particular , proximal leg fates , form independently of Dll [1] . The limited requirement of Dll in leg development raises the question of what gene ( s ) may be required to initially specify the ventral appendage primordia and proximal leg fates . A pair of genes that could fulfill a more general role in ventral appendage specification is buttonhead ( btd ) and Sp1 , which encode highly related transcription factors with three C2H2 Zn-fingers and a conserved ‘Btd box’ [11] , [12] . Both genes share a similar expression pattern throughout Drosophila development and appear to have partially redundant functions during mechanosensory organ development [13] , [14] . btd and Sp1 are also both expressed in the thoracic appendage primordia ( see Figure S1 ) [14] . Although previous work in Drosophila suggested that one or both of these genes may play a role in ventral appendage specification , these conclusions have significant limitations [14] . Embryos homozygous for a large deficiency that removes both btd and Sp1 do not express Dll in the ventral primordia [14] . In fact , these embryos appear to have no ventral appendage primordia because expression of escargot ( esg ) , a general marker for imaginal disc fates , is absent in the ventral thoracic segments of these embryos [14] . However , the large size of the deficiency used in these experiments ( Df ( 1 ) C52 ) , which removes >50 genes in addition to btd and Sp1 , leaves open the question of whether these phenotypes are due to the loss of btd , Sp1 , and/or one of the other deleted genes . Second , mis-expression experiments suggest that Btd has the ability to induce ectopic leg development and the expression of leg marker genes in dorsal tissues , such as the wing . Sp1's activity was not tested in this ectopic expression test [14] . Third , because Df ( 1 ) C52 is too large to be used for clonal analysis , loss-of-function btd and Sp1 phenotypes in the adult were analyzed by RNA interference ( RNAi ) [14] . Counter to the idea that these genes are essential for leg specification , RNAi knockdown of btd and Sp1 did not prevent leg development , but instead only resulted in a reduction of leg growth . These phenotypes are highly reminiscent to those observed when btd and Sp1 orthologs were knocked-down in the beetle Tribolium castaneum ( Sp8 ) or milkweed bug Oncopeltus fasciatus ( Sp8 and Sp9 ) [15] , [16] . In sum , because previous experiments depended on a large deficiency , ectopic expression , and RNAi knockdown approaches , they do not resolve whether btd and/or Sp1 are required for the initial establishment of the ventral appendage primordia and/or for ventral appendage growth . In vertebrates , there are two genes that are closely related to btd and Sp1 , called Sp8 and Sp9 . Both Sp8 and Sp9 are initially expressed in the ectoderm of the developing limb bud but are later restricted to the Apical Ectodermal Ridge ( AER ) [17] , [18] . Interestingly , Sp8 mutant mice have truncated limbs , due to the loss of expression of several genes essential for limb formation , including those encoding FGFs , Shh and BMPs [17]–[19] . Thus , although double mutant Sp8 Sp9 mice have not been studied , the Sp8 single mutant suggests a critical role for these genes in vertebrate limb development . Here , we use a new deletion of Sp1 and btd that allows us to unambiguously analyze the function of these genes in Drosophila . Most strikingly , the complete absence of Sp1 and btd , but not btd alone , results in the loss of all leg structures and can lead to a dramatic transformation of leg into wing and notum fates , representing a complete ventral to dorsal fate change . These phenotypes are rescued by resupplying Sp1 , but not btd , suggesting that Sp1 plays an essential and early role in leg specification . Consistent with these severe phenotypes , early loss of both genes leads to the loss of expression of the telopodite genes Dll and dachshund ( dac ) . However , in contrast to previous findings [14] , appendage primordia , as assessed by esg expression , still form in the absence of btd and Sp1 . We also find that , like Btd , ectopic expression of Sp1 and vertebrate Sp8 can induce ectopic leg development in dorsal appendages , suggesting that these functions have been conserved . If , however , btd and Sp1 are removed after Dll expression is initiated , ventral appendage fates still form , but leg growth is severely compromised . Together , these results suggest that Sp1 functions upstream of Dll and that it plays an early and essential selector-like function in the specification of ventral appendage and body wall fates . Later in development , both btd and Sp1 work in parallel with Dll to control the growth and morphology of the legs .
Although btd null alleles are available , there were no mutations that eliminated both btd and Sp1 that could be used for clonal analysis , thus precluding a definitive assessment of the role these genes play in adult development . The distance between btd and Sp1 is approximately 32 kilobases ( kb ) , with no known intervening genes ( Figure 1A ) . To generate a deficiency that removes both btd and Sp1 we used the FRT-directed recombination technique [20] . Using this method , we were able to generate a deficiency that deletes btd , Sp1 , and two adjacent genes with unknown function ( CG1354 and CG32698 ) ( Figure 1A; see Materials and Methods ) . The generation of this deficiency , hereafter referred to as Df ( btd , Sp1 ) , was confirmed by the polymerase chain reaction ( PCR ) using primers that flank the FRT-containing P elements ( Figure 1B ) . In third instar imaginal discs , btd and Sp1 have indistinguishable expression patterns as assessed by in situ hybridization and by the expression of an enhancer trap inserted close to the transcription start of btd ( Figure 1C ) . Both genes were expressed in the entire leg imaginal disc except for the most proximal ring of cells ( Figure 1C ) . Based on its relationship to other markers in the leg , these genes appear to be expressed in the entire leg ( coxa through tarsus ) , but not in the body wall ( see Figure S2 ) . Thus , unlike all previously described genes , the btd and Sp1 expression domain marks the tissue that will become leg as opposed to body wall . In the antennal disc , both genes were expressed in a medial ring of cells along the PD axis ( Figure 1D ) . No expression was observed in wing , haltere , or eye discs . Consistent with these expression patterns , mitotic clones of Df ( btd , Sp1 ) initiated during the second instar or before survived poorly in most of the leg disc ( except from the most proximal domain , which gives rise to the body wall ) , but were readily recovered in wing , haltere , and eye imaginal discs ( Figure 1E and 1F ) . When we examined the cell death marker Caspase 3 ( Cas3 ) , we found that clones in the dorsal imaginal discs ( eye , wing , and haltere ) had no Cas3 staining , while the few clones that survive in the ventral discs ( antenna and leg ) express Cas3 , suggesting that cell death is occurring in these clones ( see Figure S3 ) . Consistent with this observation , when Df ( btd , Sp1 ) clones also expressed the baculovirus cell death inhibitor p35 , their growth was partially rescued ( see Figure S4 ) . Because Df ( btd , Sp1 ) removes two additional genes in addition to btd and Sp1 , we used RNAi knockdown and rescue experiments to address which genes were responsible for the poor survival of Df ( btd , Sp1 ) clones . Knocking down the expression of the other two genes deleted in Df ( btd , Sp1 ) using RNAi produced no phenotype in the legs or antennae , suggesting that their absence does not contribute to the poor survival of these clones ( data not shown ) . Using a rescue approach , we tested two different isoforms of Sp1 ( Sp1S and Sp1L; Figure 1A ) and btd . The recovery of Df ( btd , Sp1 ) clones in the leg disc was rescued to wild type by Sp1S and , to a lesser degree , by Sp1L ( Figure 1G and 1H ) . Importantly , the weak rescue provided by btd was not statistically significant ( Figure 1H ) . Together with the data described below , these results suggest that the poor survival of Df ( btd , Sp1 ) clones is largely due to the loss of Sp1 . To gain additional insights into the compromised growth of Df ( btd , Sp1 ) clones , we tested which growth-promoting pathways might be able to rescue this phenotype . Co-expressing string ( cdc25 ) and cyclinE , which promote the cell cycle by promoting the G2 to M transition [21] , failed to provide any rescue of Df ( btd , Sp1 ) clones ( see Figure S4 ) . In contrast , expressing the transcriptional co-activator Yorkie ( Yki ) , a downstream component of the Hippo tumor suppressor pathway [22] , [23] , was able to rescue the growth of Df ( btd , Sp1 ) clones , both in the leg imaginal disc and the adult leg ( see Figure S4 ) . These data suggest that Yki , which is known to activate genes required for proliferation and cell survival [22] , [23] , functions downstream of Sp1 to activate growth-promoting target genes . These results show that Df ( btd , Sp1 ) is a valuable tool to analyze the role of btd and Sp1 during Drosophila development . They further suggest that Sp1 plays a more critical role in leg development than btd , a conclusion that we further support below . To assess the role these genes play at different times during development , we first analyzed the behavior of Df ( btd , Sp1 ) clones in the adult that were induced in the second instar stage ( 48 to 72 hrs after egg laying ( AEL ) ) , long after the imaginal discs have been allocated and Dll expression has been initiated . To give these clones a growth and survival advantage , we used the Minute ( M ) technique , which allows the generation of tissue comprised of entirely , or almost entirely , homozygous mutant cells [24] . No phenotypes were observed in the dorsal appendages ( wing or haltere ) or dorsal body ( see below ) . In contrast , legs containing Df ( btd , Sp1 ) M+ clones were dramatically reduced in size ( Figure 2B ) . Growth defects were observed throughout the entire leg , from the coxa to the tip of the tarsus . However , leg identity , assessed by the presence of bracted bristles , was still maintained in Df ( btd , Sp1 ) tissue ( Figure 2B , inset ) . When recovered in the antenna , the sizes of the a1 and a2 segments were also severely reduced , while the a3 and arista segments were unaffected ( Figure 2C and 2D ) , consistent with the expression of btd and Sp1 in a medial ring in the antennal imaginal disc ( Figure 1D ) . These findings suggest that , by 48 hrs of development , neither btd nor Sp1 are required to maintain leg identities , but that one or both of these genes is required for proper leg growth and morphology . To determine which of these two genes is required for leg growth at this stage we examined the effects of eliminating or knocking down btd and Sp1 individually . Large btdXG81 M+ clones made between 48 to 72 hrs AEL only generated weak phenotypes in the femur and tibia , which were partially fused ( Figure 2E ) . These results support the idea that btd plays only a minor role in leg development . Because a Sp1 null allele is not available , we improved upon earlier RNAi knockdown experiments [14] to assess the role of Sp1 ( see Materials and Methods ) . In contrast to the weak phenotypes observed in btdXG81 clones , reducing Sp1 activity by RNAi resulted in growth defects that were similar to those observed in large Df ( btd , Sp1 ) clones ( Figure 2F–2H ) . Analogous results were observed in the antenna: btdXG81 clones had no effect , while knockdown of Sp1 phenocopied the loss of the a1 and a2 segments seen in Df ( btd , Sp1 ) mutant clones ( see Figure S5 ) . Taken together , these results suggest that Sp1 is playing a more important role than btd in leg and antennal growth after 48 hrs AEL . We next examined the behavior of Df ( btd , Sp1 ) M+ clones in the leg imaginal discs . For these experiments , we analyzed the expression of the three primary genes expressed along the proximo-distal ( PD ) axis , Dll , dachshund ( dac ) , and homothorax ( hth ) . In wild type third instar leg discs , these genes are expressed in overlapping domains along the PD axis to create five unique combinations [25] . From distal to proximal , these combinations are: 1 ) Dll only , 2 ) Dll + dac , 3 ) dac only , 4 ) Dll + dac + hth , and 5 ) hth only [26] , [27] . In 86% ( n = 23 ) of the Df ( btd , Sp1 ) M+ clones recovered in the dac only domain Dll was derepressed , without any effect on dac expression ( Figure 2I ) . In a smaller number of clones ( 34%; n = 32 ) , we observed the de-repression of hth in the dac-only domain ( Figure 2J ) . Similarly , 44% ( n = 36 ) of the Df ( btd , Sp1 ) M+ clones recovered in the Dll only domain de-repressed dac , without affecting Dll expression ( Figure 2I ) . No effect on hth expression was observed in clones present in the most proximal domain of the leg disc ( data not shown ) . Importantly , the expression patterns of Dll , dac , and hth were unaffected in btdXG81 M+ clones ( data not shown ) . These data demonstrate that Sp1 plays an important role in generating the unique domains of gene expression that comprise the leg's PD axis . Previous work using a much larger deficiency ( Df ( 1 ) C52 ) that removes btd , Sp1 , and >50 other genes suggested that btd and Sp1 are required for the embryonic expression of Dll [14] . Using Df ( btd , Sp1 ) , we find that Dll expression is barely detectable in the leg primordia of stage 15 embryos ( see Figure S6 ) . In addition , in contrast to what was previously suggested based on the larger deficiency , formation of the leg primordia , as monitored by escargot ( esg ) expression , does not require btd and Sp1 , because esg expression was still observed , although reduced , in Df ( btd , Sp1 ) homozygous embryos ( see Figure S6 ) . As will be described below , the weak residual Dll protein that is observed in older Df ( btd , Sp1 ) embryos is likely due to the activity of the early Dll-304 enhancer , which does not require btd or Sp1 inputs . The near absence of Dll expression in older embryos contrasts with the relatively subtle effects on Dll expression when btd and Sp1 activities are removed 48 hrs AEL or later ( Figure 2I and data not shown ) . One possible scenario to reconcile this difference is that btd and Sp1 have two temporally distinct functions during leg development: early , during embryogenesis , they would be required to maintain or perhaps establish ventral appendage fates , in part by activating Dll . Later in development , during larval stages , they would only be required for the proper growth of the leg . To test this idea , we analyzed the behavior of Df ( btd , Sp1 ) M+ clones generated during embryogenesis . Using Dll-Gal4; UAS-flp to induce mitotic recombination ( see Materials and Methods ) , 100% of the adults had severely aberrant legs . In 90% of the adults , the legs were completely absent or consisted of only a small patch of residual leg tissue ( Figure 3G ) . When leg tissue was observed , it was invariably associated with non-mutant tissue , suggesting that these clones were generated slightly later than those samples in which no leg tissue remained . Generating btdXG81 clones at this early time produced relatively minor fusions of the femur and tibia , but left the tarsal segments largely unaffected ( Figure 3E ) . These phenotypes are similar to those observed in later-induced btd clones ( compare with Figure 2E ) . In contrast , reducing Sp1 activity by RNAi resulted in severe defects throughout the entire leg ( Figure 3F ) . These results suggest that Sp1 is playing a more important role than btd , a conclusion that is supported by rescue experiments . When btd+ was resupplied in the Df ( btd , Sp1 ) M+ legs , the resulting appendages were still highly abnormal , indicating poor rescue ( Figure 3J ) . In contrast , when Sp1S or , to a lesser degree , Sp1L , were resupplied in Df ( btd , Sp1 ) M+ legs , significant rescue of leg development was observed ( Figure 3H and 3I ) . Similar conclusions come from the analysis of leg imaginal discs containing Df ( btd , Sp1 ) M+ clones generated during embryogenesis . Because these clones were generated early , entirely mutant leg discs could be obtained . In most cases , the discs were greatly reduced in size , with no or little Dll expression , and only a small patch of residual dac expression ( Figure 3C ) . Strikingly , the normal expression domains of Dll and dac could be fully rescued by resupplying Sp1S or Sp1L ( Figure 3H' and 3I' ) . In contrast , resupplying btd to Df ( btd , Sp1 ) M+ discs provided a very weak rescue of these PD expression domains ( Figure 3J' ) . Together , these experiments suggest that Sp1 is required during embryogenesis to generate leg fates , while btd plays a much more restricted role in leg development . Similarly , as noted above , Sp1 plays a much more important role in antennal development than btd ( see Figure S5 ) . In addition to observing severely truncated or no legs , in about 10% of the adults with Dll>flp induced Df ( btd , Sp1 ) M+ clones one or two of the legs were transformed towards a dorsal thoracic fate , including elements of the wing or haltere , and notum ( Figure 4A and Table S1 ) . In some examples we observed the triple row of bristles characteristic of the dorso-ventral border of the wing blade ( data not shown ) . These ventral to dorsal homeotic transformations were confirmed by the presence of wing and notum molecular markers in mutant Df ( btd , Sp1 ) leg discs , including vestigial ( vg ) and eyegone ( eyg ) ( Figure 4B ) [28] , [29] . In addition to observing wing tissue , we also observed haltere tissue in place of the third thoracic legs in some of these animals ( Figure S7 and Table S1 ) . Curiously , these ventral to dorsal transformations did not always respect the normal thoracic identities because wing tissue , which normally develops in the second thoracic ( T2 ) segment , was frequently observed in the T1 , T2 , and , to a lesser extent , the T3 segments ( see Table S1 ) . Nevertheless , these dramatic transformations indicate that btd and Sp1 are required for establishing adult ventral fates and that they inhibit the establishment of dorsal fates . Ventral to dorsal transformations were never observed when only btd function was removed at this early time , arguing that Sp1 is sufficient for executing these selector-like gene functions . Ectopic expression experiments also support the idea that Sp1 behaves as a ventral appendage selector gene . Using either flip-out Gal4 ( not shown ) or dpp-Gal4 ( Figure 4 ) , Sp1L was able to activate both Dll and dac and inhibit vg expression in the wing imaginal disc and produce wing to leg transformations in the adult appendage ( Figure 4C and 4E ) . Ectopic expression of Sp1L was also able to induce another proximal leg gene , teashirt ( tsh ) , in the wing , in a pattern that was reminiscent of that seen in wild type leg discs ( see Figure S8 ) . Moreover , this property has been evolutionarily conserved in this gene family because mouse Sp8 can also activate Dll and dac and induce dramatic dorsal to ventral homeotic transformations ( Figure 4D and 4F ) . To examine the connection between btd , Sp1 , and Dll at higher resolution , we analyzed the dependencies of two Dll enhancers , Dll-304 and Dll-LT , on btd and Sp1 activities . Dll-LT is directly activated by Wg and Dpp inputs [30] . Consequently , a LT-lacZ reporter gene is expressed in the center of the leg imaginal disc , in cells that receive strong input from both of these signaling pathways [30] , [31] . One question that stems from these previous studies is why Dll-LT activity is specific to the ventral appendages and is not activated in other tissues where Wg and Dpp activities intersect such as the wing disc . The data described above suggest that btd and/or Sp1 may be the answer . To test this idea , we generated Df ( btd , Sp1 ) M+ clones and analyzed the effects on the LT-lacZ reporter gene in leg imaginal discs . Strikingly , LT-lacZ expression was absent in these clones ( Figure 5A ) . This appears to be a consequence of the loss of Sp1 and not btd because btdXG81 clones had no effect on LT-lacZ expression ( Figure 5C ) . Further , the down regulation of Sp1 by RNAi was sufficient to strongly reduce , but not eliminate , LT-lacZ expression ( Figure 5B ) . Note that , consistent with our earlier studies [30] , no effect on Dll expression was observed in these clones because the maintenance of Dll expression in larval stages is independent of Sp1 and btd ( Figure 5A–5C ) . Ectopic expression of Sp1 also induced the expression of LT-lacZ and Dll in the wing disc ( Figure 5E ) . LT-lacZ and Dll can also be activated by mouse Sp8 and by btd ( Figure 5D and 5F ) . Thus , although btd is not required for LT activity , it has the capacity to induce its activity when ectopically expressed . This gain-of-function property of btd is consistent with previous observations that btd is sufficient to induce leg development when ectopically expressed in the wing [14] . LT-lacZ is first activated in stage 14 embryos [2] . Consistent with the above findings , Df ( btd , Sp1 ) embryos failed to express LT-lacZ ( Figure 6D ) . In contrast , LT-lacZ is expressed in btd embryos , although at reduced levels [2] . The lack of LT-lacZ expression in Df ( btd , Sp1 ) embryos could be rescued by resupplying only Sp1 ( Figure 6D ) . Remarkably , mouse Sp8 was also able to rescue Dll expression and LT activity in Df ( btd , Sp1 ) embryos ( Figure 6E ) and Dll and dac expression in Df ( btd , Sp1 ) mutant leg imaginal discs ( Figure 6F ) . In contrast to Dll-LT , the earlier-acting Dll enhancer , Dll-304 , did not require btd or Sp1 because a 304-lacZ reporter gene was expressed in Df ( btd , Sp1 ) embryos ( Figure 6B ) . The independence of Dll-304 , but dependence of Dll-LT , on Sp1 activity accounts for the observation that Df ( btd , Sp1 ) stage 14 embryos show very weak , residual Dll protein in the leg primordia ( Figure 6D and 6E ) . As described above , btd and Sp1 both have the ability to induce ectopic leg development when expressed in the dorsal imaginal discs , and both have the ability to induce Dll expression . Given our observation that the initiation of LT activity is also dependent on btd and Sp1 , we reasoned that the ability of these factors to induce leg development , especially distal leg fates , might depend on Dll . To test this , we used the MARCM method [32] to generate clones that ectopically express btd or Sp1L and at the same time were mutant for Dll ( tub>btd; Dll– or tub>Sp1L; Dll– ) . In control tub>btd clones ( wild type for Dll ) , ectopic leg tissue was observed in the wing , and markers for leg development ( Lim1 , dac , and hth ) were activated in the wing imaginal disc ( Figure 7A and 7B ) . In contrast , when these clones were also mutant for Dll , the activation of Lim1 and dac , which are markers for the distal leg , was not observed ( Figure 7C ) . However , tub>btd; Dll– clones close to the wing hinge were still able to activate hth , a marker for proximal fates ( Figure 7D ) . Similar observations were obtained in tub>Sp1L; Dll– clones ( see Figure S9 ) . From these data , we conclude that btd requires Dll to generate the Lim1+ , dac+ telopodite , but that btd can induce proximal , hth+ , leg fates in the absence of Dll . This conclusion is further supported by the behavior of tub>btd+; Dll– clones that arise in the adult wing . Although these clones cannot generate distal leg fates , they are able to produce what appears to be proximal leg tissue ( Figure 7F ) . In contrast , tub>btd clones have the ability to induce both proximal and distal leg structures in the adult wing ( Figure 7E ) . Finally , the epistatic relationship between btd , Sp1 , and Dll was further supported by analyzing the consequences of resupplying Dll+ in Df ( btd , Sp1 ) M+ legs and leg discs . The resulting legs were still severely truncated , indicating poor rescue , while in the imaginal discs Dll and dac expression was only partially rescued ( Figure 3K ) . These phenotypes likely reflect the later requirement of Sp1 and , to a lesser extent , btd , in leg growth and cell survival ( see above ) . In summary , btd and Sp1 have the capacity to induce telopodite fates , which depend on Dll , as well as more proximal coxapodite fates , which do not require Dll .
During larval development , we find that Sp1 is required for the proper growth of the entire leg , from the coxa through the tarsus . In contrast , btd plays a much more limited role in the tibia and femur . At this stage , neither gene is required for leg identity , nor are they required for the development of ventral body structures that arise from the most proximal cells in the leg imaginal disc . These ‘late’ phenotypes are consistent with the expression patterns of these genes in the third instar leg imaginal discs , where they appear to mark the entire presumptive leg , but not more proximal cells . This is interesting , because prior to these observations there were no markers that distinguished between the hth-expressing cells that give rise to the coxa from the hth-expressing cells that give rise to the ventral body wall . Dll , for example , is expressed in the cells that give rise to the distal tibia and tarsus , and lineage tracing with the Dll-LT element marks the entire telopodite ( trochanter , femur , tibia , and tarsus ) [2] . The addition of the btd and Sp1 expression patterns and mutant phenotypes to previously characterized PD genes therefore adds an important demarcation that distinguishes leg from body fates . Our analysis also reveals dramatic differences in the post-embryonic functions of btd and Sp1 . Specifically , most of the growth phenotypes observed when both genes are removed can be phenocopied by knocking down only Sp1 . In contrast , btdXG81 clones ( or btdXA clones , see Materials and Methods ) have no phenotypes in the antenna , and , in the leg , result in only partial fusions between the femur and tibia . Thus , Sp1 , not btd , plays an important and non-redundant function in ventral appendage development at this stage . Selector and selector-like genes have the property that they specify an entire organ or body part [33] . The classic example is engrailed ( en ) which ‘selects’ posterior compartment identities in Drosophila [34] . Another example is eyeless ( ey ) , which is both necessary and sufficient for eye development in Drosophila [35] . In the leg , previous work highlighted the role of Dll in ventral appendage specification . In the absence of Dll , the distal portion of the leg fails to develop , while dorsal appendages remain wild type [1] . Moreover , ectopic expression of Dll can induce distal legs to develop in dorsal positions [10] . Taken together , these observations suggested that Dll is a selector-like gene for the distal leg . Despite the requirement for Dll in leg development , it has been known for sometime that the ventral appendage primordia form in the absence of Dll [1] , [9] . Moreover , homeotic transformations are not observed in the absence of Dll . Thus , Dll cannot be considered a selector-like gene for the entire ventral appendage . These observations raise the question of what factor or factors initially specify the cells that will give rise to the ventral appendage . We propose that Sp1 fulfills this selector-like role . The suggestion that Sp1 is a selector-like gene for the entire ventral appendage stems in part from the observation that when the function of this gene is removed early in development , ∼10% of the animals have dramatic transformations of ventral structures to dorsal structures . In many of these cases , we observe both wing and notum tissue developing in ventral positions . Molecularly , Dll and dac expression is lost in transformed leg discs , and ectopic expression of vg and eyg , two markers for the dorsal appendages , are observed instead . The expression of Dll-304 , which is traditionally been considered a marker for the ventral appendage , in Df ( btd , Sp1 ) embryos may seem at odds with the idea that Sp1 is required for the initial specification of leg fates . However , fate-mapping studies show that Dll-304-expressing cells give rise to both the ventral ( leg ) and dorsal ( wing and haltere ) appendages [2] . Thus , Dll-304 cannot be considered a ventral marker , and its activity in Df ( btd , Sp1 ) embryos only confirms the establishment of appendage primordia without ventral or dorsal identity . In sum , the striking transformations of fate seen in Df ( btd , Sp1 ) animals suggest that Sp1 promotes ventral fates , both the entire leg and ventral body wall , and that in the absence of this gene , dorsal fates are de-repressed . This change in developmental fate is analogous to other classical homeotic transformations , for example , when the leg is transformed to antenna in the absence of Antennapedia ( Antp ) [36] . Note that btd null clones made at the same early time in development only result in mild growth defects , but legs are still generated . Thus , btd is not required for this function . However , because an Sp1 null allele ( btd+ ) is not currently available , we cannot at this time be completely certain that btd plays no role in this process . Because wing development is normally limited to T2 , it was unexpected to observe leg to wing transformations in the T1 and , to a lesser extent , T3 segments . One potential explanation for this violation of antero-posterior identity is due to the timing of clone induction . Although the Hox genes are responsible for determining the segmental identities of the dorsal appendages [37] , [38] , it may be that they are deployed at different times in the ventral and dorsal primordia in the different thoracic segments . If this is the case , then the resulting transformations may be very sensitive to the time they were generated and to their segmental origins . It is also worth noting that the wing primordia and T2 identity can be generated in the absence of Hox input [39] , [40] . Thus , wing fates , as opposed to haltere or humeral ( dorsal T1 ) fates , represent a Hox-free default state , which may predominate in these aberrant developmental situations . Together with previous studies , these findings allow us to present a more complete view of ventral appendage specification , which we breakdown into three main phases ( Figure 7G ) . In the first phase , Sp1 , btd , and Dll ( via it’s early Dll-304 enhancer ) are initially activated in parallel in a ventral domain in each thoracic hemisegment of stage 11 embryos . The activation of all three genes is dependent on Wg signaling [1] , [14] . This early , Dll-304-driven expression of Dll does not require either btd or Sp1 . This initial group of cells is fated to give rise to both the entire ventral and dorsal thoracic imaginal discs , in other words , the entire adult thorax . In the second phase , which begins at stage 14 , Dll-304 is no longer active and Dll is controlled by late-acting enhancers such as Dll-LT , which is activated by Wg and Dpp signaling [2] , [30] . Interestingly , as shown here , these late-acting Dll enhancers also require Sp1 , but not btd [2] , thus placing Sp1 genetically upstream of Dll . At this stage , the Dll+ cells will only give rise to the leg telopodite . Sp1 is also required for telopodite formation but is carrying out at least two additional functions . One is that , unlike Dll , Sp1 is required to specify more proximal leg segments ( the coxapodite ) . Second , the ventral to dorsal homeotic transformations described above suggest that Sp1 is also required to repress dorsal fates . Finally , in the third phase , Dll begins to autoactivate it’s expression and no longer depends on Wg and Dpp inputs [30] , [41] . At this stage , Dll also no longer requires Sp1 to be expressed . Instead of working through Dll , btd and Sp1 continue to play a critical role in leg development but now work in parallel to Dll to promote the growth of the entire leg . Thus , the specification of the ventral primordia depends on a feed-forward logic in which Sp1 activates late embryonic Dll expression followed by a phase in which both btd and Sp1 contribute to appendage growth in parallel to Dll ( Figure 7G ) . Besides having a PD axis , arthropod and vertebrate appendage morphologies have little in common . Moreover , the developmental logic of limb formation in Drosophila is very different from that of vertebrate limb development . In flies , Hedgehog signaling induces two antagonistic secondary signals , Dpp and Wg , which in turn establish the PD axis by activating genes such as Dll and dac [41] , [42] . In vertebrate limb development , Sonic hedgehog induces the activity of fibroblast growth factor-like molecules such as FGF8 in the ectoderm , which drives the proliferation of the underlying mesenchyme and the nested expression of Hox genes to create a PD axis [43] , [44] . Despite these differences , it is striking that multiple vertebrate orthologs of both Sp1 and Dll are expressed during vertebrate limb development . In addition , orthologs of both hth and exd ( Meis and pbx , respectively ) are expressed in the proximal domain of the developing mouse limb [45] , [46] . Although the existence of multiple Dll and Sp1 orthologs ( Dlx1/Dlx2/Dlx5/Dlx6 and Sp8/Sp9 , respectively ) makes it much more challenging to assess their functions in detail , the available data demonstrate that , as in flies , both sets of genes are critical for vertebrate limb development [17]–[19] , [47] , [48] . Our results , illustrating that vertebrate Sp8 can rescue many of the Sp1 and btd loss of function phenotypes in Drosophila , support the idea that appendage development in these two phyla represents a case of ‘deep homology’ [49] , [50] . Interestingly , that orthologs of both Sp1 and Dll gene families are used in both phyla argue that , for appendage development , the functions of these transcription factors have been much more conserved than those of the signaling pathways used in limb development . The same conclusion holds for eye development where the transcription factors , more than the deployment of specific signaling pathways , have been conserved over vast evolutionary distances [49] , [51] . These observations imply that , once established , transcription factor networks may be very stable , while the organization of signaling pathway networks may be much more plastic and easily modified to accommodate radically distinct morphologies .
To generate Df ( btd , Sp1 ) we used the FRT-directed recombination technique using two FRT-containing P elements ( PBac{XP}d01932 and PBac{RB}CG32698e03908 ) . Recombinants lose the miniwhite gene , providing a positive identification for the recombination event . Two independent deletions were generated and confirmed by PCR using primers flanking the genomic region or within the P elements . Besides btd and Sp1 this deletion also removes CG1354 ( molecular function: GTP binding ) and partially deletes CG32698 ( molecular function: carbonate dehydratase activity ) ( DrosDel FDD-0029282 , http://www . drosdel . org . uk ) . Sp1L and Sp1S are called Sp1-RD and Sp1-RB , respectively , by FlyBase ( http://flybase . org ) . For the UAS-Sp1L construct we isolated RNA from leg imaginal discs to generate cDNA ( SuperScript III First Strand Synthesis System for RT-PCR , Invitrogen ) . This served as a template to amplify the Sp1L isoform by PCR , which was sequenced and cloned into the pUAST attB vector . For mouse UAS-Sp8 we cloned the mouse Sp8 cDNA ( gift from A . Mansouri ) into a 3XHA-tagged pUAST attB vector . Two btd mutations were studied , the strong btdXG81 mutation and the amorph btdXA [52] . We found that btdXG81 phenotypes are stronger than btdXA , in agreement with Cohen and Jurgens [52] . To knock down Sp1 function , we combined two UAS-RNAi hairpin transgenes , one described by Estella et al . [14] and one from the Vienna Drosophila Resource Center ( VDRC; line #4097 ) . The Vienna RNAi stock is reported to have no off-target affects . As confirmation of this , we only observed phenotypes in tissues where Sp1 is expressed . Both transgenes , which target both Sp1 isoforms , were used in conjunction with UAS-dicer to enhance the RNAi , and thus generated much stronger phenotypes than were previously reported [14] . Dll SA1 [8] , UAS-Dll [10] , UAS-btd [13] , Dll-Gal4 line 212; [10] , btd-Gal4 [14] , dac-Gal4 [53] , and prd-Gal4 [54] have been described . The dpp-Gal4; UAS-GFP , tub-Gal80ts and UAS-flp were from the Bloomington Stock Center . The UAS-Sp1 was from [13] was renamed UAS-Sp1S because it encodes the short Sp1 isoform . The two Dll elements , Dll-304-lacZ [8] and LT-LacZ [14] were described . ubi-GFP FRT19A; hs-flp and ubi-GFP M ( 1 ) osp FRT19A were gifts from G . Struhl . UAS-yki was from D . J . Pan [55] and UAS-p35 , UAS-string , and UAS-cycE were from L . Johnston . To generate these genotypes we used a duplication on the Y chromosome that covers the btd and Sp1 genes ( Dp ( 1;Y ) lz+ ) [13] . -btd clones yw btdXG81 or btdXA FRT19A/ubi-GFP M ( 1 ) osp FRT19A; Dll-Gal4 , UAS-flp or hs-flp . -Df ( btd , Sp1 ) loss of function clones yw Df ( btd , Sp1 ) FRT19A/ubi-GFP M ( 1 ) osp FRT19A; Dll-Gal4 , UAS-flp or hs-flp . yw Df ( btd , Sp1 ) FRT19A/yw ubi-GFP FRT19A; hs-flp Because Minute/+ flies are developmentally delayed by approximately 1 day we adjusted the time of the heat-shock to induce clones at the correct developmental stage . Larvae were heat shocked for 1 hour at 37°C . btd , Sp1S , Sp1L , and Sp8 gain of function clones . yw hs-flp; act>y+>Gal4 UAS-GFP . The larvae were heat shocked for 10 minutes at 37°C . Dll-; UAS-btd or UAS-Sp1L MARCM clones . yw hs-flp , UAS-GFP; FRT42D y+ tubG80/DllSa1 FRT 42D; tub-Gal4 Df ( btd , Sp1 ) ; UAS-btd , UAS-Sp1S or UAS-Sp1L MARCM clones . tubGal80 FRT19A/Df ( btd , Sp1 ) FRT19A; tub-Gal4 , UAS-lacZ Df ( btd , Sp1 ) ; UAS-btd , UAS-Sp1S or UAS-Sp1L , or UAS-Sp8 rescue experiments . yw Df ( btd , Sp1 ) FRT19A/ubi-GFP M ( 1 ) osp FRT19A; Dll-Gal4 , UAS-flp Imaginal discs and embryos were prepared and stained using standard procedures . RNA in situ hybridizations were carried out with digoxigenin-labeled RNA probes against btd and Sp1 [14] . For the Sp1 probe , the first and second exons of Sp1L were cloned in pBSK and transcribed to generate the anti-sense probe . These exons partially overlap a non-coding exon of Sp1S , so is likely to hybridize to both Sp1 transcripts . The primary antibodies used were: rabbit and mouse anti-ßGal ( Capell and Promega ) , rabbit anti-GFP ( Invitrogen ) , mouse anti-Dachsund and mouse anti-Dlg ( Developmental Studies Hybridoma Bank ( DSHB ) ) , rabbit anti-caspase-3 ( Upstate biotechnologies ) , guinea pig anti-Distalles , rabbit anti-Homothorax , rat anti-Lim1 ( gift from Gerard Campbell ) , guinea pig anti-Vestigial ( gift from M . Zecca ) and rabbit anti-Vestigial ( gift from Sean Carroll ) and guinea pig anti-Eyegone ( gift from N . Azpiazu ) .
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The development of vertebrate and invertebrate appendages differs in many respects . Yet , despite these differences , genes related to the Distalless ( Dll ) gene of Drosophila ( vertebrate Dlx genes ) are important for the development of appendages in multiple animal phyla . Such findings raise the question of whether disparate animal appendages have a common evolutionary origin . In vertebrates , a second gene family , related to Drosophila Sp1 , also plays a fundamental role in appendage development . Although there was some evidence to suggest that Sp1 family members may play a role in Drosophila appendage development , definitive data were lacking . Using a new deficiency that removes both Drosophila sister genes , Sp1 and buttonhead ( btd ) , we unambiguously assess their role in Drosophila development . We find that Sp1 , but not btd , is critical for specifying leg ( ventral ) development , and that neither gene is required for wing ( dorsal ) development . We also show that Sp1 lies genetically upstream of Dll . The fact that both Sp1 and Dlx gene families are used for appendage development in vertebrates and invertebrates provides striking evidence that the Sp1–Dlx relationship represents an ancient gene network that was used in a common ancestor for appendage-like outgrowths .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/embryology",
"developmental",
"biology",
"developmental",
"biology/developmental",
"evolution",
"developmental",
"biology/cell",
"differentiation",
"developmental",
"biology/molecular",
"development",
"developmental",
"biology/organogenesis"
] |
2010
|
Non-Redundant Selector and Growth-Promoting Functions of Two Sister Genes, buttonhead and Sp1, in Drosophila Leg Development
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Human alpha and beta defensins contribute substantially to innate immune defenses against microbial and viral infections . Certain nonhuman primates also produce theta-defensins—18 residue cyclic peptides that act as HIV-1 entry inhibitors . Multiple human theta-defensin genes exist , but they harbor a premature termination codon that blocks translation . Consequently , the theta-defensins ( retrocyclins ) encoded within the human genome are not expressed as peptides . In vivo production of theta-defensins in rhesus macaques involves the post-translational ligation of two nonapeptides , each derived from a 12-residue “demidefensin” precursor . Neither the mechanism of this unique process nor its existence in human cells is known . To ascertain if human cells retained the ability to process demidefensins , we transfected human promyelocytic cells with plasmids containing repaired retrocyclin-like genes . The expected peptides were isolated , their sequences were verified by mass spectrometric analyses , and their anti-HIV-1 activity was confirmed in vitro . Our study reveals for the first time , to our knowledge , that human cells have the ability to make cyclic theta-defensins . Given this evidence that human cells could make theta-defensins , we attempted to restore endogenous expression of retrocyclin peptides . Since human theta-defensin genes are transcribed , we used aminoglycosides to read-through the premature termination codon found in the mRNA transcripts . This treatment induced the production of intact , bioactive retrocyclin-1 peptide by human epithelial cells and cervicovaginal tissues . The ability to reawaken retrocyclin genes from their 7 million years of slumber using aminoglycosides could provide a novel way to secure enhanced resistance to HIV-1 infection .
Nearly 33 million people are infected with HIV worldwide [1 , 2] , and despite extensive efforts there are no effective vaccines or other countermeasures to protect against HIV transmission [3] . In our attempts to find effective anti-HIV agents , our group determined that certain synthetic θ-defensins called “retrocyclins” are potent inhibitors of HIV-1 infection [4–8] . Retrocyclins belong to a large family of antimicrobial peptides known as defensins , all of which are cationic , tri-disulfide bonded peptides that have important roles in innate host defense . On the basis of the position of the cysteines and the disulfide bonding pattern , defensins are grouped into three subfamilies: α-defensins , β-defensins , and θ-defensins [9 , 10] . θ-Defensins such as retrocyclin have a cyclic peptide backbone , derived from the head-to-tail-ligation of two peptides that each contributes nine amino acids to form the 18-residue mature peptide [11] . θ-Defensins are the only known cyclic peptides in mammals and were originally isolated from rhesus macaque leukocytes and bone marrow [11–13] . While θ-defensin peptides are produced in old world monkeys and orangutans , in humans they exist only as expressed pseudogenes [14] . A premature termination codon in the signal peptide portion of human retrocyclin mRNA prevents its translation . The retrocyclin gene is otherwise remarkably intact , showing 89 . 4% identity with rhesus θ-defensins . Its genetic information was utilized to recreate retrocyclins synthetically and confirm their activity against both X4 and R5 strains of HIV-1 [4–7] . Retrocyclins inhibit the fusion of HIV-1 Env by selectively binding to the C-terminal heptad repeat region on gp41 blocking 6-helix bundle formation [15 , 16] . RC-101 is a congener of retrocyclin with a single arginine to lysine substitution that retains structural and functional similarity to retrocyclin [4] . RC-101 exhibited enhanced anti-HIV-1 activity against over two dozen primary isolates from several clades [7 , 8] , and did not induce inflammation or toxicity in organotypic models of human cervicovaginal tissue [17] . Continuous passaging of HIV-1 BaL in the presence of subinhibitory concentrations of RC-101 for 100 days induced only minimal viral resistance [18] . Given these beneficial attributes , we envisioned that restoring the endogenous expression of retrocyclins in humans would provide an effective and natural way of combating HIV-1 infection . In the current study we restored the translation of this evolutionarily lost retrocyclin peptide by ablating the premature termination codon using site-directed mutagenesis , and analyzed whether human cells can synthesize biologically active retrocyclins . We found that promyelocytic HL60 cells stably transfected with retrocyclin constructs in which the premature termination codon was corrected could express retrocyclins . Application of the expressed retrocyclins to TZM-bl cells , PM1 cells , and peripheral blood mononuclear cells ( PBMCs ) conferred protection against HIV-1 infection . Moreover , mass spectrometric techniques confirmed the presence of correctly folded mature retrocyclin peptides . We also explored methods to read-through the premature termination codon within the retrocyclin pseudogene . Previous reports revealed that aminoglycoside antibiotics could suppress the termination codon of pseudogenes and disease-associated nonsense mutations [19–25] . In bacteria , aminoglycosides bind strongly to the decoding site on the 16S rRNA , thereby hindering protein synthesis [26] . However , in eukaryotes , aminoglycosides bind to the eukaryotic decoding site with low affinity and induce a low level of translational misreading , which suppresses the termination codon through the incorporation of an amino acid in its place [27] . Herein , we utilized aminoglycosides to induce translational read-through of the θ-defensin pseudogene , which restored the expression of functional anti-HIV-1 retrocyclin peptides in human cervicovaginal tissue models . Topical application of aminoglycosides to produce endogenous retrocyclins in the vaginal mucosa might soon be an effective preventative to combat sexual transmission of HIV-1 .
θ-Defensins are formed by post-translational modification of two 12-residue gene products , each of which is processed to give a nonapeptide that contains three cysteines . The N-terminus of one nonapeptide forms a peptide bond with the C-terminus of another nonapeptide , resulting in a cyclic 18 residue peptide with three intramolecular disulfide bonds [11 , 14] . To determine if human cells have retained the ability to process θ-defensins , we transfected promyelocytic HL60 cells with retrocyclin constructs each encoding a nonapeptide in which the premature termination codon was replaced with a glutamine ( ⊗17Q ) . Four types of constructs were produced: R1 , R3 , A1 , and A3 ( Figure 1 ) . Aside from the corrected premature termination codon ( ⊗17Q ) , all constructs were engineered to contain two termination codons at the end of the gene to ensure read-fidelity . Constructs with an “R” designation terminate after the retrocyclin portion of the gene , while constructs with an “A” designation contain the retrocyclin portion with additional downstream residues that might be critical for translation and/or processing [14 , 28] . Constructs with a “1” designation do not have any additional residues mutated , while constructs with a “3” designation have the additional Arg → Lys mutation ( R70K ) encoding the RC-101 nonapeptide . HL60 cells were cotransfected by electroporation with either R1 and R3 , or A1 and A3 , and propagated in the presence of G418 ( 300 μg/ml ) to create stably transfected cell lines . Stable transfection was verified by analyzing genomic DNA and mRNA ( Figure S1 ) . Since two different constructs were cotransfected for each condition , combinatorially it would be possible to generate three different retrocyclin peptides as illustrated in Figure 1B . For example , if cells were cotransfected with the R1 and R3 constructs , they could theoretically generate a heterodimer ( HL60 cells containing retrocyclin constructs R1 and R3 [R1R3] ) or homodimers ( R1R1 or R3R3 ) . We next analyzed if correcting the termination codon in the retrocyclin constructs could restore the translation of biologically active retrocyclin peptides . The infection of TZM-bl cells with HIV-1 BaL was significantly reduced when cells were treated with cellular acid extracts of R1R3 cells ( p < 0 . 004 ) and HL60 cells containing retrocyclin constructs A1 and A3 ( A1A3 ) ( p < 0 . 002 ) ( Figure 2A ) . A standard tetrazolium MTT assay revealed that the extracts did not affect cellular metabolism at the concentrations used in the experiment ( Figure 2E ) . Addition of A1A3 cell extracts to HIV-1 infected PM1 cells ( Figure 2B ) and PBMCs ( Figure 2C ) showed significant ( p < 0 . 002 and p < 0 . 004 , respectively ) decrease in the viral titer as compared to cells treated with control HL60 cell extract . A trypan blue exclusion assay was performed in PBMCs to monitor cell viability ( Figure 2F ) . We next affinity purified R1R3 and A1A3 cell extracts using anti-RC-101 antibody and confirmed the antiviral activity in a luciferase-based assay system ( Figure 2D ) . Interestingly , A1A3 cell extracts were found to be consistently more active than equivalent amounts of R1R3 cell extract , which suggests a role for the downstream residues in retrocyclin processing . These results indicate that biologically active recombinant retrocyclin peptides can be synthesized in human promyelocytic cells . As a next step we tested the presence of retrocyclin in promyelocytic cells using immunostaining . Immuno-dotblot analyses revealed that our anti-RC-101 antibody specifically recognized lysine-containing human retrocyclin analogs ( synthetic RC-101 and RC-101_2K ) and RC-100 ( i . e . , wild-type form ) to a lesser extent ( Figure 3A ) but not human neutrophil peptides 1–3 , or peptides with very similar tertiary structure including rhesus theta defensin-1 ( RTD-1 ) and protegrin-1 ( PG-1 ) ( Figure 3B ) . This antibody was used to visualize the expressed retrocyclin peptides in the stably transfected HL60 cells by immunofluorescence staining , which revealed that R1R3 cells and A1A3 cells were brightly stained as compared to vector control ( VC ) cells ( Figure 3C ) . Slides treated with preimmune serum showed no staining ( unpublished data ) . Note that the staining of A1A3 was brighter than R1R3 and the morphology of A1A3 cells was smaller than VC cells . Experiments were next designed to purify and confirm the identity of the expressed retrocyclin peptides from the cell extracts . Reverse-phase high-performance liquid chromatography ( RP-HPLC ) was utilized to purify the recombinant retrocyclin peptides from stably transfected HL60 cell extracts . Figure 4A shows the RP-HPLC trace of A1A3 and synthetic RC-101 . Synthetic RC-101 was recovered in fractions collected at 26–28 min . A1A3 HPLC Fractions collected from 23–30 min were analyzed on a 16% Tricine-SDS-gel . Control samples did not contain any protein bands at the expected size , whereas fractions from R1R3 cell extracts revealed protein bands of about 6-kDa size ( unpublished data ) . Interestingly , A1A3 HPLC fractions revealed multiple protein bands , which we further analyzed by western blot ( Figure 4B ) . The western blot analysis revealed bands at sizes corresponding to a monomer , dimer , and trimer of retrocyclin . Interestingly , the presence of multimeric forms of retrocyclin has been independently observed by Daly and colleagues [29] . Furthermore , the RP-HPLC purified A1A3 fractions inhibited entry of HIV-1 BaL in TZM-bl cells ( Figure 4C ) . The IC50 of retrocyclin peptides expressed by A1A3 cells ( 2 μg/ml ) was similar to that of synthetic RC-101 ( 1 . 25 μg/ml ) [8] . To determine the identity of the retrocyclin peptide expressed by A1A3 cells , HPLC fraction 26 was analyzed by mass spectrometric analysis ( MALDI-TOF-MS ) at the Microchemical and Proteomics Facility , Emory University ( Atlanta , Georgia , US ) . Analysis of A1A3 Fraction 26 revealed peaks with masses 1 , 889 . 775 Da ( oxidized ) and 1895 . 890 Da ( reduced ) , which is nearly identical to the expected mass of synthetic cyclic RC-101 ( 1 , 889 . 85 Da and 1 , 895 . 96 Da , respectively; unpublished data ) and is in agreement with reduction of the three disulfide bridges in the molecule . Furthermore , treatment with iodoacetamide yielded mass species of 2 , 238 . 081 Da for the A1A3 fraction 26 and 2 , 238 . 071 Da for RC-101 corresponding to the predicted 6-fold–alkylated form of RC-101 ( expected mass = 2 , 238 . 097 Da ) . Comparison of spectrum of the Lys-C digest of reduced/alkylated synthetic RC-101_2K ( peak at 1 , 123 . 577 Da; peptide cleaved at two Lys-Gly bonds; Figure 4D ) , synthetic RC-101 ( peak at 2 , 256 . 097 Da; peptide cleaved at a single Lys-Gly bond; N-terminal sequence determined as: Gly-Ile-Cys-Arg-; Figure 4E ) , and A1A3 fraction 26 ( peak at 2 , 256 . 010 Da ) suggests that the A1A3 cells are expressing RC-101 ( Figure 4F ) . These data confirmed that correctly folded mature retrocyclin peptides can be expressed by human cells . In the following experiments we explored alternative methods to express the peptide endogenously . Of particular interest was the effect of aminoglycosides in mediating varying degrees of termination codon read-through as previously described [19–25] . We tested the ability of three commonly used aminoglycosides ( gentamicin , amikacin , and tobramycin ) to induce termination codon read-through of retrocyclin cDNA . The native retrocyclin gene was fused with a luciferase reporter at the C terminus to create two constructs: unrescued RC-101 and rescued RC-101 ( positive control ) as shown in Figure 5A . These constructs were transfected into HOS-CD4-CCR5 cells , grown in the presence of varying concentrations of aminoglycosides , and the degree of read-through quantified by measuring luciferase . Application of tobramycin ( 10 μg/ml ) was the most effective , producing a 26-fold increase in read-through ( p < 0 . 0007; Figure 5B ) . Having thus established the optimal aminoglycoside concentration required to achieve read-through of retrocylin cDNA , we next determined if aminoglycosides could restore the translation and anti-HIV-1 activity of native retrocyclin peptides . HeLa-derived cells lines such as TZM-bl cells can natively express retrocyclin mRNA ( unpublished data ) . We applied aminoglycosides to TZM-bl cells and challenged them with HIV-1 BaL . We found that cells treated with gentamicin and tobramycin significantly ( p < 0 . 0005 and p < 0 . 0001 , respectively ) inhibited HIV-1 infection as compared to untreated cells ( Figure 5C ) . The effect was modest when compared to inhibition by synthetic peptides . Cell viability , determined by a tetrazolium-based MTT assay , was not affected by the application of aminoglycosides at the mentioned concentrations ( Figure 5E ) . In order to visualize the retrocyclins expressed by application of aminoglycosides , we performed immunostaining . TZM-bl cells were treated with PBS control or 10 μg/ml tobramycin and stained with anti-retrocyclin antibody or preimmune serum . Control cells showed no staining while cells treated with tobramycin revealed brightly stained cells suggesting that aminoglycosides can induce the expression of retrocyclin peptides ( Figure 5D ) . We next incubated TZM-bl cells with tobramycin ( 10 μg/ml ) for 24 h , and then treated the cells with preimmune or anti-retrocyclin serum followed by infection with HIV-1 . Figure 5F reveals that cells treated with preimmune serum showed a modest yet significant reduction in infection as compared to cells treated with anti-retrocyclin antibodies ( p < 0 . 018 ) , suggesting that the antibody inhibited the endogenous retrocyclins . These data confirm that the anti-HIV-1 activity observed is due to the endogenous retrocyclin peptides expressed when tobramycin was applied to cells . We next analyzed the ability of aminoglycosides to induce the expression of retrocyclin peptides in an organotypic model cervicovaginal tissue . Tissues were treated apically with tobramycin or control ( PBS ) for 24 h and anti-retrocyclin immunohistochemical analysis was performed . Interestingly , tissues treated with tobramycin alone and stained with anti-retrocyclin antibody revealed brightly stained cells ( Figure 6A ) suggesting that production of retrocyclin peptides is induced upon application of aminoglycosides . Lactate dehydrogenase ( LDH ) activity in the medium underlying the tissues was performed to determine tissue cytotoxicity . The LDH assay revealed that application of 10 μg/ml tobramycin was not cytotoxic to the tissues ( Figure 6B ) . In addition , treatment of tobramycin did not affect the metabolic activity adversely , which was determined by an MTT assay performed on one tissue ( unpublished data ) . In order to purify endogenous retrocyclins expressed in the tissues , we utilized RP-HPLC . Figure 6C shows an HPLC trace of control , tobramycin-treated tissue extracts as compared to synthetic RC-100 peptide . Synthetic RC-100 peptide was recovered in fractions collected at 27–29 min . Corresponding fractions from control and tobramycin-treated tissues were analyzed by immuno-dotblot analysis using the anti-RC-101 antibody . Figure 6D shows that retrocyclin peptides were recovered in fractions 27–29 min in tobramycin-treated tissue samples but not in control tissue samples . The amount of retrocyclin ( RC-100 ) expressed in tobramycin-treated cervicovaginal tissues was estimated by densitometry to be approximately 1 . 6 μg/tissue . Together these studies show that aminoglycosides are promising molecules to suppress the premature termination codon of retrocyclin transcripts and restore the ability of cervicovaginal tissues to protect cells from HIV-1 . Identifying effective drugs to prevent HIV-1 infection and other viral infections is essential for countering the spread of these diseases . Exogenous ( synthetic ) retrocyclins exhibit full activity in the complex environment of vaginal fluid and the peptide is very well tolerated in organotypic human cervicovaginal tissue models [17] . Moreover , HIV-1 evolves little resistance during continued passaging in the presence of the peptide [18] . For these and other reasons , retrocyclins have emerged as potential topical microbicides to protect against sexually transmitted HIV-1 infections . In this study we have taken a different path towards developing θ-defensin therapeutics . The human pseudogenes that encode the demidefensin precursors whose post-translational processing gives rise to mature retrocyclin are expressed at the mRNA level in multiple organs , including the spleen , bone marrow , thymus , testis , and skeletal muscle [14] , and cervicovaginal epithelia ( A . M . Cole , unpublished data ) . By transfecting human myeloid cells with plasmids containing retrocyclin genes without a premature termination codon , we demonstrated that the “machinery” needed to process , trim , splice , and oxidize retrocyclin precursors was available in human myeloid cells . Two sets of expression constructs were transfected into cells: a shorter form ( R1R3 ) that terminates at the end of the retrocyclin gene and a longer form that contains ( A1A3 ) additional 3′ untranslated residues ( UTR ) . Interestingly , A1A3 cells expressed higher levels of retrocyclin peptides as compared to R1R3 cells indicating a role for additional residues in the translational efficiency of these peptides . This was not altogether surprising as other studies have shown that the length of the 3′-UTR regulates translation efficiency [28 , 30] . Finally , we showed that aminoglycoside-treated cells and cervicovaginal tissues could produce retrocyclins endogenously by suppressing the premature termination codon in their endogenous mRNA transcript . Since approximately 30% of inherited disorders may result from premature termination codon mutations , there has been tremendous interest and some progress in developing and applying agents that can read-through premature UAA , UAG , or UGA termination codons [25] . Although aminoglycosides , as used in this study , have been most widely investigated , exciting new agents such as PTC-124 , have also appeared [31 , 32] . In a sense , human retrocyclin-deficiency is also an inherited disorder , albeit one with an incidence of 100% . It is caused by a premature termination codon mutation that occurred after human lineage diverged from the lineage we share with orangutans , lesser apes , and old world monkeys . Since HIV-1 and other viruses that currently infect humans have evolved in the absence of selective pressure exerted by retrocyclins , the ability to reawaken this ancestral molecule could be used to strengthen the innate immune system's ability to prevent or limit the infections they now induce .
HL60 cells [33 , 34] obtained from ATCC were cultured in Iscoves's DMEM with 20% FBS , 100 U/ml penicillin , and 100 μg/ml streptomycin ( I20 ) . TZM-bl cells [35] stably expressing CD4 , CCR5 , and CXCR4 , has firefly luciferase gene under the control of HIV-1 promoter ( from J . C . Kappes , X . Wu , and Tranzyme Inc ) . TZM-bl , HOS-CD4-CCR5 [36 , 37] ( from N . R . Landau ) , PM1 cells [38] , ( from M . Reitz ) , and HIV-1 BaL , an R5 tropic strain , were all procured through the National Institutes of Health ( NIH ) AIDS Research and Reference Reagent program . HIV-1 BaL viral stocks were prepared by infecting PM1 cells [18] . PBMCs were isolated from blood drawn from a healthy HIV-1 seronegative donor as per the guidelines of the institutional review board of University of Central Florida . PBMCs were isolated using Lymphosep ( MP biomedicals LLC ) , and cultured in RPMI-1640 medium with 10% FBS ( R10 ) supplemented with 50 units of IL-2 ( R10-50U ) and 5 μg/ml of phytohemagglutinin ( PHA ) for 3 d . The cells were then resuspended in R10-50U at a density of 0 . 8 × 106 cells/ml and grown for 5–6 d . Cervicovaginal tissues ( EpiVaginal ) were obtained from MatTek Corporation and maintained in proprietary growth medium as per the company's guidelines . The tissues were composed of a full-thickness , stratified vaginal-ectocervical layer intermixed with Langehans cells and underlying lamina propria . The tissues were allowed to grow on transwell cell culture inserts at the air-liquid interface .
|
Defensins are a large family of small antimicrobial peptides that contribute to host defense against a broad spectrum of pathogens . In primates , defensins are divided into three subfamilies—alpha , beta , and theta—on the basis of their disulfide bonding pattern . Theta-defensins were the most recently identified defensin subfamily , isolated initially from white blood cells and bone marrow of rhesus monkeys . They are the only known cyclic peptides in mammals and act primarily by preventing viruses such as HIV-1 from entering cells . Whereas theta-defensin genes are intact in Old World monkeys , in humans they have a premature stop codon that prevents their expression; they thus exist as pseudogenes . In this work , we reveal that , upon correction of the premature termination codon in theta-defensin pseudogenes , human myeloid cells produce cyclic , antiviral peptides ( which we have termed “retrocyclins” ) , indicating that the cells retain the intact machinery to make cyclic peptides . Furthermore , we exploited the ability of aminoglycoside antibiotics to read-through the premature termination codon within retrocyclin transcripts to produce functional peptides that are active against HIV-1 . Given that the endogenous production of retrocyclins could also be restored in human cervicovaginal tissues , we propose that aminoglycoside-based topical microbicides might be useful in preventing sexual transmission of HIV-1 .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases",
"immunology"
] |
2009
|
Reawakening Retrocyclins: Ancestral Human Defensins Active Against HIV-1
|
Recombinant adenoviral ( rAd ) vectors elicit potent cellular and humoral immune responses and show promise as vaccines for HIV-1 , Ebola virus , tuberculosis , malaria , and other infections . These vectors are now widely used and have been generally well tolerated in vaccine and gene therapy clinical trials , with many thousands of people exposed . At the same time , dose-limiting adverse responses have been observed , including transient low-grade fevers and a prior human gene therapy fatality , after systemic high-dose recombinant adenovirus serotype 5 ( rAd5 ) vector administration in a human gene therapy trial . The mechanism responsible for these effects is poorly understood . Here , we define the mechanism by which Ad5 targets immune cells that stimulate adaptive immunity . rAd5 tropism for dendritic cells ( DCs ) was independent of the coxsackievirus and adenovirus receptor ( CAR ) , its primary receptor or the secondary integrin RGD receptor , and was mediated instead by a heparin-sensitive receptor recognized by a distinct segment of the Ad5 fiber , the shaft . rAd vectors with CAR and RGD mutations did not infect a variety of epithelial and fibroblast cell types but retained their ability to transfect several DC types and stimulated adaptive immune responses in mice . Notably , the pyrogenic response to the administration of rAd5 also localized to the shaft region , suggesting that this interaction elicits both protective immunity and vector-induced fevers . The ability of replication-defective rAd5 viruses to elicit potent immune responses is mediated by a heparin-sensitive receptor that interacts with the Ad5 fiber shaft . Mutant CAR and RGD rAd vectors target several DC and mononuclear subsets and induce both adaptive immunity and toxicity . Understanding of these interactions facilitates the development of vectors that target DCs through alternative receptors that can improve safety while retaining the immunogenicity of rAd vaccines .
The efficacy of adenovirus vectors as vaccines in many animal models of infectious diseases [1–3] and their immunogenicity in early clinical evaluation indicate their potential for human use . The mechanism underlying their strong immunogenicity and their relationship to adverse responses has not been well defined , nor has the connection between immunogenicity and adverse responses [4–6] . To address this issue , we evaluated the contribution of known receptor binding domains in the recombinant adenovirus serotype 5 ( rAd5 ) fiber and penton base . The primary receptor recognition sequence resides in the knob region of the fiber . This domain has been localized to regions identified structurally [7] , specifically in the AB loop , the B β-sheet , and the DE loop of the knob , and interacts with coxsackievirus and adenovirus receptor ( CAR ) [8–10] . Binding and internalization are facilitated through an interaction of an RGD motif in the penton base with integrin receptors [11 , 12] . To evaluate the contributions of these regions to targeting of rAd vectors to different cell types , we prepared vectors with mutations in these domains . Previous studies have shown that mutations in the CAR-binding domain inhibit infection of many cell types [10 , 13] , and modifications of the RGD domain also affect targeting . Investigations of cell culture transduction do not always predict transduction of similar cell types in vivo . However , analysis of tissue transduction following intramuscular injection shows that elimination of both CAR and integrin receptor interactions greatly diminishes local transduction in muscle [14] . On the other hand , the long shaft in the fiber of Ad5 determines its hepatic tropism for systemic administration in mice [15 , 16] . In this study , we systematically investigated the contribution of these domains to the immunogenicity of Ad5-based vaccine vector when administered intramuscularly in mice and defined the molecular basis of its toxicity in a rabbit pyrogenicity model .
The construction and propagation of the rAd5 vectors with wild-type ( WT ) capsid proteins and with mutated CAR- and integrin-binding motifs were previously described [14] . Total particle unit ( PU ) titer was determined by absorbance [17] . The chimeric rAd5+Ad35knob vector was a kind gift of Andre Leiber and is E1 and E3 deleted with the green fluorescent protein ( GFP ) expression cassette located in the E3 region [18] . Human peripheral blood was obtained from the National Institutes of Health Clinical Center Blood Bank ( Bethesda , Maryland , United States ) , and mononuclear cells ( PBMCs ) were isolated by gradient centrifugation with Ficoll-Paque PLUS ( Amersham Biosciences , http://www . amersham . com ) as buffy coat and cultured in 10% RPMI medium ( Invitrogen , http://www . invitrogen . com ) . Plasmacytoid dendritic cells ( DCs ) were isolated by magnetic cell sorting with BDCA-4 cell isolation kit ( Miltenyi Biotec , http://www . miltenyibiotec . com ) . Bone marrow ( BM ) -derived DCs were obtained from BM of BALB/c mice and cultured according to published methods [19] . More than 80% of these cells cultured with mGM-CSF after 1 wk expressed DC surface markers CD11b and CD11c as measured by flow cytometry . Lymphoid DCs ( CD8+ DCs ) and plasmacytoid DCs ( B220+ DCs ) were isolated from mouse spleens by magnetic cell sorting according to the manufacturer's protocol ( Miltenyi Biotec ) . More than 90% of these purified cells expressed CD8 or B220 as measured by antibody staining of the cells . Six- to 8-wk-old BALB/c female mice were used for immunogenicity studies . Mice were injected once with 100 μl of the specified rAd vectors encoding GFP or HIV Env ( gp140ΔCFI ) as the control vector at the indicated particle concentration bilaterally in the muscle with the use of needle and syringe . For each vector and dose , a group of five mice was injected with vector in PBS . All animal experiments were reviewed and approved by the Animal Care and Use Committee , Vaccine Research Center ( VRC ) , National Institute of Allergy and Infectious Diseases ( http://www . niaid . nih . gov/vrc ) and performed in accordance with all relevant federal and National Institutes of Health guidelines and regulations . Three weeks after vaccination , mouse spleens were removed aseptically , gently homogenized to a single-cell suspension , washed , and resuspended to a final concentration of 106 cells/ml . Harvested spleen cells ( 106 cells/peptide pool ) were stimulated for 6 h in the presence of 2 μg of anti-CD28 and anti-CD49d MAbs/ml ( BD PharMingen , http://www . pharmingen . com ) . The last 5 h of stimulation occurred in the presence of 10 μg/ml brefeldin A ( Sigma , http://www . sigmaaldrich . com ) , with no stimulation as the background control or with phorbol myristate acetate ( PMA ) as the positive control , or peptide pools having the same amino acid sequences as GFP , or Ebola GP protein as the negative control ( Figures S2 and S3 ) . All peptides used in this report were 15 mers overlapping by 11 amino acids that spanned the complete sequence of the protein . Cells were permeabilized and fixed with Cytofix/Cytoperm and stained with monoclonal antibodies ( rat anti-mouse cell surface antigens CD3-PE , CD4-PerCP and CD8-APC; BD PharMingen ) followed by multiparametric flow cytometry to detect the IFN-γ ( IFN-γ-FITC ) and TNF-α ( TNF-α-FITC ) –positive cells in the CD4+ or CD8+ T-cell population . Statistical analyses in observed CD4+ and CD8+ responses between control-vaccinated and test article–vaccinated mice were performed by the t-test using Microsoft Excel software ( http://www . microsoft . com ) . Samples were assayed on an FACSCalibur instrument using CELLQuest software ( BD Biosciences , http://www . bdbiosciences . com ) . The collected data were analyzed with FlowJo 6 . 1 software ( Tree Star , http://www . flowjo . com ) . ELISA plates of 96 wells were coated with 100 μl/well purified GFP ( BD Biosciences ) at 2 . 5 μg/ml and kept overnight at 4 °C . The GFP was removed , and each well was blocked with 200 μl of PBS containing 10% FBS for 2 h at room temperature . The plates were washed twice with PBS containing 0 . 2% Tween-20 ( PBS-T ) . Then , 100 μl of serum from vaccinated mice was added to each well at a dilution of 1:100 . The plates were incubated for 1 h at room temperature and washed . Afterward , 100 μl of horseradish peroxidase–conjugated goat anti-mouse IgG was added to each well . The plates were again incubated for 1 h at room temperature and washed . Subsequently , 50 μl of substrate ( fast o-phenylenediamine dihydrochloride; Sigma ) was added to each well . The plates were then incubated for 30 min at room temperature . The reaction was stopped by the addition of 100 μl of 1 ( N ) H2SO4 , and the optical density was read at 450 nm . One hundred samples of human sera from volunteers enrolled in VRC-sponsored HIV trials in the United States were obtained from the VRC Immunology Core Laboratory . The sera were diluted with Dulbecco's modified Eagle's medium ( 1:12 ) and mixed with the indicated rAd vector encoding GFP for 1 h at room temperature . The neutralized virus was used to infect 293 cells at 500 PU/cell for 2 h , and GFP expression was analyzed by flow cytometry at 24 h post transduction . The 11- to 16-wk-old New Zealand White rabbits were administered 1012 particles of vector intramuscularly . This dose ensured that 100% of the animals injected with WT capsid rAd5 would have elevated body temperature because 1011 particles induced fevers in 60% of the animals ( unpublished data ) . Body temperature was measured using subcutaneously implanted thermometers ( BMDS transponder IPTT-200; BioMedic Data Systems Inc , http://www . bmds . com ) at the nape of the neck .
To identify the viral component responsible for gene transfer into specific cell types , mutant adenoviral vectors were constructed and tested in vitro for gene transfer and expression . The CAR−RGD− rAd vector failed to mediate gene transfer into a number of epithelial and fibroblast cell lines of human or mouse origin that were readily transduced with a similar amount of WT rAd expressing a GFP reporter ( Figures 1A , left , and S1 ) . The specificity of CAR−RGD− rAd was evaluated further in DC subsets and mononuclear cells derived from alternative tissues . Murine BM cells were isolated and incubated with mGM-CSF to promote differentiation into BM DCs [19] . The CAR−RGD− rAd readily transduced these cells as measured with a GFP reporter , although transduction of this mixed population was more efficient with WT virus ( Figure 1A , center ) . Unseparated human CD11c+ mononuclear cells derived from peripheral blood were also transduced by the mutants with similar efficiency to the WT virus ( Figure 1A , right ) . When these cells were purified to yield murine BM DCs by sorting CD19−CD11chigh cells , the CAR−RGD− rAd vector transduced these cells with similar efficiency to the WT virus , as determined with a luciferase reporter ( Figure 1B , left ) . The CAR−RGD− rAd vector was able to transduce other DC subsets from alternative tissues: both mouse B220+ ( plasmacytoid ) DCs and CD8+ ( lymphoid ) DCs derived from spleen cells were readily transduced by the mutant virus ( Figure 1B , middle , right ) . A titration of input vector showed slightly lower transduction efficiencies by the CAR−RGD− rAd vector , as measured by slightly reduced luciferase reporter activity in murine BM-derived and plasmacytoid DCs; nonetheless , the transduction was comparable to the WT capsid vector over a 2-log range of multiplicities of infection ( Figure 2A and 2B ) . These findings indicate that transduction of several DC and mononuclear subsets is independent of CAR and integrin binding . Adenoviral infection mediated by the shaft region of the fiber protein has been reported for certain cell types . Fiber shaft structure is specific to different serotypes , with respect to both the specific amino acid sequence and shaft length as measured by the number of repeats in each fiber . A KKTK motif has been identified in the Ad5 shaft that mediates transduction of some cells , and transduction mediated by this motif is sensitive to inhibition by heparin [20–22] . To test the role of the KKTK motif , we evaluated transduction of DCs in the presence of heparin sulfate or heparan sulfate , an alternative proteoglycan that served as a negative control . Heparin sulfate nearly completely inhibited CAR−RGD− rAd gene transfer to murine BM-derived DCs , in contrast to heparan sulfate ( Figure 2C ) , which suggests that ionic interactions contained within the fiber shaft are necessary . We also substituted the Ad5 shaft with the Ad35 shaft , which lacks the KKTK motif , in the CAR−RGD− mutant . Gene transfer to murine BM-derived DCs by this mutant was substantially reduced ( Figure 2D ) . To define the Ad5 viral determinants of immunogenicity in vivo , mice were injected with increasing amounts of different recombinant viruses expressing GFP as the antigen . When the WT and CAR−RGD− rAd were analyzed , there was no significant difference ( p > 0 . 05 ) in the peak levels of cellular immune response elicited by the WT and double-mutant vectors ( Figure 3 ) . Thus , although the specificity of the CAR−RGD− adenoviral vector differs markedly from the WT vector , its ability to bind to DCs remains unchanged , and it is potently immunogenic in vivo . Finally , to confirm the role of the shaft in stimulating immune responses by rAd vectors , the immunogenicity of rAd5 was compared to rAd5 with Ad35 knob transposed onto the fiber . Both vectors stimulated responses that were significantly above those from a vector containing a control insert ( Figure 4A ) . Instead of binding to CAR , the Ad35 knob normally binds to CD46 in humans , but CD46 is absent from nearly all mouse tissues [23] . Consequently , the rAd5 vector containing the Ad35 knob is expected to have little cellular binding dependent on the knob . However , both vectors can utilize the Ad5 shaft and showed comparable CD4 and CD8 intracellular cytokine staining and increased antibody titers ( Figure 4A ) , confirming the role of the Ad5 shaft independent of the Ad5 knob in T-cell immunogenicity . Because the Ad5 shaft had a significant role in the adaptive immune response , it was possible that the shaft was a target for neutralizing antibody . A panel of rAd35 vectors engineered with Ad5 fiber domains were compared to rAd5 for susceptibility to neutralization by human serum samples obtained from healthy adults . Transposition of the Ad5 shaft to rAd35 did not affect neutralization; however , the presence of the Ad5 knob alone changed the neutralization profile of rAd35 to that of rAd5 ( Figure 4B ) . Thus , the shaft region did not contain human neutralization epitopes . To determine whether the Ad5 shaft contributed to the pyrogenicity observed after the administration of rAd vectors , rabbits were injected with a high dose , 1012 particles , of rAd5 vectors . All vectors containing the rAd5 shaft , including those with mutations in the penton base RGD and fiber CAR-binding domain , induced a statistically significant increase in body temperature ( Figure 4C , left ) , including those in which both the CAR and integrin interactions had been ablated . In contrast , the group of animals that received the CAR−RGD− rAd5 vector without the Ad5 shaft had significantly lower mean body temperature relative to the group with the Ad5 shaft ( Figure 4C , right ) . Thus , the ability of a vector to induce a potent pyrogenic response was also associated with the Ad5 shaft , consistent with the observation that the Ad5 shaft is associated with transduction of DCs and stimulation of adaptive immunity .
In this paper , we have evaluated the contribution of the known receptors of adenoviral vectors as vaccines . Previous studies have shown that the CAR-binding region of the adenoviral knob and the integrin-binding motif RGD in the penton base protein are responsible for targeting the adenovirus to a variety of cell and tissue types in vivo [7 , 12 , 24–26] . In skeletal muscle , the CAR−RGD− rAd was previously shown to transduce 100-fold less efficiently compared to a WT capsid vector [14] . The present study evaluates the contribution of these receptors to adenovirus infectivity and immunogenicity . Mutations in the CAR- and integrin-binding domains were not required for immunogenicity , despite their significant effect on targeting of the virus to many cell types including skeletal muscle . The CAR−RGD− vector was clearly still competent for transduction , but the cell types transduced following an intramuscular administration must have been a subset of those transduced by the WT capsid vector . It has been previously suggested that DCs likely play a critical role in the ability of the adenovirus to elicit immunity in vivo [27 , 28] , but the mechanism by which rAd5 targets these cells was unknown . It has been suggested that immature cells are infected by the virus , leading to differentiation to more mature DCs that may more effectively present antigen in vivo [29–33] . In vitro , adenovirus can induce maturation of BM DCs , and the penton RGD has been shown to be involved in such stimulation [31 , 34] . Our data suggest that adenovirus can utilize pathways other than the RGD–integrin interaction to mature DCs in vivo , as deletion of the penton RGD has no effect on immunogenicity of the vector . An alternative proposal is that adenovirus targets more mature DCs more effectively in vivo . Because of the multiple cell-binding specificities of adenoviral vectors , the relative contributions of these determinants to immunogenicity and pyrogenic toxicity provide important information relevant to vaccine design for gene-based and other modes of antigen delivery in vivo . This study suggests that the shaft contributes to the targeting of the adenovirus to DCs , which likely mediate antigen presentation and enhance immune reactivity in vivo . Within the shaft sequence is a repetitive heparin-binding motif , KKTK , that may mediate this effect . The shaft domain has also been implicated in Ad5 targeting to hepatic cells and cytokine release when administered through intravenous injection in mice [15 , 16]; however , its relation to the pyrogenic response could not be defined in this model . Although CAR−RGD− virus can bind to DCs and gene transfer is blocked by heparin , but not heparan sulfate , it is not clear that the KKTK domain alone mediates interaction with DCs; nonetheless , it is clear that the shaft domain is involved and that this interaction is dependent on a heparin-like receptor interaction . This finding suggests that modifications of the adenoviral fiber may allow design of targeted adenovirus vectors modified to avoid toxicity and reactogenicity . Fever that was observed as a component of a serious adverse event in a human gene therapy trial [6] after intraportal artery infusion of high doses of rAd5 could possibly have been mediated by fiber interactions . Selective modification of the shaft region , for example , by substitution with the Ad35 shaft ( which does not have putative heparin sulfate proteoglycan-binding motifs ) , as shown here , may assist in avoiding this complication . In addition , the immune response to vector capsid proteins has limited the repetitive use of adenoviral vectors for vaccine-induced immune responses . The ability to define specific motifs within the adenoviral fiber that facilitates DC binding and entry may assist in the development of synthetic vectors that target these cell types specifically . Interestingly , it has been difficult to identify antibodies directed to the shaft region of adenoviral vectors ( Figure 4B ) , raising the possibility that this motif may be protected from immune recognition and could be retained in chimeric rAd from different serotypes or with synthetic vectors . This knowledge may also assist in the design of Ad vectors that could target DCs by other receptors that may not lead to the pyrogenic response . For example , DC-specific ligands , such as DC-SIGN or Langrin , can be incorporated into the detargeted CAR−RGD− vector with the shaft region from Ad35 that does not cause a pyrogenic response , to build alternative DC-targeted adenovirus vectors . These studies therefore lend insight into the mechanisms of adenoviral immune targeting at the same time they suggest possible means for targeting specific cells in vivo for future gene-based vaccines .
|
Recombinant adenovirus ( rAd ) vectors are remarkable for their ability to stimulate potent immune responses and to mediate highly efficient gene transfer . These vectors have been used extensively in human studies with generally acceptable tolerability . As with many bioactive compounds , adenoviruses can also cause potentially serious side effects , as observed in a human gene therapy trial several years ago that led to a fatality . The first manifestation of this toxicity is fever , but the relation of this side effect to the ability of the vector to stimulate immunity was unknown . We show that targeting of rAd vectors induces vaccine responses and toxicity through a previously unrecognized mechanism related to its attachment and entry into cells . We find that both adaptive immunity and fever are mediated by targeting of the rAd vector to dendritic cells and some monocytes , independent of the coxsackievirus and adenovirus receptor and RGD binding domains , mediated instead by the fiber shaft . This finding suggests that a distinct receptor present on dendritic and mononuclear cells mediates both effects . The immunogenicity of rAd vectors is dependent on targeting of virus through a specific fiber region and mediates rAd toxicity , which has implications for vaccine and gene therapy vector design that may help to improve rAd safety and efficacy .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"immunology"
] |
2007
|
Mechanism of Ad5 Vaccine Immunity and Toxicity: Fiber Shaft Targeting of Dendritic Cells
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Understanding complexity in physical , biological , social and information systems is predicated on describing interactions amongst different components . Advances in genomics are facilitating the high-throughput identification of molecular interactions , and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity . Here , we describe the architectural organization and associated emergent topological properties of gene regulatory networks ( GRNs ) that describe protein-DNA interactions ( PDIs ) in several model eukaryotes . By analyzing GRN connectivity , our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents . These exponents are independent of the fraction of the GRN experimentally sampled , enabling prediction of properties of the complete GRN for an organism . We further demonstrate that the exponents describe inequalities in transcription factor ( TF ) -target gene recognition across GRNs . These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies . Consequently , architectural GRN organization drives not only phenotypic plasticity within a species , but is also likely implicated in species-specific phenotype .
Complex systems are formed by large numbers of components organized into networks , and modelled by graphs in which nodes are connected by edges . Network architecture is established by topological and statistical analyses , ultimately leading to inference of functional roles played by the nodes in the network , and prescribed by the observed architecture . Efforts to infer information flow , which ultimately leads to functional outputs , have been applied to different types of networks , including social communication , electrical power [1] , and biological [2–10] . A general characteristic of many real-world networks is their scale-free topologies , which exhibit a node degree distribution that can be described with a power law function: P ( k ) =Ck−α ( 1 ) where P ( k ) is the probability of a randomly selected node having degree k ( that is k connections ) , and α is the power law scaling exponent ( hereafter referred to as the exponent ) . The constant C is a Riemann’s zeta function that normalizes the power law probability distribution , such that: ∑k=1∞P ( k ) =1 ( 2 ) In scale-free networks , most nodes have comparatively few interactions manifested as a lower degree , while a small number of nodes , the ‘hubs’ , have a higher degree [11 , 12] . This scale-free connectivity distribution is observed at different levels of biological organization ranging from the cellular and molecular , to the ecological level . Gene regulatory networks ( GRNs ) , characterized by the interaction of a specific type of proteins , the transcription factors ( TFs ) with the regulatory DNA regions in the genes that the TFs control , provide excellent examples of molecular-level scale-free networks [2 , 6 , 10 , 13–15] . GRNs can be represented by directed graphs in which the edges have a polarity , because a TF can bind to the regulatory region of a gene ( which may encode for another TF ) and modulate its expression , but not vice versa . Thus , GRNs can be visualized from the perspective of incoming connectivity ( i . e . , how many TFs bind to a specific gene regulatory region ) , or from the outgoing connectivity perspective ( i . e . , how many regulatory regions does a TF recognize ) . The molecular tools available to identify incoming and outgoing connectivity are different . Incoming connectivity is usually mapped using gene-centered approaches such as yeast one-hybrid ( Y1H ) assays [16] , and outgoing connectivity is evaluated by TF-centered approaches such as chromatin immunoprecipitation ( ChIP ) -based ( e . g . , ChIP-Seq and ChIP-chip ) [17] or DNA affinity purification sequencing ( DAP-Seq ) methods [18] . While the integration of results derived from gene- and TF-centered procedures should ultimately converge into the same GRN , much of the unbiased data available today derives from TF-centered approaches , providing a much clearer perspective of outgoing connectivity . We anticipate that the advent of new experimental approaches to map PDIs and place them in a biological context will permit to explore the convergence of incoming and outgoing connectivity in many organisms . Organismal phenotypic plasticity is driven in part by the underlying GRNs [19–21] . Therefore , the reconstruction and topological analysis of GRNs provides an excellent opportunity for elucidating molecular mechanisms that drive phenotypic plasticity . However , despite significant research in this area , little is known with regards to whether GRNs from different organisms have similar emerging properties that only depend on node number , or whether properties such as network connectivity , manifested for example in the exponent of the power law , are unique to each organism . Experimentally , most studies will be able to provide at best an observed network , which corresponds to a subset of the complete true network ( Fig 1 ) . It is unclear to what extent properties of the observed network can be used to infer properties of the complete network ( Fig 1 ) . Conversely , several studies have investigated the properties of subnetworks , starting from synthetic or natural networks . The conclusions derived from these studies depend on the sampling method used [22–24] . For example , it was argued that randomly selected subnets of scale-free networks are not scale-free themselves , and that therefore inferences about the complete network had to be treated with caution [25] . However , the node sampling methodology used in that study results in a loss of degrees because , by targeting nodes rather than edges , all the edges associated with a node are lost , resulting in the enhanced decrease in degrees . In addition , that study did not model the stochasticity inherent in the sampling process , thereby not capturing the possible range of degree exponents that a subnetwork can take . As described here , sampling edges while accounting for stochasticity gives a very different result . In our study , we take four representative model organisms that represent major eukaryotic evolutionary groups ( the yeast Saccharomyces cerevisiae , the worm Caenorhabditis elegans , the fruit fly Drosophila melanogaster and the flowering plant Arabidopsis thaliana ) and for which a wealth of PDI data is publicly available , to reconstruct GRNs followed by network connectivity analysis . Following simulations and rigorous statistical analyses , we demonstrate that GRNs exhibit organism-specific scale-free connectivity , revealed by distinct exponents of the out-degree . Further , we show that the observed coefficients are unbiased estimates of exponents derived from the degree distribution of the inferred complete GRNs . As a result , we apply a Monte-Carlo simulation approach for the estimation of the number of PDIs in complete GRNs . To provide an interpretation of the out-degree exponent , we employ ‘inequality’ analyses using Lorenz curves . We show that the exponents describe the relationship between the proportions of TFs binding to the corresponding fraction of the target genes . The resulting GRN topologies can therefore be classified as either ‘capitalistic’ , exemplified by the presence of a handful of hub TFs that bind a significant and disproportionate number of target genes , or ‘socialistic’ , in which TFs bind a near corresponding proportion of targets . Collectively , these observations demonstrate the utility of the observed GRNs in predicting properties of complete GRNs , with important implications for understanding the complex regulatory repertoire of eukaryotic organisms .
We constructed GRNs using all available experimentally determined PDIs derived from ChIP-Seq , ChIP-chip , and yeast one-hybrid assays ( Table 1 ) . To determine the connectivity of these observed GRN , we enumerated the target genes bound by each TF ( out-degree , Fig 2 ) , and the number of TFs binding each target gene ( in-degree , S1 Fig ) . We observed that , in all four organisms investigated , a majority of TFs bind comparatively to few target genes ( low degree TFs ) , while a small number of TFs bind to a large proportion of target genes ( high degree TFs ) . A linear relationship of the probability density function on a log-log scale was observed ( Fig 2 , inset ) , indicative of the scale-free property of the interaction distribution . To unequivocally confirm the scale-free properties of the resulting observed GRNs , we implemented a formal statistical analysis framework consisting of the following steps: ( i ) Fitting node-degree distribution to a power law function and estimating the power law function exponent parameter ( α ) using the maximum likelihood approach; ( ii ) testing goodness-of-fit by comparing the fitted power law distribution and the empirical node degree distribution using the Kolmogorov-Smirnov ( KS ) D statistic; and ( iii ) performing pairwise model selection by comparing the fitted power law distributions to Poisson and exponential functions . A non-nested model selection approach that uses the Kullback-Leibler information criterion ( Vuong’s closeness test ) was employed for the pairwise model comparisons ( see Methods for details ) . We observed a significant fit of power law functions on the out-degree distribution ( Table 2 ) , thereby confirming the scale-free nature of the observed GRNs . Further , model selection likelihood ratio tests comparing fitted power law and Poisson distribution functions ( a descriptor of non-scale-free random networks ) revealed that fitted power law distributions are significantly favored ( Table 2 ) . As anticipated , given the biased nature and insufficient sampling of most gene-centered PDI determination studies , in-degree distribution of the available experimental data could not be described by a power law function . Unexpectedly , the out-degree power law exponents were different for the observed GRNs obtained from the four organisms , with values of 4 . 12 for C . elegans , 3 . 04 for the fruitfly , 2 . 0 for yeast and 1 . 73 for Arabidopsis ( Table 2 , first row ) . To determine the difference between the empirical distributions of out-degrees for pairs of observed GRNs , the two-sample Kolmogorov-Smirnov ( KS ) test was employed , with the null hypothesis testing whether two samples have been drawn from the same distribution . We observed that pairs of out-degrees between organisms have distinct distributions , with the exception of A . thaliana—S . cerevisiae and D . melanogaster—S . cerevisiae comparisons ( Table D in S1 Information ) . To investigate the possible biological consequence of the different out-degrees in the scale-free topology of GRNs for the four organisms , we investigated how ‘inequality’ in TF-target gene binding distributions is affected by the power law degree exponent in the different GRNs , using Lorenz curves [26] . We ranked TFs based on increasing number of target genes and plotted the cumulative proportion of target genes as a function of the corresponding cumulative proportion of TFs . Interestingly , we observed an increase in degree ‘equality’ for each increase in the value of the exponent ( Fig 3 ) . This contrasts with a perfectly egalitarian distribution of degrees where all TFs have approximately the same degree , and for which the associated Lorenz curve becomes the diagonal of the plot , referred to as the line of equality . Thus , GRNs with smaller exponents , such as the S . cerevisiae GRN , have hub TFs that bind disproportionately more target genes , compared to the same number of hub TFs in GRNs with higher exponents ( Fig 3 ) . Indeed , we observed that the top 20% of TFs with the highest number of target genes in S . cerevisiae bind about 50% of the target genes . In C . elegans , a similar proportion ( the top 20% ) of TFs binds to 30% of target genes . In an egalitarian binding scenario , 20% of the top TFs would bind 20% of the target genes . Note that our analyses here and henceforth did not include Arabidopsis out-degrees due to the low number of target genes ( 37% of all coding genes ) represented in the observed GRN ( Table 1 ) , corresponding to insufficient sampling of out-degrees . Next , we investigated potential biases in the estimation of the exponents that might be driven by data sources . In this regard , we estimated exponents from subnetworks derived from specific experimental data source ( ChIP-Seq , ChIP-chip or Y1H ) and tissues type , where data is available . Investigating the influence of experimental data source in D . melanogaster GRN revealed similar exponents for the data source-specific subnetworks ( Table A in S1 Information ) . For instance , with the exception of a slight increment of 0 . 32 for the ChIP-Seq-derived subnetwork , the exponents of ChIP-derived subnetworks are similar ( rows 2 , 3 , and 4 of Table A ) . In the case of Y1H , it is evident that gene-centered approaches employed in building GRNs can inadvertently introduce bias due to insufficient sampling of out-degrees . Y1H contributed only 406 ( ~ 0 . 18% of total GRN ) interactions to the D . melanogaster GRN and 136 TFs , indicating that on average one Y1H-derived TF binds 2 target genes ( 208/136 ) –an unlikely in vivo phenomenon . Indeed , fewer out degrees are sampled in Y1H because the technique depends on cloning promoters of target genes . Difficulties in cloning promoters , as well as the comparatively higher numbers of targets in a genome ( unlike TFs ) , results in an underrepresentation of out degrees in Y1H-derived GRNs . TF-based techniques ( ChIP-Seq and ChIP-chip ) such are more attractive in construction of GRNs that capture the expected connectivity because of the near complete sampling of out degrees ( targets ) . TF-centered approaches are however not immune from potential bias , primarily the inclusion of false-positives targets . The ChIP-Seq and ChIP-chip analysis pipelines attempt to account for the false positive rates by determining the false discovery rates ( FDRs ) in cases where biological replicates exist . It’s important to note that in our analysis , inclusion of the Y1H data did not result in a deviation of the power law exponent ( compare rows 2 and 3 in Table A of S1 Information ) . Minimal deviations were also observed in the data-specific subnetworks of C . elegans ( Table B ) and S . cerevisiae ( Table C ) . Another potential bias in estimation of exponents is tissue- ( or developmental ) specific sampling of targets . To address this , we sampled a subnetwork from the D . melanogaster GRN PDIs derived from the embryo stage . The choice for D . melanogaster was largely due to availability of tissue-specific data . The analyses resulted in a subnetwork with 47 TFs , 178 , 224 PDIs , and a total of 15 , 016 nodes . Fitting a power law function on the ‘embryonic’ out-degrees resulted in an exponent of 3 . 10 and KS P-value of 0 . 86 ( row 7 of Table A ) . It is worth noting that these deviations fall within the 95% prediction intervals of the expected range of exponents for complete GRNs ( see next subsection on inference of properties of complete GRNs ) . Taken together , these findings demonstrate that , while GRNs are characterized by the unifying scale-free network property as opposed to random degree distribution , the GRN connectivity is quantitatively organism-specific , suggesting intrinsic organismal properties that define TF binding landscapes . The ‘observed’ GRNs described in the prior section correspond to a fraction of the ‘complete’ GRNs that remain to be experimentally determined . A fundamental question that this study intends to address is to what extent can the observed GRNs be used to infer properties of complete GRNs ( Fig 1 ) . The answer serves two main purposes: first , one goal of systems biology is to describe all system components and their associated interactions . Since current GRNs are incomplete , but are samples from complete yet unobserved GRNs , there is need to determine whether properties of current GRNs sufficiently describe properties of the intended complete GRNs . Second , decisions on whether additional experiments need to be performed will be based on the ability of the current GRNs in describing properties of complete GRNs , an important consideration in experimental design . Therefore , to determine whether complete GRNs are scale-free with organism-specific degree scaling exponents , we evaluated the distribution of degrees of nodes sampled from large populations of simulated node degrees whose power law exponents are known . We describe the approach below . We implemented a Monte-Carlo ( MC ) simulation approach to generate large populations of simulated nodes , each population exhibiting a distinct and known power law exponent of the node degrees ( see Note A in S1 Information for a detailed description on sampling ) . Briefly stated , we first generated three sets of a large number of simulated nodes ( n = 10 , 000 ) , each with population degree power law exponent , αpop , corresponding to the three observed exponents of 4 . 12 , 3 . 04 and 2 . 00 . In the MC simulation , the number of computationally-generated nodes significantly exceeded the number of TFs in any organism in order to model a theoretically large population of nodes , a condition required for the central limit theorem ( CLT ) to be applicable ( see Note A in S1 Information ) . Next , we randomly drew nodes from each population ( with replacement ) to generate samples ( r = 1 , 000 ) of different sizes , followed by estimation of the scaling exponent of each sample using the maximum likelihood method . The distribution of exponents for large sample sizes ( e . g . , n = 5 , 000 ) followed normality with their average corresponding to the population exponent ( S2A Fig ) . As anticipated , we observed a marked deviation from normality coupled with increased variance whenever smaller samples ( n < 30 ) were drawn ( S2B Fig ) . To predict the range of exponents for the complete GRNs , we calculated prediction intervals ( PIs ) using standard deviation ( SD ) of their distribution derived from the MC sampling procedure . We specifically used the MC-derived SDs corresponding to the number of TFs in the genome to construct 95% PIs ( Note A in S1 Information ) . We observed 95% PIs falling in the {3 . 51–4 . 73} , {2 . 76–3 . 32} , and {1 . 87–2 . 13} intervals for the starting degrees of 4 . 12 , 3 . 04 and 2 . 00 , respectively . Notably , the PIs for exponents of complete GRNs do not overlap , thereby underscoring the organism-specific nature of power law scaling exponents in GRNs of the organisms investigated here . From this analysis , we conclude that , a scale-free observed GRN with exponent αobs is likely derived from a complete true GRN , which is also scale free with exponent αobs ± c , where c is the upper and lower bounds of the 95% PI . In the following section , we capitalize on the predicted exponents of complete GRNs to estimate the size of their corresponding complete GRNs . There is a pressing need to infer properties of complete GRNs in order to capture the system-wide regulatory landscape of a particular organism . However , experimental limitations ( such as challenges in generating TF-specific antibodies for ChIP , limitations in genome sequence and annotation ) and lab-specific research questions have resulted in incomplete and often fragmented GRNs , whose properties may fail to adequately capture the intended entire regulatory repertoire . To address this challenge , we undertook a simulation approach to estimate the expected number of PDIs in the complete GRNs . Our method is predicated on the finding that the observed out-degree exponent of a GRN is an unbiased estimate of the respective complete GRN exponent . As a consequence , out-degrees of an observed GRN can be described as a random sample from a population of degrees corresponding to the number of TFs in the genome . In the observed GRNs , the number of interactions ( Iobs ) is obtained by the summation of the out-degrees in the network , as follows: Iobs=∑i=1nkobs , i ( 3 ) where n is the total number of TFs ( number of out-degree nodes ) in the observed GRN , and kobs , i is the ith observed out-degree value . We extend this framework to identify the number of interactions for a complete GRN ( Icomp ) , and posit that: Icomp=∑i=1Nkcomp , i ( 4 ) where N is the total number of TFs in the genome and kcomp , i is the ith out-degree value of the complete GRN ( see Note B in S1 Information for a detailed description on derivation of simulated degrees ) . To test the feasibility and accuracy of the simulation approach , we estimated the actual number of interactions ( Iobs ) for the observed GRNs . We determined that , on average , the number of PDIs estimated by the method was equal to the number of PDIs of the observed GRNs ( Table 3 , column 3; refer to Note B in S1 Information for description on hypothesis testing ) . We subsequently used this method to predict the number of interactions of the complete GRNs ( Icomp ) based on the total number of TFs and genes that have been described in the organisms ( Table 1 ) . The maximum possible number of PDIs ( upper bound ) corresponds to the product of the total number of TFs and the total number of genes , as this would imply that every TF binds to every gene in the genome ( Table 3 , column 5 ) . When we computed Icomp for the organisms we investigate here , we found that the budding yeast S . cerevisiae would have a total of ~60 , 000 PDIs , the fruitfly Drosophila has ~1 . 5M PDIs and the worm C . elegans has ~2M PDIs ( Table 3 , column 4 ) . These estimates suggest that the number of observed PDIs represents ~45% , 14% , and 23% of the respective complete GRNs . When we compare the predicted PDI number of the complete GRNs with the maximum possible , we find that it is only 4% for yeast , 8% for Drosophila and 10% for C . elegans ( Table 3 , column 5 ) , indicating that combined , TFs are sampling only a fraction of all the possible TF-target gene combinations . This observation contrasts the continuous network model that proposes in vivo binding of each TF to essentially all target genes in an organism . Having demonstrated that complete GRNs are scale free , we set out to determine whether subnetworks of observed scale-free GRNs are equally scale-free . By sampling edges from observed GRNs , we mimic the experimental approach involved in constructing GRNs . Indeed , construction of GRNs largely involves identifying interactions ( edges ) between known cellular components ( TFs and potential target genes ) . Below , we first show analytically followed by sampling , that subnetworks of scale-free GRNs are scale-free . When drawing edges from a GRN , the probability Pr ( i ) of a node i in the GRN becoming node i* in the subnetwork given that its edge has been randomly selected is dependent on node i degree , ki . This relationship is described by: Pr ( i* ) =kikT ( 5 ) where kT denotes the total number of out-degrees in a GRN . To sample subnetworks of different sizes , edges are sampled with probabilities 0<p<1 . Therefore , the probability of including node i* in the subnetwork when edges are sampled with a probability p is: Pr ( ip* ) =p×kikT ( 6 ) When sampling subnetworks of specific sizes ( e . g . , half the observed GRN , where p = 0 . 5 ) , p and kT are constants in Eq 6 , which can be rewritten as: Pr ( ip* ) =pkTki+ε ( 7 ) where ε is an error term accounting for the pseudorandom number generator ( PRNG ) since the PRNG algorithm is not strictly random but depends on an initial value , the seed . It is clear from Eq 7 that the probability of a node being included in the subnetwork , when its edges are sampled , is a linear function of the degree of the node being sampled . Thus , analytically , the degree distributions of a GRN and its associated subnetworks are similar . For computationally validating the aforementioned analytical procedure while accounting for stochasticity , we randomly sampled subnetworks of varying sizes from the observed GRNs and from one synthetic complete GRN , followed by a determination of their respective out-degree exponents ( note here that sampled subnetworks do not have random degree connectivity , but are rather randomly sampled from GRNs ) . We discovered that for the observed GRNs , a majority of subnetworks exhibited exponents similar to the exponent of their respective GRNs ( Fig 4 ) . However , there exists a subnetwork size below which there is an increased uncertainty in the determination of the exponents . This is evident in the marked increase and overlap in variation of the subnetwork exponents across organisms at lower subnetwork sizes ( Fig 4 ) . To sample from synthetic networks , we first constructed in silico networks that capture the expected connectivity of the complete yeast GRN as prescribed by the predicted exponents and number of PDIs from the previous sections ( see Methods for procedure on creating in silico GRNs ) . Expectedly , the out-degree distribution of the synthetic GRN was strikingly similar to the observed GRN ( Fig 5A ) . Fitting power law functions on the out-degrees of synthetic GRN resulted in exponents ranging from 1 . 98 to 2 . 14 , capturing the exponent ( 2 . 0 ) of the observed GRN ( Fig 5B ) . Further , the power law fit to the out-degrees of subnetworks drawn from synthetic GRN was significant , as indicated by the large KS test P values ( Fig 5C ) . In sharp contrast with previous studies that investigated properties of subnetworks by sampling nodes , rather than edges as done here , a majority of exponents of subnetworks were similar to the exponent of the complete GRN ( Fig 5D ) , a further indication of organism-specific GRN connectivity . In addition , maintenance of the network connectivity in randomly sampled subnetworks demonstrated an important network property that distinguishes random network from scale-free networks: robustness . However , there is a subnetwork size threshold below which the organism-specific connectivity deviates from the expected . Our analysis revealed that whenever less than 10% of the complete C . elegans GRN; or 2% of the yeast and Drosophila GRNs are sampled , the expected scale-free property no longer holds ( Fig 4 ) . Collectively , these observations have an important implication that is likely general to other scale-free GRNs: whenever subnetworks are randomly sampled from scale-free complete and sufficiently large incomplete GRNs that are either experimentally-determined or synthetic , the sampled subnetworks are scale-free , at least for a given subnetwork size threshold .
The ultimate goal in the characterization of TF-target gene interactions is to describe the complete genome-wide regulatory repertoire of an organism . However , current GRNs are incomplete . Two related challenges have impeded the understanding of an organism's complete regulatory repertoire: Establishing the full range of PDIs that characterize complete GRN , and anticipating the properties of complete GRNs given properties of experimentally-determined but incomplete GRNs , We have addressed these challenges by a topological analysis of current GRNs across a diversity of organisms , and discovered that observed GRNs , and their respective complete GRNs , have organism-specific topologies . This finding has profound biological implications: while GRNs are largely scale-free , there exists an organism-specific GRN architecture that drives organism-specific developmental trajectories and phenotypic uniqueness . Taking advantage of conservation of network topology between observed and complete GRNs , we predicted the possible range of PDI numbers for complete GRNs . Indeed , the forecasted complete GRN PDI numbers are just a fraction of the maximum number of PDIs that result when each TF binds all target genes . This observation deviates from the previously proposed continuous network model , whose fundamental property is that TFs have the potential to bind all genes in an organism [27] . For broader applications , our simulation method employed in estimation of the expected number of PDIs can be applied to different types of biological networks such as protein-protein interaction , protein phosphorylation , metabolic interactions , and genetic interaction . In contrast with previous work by Stumpf and colleagues [25] , we demonstrate here that subnetworks sampled from scale-free networks are scale-free . Several differences exist between our approach and the one previously published [25] . First , sampling nodes leads to a loss of out-degrees resulting in a deviation between out-degrees of the observed network and sampled subnetworks . In addition , sampling nodes leads to the generation of singletons ( nodes without edges or targets ) in the subnetworks . In contrast , we sampled edges ( with their associated nodes ) from observed GRNs , thereby capturing both a TF and its potential targets . Indeed , this approach mimics the expected experimental sampling . Second , stochasticity inherent to sampling procedures was not previously accounted for [25] . Here , we account for stochasticity in sampling procedure by repeated sampling of subnetwork and estimation of the variance of the sampling distribution of subnetwork exponents at each subnetwork size . Third , estimation of power law exponents using the graphical method of ordinary least squares ( OLS , see Fig 2 of reference [25] ) might not be a robust approach for parameter estimation . The OLS method is based on the following assumptions: ( a ) regression errors are identically and independently distributed ( iid ) random variables with mean zero , and ( b ) the standard deviation of the error is independent of the independent variable ( out-degrees ) . The OLS method is expected to perform poorly in the estimation of the power law exponents because these assumptions are not met in empirical data of power law distributions [28 , 29] . In our analysis , we fit the data ( out-degrees of observed and sampled subnetworks ) to the power law function using the maximum likelihood estimation ( MLE ) method , since MLE has been shown to be asymptotically efficient and can be applied to a wide range of data with skewed distributions [28] . Our study also provides an interpretation of the organism-specific power law exponent by use of Lorenz curves: GRNs with higher values of exponents are ‘egalitarian’ in their TF-target gene binding . Simply put , GRN architectures can either be ‘capitalistic’ , exhibited by a highly skewed TF-target gene binding landscape described by low exponents; or ‘socialistic’ , described by high exponents . Just like skewed distributions of incomes of individuals describe less egalitarian capitalist societies , we envisage a more skewed TF-target gene binding landscape in GRNs with comparatively low exponents , wherein the number of target genes bound by a TF is analogous to an individual’s income . An increase in the exponent value denotes a decrease in skewness of TF binding . Taken together , findings reported herein provide opportunity to understand complex regulatory mechanisms from a genome-wide perspective , while paving way for construction and analyses of GRNs in non-model organisms whose complete regulatory repertoire is yet to be deciphered .
PDIs for C . elegans , D . melanogaster , S . cerevisiae and A . thaliana were extracted from regulatory databases and literature . In cases where regulatory interactions comprised of only DNA-binding sites ( such as in ChIP-Seq , DAP-Seq , and ChIP-chip binding ‘peak’ location ) , the target genes associated with the binding sites were located within 2 kb of the ‘peak’ location . Transcriptional GRNs were subsequently modelled using directed graphs , Gn , v , with n nodes and v vertices ( edges , PDIs ) . Nodes in GRNs represent both target genes and their associated protein products in cases where a target gene is a TF . A PDI is represented by a directed edge emanating from the TF and ending in the target gene . Node degrees were determined by enumerating the number of TFs binding to each target gene ( out-degree ) and the number of target genes bound by each TF ( in degree ) . A formal statistical framework that tests scale-free property in GRNs was developed involving the following steps: Note that implementations of the methods presented above can be found in the R statistical packages ‘igraph’ and ‘poweRlaw’ . Sampling subnetworks involved randomly selecting a number of PDIs from the observed GRNs , followed by construction of the subnetworks from the sampled PDIs . The sizes of the subnetworks correspond to the proportion of PDIs sampled . One thousand subnetworks were sampled for each proportion . A degree distribution was determined for each sampled subnetwork . Below is the pseudocode implemented for sampling and fitting power law function on the sampled subnetworks , from each GRN: For i in {0 , . . , 1} // where i is a proportion ( size ) of the GRN For j in {1 , . . , 1000} // 1000 iterations Select edges uniformly at random from edge-list of GRN Construct subnet Gi , j* Fit out-degree of subnet to Power law function Estimate exponent αi , j of subnet out-degree END For END For To sample from synthetically-generated out-degrees , the following pseudocode was implemented: Generate 10 , 000 degrees that follow a specified exponent ( Note B in S1 Information ) For i in in {0 , . . , 1} where i is a proportion of the number of TFs ( degrees ) in the GRN of interest For j in {1 , … , 1000} //1000 iterations Select j * i random TFs from the population Construct subnet Gi , j* Fit Gi , j* out-degree to Power law function Estimate exponent αi , j END For END For Estimation of the expected number of PDIs in complete GRNs , and the power law exponent alpha for observed GRNs , enabled creation of in silico GRNs that recapitulate the expected complete GRNs . Complete GRNs were built using the edited ‘igraph’ function ‘static_power_law_pl’ which takes exponent and number of PDIs as inputs . In order to generate a biologically comparative network , the number of TFs in the function ‘static_power_law_pl’ was edited so that only 5% of genes can have out-going edges . The scale-free property , sampling of subnetworks , and the determination of the clustering coefficients of the in silico GRNs were performed using methods outlined above . The threshold where the exponents of samples start to deviate significantly is called the knee point of exponential function . A MATLAB code written by Dimitry Kaplan called Knee Point finds the knee point by fitting two lines ( in each direction ) at each bisection point and calculating the sum of errors of points along those lines . The knee is judged to be at the bisection point which minimizes the sum of errors of the two fits .
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The translation of genotype to phenotype is a tightly regulated process that is mediated by specific interactions between a variety of cellular components . Central to this is the transcription of genes , a process regulated by proteins that bind DNA , including the transcription factors ( TFs ) . Gene regulatory networks ( GRNs ) describe the web of protein-DNA interactions essential for the regulation of biological pathways and developmental processes . Here , we describe fundamental properties of the architectural organization of eukaryotic GRNs . Using protein-DNA interaction data derived from the budding yeast , the fruit fly , Caenorhabditis elegans , and Arabidopsis , we determine that GRNs are scale-free , wherein a majority of TFs bind comparatively fewer target genes , while a small number of TFs bind a large number of genes . Further , we show that the scale-free connectivity power-law coefficient is organism-specific , suggesting differential wiring patterns across GRNs . We then capitalize on the organism-specific connectivity to develop a mathematical framework to predict the number of total interactions in the complete GRNs , important for understanding how the expression of all genes in an organism is regulated , and for experimental design purposes . Finally , we demonstrate numerically and by simulations that subnetworks sampled from scale-free GRNs are scale-free , as long as edges are sampled . This finding has important real-life implications to infer properties of a network with limited experimental data .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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] |
2018
|
Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties
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The Epstein-Barr virus ( EBV ) encoded oncoprotein Latent Membrane Protein 1 ( LMP1 ) signals through two C-terminal tail domains to drive cell growth , survival and transformation . The LMP1 membrane-proximal TES1/CTAR1 domain recruits TRAFs to activate MAP kinase , non-canonical and canonical NF-kB pathways , and is critical for EBV-mediated B-cell transformation . TRAF1 is amongst the most highly TES1-induced target genes and is abundantly expressed in EBV-associated lymphoproliferative disorders . We found that TRAF1 expression enhanced LMP1 TES1 domain-mediated activation of the p38 , JNK , ERK and canonical NF-kB pathways , but not non-canonical NF-kB pathway activity . To gain insights into how TRAF1 amplifies LMP1 TES1 MAP kinase and canonical NF-kB pathways , we performed proteomic analysis of TRAF1 complexes immuno-purified from cells uninduced or induced for LMP1 TES1 signaling . Unexpectedly , we found that LMP1 TES1 domain signaling induced an association between TRAF1 and the linear ubiquitin chain assembly complex ( LUBAC ) , and stimulated linear ( M1 ) -linked polyubiquitin chain attachment to TRAF1 complexes . LMP1 or TRAF1 complexes isolated from EBV-transformed lymphoblastoid B cell lines ( LCLs ) were highly modified by M1-linked polyubiqutin chains . The M1-ubiquitin binding proteins IKK-gamma/NEMO , A20 and ABIN1 each associate with TRAF1 in cells that express LMP1 . TRAF2 , but not the cIAP1 or cIAP2 ubiquitin ligases , plays a key role in LUBAC recruitment and M1-chain attachment to TRAF1 complexes , implicating the TRAF1:TRAF2 heterotrimer in LMP1 TES1-dependent LUBAC activation . Depletion of either TRAF1 , or the LUBAC ubiquitin E3 ligase subunit HOIP , markedly impaired LCL growth . Likewise , LMP1 or TRAF1 complexes purified from LCLs were decorated by lysine 63 ( K63 ) -linked polyubiqutin chains . LMP1 TES1 signaling induced K63-polyubiquitin chain attachment to TRAF1 complexes , and TRAF2 was identified as K63-Ub chain target . Co-localization of M1- and K63-linked polyubiquitin chains on LMP1 complexes may facilitate downstream canonical NF-kB pathway activation . Our results highlight LUBAC as a novel potential therapeutic target in EBV-associated lymphoproliferative disorders .
Epstein-Barr virus ( EBV ) is an oncogenic gamma-herpesvirus that is the causative agent of infectious mononucleosis . While EBV infection generally results in subclinical lifelong infection for most individuals , EBV is nonetheless associated with multiple human malignancies [1 , 2 , 3 , 4 , 5] . These include Hodgkin lymphoma , post-transplant lymphoproliferative disease ( PTLD ) , and HIV-associated lymphomas . In these malignancies , the principal EBV oncoprotein , Latent Membrane Protein 1 ( LMP1 ) , is often expressed . LMP1 constitutively activates growth and survival pathways by mimicking CD40 signaling [6 , 7 , 8] . CD40 is a member of the tumor necrosis factor receptor ( TNFR ) family and serves as a key B-cell costimulatory molecule [9 , 10 , 11] . LMP1 expression transforms rodent fibroblasts and murine B-cells , and is necessary for EBV-mediated conversion of human B lymphocytes into immortalized lymphoblastoid cell lines ( LCLs ) [12 , 13 , 14 , 15 , 16 , 17] . LMP1 is comprised of a 24-residue N-terminal cytoplasmic tail , 6 transmembrane domains ( TM ) , and a 200 residue C-terminal cytoplasmic tail . Deletion of the LMP1 N-terminus abrogates EBV-mediated B-cell transformation and alters LMP1 localization [18] . However , specific roles of the LMP1 N-terminus remain to be defined at the molecular level . The LMP1 TM domains drive assembly of LMP1 signalosome oligomers , which constitutively signal in a ligand independent manner from C-terminal tail domains [19 , 20 , 21 , 22 , 23] . The membrane proximal Transformation Effector Site ( TES1 ) /C-terminal Activation Domain ( CTAR1 ) spans residues 186–231 . The TES1 P204QQAT210 motif binds directly to the TRAF domain of TNF receptor associated factor 2 ( TRAF2 ) , and likely also to conserved residues in the TRAFs 1 , 3 , and 5 domains [24] . The LMP1 PQQAT motif is necessary for TES1/CTAR1-medaited MAP kinase , canonical and non-canonical NF-kB pathway activation [24 , 25 , 26 , 27 , 28 , 29 , 30] . LMP1 TES1 activates additional pathways , including PI3K [31] . The LMP1 TES2/CTAR2 domain spans residues 351–386 and uses TRAF6 to further activate canonical NF-kB , MAP kinase , and IRF7 pathways [32 , 33 , 34] . The LMP1 CTAR3 domain , located between residues 231 and 350 , associates with UBC9 and contributes to LMP1-mediated cellular migration [35] . The composition of TRAF complexes in LMP1-expressing cells has yet to be fully defined , and important components have recently been described [36 , 37 , 38 , 39] . LMP1 TES1 is critical for primary B lymphocyte growth transformation , since recombinant EBV that lacks LMP1 residues 185–211 does not initiate LCL outgrowth in tissue culture [25 , 40] . By contrast , the N-terminal 231 LMP1 residues support EBV-mediated B-cell outgrowth for up to five weeks in culture [41] , and long-term on fibroblast feeder layers [40] . Interestingly , while LMP1 regulates the expression of a wide-array of host cell genes [42 , 43 , 44 , 45] , a subset are uniquely induced by TES1 signaling . Notably , TRAF1 is amongst the earliest and most highly up-regulated LMP1 B-cell targets [29 , 46] . TRAF1 is abundantly expressed in EBV-infected immunoblasts in patients with infectious mononucleosis [47] , and is also highly expressed in EBV-associated PTLD and Hodgkin lymphoma , where TRAF1 serves as an important biomarker [47 , 48 , 49] . TRAF1 expression is higher in EBV-positive Hodgkin lymphoma than in EBV-negative tumor samples [49] . Some nasopharyngeal carcinomas express LMP1 and TRAF1 [50] . In LCLs , most TRAF1 is associated with LMP1 , either as TRAF1 homotrimers , or as TRAF1:TRAF2 heterotrimers [29] . LMP1 and TRAF1 co-localize in acquired immunodeficiency syndrome ( AIDS ) -associated lymphoma , PTLD , and Hodgkin lymphoma samples [51] . Despite these intriguing associations , little is known about the extent to which TRAF1 plays a pathogenic role in EBV-associated malignancy . LMP1 TES1-mediated activation of the JNK/AP-1 axis is critically dependent on both TRAF1 and TRAF2 [52] . Although TRAF1 is the only TRAF family member that is not equipped with an N-terminal RING finger domain , TRAF1 is nonetheless the only TRAF that co-activates TES1-mediated NF-kB and JNK pathway activation [29 , 52] . Likewise , TRAF1 enhances signaling from the TNF receptor family member 4-1BB , and is important for CD8+ T-cell responses during chronic viral infection [53 , 54] . The mechanism by which TRAF1 enhances LMP1 signaling remains incompletely understood . The role of TRAF proteins in NF-kB and MAP kinase pathway activation is perhaps best understood in the context of TNF receptor signaling . Upon TNF stimulation , TNF receptor 1 binds TRADD , which in turn recruits TRAF2 , cIAP1 and cIAP2 , and subsequently the linear ubiquitin assembly complex ( LUBAC ) [55] . LUBAC is comprised of two ring-in-between-ring E3 ligases subunits , HOIP and HOIL-1L , and the scaffold protein SHARPIN . LUBAC catalyzes a peptide bond between the N-terminal methionine alpha-amino group of one ubiquitin molecule and the C-terminal glycine of another ubiquitin molecule [56] . Linear ( Met1 or M1 ) linked poly-Ub ( pUb ) chains stabilize the TNFR1 complex and enable recruitment of downstream activators [57 , 58] . LUBAC is also recruited to CD40 in a TRAF2-dependent manner , and SHARPIN deficiency impairs CD40-mediated NF-kB activation [55 , 59 , 60] . TNFR1 induces linear ubiquitination of RIP1 and IKK-gamma , and CD40 signaling also induces linear ubiquitination of IKK-gamma [55] . Interestingly , the IKK-gamma UBAN domain strongly associates with M1 chains , and thereby recruits the IKK-alpha and IKK-beta kinases to activated receptors to activate canonical NF-kB [61 , 62 , 63] . The extent to which M1-linked pUb chains participate in LMP1 signaling remains unknown . To gain insight into the molecular mechanism by which TRAF1 functions downstream of LMP1 , we performed proteomic analysis of immuno-purified TRAF1 complexes from cells uninduced or induced for LMP1 1–231 expression . We identified LUBAC components as high-confidence TRAF1 interactors in cells that express LMP1 residues 1–231 , and found that TRAF1 and LMP1 complexes are highly M1-pUb linked in LCLs . LMP1 and TRAF1 complexes immuno-purified from LCL extracts were each K63-pUb chain modified .
Human embryonic kidney ( HEK ) -293 cells were obtained from Elliott Kieff ( Brigham and Women’s Hospital , Boston , MA ) . 293 cells with inducible LMP1 1–231 expression were constructed , using a tightly regulated Tet-on inducible system that was previously described [43] . Briefly , the inducible system for LMP1 1–231 expression consisted of three parts: ( i ) an untagged LMP1 1–231 cDNA with stop codon after residue 231 , cloned into the tetracycline-regulated pTRE-tight vector ( Clontech ) ; ( ii ) a tetracycline suppressor ( tTS ) that binds Tet operator sites in the absence of tetracycline and silences expression; ( iii ) a reverse tetracycline transactivator fused to the 4-hydroxy tamoxifen ( 4HT ) ligand-binding domain ( rTTA M2 ) . LMP1 expression was induced by addition of doxycycline ( 1ug/ml ) and 4HT ( 100 nM ) . A clone that had undetectable LMP1 1–231 expression at baseline , and inducible LMP 1–231 expression upon doxycycline and 4HT addition , was selected . Conditional LMP1 1–231 cell lines with stable N-terminal FLAG epitope-tagged TRAF1 , TRAF2 , TRAF3 , or green fluorescence protein ( GFP ) expression were established by murine stem cell leukemia virus ( MSCV ) transduction and puromycin selection , as previously described [64–65] . GM12878 cells were provided by Elliott Kieff . MSCV transduction was also used to derive GM12878 LCLs with stably expressed N-terminally epitope-tagged TRAF1 , GFP , SHARPIN , IKK-gamma or IKK-epsilon . LCLs that express N-terminal FLAG-tagged LMP1 at physiological levels , in place of untagged LMP1 , were previously described [66] , and were generously provided by Elliott Kieff . Briefly , a FLAG-tagged LMP1 cDNA was recombined into the P3HR-1 genome LMP1 locus by second site homologous recombination . 293 cell lines were cultured in DMEM with 10% tetracycline-free fetal calf serum ( FCS ) , LCLs and EBV-negative BL2 Burkitt lymphoma B-cells ( kindly provided by Elliott Kieff ) were cultured in RPMI with 10% FCS . All cell lines were grown in a humidified ThermoFisher incubator at 37 C , with 5% CO2 . The following vectors were used in transient transfection assays: empty pSG5 , pSG5 vectors with N-terminally FLAG-tagged GFP , TRAF1 , TRAF2 , or TRAF3 cDNAs; pSG5 expression vectors with untagged or N-terminally epitope-tagged full length LMP1 ( wildtype ) , LMP1 residues 1–231 , C-terminally HA-tagged LMP1 1–231; the TES2 null mutant 384YYD386 ->384ID385 , the TES1 null alanine point mutant LMP1 204PQQAT208-> 204AQAAA208 , or the TES1/TES2 null double mutant ( LMP1 DM ) containing both of these mutations [25 , 67]; a PGK-puro vector with untagged TRAF1 cDNA . N-terminally GST-tagged TRAF1 was cloned into a modified Gateway-compatible pSG5 expression vector by Gateway cloning . For western blot analysis , antibodies against the following were used: Cell Signaling Technologies TRAF3 ( #4729 ) , cIAP1 ( #7065 ) , cIAP2 ( # 3130 ) , phospho-JNK ( #9251 ) , phospho-p38 ( #9211 ) , total JNK ( #9252 ) , total p38 ( #9212 ) , phospho-ERK ( # 4377 ) , total ERK ( # 9102 ) , RelA-phosphoserine 536 ( 3033 ) ; total RelA ( 8242 ) total Ub ( 3936 ) , ABIN1 ( 4664 ) , A20 ( cat #4625 ) ; Bethyl Laboratories SHARPIN ( #A303-559A ) , TRAF2 ( #A303-460A ) , HOIP ( A303-560A ) ; Sigma Aldrich tubulin ( # T5168 ) and FLAG M2; Santa Cruz TRAF1 ( #sc1831 ) and NEMO ( sc-8330 ) ; Covance HA . 11; EMD Millipore p100/p52 ( 05–361 ) and anti-K63 05–1308; Chemicon/Millipore GAPDH ( MAB375 ) ; Genentech anti-M1 Ub 1F11/3F5/Y102L . Detection of endogenous phospho-TAK1 was performed on inducible 1–231 LMP1 293 TRAF1 cells uninduced or induced for LMP1 expression overnight , and treated with 50 nM Calyculin A ( Cell Signaling #9902 ) for 5 minutes prior to harvest . Cell Signaling antibodies against phospho-TAK1 ( #4508 ) and total TAK1 ( #4505 ) were used . LMP1 monoclonal antibodies OT22CN against the LMP1 N-terminus , or S12 against the LMP1 C-terminus ( recognizes an epitope between TES1 and TES2 ) , were used . Cell Signaling HRP-tagged secondary antibodies , or with Rockland TrueBlot HRP-tagged secondary antibodies were used for western blot . All immune-purified and whole cell lysate samples were boiled for 5 minutes in Laemmli SDS-loading buffer with a final concentration of 1% SDS and 5% beta-mercaptoethanol , at 95°C for 5 min . SDS/PAGE was performed on Bio-Rad precast gels . 4–20% gradient gels were used for experiments with anti-ubiquitin western blots . Proteins were transferred to nitrocellulose filters for 1 hour at 100V using Bio-rad a power pack and minigel transfer apparatus . Blots were blocked with 5% non-fat dry milk for 30 minutes , then probed overnight with primary antibody at 4 degrees C , washed in TBST for 5 minutes x 4 cycles , incubated with secondary antibody for 1 hour , washed in TBST for 5 minutes x 4 cycles , developed with Western Lightening ECL developer , and imaged on a Carestream Molecular Imaging workstation . Where indicated , western blot band intensities were measured using Carestream software , using background subtracted net values . M1-pUb and K63-pUb linked chains were purified under denaturing conditions , according to the manufacturer’s instructions . Briefly , for M1-pUb purification , cells were lysed in buffer containing 8M urea and 20mM Tris ( pH 7 . 4 ) , 135 mM NaCl , 1% Triton-X100 , 10% glycerol , 1mM EDTA , and 1 . 5mM MgCl2 , supplemented with Roche complete EDTA-free protease inhibitor tablet , 1 mM PMSF , 4mM 1 10 o-phenanthroline , sodium pyrophosphate , 10 mM-glycerophosphate , 2 mM sodium pyrophosphate , 1% aprotinin , and 2 mM N-ethylmaleimide , at RT for 10min . Insoluble debris was pelleted by 13 , 000 RPM microfuge , and urea concentration was then reduced to 7M by addition of lysis buffer . 2ug of M1-Ub antibody was added , and samples were rotated at RT overnight . Precipitate was pelleted by microcentrifuge , and then 20ul of Protein A sepharose beads ( Invitrogen 101042 ) were added , and rotated for 2 hours at RT . Beads were washed 5X with 7M urea lysis buffer , with inhibitors . Western blots were preformed according to Genentech instructions , using wet transfer at 30V for 2 hours to nitrocellulose membranes . Primary antibody was added to blots for 1 hr at RT . NF-kB activity was measured by a GFP reporter assay , as previously described [38] . Briefly , conditional LMP1 1–231 and LMP1-231 TRAF1 293 cells with a stably integrated NF-kB GFP reporter were used . LMP1 1–231 expression was induced for 20 hours by the addition of doxycycline and 4HT . NF-kB GFP reporter values were measured on a FACScalibur flow cytometer ( BD Biosciences ) , and analyzed by Cell Quest software ( BD Biosciences ) . The SMAC mimetic TL-32711 was obtained from Active Biochem ( #A-1901 ) , and used according to the manufacturer’s instructions at a concentration of 20 uM . 293 inducible LMP1 cells were treated with Dharmacon/Thermo Fisher siRNAs for 72 hours prior to LMP1 induction , as previously described [38] . Briefly , LMP1 1–231 conditional 293 TRAF1 cells were reverse transfected with Dharmafect I lipid in 12-well plates . 72 hours later , LMP1 1–231 expression was induced by addition of 4HT and doxycycline , where indicated , for 16 hours . The non-targeting siRNA control ( Catalogue # D-001810-10-20 ) , and siGenome siRNAs against TRAF2 , TRAF3 , RNF31/HOIP , RBCK1/HOIL-1L and SHARPIN were used at a final concentration of 50 nM per siRNA pool . For GM12878 shRNA analysis , LCLs were transduced with VSV-G pseudotyped lentiviral vectors from the Broad Institute of Harvard and MIT RNAi consortium on day 0 and 1 . Anti-GFP shRNA was used as a control . On day 2 , LCLs were selected with puromycin ( 3 ug/ml ) , and analyzed at the indicated timepoints . Knockdowns were validated by western blot and qPCR analysis . All shRNA sequences are available upon request . GM12878 LCLs with stable S . pyogenes Cas9 expression were established by infection by lentiviral transduction and blasticidin selection , using pLentiCas9-Blast ( Addgene plasmid # 52962 ) . We verified that Cas9 was highly active in the selected LCL pool by transduction with a lentivirus that encodes GFP , and a sgRNA against GFP [68] . The PXPR-011 plasmid was kindly provided by John Doench , Broad Institute , and encodes GFP , as well as an sgRNA against GFP . PXPR-011 is therefore a convenient way to monitor Cas9 activity in cell lines . GM12878 cells transduced with PXPR-011 based lentivirus and selected with puromycin initially expressed GPF , but were then found to lose GFP expression in >85% of transduced cells ( the residual 15% of cells that continue to express GFP despite sgRNA against GFP may be cells where the non-homologous end-joining pathway correctly repaired the Cas9-induced DNA double strand break ) [68] . By contrast , nearly 100% of Cas9 negative GM12878 cells were GFP positive after transduction with the same lentivirus and puromycin selection . CRISPR single guide RNAs ( sgRNA ) targeting human RNF31 ( which encodes HOIP ) were designed using the online program CRISPRdirect ( http://crispr . dbcls . jp/ ) [69] , and the oligo GCCCTCAGCGGCCTCGGTAC was Synthesized by Life Technologies , cloned into the lentiGuide-Puro vector ( Addgene plasmid # 52963 ) , according to the protocol from the Zhang laboratory website ( http://genome-engineering . org/ ) [70] , and sequence verified . Lentiviruses encoding the HOIP sgRNA were constructed and used to transduce GM12878 Cas9+ cells . Transduced cells were selected by purmoycin . HOIP depletion efficiency was validated by western blot . 293 cell lines were transiently reverse transfected as previously described [38] , using Effectene lipid ( Qiagen ) . For most experiments , cells were transfected for 18 hours . 293 cells were transiently transfected with the indicated plasmids ( pSG5 LMP1 , pSG5 FLAG-TRAF1 , and/or the UBAN-GFP sensor [71 , 72] ) . UBAN-GFP is a fusion between the conserved linear Ubiquitin Binding domain of ABIN1 and NEMO/IKK-gamma and GFP . The UBAN-GFP biosensor has been validated to be highly specific for M1-pUb chains in vitro and in vivo [71 , 72] . 20 hours after transfection , cells were fixed , permeabilized , and stained where indicated with antibodies against LMP1 or TRAF1 ( Santa Cruz , rabbit polyclonal ) . Secondary antibodies used were Alexa-561-conjugated anti-mouse and Alexa-633-anti-rabbit ( both from Life Technologies ) . Cells were analyzed by confocal microscopy , and images were processed with Fiji ( http://wiki . imagej . net/Fiji ) . N-terminally GST-tagged TRAF1 expression vectors were constructed using Gateway cloning , and used for purification of recombinant TRAF1 from unstimulated HEK-293 cells . In vitro ubiquitin assays were performed as previously described [73] . Briefly , in vitro ubiquitination assays were performed according to the manufacturer’s protocol ( Boston Biochem ) . Ubiquitin ( 5 μg ) , the E1 enzyme ( 200 ng ) , UBE2L3 ( 300 ng ) ( Boston Biochem ) , the indicated LUBAC components ( 0 . 8 μg ) and TRAF1-GST ( 2μg ) were co-incubated with 2 mM ATP ( Sigma ) at 37°C 2 hours , in ubiquitin assay buffer ( 20 mM Tris-HCl pH7 . 5 , 5 mM MgCl2 , 2 mM DTT ) . 1x stop solution ( Boston Biochem ) was added to terminate the reaction . Following GST pull-down , beads were washed four times , and then boiled in Laemmli SDS-loading buffer with 5% beta-mercaptoethanol at 95°C for 5 min . The samples were subsequently analyzed by SDS-PAGE followed by Western blotting using a PVDF membrane . FLAG affinity purification and liquid chromatography-mass spectrometry analysis were performed , as previously described[74] . Seven 15 cm^2 dishes ( approximately 100 million cells ) of 293 TRAF1 or GFP control cells , either uninduced or induced for LMP1 1–231 expression for 16 hours , were washed twice with PBS and then lysed on ice for 30 minutes in lysis buffer with protease inhibitors ( Roche EDTA Free Complete , Cat #11836145001 , 1% aprotinin ( Sigma Cat #A6279 ) , 1 mM PMSF ( Sigma ) , and 4 mM 1 , 10 o-phenanthroline ( Sigma ) , and the phosphatase inhibitors 10 mM beta-glycerophosphate and 2 mM sodium pyrophosphate ( Sigma ) [64] . 30 uL of packed anti-FLAG beads ( Sigma Cat #A2220 ) were added into the lysates and were rotated at 4 degrees C for 4 hours , then washed 5 times in lysis buffer with protease and phosphatase inhibitors , with Eppendorf tube change prior to the last wash , and eluted with 50 ul of 3X-FLAG peptide ( 0 . 5 mg/ml , Sigma #F4799 ) at room temperature for 30 minutes , three times sequentially . Samples were analyzed by WB to confirm absence of antibody heavy/light chain contamination , and then run into a 10% pre-cast mini-gel ( Bio-Rad ) for 1 cm , cut into two equal slices , and sent for liquid chromatography mass spectrometry ( LC/MS-MS ) analysis at the Harvard Taplin Biological Mass Spectrometry Facility ( Harvard Medical School ) . Gel slices were processed by the Taplin Proteomics facility staff . Briefly , gel slices were subjected to a modified in-gel trypsin digestion procedure . Gel pieces were washed and then dehydrated with acetonitrile for 10 min . Following acetonitrile removal , slices were speed-vac dried , rehydrated with a 50 mM ammonium bicarbonate solution containing 12 . 5 ng/μl modified sequencing-grade trypsin ( Promega , Madison , WI ) at 4°C . After 45 min . , the excess trypsin solution was removed and replaced with 50 mM ammonium bicarbonate solution to just cover the gel pieces . Peptides were then extracted by removing the ammonium bicarbonate solution , followed by one wash with a solution containing 50% acetonitrile and 1% formic acid . Extracts were speed-vac dried for ~1 hr and stored at 4°C until analysis . On the day of analysis , samples were reconstituted in 5–10 μl of HPLC solvent A ( 2 . 5% acetonitrile , 0 . 1% formic acid ) , subjected to nano-scale reverse-phase HPLC using a capillary column ( 5 μm C18 spherical silica beads packed into a fused silica capillary ( 100 μm inner diameter x ~12 cm length ) with a flame-drawn tip . After equilibrating the column , each sample was loaded via a Famos auto sampler ( LC Packings ) onto the column . A gradient was formed and peptides were eluted with increasing concentrations of solvent B ( 97 . 5% acetonitrile , 0 . 1% formic acid ) . Upon elution , peptides were subjected to electrospray ionization and analyzed by a LTQ Velos ion-trap mass spectrometer ( ThermoFisher , San Jose , CA ) . Peptides were detected , isolated , and fragmented to produce a tandem mass spectrum of specific fragment ions for each peptide . Dynamic exclusion was enabled such that ions were excluded from reanalysis for 30 s . Peptide sequences ( and hence protein identity ) were determined by matching protein databases , using Sequest ( ThermoFisher ) . The human IPI database ( Ver . 3 . 6 ) was used for searching . Precursor mass tolerance was set to +/- 2 . 0 Da and MS/MS tolerance was set to 1 . 0 Da . A reversed-sequence database was used to set the peptide false discovery rate at 1% . Filtering was performed using the Sequest primary score , Xcorr and delta-Corr . Spectral matches were further manually examined . To assign statistical significance and to identify high-confidence TRAF1 interacting proteins , our data were compared with a publicly-available database of 30 negative control FLAG-tagged baits , purified from HEK-293 stable cell lines under similar conditions ( http://www . crapome . org ) [75] . Control dataset IDs and bait peptide counts are also provided for comparison in S1 Table . The SAINT algorithm ( http://sourceforge . net/projects/saint-apms ) was used to evaluate the MS data [76 , 77] . SAINT is designed for AP-MS analysis and has been validated in analysis of several protein interactomes [76 , 78 , 79] . The default SAINT options were low Mode = 1 , min Fold = 0 , norm = 0 . SAINT probabilities computed independently for each biological replicate were averaged ( AvgP ) and reported as the final SAINT score . Fold change was calculated for each prey protein as the ratio of average spectral counts from replicate bait purifications over the average spectral counts across all negative controls ( total peptide spectral counts were summed for each protein ) . A background factor of 0 . 1 was added to the average spectral counts of negative controls to prevent division by zero . The highest number of spectral counts for each protein were selected to establish the negative control database for SAINT analysis . Selection of the threshold for SAINT scores was based on receiver operating curve analysis performed using publicly available protein interaction data and the FLAG AP-MS data set as a list of true positive interactions . A SAINT score of AvgP ≥ 0 . 80 was considered a high-confidence interacting protein , with an estimated FDR of ≤1% . Real-time reverse transcription-PCR ( qPCR ) . qPCR was performed on a Bio-Rad CFX Connect Real-time system , using the Power SYBR green RNA-to-CT 1-step kit ( Applied Biosystems ) , for 40 cycles . Fold changes were determined using the CT method and normalized by 18S rRNA expression levels . The RBCK1 primers 5’-TGCAAGACCCCAGATTGCA-3’ , AND 5’-ACAGGGCAGGTGAACTCATTG-3’ were used . 96 hours after initial shRNA transduction ( and 48 hours after puromycin selection ) , LCLs were plated at a density of 300 , 000 cells/ml in 96 well plates in 100 uL of RPMI/FCS , in triplicate . Cells were fed 100 uL of RPMI at 48 hours and 96 hours thereafter . Relative live cell numbers were then accurately quantitated by the CellTiter-Glo luminescent cell viability assay ( Promega ) . All values obtained were within the linear range of the instrument . For shRNA growth curves , a LMaxII instrument was used ( Molecular Devices ) . For CRISPR/cas9 growth curve analysis , SpectraMax L ( Molecular Devices ) was used , as the LMaxII was no longer available . All bar-graphs and growth curves were produced using GraphPad software .
To characterize signaling by the LMP1 TES1 domain , 293 cells with conditional LMP1 1–231 expression were derived ( described in detail in the Methods ) [38 , 43] . We then established conditional LMP1 1–231 293 cell lines with stable expression of N-terminally FLAG- tagged GFP or TRAF1 . FLAG-TRAF1 was expressed at LCL physiological levels ( S1 Fig ) . The conditional 293 LMP1 cell pair provided an isogenic background with which to compare the effects of TRAF1 on LMP1 TES1 domain-mediated pathway activation . Consistent with prior LMP1 studies [29 , 52] , TRAF1 co-expression markedly boosted LMP1 TES1-mediated JNK pathway activation ( Fig 1A and 1B ) . The ratio of phospho-JNK to total JNK in whole cell extracts increased from 2 . 1-fold in 293 cells to 7 . 1-fold in 293 TRAF1 cells 16 hours after LMP1 1–231 induction , as judged by western blot analyses from three independent experiments . We likewise found that TRAF1 co-expression significantly increased LMP1 1-231-mediated p38 and ERK phosphorylation ( Fig 1A and 1B ) . Our results suggest that TRAF1 enhances LMP1 TES1 domain-mediated activation at a level upstream of the three MAP kinases . Consistent with a prior report , we found that TRAF1 co-expression also significantly enhanced LMP1 TES1-mediated NF-kB activation ( Fig 1C ) [29] . We next examined whether TRAF1 co-expression affected LMP1 1-231-mediated canonical and/or non-canonical NF-kB pathway activation . TRAF1 co-expression significantly up-regulated LMP1 TES1-mediated canonical NF-kB , as judged by RelA serine 536 phosphorylation , a commonly used marker of canonical NF-kB activity ( Fig 1A and 1B ) . To examine the effect of TRAF1 co-expression on LMP1 1-231-mediated non-canonical NF-kB activation , we analyzed the p100:p52 ratio in conditional 293 and 293 TRAF1 cells . Non-canonical NF-kB activity triggers proteasomal processing of p100 to p52 . Interestingly , TRAF1 co-expression did not significantly enhance LMP1 1-231-mediated non-canonical NF-kB pathway activity , as judged by the ratio of p100:p52 , which remained 1 . 5 in both conditions ( Fig 1D ) . Since the kinase TAK1 plays key roles in LMP1 MAP kinase and canonical NF-kB pathways , we next tested whether TRAF1 enhanced LMP1 1-231-mediated TAK1 activation . Indeed , TRAF1 markedly enhanced LMP1 1–231 induction of TAK1 activation loop serine 187 phosphorylation ( S2 Fig ) . Collectively , our results suggest that TRAF1 enhanced LMP1 1-231-mediated MAP kinase and canonical NF-kB pathway activation at or above the level of TAK1 activation . To gain insights into TRAF1 effects on LMP1 TES1 signaling , we used affinity purification and mass spectrometry analysis ( AP-MS ) of TRAF1 complexes as a discovery tool for subsequent analysis . FLAG-TRAF1 complexes were immune-purified from conditional 293 cells that were uninduced or induced for LMP1 1–231 expression for 16 hours [64] . Complexes were eluted from agarose beads by co-incubation with FLAG peptide ( see Methods section for details ) . As a negative control , FLAG-GFP was immuno-purified from conditional LMP1 1–231 cells induced for 16 hours . Independent FLAG-purifications were analyzed by liquid chromatography-mass spectrometry/mass spectrometry ( LC-MS/MS ) proteomic analysis for each condition . Data resulting from AP-MS analysis are presented in S1 Table . To identify high-confidence interactions in our TRAF1 datasets , we further compared our datasets with thirty publically-available 293 cell FLAG AP/MS control datasets ( http://www . crapome . org ) [75] . This analysis provides additional statistical power to remove common 293 cell contaminants , which are frequently high abundance proteins including heat-shock , cytoskeletal , histones , ribonucleoproteins , and ribosomal proteins . We used the well-established ‘Significance Analysis of Interactome' ( SAINT ) computational algorithm to assign confidence scores to our TRAF1 datasets [76 , 77 , 80] . SAINT uses quantitative AP-MS data to derive the probability of a bona fide protein-protein interaction . At a FDR < 1% cutoff ( SAINT score ≥0 . 8 ) , we identified 19 high-confidence TRAF1 interactors in extracts from LMP1 1–231 expressing 293 cells . Three additional proteins had a SAINT score of 0 . 79 and were also considered high-confidence interactors . 23 high-confidence TRAF1 interactors were identified from uninduced 293 cells purifications ( S3 Fig and S1 Table ) . Well-characterized TRAF1 interactors were enriched in both TRAF1 datasets , including TRAF2 , cIAP1 , cIAP2 , TBK1 , and TANK . Other TRAF1 high confidence interactors were identified in either the LMP1 1–231 uninduced or induced condition . Interestingly , all seven of the LMP1 1-231-induced TRAF1 high-confidence protein interactions have established roles in ubiquitin biology , including ubiquitin itself . Notably , LMP1 1–231 expression induced association between TRAF1 and the LUBAC catalytic subunits HOIP ( SAINT score 0 without induction , 0 . 99 with induction ) and HOIL-1L ( SAINT score 0 without induction , 0 . 79 with induction ) . By comparison , HOIP or HOIL-1L peptides were not retrieved in our FLAG-GFP samples or in any of the 30 control 293 cell FLAG runs ( S1 Table ) . LMP1 1–231 expression also induced TRAF1 association with the ubiquitin editor protein A20 ( SAINT score 0 without induction , 0 . 96 with induction ) . A20 contains deubiquitinase , ubiquitin ligase , and ubiquitin-binding zinc finger domains , the latter of which bind to both M1- and K63-pUb chains . The K63-polyubiquitin binding protein SQSTM1/P62 was also identified as a high-confidence LMP1-induced TRAF1 interactor ( uninduced SAINT score 0 , induced score 0 . 79 ) . The K63- and M1-pUb sensor ABIN1 nearly reached significance ( SAINT score without induction , 0 . 49 with induction ) . The LMP1 1-231-induced association between TRAF1 and LUBAC was further validated by IP/western blot analysis , using both conditional 293 cells and GM12878 LCLs with stably FLAG-TRAF1 expression at physiological levels ( S1 Fig ) . First , FLAG-TRAF1 complexes were immuno-purified from conditional 293 cells , either undincued or induced for LMP1 1–231 expression for 16 hours ( Fig 2A ) . LMP1 induction increased the level of HOIP and SHARIPN in FLAG-TRAF1 complexes . By contrast , we did not observe significant co-purification of LUBAC components with FLAG-TRAF3 complexes immuno-purified from conditional LMP1 1–231 293 cells with stable FLAG-TRAF3 expression . Of note , twice as many FLAG-TRAF3 cells were used in this experiment , to achieve similar levels of immuno-purified FLAG-TRAF1 and FLAG-TRAF3 . We were unable to find a suitable antibody for analysis of endogenous HOIL-1L . Also of note , LMP1 1–231 induction caused TRAF1 and TRAF3 steady state levels to decrease , perhaps as a result of increased turnover . To validate that TRAF1 associates with LUBAC in LCL extracts , we tested whether FLAG-TRAF1 purified from GM12878 cells also retrieved LUBAC components . Both HOIP and SHARPIN co-immunoprecipitated with FLAG-TRAF1 , but not with a FLAG-GFP control ( Fig 2B ) . Likewise , HA-SHARPIN complexes immuno-purified from GM12878 cells with stable HA-SHARPIN expression co-immunoprecipitated TRAF1 and LMP1 ( S4 Fig ) . Taken together , our data suggest that TRAF1 and LUBAC are present together in protein-protein complexes in LMP1+ cells . Most TRAF1 in LCLs is associated with LMP1 [29] . We therefore investigated whether LMP1 complexes are modified by M1-linked pUb chains in LCL extracts . FLAG-LMP1 was immuno-purified from LCLs established from recombinant EBV , in which FLAG-tagged LMP1 is expressed at physiological levels from the EBV genome [18] . As a negative control with physiologic LMP1 expression from the EBV genome , we used GM12878 LCLs ( where LMP1 is untagged ) . FLAG immuno-purified material was subjected to western blot analysis , using a M1-linked pUb ( M1-pUb ) chain specific monoclonal antibody [81] . M1-pUb chains were readily detected in FLAG-LMP1 purified from FLAG-LMP1 LCL extracts , but not from GM12878 extracts , suggesting that either LMP1 , or an LMP1 signalosome protein , was modified by LUBAC-catalyzed M1-pUb chains ( Fig 3A ) . We next investigated whether TRAF1 complexes purified from GM12878 LCLs were likewise decorated with M1-pUb chains . As a negative control , we used GM12878 that express N-terminally FLAG-tagged GFP at similar levels . As shown in Fig 3B , FLAG-immuno-purified TRAF1 complexes , but not FLAG-GFP complexes , were highly modified by M1-pUb chains . Thus , LMP1 and TRAF1 are each present in complexes that are highly modified by M1-pUb chains . To determine whether LMP1 induces M1-pUb chain attachment to TRAF1 complexes , we used 293 cell transient transfection assays . 293 cells were co-transfected for 24 hours with N-terminally FLAG-tagged GFP , TRAF1 or TRAF2 and either empty vector or untagged wildtype LMP1 . We used the transfection system rather than the conditional system for this analysis to achieve similar FLAG-tagged protein expression levels across all three baits . FLAG immuno-purified complexes were analyzed by western blot for M1-pUb chains . Interestingly , FLAG-TRAF1 complexes , retrieved from LMP1 co-transfected cells , were highly modified by M1-pUb chains ( Fig 3C ) . By contrast , background levels of M1-pUb chains were observed in FLAG-GFP and FLAG-TRAF2 pulldowns , and in FLAG-TRAF1 pulldown from cells without LMP1 . This result suggests that LMP1 stimulates M1-pUb chain attachment to TRAF1 , or a TRAF1-associated protein , and that in the absence of TRAF1 , LMP1 does not induce M1-pUb attachment to TRAF2 complexes . To next determine whether HOIP is important for M1-pUB chain attachment to LMP1 complexes in LCLs , we depleted HOIP from FLAG-LMP1 LCLs , using two independent shRNAs . Each anti-HOIP shRNA , but not the anti-GFP shRNA control , markedly reduced M1-pUb chain decoration of FLAG-LMP1 complexes , suggesting that HOIP plays a key non-redundant role in M1-chain attachment to LCL TRAF1 complexes ( Fig 3D ) . Finally , we investigated whether LMP1 1–231 expression stimulates LUBAC activity . Whole cell lysates from 293 TRAF1 conditional cells that were uninduced , or induced for LMP1 1–231 for 16 hours , were analyzed for M1-pUb chain content by western blot ( Fig 3E ) . Extracts from induced cells had abundant immune-reactive material . Of note , similar M1-pUb chain formation in 293 cells was previously demonstrated by HOIP/HOIL-1L transient transfection [81] . To further establish that LMP1 and TRAF1 complexes are decorated by M1-pUb chains in vivo , we next tested whether LMP1 and TRAF1 co-localize with a M1-pUb chain biosensor . The biosensor is comprised of a fusion protein between GFP and the Ubiquitin Binding of ABIN1 and NEMO/IKK-gamma ( UBAN ) domain . The UBAN-GFP biosensor selectively visualizes the localization of M1-pUb chains in mammalian cells activated by multiple independent stimuli [71 , 72] . 293 cells were transiently co-transfected with UBAN-GFP , TRAF1 and or/ LMP1 . The characteristic 293 cell LMP1 punctate staining pattern was observed by confocal microscopy analysis of LMP1-transfected cells . By contrast , TRAF1 and UBAN-GFP exhibited diffuse cystosolic staining patterns in the absence of LMP1 co-expression ( Fig 4A ) . Interestingly , LMP1 co-expression with TRAF1 and UBAN-GFP altered the TRAF1 and UBAN-GFP patterns , and induced marked co-localized of all three into punctate foci ( Figs 4A and S5 ) . We did not observe similar punctate foci of UBAN-GFP or co-localization with LMP1 in the absence of TRAF1 co-expression . Of note , TRAF1 and the UBAN-GFP sensor colocalized to a lesser extent , even in the absence of LMP1 . Taken together with the proteomic and biochemical data presented above , these results are further suggest that in cells with LMP1 and TRAF1 co-expression , LMP1 and TRAF1 are present in complexes modified by M1-pUb chains . We next tested whether TRAF1 and IKK-gamma also associate in GM12878 LCL extracts . Using GM12878 cells that stably express FLAG-tagged TRAF1 or FLAG-GFP as a control , we found that endogenous IKK-gamma co-immunoprecipitated with FLAG-TRAF1 , but not with the FLAG-GFP negative control ( S6 Fig ) . Likewise , using stable GM12878 cell lines , we found that endogenous TRAF1 reciprocally co-immunoprecipitated with HA-tagged IKK-gamma , but not with the HA-GFP negative control ( S6 Fig ) . Of note , IKK-gamma expression in LCLs is significantly higher than in 293 cells , perhaps explaining why the 293 cell TRAF1 AP/MS analysis did not also identify IKK-gamma as a high confidence TRAF1 interactor , given the limit of detection of the assay . Collectively , these results are consistent with a model in which M1-pUb-linked pUb chains attached to TRAF1 complexes recruit the IKK-gamma . We further validated the AP/MS result that LMP1 1–231 expression induced association between TRAF1 and the M1- and K63-pUb sensors A20 and ABIN1 . A20 and ABIN1 are feedback regulators that each contain M1- and K63-pUb binding domains A20 down-modulates LMP1 TES1 signaling [82] . We found that immuno-purified FLAG-TRAF1 co-immunoprecipitated A20 and ABIN1 in 293 cells induced for LMP1 1–231 expression for 16 hours . By contrast , FLAG-TRAF2 complexes , purified from conditional LMP1 1–231 cells that stably express FLAG-TRAF2 , did not co-immunoprecipitate A20 or ABIN1 , even 16 hours after LMP1 1–231 induction ( Fig 4B ) . Consistent with the higher A20 SAINT score in our TRAF1 AP/MS analysis , A20 association with TRAF1 complexes appeared to be more robust than that of ABIN1 by western blot analysis . Also of note , LMP1 expression increased A20 expression levels , as has previously been reported . Finally , endogenous A20 co-immunoprecipitated with FLAG-TRAF1 , immuno-purified from GM12878 extracts ( Fig 4C ) . We previously found that HOIP and HOIL-1L are important for LMP1 TES2-mediated canonical NF-kB pathway activation in a genome-wide siRNA screen [38] , suggesting that LMP1 TES2 may also activate LUBAC activity . To determine whether signaling by LMP1 TES2 also stimulates addition of M1-pUb chains to TRAF1 complexes , 293 cells were co-transfected with either wildtype ( WT ) LMP1 , or with LMP1 mutants deficient for TES1 , TES2 or TES1/TES2 signaling and with FLAG-tagged TRAF1 . M1-pUb chains were detectable on FLAG-TRAF1 complexes immuno-purified from cells that co-expressed wildtype LMP1 , or the LMP1 384ID385 mutant , which is null for TES2 signaling . By contrast , FLAG-TRAF1 complexes purified from cells that co-expressed either a LMP1 TES1 null alanine point mutant ( LMP1 204PQQAT208-> 204AQAAA208 ) deficient for TRAF recruitment or a LMP1 double mutant ( DM ) deficient for TES1 and TES2 signaling , were not modified by M1-pUb chains ( Fig 5A ) . These results suggest that association between TRAF1 and the LMP1 TES1 domain are required for M1-pUb chain attachment to TRAF1 complexes , and that LMP1 TES2-mediated NF-kB activation does not stimulate LUBAC to modify TRAF1 . We next determined whether LMP1 TES1 signaling was important for M1-pUb chain attachment to LMP1 complexes . 293 cells were co-transfected with FLAG-tagged WT , 1–231 , or DM LMP1 vectors and with untagged TRAF1 . FLAG-LMP1 immuno-purified complexes were analyzed by western blot for M1-pUb chain attachment ( Fig 5B ) . M1-pUb chains decorated WT and LMP1 1–231 complexes , but not DM LMP1 complexes . These results again suggest that LMP1 complexes are modified by M1-pUb chains in cells with TRAF1 expression , and that LMP1 TES1 signaling is important for M1-pUb chain attachment to LMP1 complexes . To identify TRAF1 domains important for LMP1-induced M1-pUb attachment , 293 cell transient transfection assays were performed with FLAG-tagged TRAF1 constructs , co-transfected with untagged WT LMP1 . 24 hours after transfection , FLAG immuno-purified complexes were analyzed by western blot . Interestingly , FLAG-TRAF1 183–416 complexes , but not FLAG-TRAF1 264–416 complexes expressed at a similar level , were modified by M1-pUb chains ( Fig 5C ) . TRAF1 183–264 residues form a coiled-coil ( CC ) domain , which is required for the formation of TRAF homo- and hetero-trimers . Notably , FLAG-TRAF 264–416 did not associate with LMP1 , suggesting that TRAF1 trimerization and/or physical association with LMP1 are important for incorporation into complexes that contain M1-pUb chains . This result is consistent with the prior observation that TRAF trimers , rather than monomers , associate tightly with activated CD40 receptors . Loss-of-function approaches were used to test the importance of LUBAC subunits , TRAF2 , TRAF3 and cIAP1/2 in M1-pUb chain attachment to TRAF1 complexes , since each were identified as high-confidence TRAF1 interactors . First , we used an siRNA approach to investigate the role of the three LUBAC components . 72 hours after 293 TRAF1 cell siRNA transfection , LMP1 1–231 expression was induced for 16 hours . The M1-pUb chain content of FLAG- TRAF1 immuno-purified complexes was analyzed by western blot . Knockdown efficiency was measured by western blot , using whole cell lysates ( Figs 6A and S7 ) . We were unable to identify commercially available antibodies that recognized endogenous HOIL-1L in our 293 cells , and instead used quantitative PCR analysis to validate HOIL-1L mRNA depletion in a parallel experiment ( S8 Fig ) . Interestingly , we found that depletion of HOIP , HOIL-1L , or SHARPIN each impaired M1-pUb chain attachment to TRAF1 complexes , suggesting that all three LUBAC components play important and non-redundant roles , at least in 293 TRAF1 cells ( Fig 6A ) . TRAF2 depletion likewise reduced M1-pUb chain abundance in purified FLAG-TRAF1 complexes , and also diminished association between TRAF1 and the LUBAC components HOIP and SHARPIN ( Figs 6A and S7 ) . By contrast , TRAF3 depletion did not impair M1-pUb chain attachment to purified TRAF1 complexes ( S7 Fig ) . Taken together with our prior observation that TRAF2 complexes are not modified by M1-pUb in cells that lack TRAF1 expression , our results suggest that a TRAF1:TRAF2 heterotrimer , rather than a TRAF1 homotrimer , may be the functional unit that associates with LUBAC . Of note , HOIL-1L knockdown increased TRAF2 steady state levels ( Fig 6A ) , while HOIP and SHARPIN knockdown also increased TRAF2 levels to a lesser extent . To our knowledge , LUBAC has not previously been implicated in control of TRAF2 steady-state levels , though it remains possible that this effect is specific to cells that express LMP1 . Depletion of HOIP and HOIL-1L from 293 TRAF1 cells impaired LMP1 1-231-mediated p38 , JNK and canonical NF-kB activation , consistent with a role for M1-pUB chains at or above the level of TAK1 kinase activation ( S9 Fig ) . Given the key roles that TRAF2 and cIAP ligases play in TNFR1-mediated LUBAC recruitment and activation , and since the TRAF1:TRAF2 heterotrimer more efficiently recruits cIAP ligases than TRAF2 homotrimers [83] , we next tested the importance of cIAP1 and cIAP2 in M1-pUb chain attachment to TRAF1 complexes . cIAP1 and cIAP2 perform largely redundant functions , potentially complicating siRNA loss-of-function approaches that would require their compound knockdown . We therefore used a cell-permeable SMAC mimetic peptide to deplete cIAP1 and cIAP2 . SMAC mimetics induce cIAP1/2 auto-ubiquitination and rapid proteasomal degradation [84] . Interestingly , 293 TRAF1 cell treatment with SMAC mimetic prior to and throughout the 16 hours of LMP1 1–231 expression did not impair subsequent attachment of M1-pUb chains to LMP1/TRAF1 complexes , despite efficiently inducing cIAP1/2 depletion from 293 cell whole cell lysates ( Fig 6B ) . This result suggests that LMP1-induced LUBAC association with TRAF1:TRAF2 complexes differs from TNF-alpha induced LUBAC recruitment to TRAF2:cIAP complexes , which require cIAP-catalyzed pUb chain formation . The LMP1 N-terminus can be modified by ubiquitin conjugates in transient overexpression assays [85] . We therefore tested whether LMP1 itself might be modified by M1-pUb chain attachment in GM12878 LCLs . EBV-negative BL2 Burkitt lymphoma cells were used as a B-cell negative control . To disrupt protein-protein complexes , GM12878 and BL2 cells were lysed under highly denaturing conditions , using buffer containing 8M urea . M1-pUb chains were then immune-purified , using a M1-pUb-specific monoclonal antibody under denaturing conditions in a buffer that contained 7M urea ( Methods ) [81] . Immuno-purified M1-pUb material was analyzed by western blot , using an LMP1-specific monoclonal antibody ( LMP1 is not epitope tagged in GM12878 ) . Surprisingly , high molecular weight LMP1 conjugates were evident , consistent with the possibility that LMP1 is a direct target of M1-pUb chain attachment ( Fig 7A ) . Since WT LMP1 has only a single lysine residue , K330 , we hypothesized that it could be the M1-pUb attachment site . We therefore tested whether the lysine-less and untagged LMP1 1–231 molecule was modified by M1-pUb chains in conditional 293 TRAF1 cells . M1-pUb chains were immuno-purified under denaturing conditions from uninduced 293 TRAF1 cells , and from 293 TRAF1 cells induced for LMP1 1–231 expression for 16 hours . M1-pUb immuno-purified material was analyzed by western blot , using an anti-LMP1 monoclonal antibody . Surprisingly , high molecular weight LMP1 conjugates were again identified in extracts from LMP1 1–231+ cells ( Fig 7B ) . Notably , high molecular weight LMP1 1–231 species were not observed in M1-pUb pulldowns from 293 TRAF1 cells treated with HOIP siRNAs 72 hours prior to LMP1 1–231 induction , and then western blotted either for M1-pUb or total Ub ( Fig 7C ) . These results suggest that LMP1 may be a direct LUBAC target , and support the specificity of the anti-M1-pUb monoclonal antibody . Further studies are required to identify the M1-pUb attachment site in the lysine-less LMP1 1–231 molecule . Of note , all SDS/PAGE samples were boiled in loading buffer with fresh 5% beta-mercaptoethanol , which disrupts cysteine-ubiquitin linkages [86] . Possible LMP1 M1-pUb attachment sites include the LMP1 N-terminus itself or a non-lysine LMP1 residue . We next used ubiquitination assays to determine whether LUBAC ubiquitinates recombinant TRAF1 in vitro . Recombinant N-terminally GST-tagged TRAF1 was added to reaction buffer containing ubiquitin , ATP , the ubiquitin E1 enzyme , as well as the indicated combinations of the ubiquitin E2 enzyme UBE2L3 and LUBAC components ( S10 Fig and Methods ) . Reactions were stopped after two hours , boiled in Laemmli sample buffer , and were analyzed by western blot for M1-pUb chain linkages . Interestingly , high-molecular weight , immune-reactive species were abundantly present in lanes 7 and 8 , from reactions that contained E1 , E2 , HOIP , HOIL-1L and TRAF1 . The lane 8 reaction also contained SHARPIN . Since SHARPIN was found to be important for M1-pUb attachment to TRAF1 complexes in 293 cells , perhaps protein concentrations used in the in vitro Ub assay circumvent the need for SHARPIN’s regulatory role . Aberrant HOIP activity has recently been implicated in the pathogenesis of the activated B cell-like ( ABC ) subtype of diffuse large B-cell lymphoma ( DLBCL ) [87 , 88] . LUBAC inhibition was synthetically lethal to ABC DLBCL , but not the germinal center lymphoma subtype , which have lower NF-kB activity [88] . Given these and our results , we tested the effect of HOIP knockdown on GM12878 LCL growth and survival . Interestingly , by comparison with a non-targeting shGFP control , HOIP depletion by five independent shRNAs significantly impaired LCL growth in biological triplicate assays ( Fig 8A ) . While four anti-HOIP shRNAs yielded very similar effects , a fifth anti-HOIP shRNA ( shRNA #3 on the growth curve ) had a statistically significant effect in the same direction , but a more modest growth phenotype . This attenuated phenotype may reflect partial rescue by an off-target shRNA effect , or partial rescue by an alternatively spliced HOIP transcript that lacks the shRNA targeting sequence , and which results in a truncated protein not recognized by our anti-HOIP antibody . To further validate the overall shRNA result , we used CRISPR/Cas9 mutagenesis in GM12878 cells that stably express Cas9 to deplete HOIP ( Methods ) . An anti-HOIP exon 1 small guide RNA ( sgRNA ) knocked down HOIP expression and caused a statistically significant decrease in LCL growth , by comparison with a control anti-GFP sgRNA ( Fig 8B ) . CRISPR/Cas9 edited cells with residual HOIP expression , for example as a result of mono-allelic HOIP disruption , may account for residual LCL growth observed in this experiment . Anti-HOIP sgRNA expression triggered marked induction of caspase 3 and 7 activity , and to a lesser extent , caspase 8 activity ( S11 Fig ) . Western blot analysis of GM12878 whole cell lysates obtained 6 days after transduction with sgRNA-expressing lentiviruses also demonstrated cleaved caspases 3 , 7 , 9 and cleaved PARP . Overall , these results suggest that HOIP depletion predominantly triggers the intrinsic apoptosis pathway . While TRAF1-independent HOIP roles may also have contributed to this phenotype , it nonetheless suggests an important role for M1-pUb chains in LCL growth and survival , and highlights LUBAC as a potential therapeutic target in EBV-associated lymphoproliferative disorders . We next tested the effect of TRAF1 knockdown in GM12878 LCLs , and found that five independent TRAF1 shRNAs each significantly impaired LCL growth relative to the shRNA control ( Fig 8C ) . We note that all five TRAF1 shRNAs had similar effects on LCL growth , despite variation in the extent of TRAF1 knockdown evident on whole cell extract western blot four days after lentivirus transduction . This result raises the possibility that GM12878 are quite sensitive to TRAF1 depletion , and that even partial TRAF1 depletion impaired cell proliferation . Alternatively , off-target shRNA effects may also have contributed to shRNA effects on LCL growth , in particular for shRNA #3 , which depleted TRAF1 to the least extent . TRAF1 levels may also have become more similar across shRNA conditions at subsequent timepoints . Nonetheless , the ability of all five TRAF1 shRNAs to impair LCL proliferation argues against off-target effects being solely responsible for the observed phenotypes . Collectively , our results support an important role for TES1 and TRAF1-dependent M1-pUb chains in the LCL immortalized growth phenotype . K63-linked pUb chains have important roles in canonical NF-kB and MAP kinase activation pathways . Notably , our proteomic analysis suggested that TRAF1 associated with multiple E3 Ub ligases that catalyze K63-linked pUb chains , including TRAF2 , TRAF3 , cIAP1 , and cIAP2 ( S1 Table ) . Likewise , A20 , ABIN1 and SQSTM1 , each of which have domains that bind to K63-pUb chains , associated with TRAF1 in LMP1 1–231 induced cells . Given also the important role that K63-linked pUb chains play in LUBAC recruitment to TNFR1 , we examined whether LMP1 or TRAF1 complexes were decorated by K63-pUb chains . First , we used 293 TRAF1 cells to test whether LMP1 1–231 expression induces attachment of K63-pUb-linked chains to TRAF1 complexes . FLAG-TRAF1 complexes were immuno-purified from 293 TRAF1 cells uninduced or induced for LMP1 1–231 expression for 16 hours , and subjected to western blot analysis with a K63-pUb chain specific antibody [89] . While TRAF1 was not associated with K63-pUb chains in unstimulated 293 cells , TRAF1 complexes were highly modified by K63-pUb chains in extracts from cells that co-express LMP1 1–231 ( S12 Fig ) . The LMP1 TES2 domain highly activates TRAF6 K63-pUb ligase activity , but does not associate with TRAF1 . To test whether LMP1 TES2 signaling nonetheless stimulates K63-pUb attachment to TRAF1 complexes , we co-transfected 293 cells with FLAG-TRAF1 and either WT LMP1 , or LMP1 mutants deficient for TES1 and/or TES2 signaling . FLAG-TRAF1 complexes purified from cells that co-expressed wildtype LMP1 , or the TES2 null LMP1 mutant , were modified by K63-pUb chains . By contrast , the LMP1 TES1 domain 204AQAAA208 triple point mutant did not stimulate attachment of k63-pUb chains to TRAF1 complexes ( Fig 9A ) . This result is consistent with a model in which TRAF1 recruitment to LMP1 TES1 is important for subsequent K63-pUb attachment to TRAF1 complexes . We examined whether LMP1 complexes are also modified by K63-pUb chains . Indeed , FLAG-LMP1 complexes , immuno-purified from FLAG-LMP1 LCLs , were modified by K63-pUb chains ( Fig 9B ) . To investigate whether LMP1 1–231 signaling is important for K63-pUb chain attachment to LMP1 complexes , 293 cells were co-transfected with FLAG-tagged LMP1 WT , 1–231 , or DM vectors and untagged TRAF1 . 24 hours after transfection , FLAG-LMP1 immuno-purified complexes were analyzed by western blot for K63-pUb chain attachment ( Fig 9C ) . K63-pUb chains decorated WT and 1–231 LMP1 complexes , but not FLAG-LMP1 DM complexes , suggesting that TES1 signaling is important for K63-pUb chain attachment to LMP1 complexes . To identify target ( s ) of LMP1 TES1-induced K63-pUb chains , we analyzed the K63-pUb chain status of proteins known to associate with the LMP1 TES1 domain . 293 cells were co-transfected with untagged TRAF1 , HA-tagged WT LMP1 , and FLAG-tagged TRAF1 , TRAF2 , TRAF3 or GFP negative control , as indicated . To determine whether LMP1 might itself be modified by K63-linked pUb chains , 293 cells were also co-transfected with untagged TRAF1 and FLAG-LMP1 . To denature protein-protein complexes , 1% SDS was added to 293 cell lysates , and samples were boiled for 5 minutes . The SDS concentration was then reduced to 0 . 1% by addition of NP40 lysis buffer , and FLAG-tagged proteins were immune-purified . Interestingly , western blot analysis demonstrated K63-pUb chain modification of FLAG-TRAF2 ( lane 3 ) , whereas signals in other lanes were similar to the FLAG GFP negative control ( Fig 10 ) . To determine whether LMP1 signaling induced K63-pUb chain attachment on TRAF2 , we also analyzed 293 cells co-transfected with TRAF2 and untagged TRAF1 , but no LMP1 ( lane 6 ) . Interestingly , FLAG-TRAF2 complexes had only background levels of K63-pUb chains when not co-expressed with LMP1 , despite similar TRAF2 expression levels . Collectively , our results suggest that LMP1 and TRAF1 stimulate K63-pUb attachment to TRAF2 , likely in the context of a TRAF1:TRAF2 heterotrimer .
Abundant TRAF1 expression is a hallmark of multiple EBV-associated human malignancies , including Hodgkin lymphoma and post-transplant lymphoproliferative disorder [47 , 48 , 49] . TRAF1 is one of the most highly LMP1-induced genes [42 , 46 , 67] , and is up-regulated early in the course of EBV-mediated primary B-cell transformation , with close correlation to LMP1 expression [90] . TRAF1 promotes Hodgkin disease Reed-Sternberg cell survival [91] . However , the molecular mechanisms that underlie TRAF1 function downstream of LMP1 , or downstream of immune receptors more generally , have remained incompletely understood . In particular , how TRAF1 enhances LMP1 TES1 domain-mediated activation of the JNK [92] and NF-kB pathways [29] have remained uncharacterized . To gain insight into TRAF1 function , we took a proteomic approach , and found that LMP1 1–231 expression induced association between TRAF1 and LUBAC components . Indeed , TRAF1 complexes purified from GM12878 LCLs contained LUBAC components and were modified by M1-pUb chains . Likewise , we found that LMP1 complexes immuno-purified from LCLs were highly decorated by M1-pUb chains , and LMP1 1–231 expression in 293 TRAF1 cells stimulated LUBAC activity , as judged by the appearance of high molecular weight M1-pUb conjugates by western blot analysis in whole cell extracts . Since TRAF1 associates with the LMP1 TES1 PQQAT motif , likely through interactions with conserved TRAF domain residues or as a heterotrimer with TRAF2 [24] , and since most TRAF1 is associated with LMP1 in LCLs [29] , our results suggest that a complex containing LMP1 , TRAF1 and TRAF2 may be the target of M1-pUb chains . Indeed , TRAF2 depletion impaired the LMP1-induced association between TRAF1 and LUBAC , and reduced M1-pUb chain attachment to TRAF1 complexes . M1-linked-pUb chains play essential roles in NF-kB and JNK activation by TNFR1 , CD40 , IL1R1 , NOD2 , and TLR signalosomes [56 , 93 , 94 , 95 , 96 , 97 , 98] , though to our knowledge , have not previously been implicated in a pathway downstream of a viral oncoprotein . TRAF1:TRAF2 heterotrimers may also be a target of LMP1 TES1-stimulated K63-pUb chain attachment . LMP1 1–231 induced K63-pUb chain attachment to TRAF2 in 293 TRAF1 cells . K63-pUb chain attachment to TRAF2 may serve important roles in LUBAC recruitment and also in TAK1 activation ( Fig 11 ) . LUBAC has multiple zinc finger pUb-binding domains , and K63-pUb chains play an important role in LUBAC recruitment to TNFR1 [56 , 93 , 94 , 95] . LMP1-induced K63-pUb chain attachment to TRAF2 may play a similarly important role in LUBAC recruitment to LMP1 and TRAF1 complexes . Likewise , TNF-alpha induced K63-pUb chain attachment to TRAF2 is important for TAB/TAK1 complex recruitment and downstream MAP kinase and canonical NF-kB activation [99–100] . K63-pUb chain linkage to TRAF2 may play a similar role in activating TAK1 downstream of LMP1 TES1 . Colocalization of M1- and K63-linked pUb chains may serve to juxtapose the IKK and TAB/TAK1 complexes at the level of LMP1 complexes , and thereby enhance MAP kinase and canonical NF-kB activation ( Fig 11 ) . TRAF1:TRAF2 heterotrimers associate with cIAP1 and cIAP2 more tightly than TRAF1 or TRAF2 homotrimers [24] . Since cIAP1/2 catalyzes pUb chains that are essential for LUBAC recruitment to TNFR1 , we investigated whether cIAP1/2 were likewise important downstream of LMP1 . Surprisingly , cIAP1/2 depletion by SMAC mimetic did not impair LMP1 1-231-induced M1-pUb chain attachment to TRAF1 complexes . While residual cIAP activity could have been sufficient to enable LUBAC recruitment , TNFR1 and LMP1 signaling may differ in this regard . An area of future LMP1 investigation will be to identify the Ub ligase that attaches K63-pUb chains to TRAF2 . We were also intrigued to find that LMP1 may be a target of M1-linked pUb chain attachment in GM12878 LCLs and 293 cells . Western blot analysis of M1-pUb chains , immuno-purified under denaturing conditions from GM12878 or conditional 293 TRAF1 cells , demonstrated high-molecular weight bands reactive with an anti-LMP1 antibody . Since untagged LMP1 1–231 does not have a lysine residue , M1-pUb may therefore be attached to the LMP1 N-terminus or to an LMP1 non-lysine residue . While Kaposi sarcoma associated herpesvirus MIR1 can attach ubiquitin to a MHC class I cysteine residue , all of our western blot samples were treated with reducing agent , which disrupts cysteine-ubiquitin linkages [86] . We note that N-terminally FLAG-tagged LMP1 complexes purified from LCLs are modified by M1-pUb chains . Possible explanations include attachment of M1-pUb chains to FLAG tag lysine residues , to the FLAG N-terminus , to LMP1 K330 , or to an LMP1-associated protein , such as TRAF1 . Indeed , recombinant TRAF1 was found to be a LUBAC target in vitro . We identified LUBAC as a potential therapeutic target in EBV-transformed B-lymphoblastoid cells . HOIP depletion by independent shRNAs or by CRISPR/Cas9 mutagenesis impaired GM12878 LCL growth and induced apoptosis , largely through activation of the intrinsic apoptosis pathway . Interestingly , LUBAC has recently been implicated in the pathogenesis of the ABC subtype of DLBCL , and a stapled alpha-helical peptide inhibitor that blocks HOIP and HOIL-1L association is toxic to DLBCL [87 , 88] . A goal of future studies will be to identify whether anti-LUBAC stapled peptides inhibit the growth of EBV-transformed B-cells . Similarly , LMP1 highly upregulates TRAF1 expression in transfected keratinocytes , and TRAF1 expression was detectable in 17 of 42 EBV+ undifferentiated nasopharyngeal carcinomas ( NPC ) [50] . Further studies are required to determine whether TRAF1 associates with LUBAC in the context of NPC , whether TRAF1 or LMP1 co-localize with M1- or K63-chains in NPC tumor samples , and whether HOIP depletion is toxic to EBV+ NPC cells in culture .
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The linear ubiquitin assembly complex ( LUBAC ) plays crucial roles in immune receptor-mediated NF-kB and MAP kinase pathway activation . Comparatively little is known about the extent to which microbial pathogens use LUBAC to activate downstream pathways . We demonstrate that TRAF1 enhances EBV oncoprotein LMP1 TES1/CTAR1 domain mediated MAP kinase and canonical NF-kB activation . LMP1 TES1 signaling induces association between TRAF1 and LUBAC , and triggers M1-polyubiquitin chain attachment to TRAF1 complexes . TRAF1 and LMP1 complexes are decorated by M1-polyubiquitin chains in LCL extracts . TRAF2 plays a key role in LMP1-induced LUBAC recruitment and M1-chain attachment to TRAF1 complexes . TRAF1 and LMP1 complexes are modified by lysine 63-linked polyubiquitin chains in LCL extracts , and TRAF2 is a target of LMP1-induced K63-ubiquitin chain attachment . Thus , the TRAF1:TRAF2 heterotrimer may coordinate ubiquitin signaling downstream of TES1 . Depletion of TRAF1 or the LUBAC subunit HOIP impairs LCL growth and survival . Thus , although TRAF1 is the only TRAF without a RING finger ubiquitin ligase domain , TRAF1 nonetheless has important roles in ubiqutin-mediated signal transduction downstream of LMP1 . Our work suggests that LUBAC is important for EBV-driven B-cell proliferation , and suggests that LUBAC may be a novel therapeutic target in EBV-associated lymphoproliferative disorders .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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TRAF1 Coordinates Polyubiquitin Signaling to Enhance Epstein-Barr Virus LMP1-Mediated Growth and Survival Pathway Activation
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The proper regulation of apoptosis requires precise spatial and temporal control of gene expression . While the transcriptional and translational activation of pro-apoptotic genes is known to be crucial to triggering apoptosis , how different mechanisms cooperate to drive apoptosis is largely unexplored . Here we report that pro-apoptotic transcriptional and translational regulators act in distinct pathways to promote programmed cell death . We show that the evolutionarily conserved C . elegans translational regulators GCN-1 and ABCF-3 contribute to promoting the deaths of most somatic cells during development . GCN-1 and ABCF-3 are not obviously involved in the physiological germ-cell deaths that occur during oocyte maturation . By striking contrast , these proteins play an essential role in the deaths of germ cells in response to ionizing irradiation . GCN-1 and ABCF-3 are similarly co-expressed in many somatic and germ cells and physically interact in vivo , suggesting that GCN-1 and ABCF-3 function as members of a protein complex . GCN-1 and ABCF-3 are required for the basal level of phosphorylation of eukaryotic initiation factor 2α ( eIF2α ) , an evolutionarily conserved regulator of mRNA translation . The S . cerevisiae homologs of GCN-1 and ABCF-3 , which are known to control eIF2α phosphorylation , can substitute for the worm proteins in promoting somatic cell deaths in C . elegans . We conclude that GCN-1 and ABCF-3 likely control translational initiation in C . elegans . GCN-1 and ABCF-3 act independently of the anti-apoptotic BCL-2 homolog CED-9 and of transcriptional regulators that upregulate the pro-apoptotic BH3-only gene egl-1 . Our results suggest that GCN-1 and ABCF-3 function in a pathway distinct from the canonical CED-9-regulated cell-death execution pathway . We propose that the translational regulators GCN-1 and ABCF-3 maternally contribute to general apoptosis in C . elegans via a novel pathway and that the function of GCN-1 and ABCF-3 in apoptosis might be evolutionarily conserved .
Apoptosis is a naturally occurring process that eliminates unwanted cells during development and maintains tissue homeostasis [1] , [2] . For example , apoptosis removes most larval tissues of insects during metamorphosis , sculpts the future inner ear in chicks , eliminates the interdigital web in mammals and shapes the endocardial cushion into valves and septa to generate the four-chamber architecture of the mammalian heart [1] , [2] . Apoptosis also culls nearly 80% of oocytes prior to birth in humans and eliminates cells that receive insufficient cell-survival signals to maintain homeostasis [1] . The improper regulation of an apoptotic program can result in either too much or too little cell death , leading to developmental abnormalities and a wide variety of human disorders , such as cancer , neurodegenerative diseases , autoimmune diseases and developmental disorders [3] , [4] . It is important to identify mechanisms that regulate apoptosis to understand both animal development and human disorders caused by the dysregulation of apoptosis . The precise spatial and temporal expression of regulators of apoptosis is known to be crucial for initiating the apoptotic cell-killing program during development and in response to environmental stresses , including ionizing radiation , temperature change , nutrient limitation , oxidative stress and viral infection [1] , [2] . Many examples of the transcriptional control of apoptosis have been described . For example , in mammals the genes that encode the pro-apoptotic BCL-2 family member BAX , the BH3-only proteins NOXA , PUMA and BID , the apoptotic protease-activating factor-1 APAF-1 and the death receptor 5 DR5 protein are transcriptionally upregulated by the tumor suppressor p53 transcription factor in response to DNA damage or to the induced expression of p53 [5]–[11] , resulting in an induction of apoptosis . The Drosophila apoptotic activator gene reaper is upregulated by multiple transcriptional regulators , including Hox transcription factors , nuclear hormone receptors , AP-1 , Polycomb , p53 , and histone-modifying enzymes , to promote the morphogenesis of segment boundaries , metamorphosis , and DNA damage responses [1] . In C . elegans , the transcription of the pro-apoptotic BH3-only gene egl-1 is directly regulated in a cell-specific manner by transcription factors that include the Hox family proteins MAB-5 , CEH-20 , LIN-39 and CEH-34 , the E2F protein EFL-3 , the Snail family zinc finger protein CES-1 , the Gli family transcription factor TRA-1 , and the basic helix-loop-helix proteins HLH-2 and HLH-3 [12]–[15] . The caspase gene ced-3 is also upregulated by the Hox transcription factor PAL-1 in the tail spike cell before its death [16] . Recently , we showed that the Sp1 transcription factor SPTF-3 directly drives the transcription of both the pro-apoptotic BH3-only gene egl-1 , which mediates a caspase-dependent apoptotic pathway , and the AMPK-related gene pig-1 , which mediates a caspase-independent apoptotic pathway [17] . The transcriptional regulation of apoptotic genes clearly plays a crucial role in determining whether specific cells live or die during development . Translational control is also important for the apoptotic process . In mammals , expression of the pro-apoptotic protein APAF-1 and the anti-apoptotic protein X-chromosome-linked inhibitor of apoptosis XIAP are regulated at the translational level by internal ribosome-entry sites ( IRES ) [18] . Exposure of cultured mammalian cells to etoposide or UV light induces APAF-1 expression via IRES-mediated translation , resulting in the activation of the caspase-dependent apoptotic program [19] . The protein level of XIAP is increased via IRES-mediated translation under stress conditions , such as serum starvation [20] . However , the specific translational regulators involved in IRES-mediated translation of APAF-1 and XIAP are unknown . In C . elegans , the RNA-binding protein GLD-1 , which is highly expressed in the transition zone and early pachytene regions of the hermaphrodite gonad , inhibits translation of the mRNA of the p53 homolog cep-1 by directly binding to the cep-1 3′ UTR , thereby preventing cep-1-dependent apoptosis in response to DNA damage [21] . Translational initiation factors have also been reported to be involved in the control of apoptosis in C . elegans . For example , RNAi knockdown of the C . elegans eukaryotic initiation factor-4G IFG-1 induces CED-4 expression in the gonad and increases the frequency of germ-cell death [22] , [23] . The eukaryotic initiation factor-3 subunit-k eIF-3 . K is partially required for the deaths of somatic cells and acts through the caspase CED-3 to promote those cell deaths [24] . Although many studies have shown that both transcriptional and translational regulation of apoptotic genes is crucial for controlling apoptotic programs , how transcriptional and translational mechanisms are coordinated to promote apoptosis remains elusive . Here we show that the maternally-contributed translational regulators GCN-1 and ABCF-3 act together to promote the cell deaths of possibly all somatic cells and of germ cells in response to ionizing radiation in a pathway distinct from the BCL-2 homolog CED-9-regulated canonical cell-death execution pathway of C . elegans . GCN-1 and ABCF-3 are required to maintain the basal level of phosphorylation of eukaryotic initiation factor 2 ( eIF2α ) . The functions of GCN-1 and ABCF-3 in the promotion of programmed cell death are evolutionarily conserved between C . elegans and Saccharomyces cerevisiae . We show that GCN-1 and ABCF-3 cooperate with the transcriptional regulators CEH-34 , EYA-1 and SPTF-3 and the protein kinase PIG-1 to promote the death of a specific somatic cell , the sister cell of the pharyngeal M4 motor neuron . We propose that the evolutionarily-conserved translational regulators GCN-1 and ABCF-3 contribute to apoptosis in general .
The C . elegans pharyngeal M4 motor neuron is generated during embryonic development and survives to regulate pharyngeal muscle contraction in feeding behavior , whereas the M4 sister cell dies by programmed cell death soon after its generation ( Figure 1A ) [25] , [26] . We created a Pceh-28::gfp reporter transgene that expresses GFP specifically in the M4 neuron of wild-type animals and in both the M4 neuron and the surviving M4 sister of ced-3 caspase mutants defective in programmed cell death ( Figure 1B–1C ) . This reporter allowed us to easily identify mutants with a defect in M4 sister cell death . [15] . Using this reporter , we performed a genetic screen for mutations that cause a defect in M4 sister cell death . Among our isolates were two non-allelic mutations , n4827 and n4927 , that caused M4 sister survival in 12% of n4827 mutants and 13% of n4927 mutants ( Figure 1D–1F ) . We mapped n4827 to a 175 kb interval of chromosome III containing 18 predicted genes ( Figure S1A ) . We used whole-genome sequencing to identify four strain-specific unique homozygous mutations within this interval in n4827 animals ( Figure S1A ) [27] . Of the four mutations , only one was exonic . This mutation was located in the third exon of gcn-1 , which encodes a homolog of the S . cerevisiae Gcn1p protein . The n4827 mutation is predicted to change the tryptophan 164 codon to an opal stop codon , generating a small truncated protein ( Figure 1G ) . A deletion mutation of gcn-1 , nc40Δ , phenocopied the n4827 mutation [28]: 11% of gcn-1 ( nc40Δ ) mutants and 12% of n4827 mutants had a surviving M4 sister , respectively ( Figure 1F ) . The cell-death defect of n4827 mutants was partially rescued by a transgene that express gcn-1 cDNA under the control of the gcn-1 promoter ( Figure 1F ) . These results indicate that n4827 is likely a null allele of gcn-1 and that loss of gcn-1 function causes a defect in M4 sister cell death . We mapped n4927 to a 5 . 3 Mb interval of chromosome III ( Figure S1B ) . This interval contains the gene abcf-3 , which encodes a homolog of the S . cerevisiae Gcn20p protein [29] . Gcn20p physically interacts with Gcn1p , the S . cerevisiae homolog of GCN-1 [30] . We determined the sequence of abcf-3 in n4927 animals and identified a mutation that changes the arginine 206 codon to an opal stop codon ( Figure 1G ) . A deletion mutation of abcf-3 , ok2237Δ , that removes most of the abcf-3 coding region phenocopied the n4927 mutation: 13% of abcf-3 ( ok2237Δ ) mutants and 13% of n4927 mutants had a surviving M4 sister ( Figure 1F ) . Furthermore , the cell-death defect of n4927 mutants was completely rescued by a transgene carrying only the abcf-3 genomic locus . We concluded that n4927 is likely a null allele of abcf-3 and that loss of abcf-3 function causes a defect in M4 sister cell death . abcf-3 encodes an AAA ATPase protein with two AAA domains ( Figure 1G ) . In many proteins AAA domains have ATPase activity . To determine whether ATPase activity is important for ABCF-3 to promote M4 sister cell death , we generated abcf-3 transgenes carrying mutations that presumably inactivate the ATPase activity of each AAA domain by altering the lysine residues known to be catalytically essential for other AAA ATPases [31] . A wild-type abcf-3 transgene as well as mutant abcf-3 transgenes that changed lysine 217 of the first AAA domain to methionine [abcf-3 ( K217M ) ] , lysine 536 of the second AAA domain to methionine [abcf-3 ( K536M ) ] or both lysine residues [abcf-3 ( K217M , K536M ) ] completely rescued the defect in M4 sister cell death of abcf-3 ( n4927 ) mutants ( Figure 1F ) . These results support the idea that the ATPase activity of ABCF-3 is dispensable for M4 sister cell death . This result is consistent with studies of S . cerevisiae Gcn20p , the homolog of C . elegans ABCF-3 . Gcn20p that lacks the ATPase activities of both AAA domains because of mutations in conserved glycine residues ( Gly371 and Gly654 ) or because of the deletion of two AAA domains still retains Gcn20p function comparable to that of wild-type Gcn20p [32] . GCN-1 and ABCF-3 are evolutionarily conserved among S . cerevisiae , C . elegans and humans ( Figure 1H , Figure S2 and S3 ) . Expression of S . cerevisiae GCN1 , the homolog of C . elegans gcn-1 , and GCN20 , the homolog of C . elegans abcf-3 , under the control of the abcf-3 promoter rescued the defect in M4 sister cell death of C . elegans gcn-1 and abcf-3 mutants , respectively , indicating that S . cerevisiae GCN1 and GCN20 are functional homologs of C . elegans gcn-1 and abcf-3 , respectively ( Figure 1F ) . S . cerevisiae Gcn1p has a domain ( amino acids 1350–2152 ) similar to that of translation elongation factor 3 ( EF3 ) . The EF3-like domain is highly conserved among species ( Figure 1H and Figure S2 ) and is necessary and sufficient for binding to Gcn20p [32] . We therefore tested whether C . elegans GCN-1 can physically interact with ABCF-3 using the yeast two-hybrid assay ( Figure 2A ) . Full-length GCN-1 ( 1–2634 ) interacted with full-length ABCF-3 ( 1–712 ) . To identify the protein domains important for GCN-1 to bind to ABCF-3 , we generated a series of deletion constructs of GCN-1 and assayed each for ABCF-3-binding activity using the yeast two-hybrid assay . GCN-1 fragments not containing entire the EF3 domain ( 1–1760 , 1–880 , 880–1760 and 1760–2634 ) or containing only the EF3 domain ( 1350–2150 ) failed to bind ABCF-3 , whereas GCN-1 fragments containing the EF-3 domain and surrounding regions ( 880–2634 ) bound ABCF-3 . These results suggest that GCN-1 physically interacts with ABCF-3 but that unlike in yeast the EF3-like domain is not sufficient for GCN-1 to bind to ABCF-3 . We also defined the domains of ABCF-3 important for ABCF-3 to bind to GCN-1 ( Figure 2A ) . ABCF-3 fragments lacking the N-terminal region ( 202–712 , 512–712 and 202–512 ) failed to bind GCN-1 , whereas ABCF-3 fragments containing the N-terminal region ( 1–712 , 1–512 and 1–202 ) bound GCN-1 , suggesting that the N-terminal portion of ABCF-3 ( which does not include the first AAA domain ) is necessary and sufficient for binding to GCN-1 , just as the N-terminal region of S . cerevisiae Gcn20p is necessary and sufficient for binding to Gcn1p , the S . cerevisiae homolog of GCN-1 . To determine whether GCN-1 and ABCF-3 interact in vivo , we generated antibodies against GCN-1 and ABCF-3 and performed co-immunoprecipitation experiments . We first tested whether these antibodies specifically recognize GCN-1 or ABCF-3 protein using western blot analysis . The antibodies against GCN-1 or ABCF-3 recognized proteins of the sizes predicted for the GCN-1 or ABCF-3 proteins in wild-type animals but not in gcn-1 ( n4827 ) or abcf-3 ( n4927 ) animals , respectively , confirming the specificity of these antibodies ( Figure 2C ) . Then we tested whether GCN-1 could be co-immunoprecipitated with ABCF-3 . Whole-protein extracts from wild-type animals were subjected to immunoprecipitation using an anti-ABCF-3 antibody ( or normal IgG as a control ) , and then immunocomplexes were analyzed by western blotting using antibodies against ABCF-3 or GCN-1 . Both ABCF-3 and GCN-1 were recovered in an immunocomplex purified with the anti-ABCF-3 antibody , whereas neither ABCF-3 nor GCN-1 was recovered in an immunocomplex purified with normal IgG ( Figure 2B ) . We conclude that GCN-1 and ABCF-3 are present in the same protein complex in vivo . Since GCN-1 and ABCF-3 form a complex in vivo , we suspected that deletion of either protein might affect the stability of the other protein [29] , [33] . To test this hypothesis , we examined the levels of GCN-1 and ABCF-3 proteins by western blot analyses of whole-protein extracts prepared from wild-type , gcn-1 ( n4827 ) and abcf-3 ( n4927 ) animals using antibodies against ABCF-3 or GCN-1 . The steady-state level of ABCF-3 protein was decreased in gcn-1 ( n4827 ) animals by 3 . 6 fold compared to that of wild-type animals . Similarly , the steady-state level of GCN-1 protein was decreased in abcf-3 ( n4927 ) animals by 4 . 4 fold ( Figure 2C ) . These results suggest that a lack of ABCF-3 or GCN-1 protein affects the stability of the other protein and support our conclusion that GCN-1 and ABCF-3 are in a protein complex together in vivo . If GCN-1 and ABCF-3 physically interact in vivo to promote M4 sister cell death , GCN-1 and ABCF-3 should act together in the same pathway . Since gcn-1 ( n4827 ) and abcf-3 ( n4927 ) are likely null mutations , the gcn-1 ( n4827 ) mutation would not enhance the M4 sister cell-death defect of abcf-3 ( n4927 ) mutants if gcn-1 and abcf-3 function in the same process or pathway . Indeed , we observed no enhancement of the M4 sister cell-death defect of gcn-1 ( n4827 ) abcf-3 ( n4927 ) double mutants compared to that of either single mutant: there was 13% M4 sister survival in gcn-1 ( n4827 ) abcf-3 ( n4927 ) double mutants , 12% M4 sister survival in gcn-1 ( n4827 ) mutants and 13% M4 sister survival in abcf-3 ( n4927 ) mutants ( Figure 1F ) . We conclude that gcn-1 and abcf-3 function together in the same process of pathway to promote M4 sister cell death , consistent with our finding that GCN-1 and ABCF-3 physically interact in vivo . In S . cerevisiae , Gcn1p and Gcn20p are required for the efficient phosphorylation of eukaryotic initiation factor 2 ( eIF2α ) under both normal conditions and conditions of amino-acid starvation [29] , [30] . Gcn1p and Gcn20p form a protein complex that activates the serine-threonine protein kinase Gcn2p , which then phosphorylates an evolutionarily conserved serine residue of eIF2α . The amino acid sequences surrounding the eIF2α phosphorylation site are identical in S . cerevisiae , C . elegans and humans , suggesting a conserved regulatory mechanism of eIF2α [28] . We tested whether GCN-1 and ABCF-3 promote the phosphorylation of eIF2α in C . elegans using an antibody that specifically recognizes eIF2α that is phosphorylated at serine 49 ( P-eIF2α ) . From wild-type animals cultivated under normal physiological conditions , a single band of eIF2α was detected in western blotting analyses using either the anti-P-eIF2α antibody or an antibody that recognized total eIF2α ( Figure 2D ) . In gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutants , the phosphorylation levels of eIF2α in physiological conditions were 52% and 54% of the levels in wild-type animals , respectively ( Figure 2D and 2E ) . We conclude that gcn-1 and abcf-3 are required to maintain the steady-state level of the phosphorylation of eIF2α . The regulation of phosphorylation of eIF2α plays an essential role in the initiation of translation . We therefore directly tested whether gcn-1 and abcf-3 affect gene expression at the translational level . Since gcn-1 and abcf-3 are highly expressed in the gonads at the fourth larval stage , maternally contribute to the death of the M4 sister and affect most programmed cell deaths ( see below , Figure 3D and 3I , Table 1 and Table S5 ) , we isolated both wild-type animals and gcn-1 and abcf-3 mutants at the fourth larval stage and performed mRNA-seq and ribosome profiling ( Ribo-seq ) to generate quantitative genome-wide information concerning mRNA abundance and the locations of mRNAs occupied by ribosomes [34] . Parallel analyses of data from Ribo-seq and mRNA-seq studies allowed us to distinguish differences in mRNA abundance from differences in translational control and to generate a quantitative and comprehensive list of genes the expression of which is likely regulated by gcn-1 and abcf-3 at the translational level . Loss of gcn-1 or abcf-3 function affected the expression of a large number of genes at either the transcriptional or translational level or at both ( Figure S4A and S4B and Table S1 ) . Since GCN-1 and ABCF-3 very likely function in translational control , their effects on transcript levels are likely indirect . Changes in gene expression compared to wild-type animals were similar between gcn-1 and abcf-3 mutants , supporting our conclusion that gcn-1 and abcf-3 act together ( Figure S4C and S4D ) . The expression of 464 genes or 217 genes changed in both gcn-1 and abcf-3 mutants compared to wild-type animals at least two-fold ( p<0 . 1 ) in mRNA-seq or Ribo-seq analyses , respectively ( Figure S4E and S4F ) . Of the 217 genes altered in translational expression , 98 genes showed no alterations in mRNA levels using our standards of a two-fold change and p<0 . 1 ( Table S2 and Table S3 ) . These genes are candidates for being directly regulated by both gcn-1 and abcf-3 translationally . These results suggest that gcn-1 and abcf-3 function together in the translational control of many genes . In mammals , eIF2α phosphorylation is mediated by at least four different protein kinases: PKR-like endoplasmic reticulum kinase ( PERK ) , general control non-derepresessible-2 ( GCN2 ) , double-stranded RNA-activated protein kinase ( PKR ) and heme-regulated inhibitor kinase ( HRI ) ; each of these kinases is activated by a distinct stress signal [18] . These kinases share homology in their kinase catalytic domains , but their effector domains are distinct and are subject to different regulatory mechanisms . Homologs of genes encoding two of these protein kinases exist in the C . elegans genome: the PERK homolog PEK-1 and the GCN2 homolog GCN-2 . Y38E10A . 8 has a kinase domain similar to that of mammalian eIF2α kinases but does not have an obvious homolog . We tested whether these three protein kinases are required for the programmed cell death of the M4 sister . Neither single mutants of each kinase gene nor the triple mutant was defective in M4 sister cell death ( Table S4 ) , suggesting that one or more unidentified protein kinase ( s ) regulated by GCN-1 and ABCF-3 are responsible for phosphorylating eIF2α in the regulation of M4 sister cell death . Alternatively , it is possible GCN-1 and ABCF-3 promote M4 sister cell death through one or more targets other than eIF2α . To determine the expression patterns of gcn-1 and abcf-3 , we generated transgenes expressing a reporter GFP under the control of the endogenous gcn-1 or abcf-3 promoter . Both gcn-1 and abcf-3 were expressed in most cells during all stages of development . We observed gcn-1 and abcf-3 expression in head neurons , hypodermal cells , intestinal cells , body wall muscles , and pharyngeal neurons , including the M4 neuron ( Figure 3A–3C and 3F–3H ) . We also used the technique of fluorescence in situ hybridization ( FISH ) with a level of sensitivity sufficient to detect single mRNA molecules [35] to observe endogenous gcn-1 and abcf-3 transcripts . Consistent with the expression of the GFP reporter transgenes , gcn-1 and abcf-3 mRNAs were observed in most somatic cells . In addition , gcn-1 and abcf-3 mRNAs were abundant in the germ cells in the hermaphrodite gonad ( Figure 3D , 3E , 3I and 3J ) . The similar expression patterns of gcn-1 and abcf-3 are consistent with our observations that GCN-1 and ABCF-3 physically interact and act together to promote the death of the M4 sister . Since gcn-1 and abcf-3 are ubiquitously expressed and required to broadly maintain the basal level of phosphorylation of eIF2α , we tested whether gcn-1 and abcf-3 might be involved in other biological processes . We did not observe abnormalities in the morphologies of the hermaphrodite vulva , the male tail or the neurite processes of the M4 , I2 and PVQ neurons . However , the growth rate of gcn-1 and abcf-3 mutants from embryogenesis to the fourth larval stage was around 24 hours longer than that of wild-type animals , and the mitotic pachytene region of the hermaphrodite gonad was expanded over the loop regions of the gonads . ( data not shown ) . These observations suggest that gcn-1 and abcf-3 affect biological processes in addition to programmed cell death . Given the ubiquitous expression patterns of gcn-1 and abcf-3 , we tested whether gcn-1 and abcf-3 promote programmed cell deaths in addition to that of the M4 sister . We examined gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutants for defects in the deaths of the NSM sisters , the PVQ sisters , the g1A sisters , the RIM and RIC sisters and multiple cells in the anterior pharynx . gcn-1 ( n4827 ) and abcf-3 ( n4927 ) single mutants did not exhibit defects in the deaths of these cells ( Table 1 ) . However , when either the gcn-1 ( n4827 ) or the abcf-3 ( n4927 ) mutation was combined with the partial loss-of-function ced-3 ( n2427 ) mutation , which sensitizes strains to weak defects in cell death [36] , we observed significant cell-death defects for all cell types tested ( Table 1 ) . For example , the gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutations enhanced the ced-3 ( n2427 ) defect from 16% to 38% and 34% , respectively , for the NSM sister and from 13% to 36% and 45% , respectively , for the g1A sister . We conclude that gcn-1 and abcf-3 promote programmed cell death generally rather than specifically affecting the M4 sister cell death . We next tested whether gcn-1 and abcf-3 are involved in the deaths of germ cells in the gonad of the adult hermaphrodite . More than half of germ cells stochastically undergo programmed cell death under normal conditions during oocyte differentiation [37] . We scored the number of apoptotic germ cells using the vital dye acridine orange ( AO ) , which stains nucleic acids within apoptotic cells in living animals [38] . gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutants had 9 . 1 and 9 . 5 apoptotic germ cells per gonadal arm on average , respectively , similar to wild-type animals , which had 8 . 6 apoptotic germ cells per gonadal arm ( Figure 4I ) . We also scored the number of apoptotic germ cells by direct observation of the gonads of engulfment-defective ced-1 ( e1735 ) mutants , in which cell corpses accumulate because of a defect in cell-corpse engulfment , facilitating a sensitive assay for the deaths of germ cells [37] . ced-1 ( e1735 ) mutants had an average of 14 . 4 cell corpses per gonadal arm ( Figure S5 ) . ced-1 ( e1735 ) double mutants with gcn-1 ( n4827 ) or abcf-3 ( n4927 ) had nearly identical numbers of cell corpses per gonadal arm , 13 . 9 and 14 . 0 , respectively ( Figure S5 ) . These results indicate that gcn-1 and abcf-3 are dispensable for germ-cell death under physiological conditions . Since many germ cells undergo apoptosis in response to genotoxic stresses such as ionizing radiation [39] , we tested whether gcn-1 and abcf-3 mediate ionizing radiation damage-induced germ cell death . As assayed with AO , wild-type animals normally contained an average of 8 . 6 apoptotic germ cells per gonadal arm , while wild-type animals exposed to ionizing radiation contained on average 27 . 1 apoptotic germ cells ( Figure 4A , 4E and 4I ) . This germ-cell death was completely blocked by a mutation in the caspase gene ced-3 in wild-type , gcn-1 ( n4837 ) and abcf-3 ( n4927 ) animals ( Figure 4B , 4F and 4I ) . Strikingly , ionizing radiation failed to increase the number of apoptotic germ cells in gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutants ( 10 . 9 and 10 . 4 apoptotic germ cells per gonadal arm in gcn-1 and abcf-3 mutants , respectively , 24 hours after gamma ray irradiation ) ( Figure 4C , 4D , 4G , 4H and 4I ) . These results indicate that gcn-1 and abcf-3 are required for ionizing radiation-induced germ cell death but not for the stochastic germ cell death that occurs in physiological conditions . The gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutations partially blocked both the programmed cell deaths of somatic cells ( Table 1 ) and the deaths of germ cells in response to ionizing radiation ( Figure 4 ) . Both somatic and ionizing radiation-induced germ cell deaths involve the canonical cell-death execution pathway consisting of the BH3-only gene egl-1 , the BCL-2 homolog ced-9 , the pro-apoptotic APAF-1 homolog ced-4 , and the caspase gene ced-3 [40] . Interestingly , animals doubly heterozygous for gcn-1 and ced-3 , ced-4 or egl-1 had a defect in M4 sister cell death ( gcn-1/+; ced-3/+ 18% , gcn-1/+; ced-4/+ 12% or gcn-1/+; egl-1/+ 17% , respectively ) significantly higher than that of singly heterozygous animals ( gcn-1/+ 4% , ced-3/+ 0% , ced-4/+ 0% or egl-1/+ 1% , respectively ) ( Table S5 ) . These results indicate that the simultaneous reduction by half of the dosage of gcn-1 and of genes in the canonical cell-death execution pathway causes a significant defect in M4 sister cell death . We observed a similar genetic interaction in animals heterozygous for abcf-3 and ced-3 , ced-4 or egl-1 ( Table S5 ) . We observed that maternal gcn-1 and abcf-3 contribute to zygotic programmed cell death . While gcn-1 ( − ) animals generated by gcn-1 ( − ) hermaphrodites and gcn-1 ( − ) males exhibited a defect in M4 sister cell death ( 12% of M4 sister survival ) , gcn-1 ( − ) animals produced from gcn-1/+ hermaphrodites and gcn-1 ( − ) males did not ( 0% of M4 sister survival ) ( Table S5 ) , indicating that maternal gcn-1 is sufficient to promote programmed cell death . gcn-1 ( − ) animals generated by gcn-1 ( − ) hermaphrodites and gcn-1/+ males exhibited a defect in M4 sister cell death ( 13% of M4 sister survival ) . gcn-1/+ animals generated by gcn-1 ( − ) hermaphrodites and gcn-1 ( + ) males exhibited a very weak defect in M4 sister cell death ( 4% of M4 sister survival ) compared to 12% of M4 sister survival in gcn-1 ( − ) self-progeny of gcn-1 ( − ) hermaphrodites . By contrast , gcn-1/+ animals produced from gcn-1 ( + ) hermaphrodites and gcn-1 ( − ) males exhibited no defect in M4 sister cell death ( 0% of M4 sister survival ) ( Table S5 ) . These results indicate that maternal gcn-1 is partially required for the M4 sister to undergo programmed cell death . We observed a similar maternal requirement and sufficiency for abcf-3 ( Table S5 ) . We conclude that maternal gcn-1 and abcf-3 are sufficient and partially required for the M4 sister to undergo programmed cell death . To examine interactions between gcn-1 and abcf-3 and the canonical cell-death execution pathway , we performed epistasis analyses between gcn-1 or abcf-3 and ced-9 , which functions downstream of egl-1 and upstream of ced-4 and ced-3 in the cell-death execution pathway [40] . Because the ced-9 ( n2812 ) null mutation causes ectopic cell deaths and organismic inviability , we used the ced-3 partial loss-of-function mutation n2446 to suppress ced-9 ( n2812 ) lethality [41] . We observed that 50% of ced-9 ( n2812 ) animals had a surviving M4 sister in the ced-3 ( n2446 ) mutant background . This increase over the 5% frequency of M4 sister survival in ced-3 ( n2446 ) mutants is consistent with the proposal that ced-9 has a cell-killing activity [42] . We observed that gcn-1 ced-9 and abcf-3 ced-9 double mutants were more highly penetrant for M4 sister survival ( 90% and 89% , respectively ) than either single mutant in the ced-3 ( n2446 ) mutant background: gcn-1 ( 45% ) , abcf-3 ( 40% ) and ced-9 ( 50% ) , respectively ( Table 2 ) . These results indicate that ced-9 is not required for gcn-1 and abcf-3 to promote programmed cell death . Thus , gcn-1 and abcf-3 function downstream of or in parallel to ced-9 in the regulation of programmed cell death . We next tested whether the activity of gcn-1 and abcf-3 can act cell-autonomously to promote programmed cell death . Previous studies showed that expression of a ced-3 , ced-4 or egl-1 cDNA under the control of the mec-7 promoter can act cell-autonomously to cause the deaths of a set of touch neurons , including the PLML and PLMR cells . We expressed gcn-1 and abcf-3 cDNAs in the PLM neurons under the control of the mec-7 promoter . We observed that 100% of the PLM neurons survived in wild-type animals , whereas only 50% or 89% of the PLM neurons survived in animals expressing gcn-1 or abcf-3 , respectively , under the control of the mec-7 promoter ( Figure 5A ) . Expression of both gcn-1 and abcf-3 also reduced a survival of the PLM neurons: 61% of the PLM neurons survived . These results indicate that expression of gcn-1 and abcf-3 are sufficient to induce cell death and suggest that gcn-1 and abcf-3 acts cell-autonomously to promote programmed cell death . Our genetic screen for mutants defective in M4 sister cell death identified other genes in addition to gcn-1 and abcf-3: ceh-34 , eya-1 , sptf-3 and pig-1 [15] , [17] . We previously showed that the Six family homeodomain protein CEH-34 and the Eyes absent homolog EYA-1 directly drive the transcription of the BH3-only gene egl-1 in the M4 sister to promote M4 sister cell-type specific death [15] and that the SP1 family transcription factor SPTF-3 directly drives the transcription of both egl-1 and the AMPK-related protein kinase gene pig-1 , which also promotes M4 sister cell death [17] . We determined how gcn-1 and abcf-3 interact with these genes by examining double mutants . The partial loss-of-function alleles ceh-34 ( n4796 ) and sptf-3 ( n4850 ) and the null allele pig-1 ( gm344Δ ) enhanced the M4 sister-cell death defect of gcn-1 ( n4827 ) and abcf-3 ( n4927 ) null mutants ( Table 3 ) . These results indicate that ceh-34 , sptf-3 and pig-1 function in pathways distinct from that of gcn-1 and abcf-3 to promote M4 sister cell death .
We demonstrated that the translational regulators GCN-1 and ABCF-3 are pro-apoptotic factors that maternally contribute to the programmed cell death of the M4 sister in C . elegans . GCN-1 and ABCF-3 promote the deaths of all somatic cells tested . Essentially all somatic cell deaths are mediated by an evolutionarily conserved cell-death execution pathway consisting of the BH3-only gene egl-1 , the BCL-2 homolog ced-9 , the APAF-1 homolog ced-4 and the caspase gene ced-3 [40] . How do gcn-1 and abcf-3 interact with this pathway to regulate apoptosis ? We propose that gcn-1 and abcf-3 likely act in a novel pathway distinct from the canonical cell-death execution pathway . First , gcn-1 and abcf-3 promote apoptosis in the absence of ced-9 activity , indicating that gcn-1 and abcf-3 function independently of ced-9 in the regulation of apoptosis and hence do not regulate either ced-9 or egl-1 . Second since ced-3 and ced-4 function downstream of ced-9 in the cell-death execution pathway , gcn-1 and abcf-3 could act through these genes to promote cell death . However , our mRNA-seq and Ribo-seq results indicate that gcn-1 and abcf-3 do not have major effects on mRNA abundance or the ribosome footprint density of ced-3 and ced-4 ( Figure S6 ) . Our preferred model is that GCN-1 and ABCF-3 function in a pathway that acts in parallel to the canonical cell-death execution pathway , although we cannot preclude the possibility that GCN-1 and ABCF-3 translationally regulate unidentified factors that act through ced-3 or ced-4 without changing the transcriptional and translational levels of the products of these genes . Also , since we used whole animals for our mRNA-seq and Ribo-seq analyses , we would not have detected alterations in CED-3 or CED-4 levels that were specific to a small subset of cells , including the M4 sister . Our genetic analyses revealed that gcn-1 and abcf-3 maternally contribute to the death of the M4 sister , which undergoes programmed cell death during embryogenesis . Maternally-contributed factors might act to ensure the rapid deaths of cells during embryogenesis; perhaps zygotic expression of apoptotic genes would be too slow . Also , the maternal effects of gcn-1 and abcf-3 might explain why we discovered new general cell-death genes , despite the fact that many genetic screens have been performed in search of C . elegans mutants defective in somatic cell deaths . Most such genetic screens have examined F2 animals after mutagenesis , and would have missed maternally-contributed genes that affect general cell death . Perhaps , additional maternal-effect genes with functions in apoptosis exist in C . elegans . Such genes might be efficiently identified by screening in the third generation after mutagenesis . gcn-1 ( n4827 ) and abcf-3 ( n4927 ) single mutations appeared to cause a defect in only M4 sister cell death , and these mutations both affected other cell deaths to differing extents in strains sensitized to weak defects in cell death . For example , the death of the M4 sister was most sensitive and the deaths of the PVQ sisters were least sensitive to the gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutations among the cells we tested ( Table 1 ) . We speculate that sensitivity to perturbation of cell-death genes is different among different cell types . This hypothesis is supported by the observations that penetrance of cell-death defects varies among different cell types in partial loss-of-function ced-3 ( n2427 ) mutants and that the extent of the cell-death defect of ced-3 ( n2427 ) mutants is well correlated with that of gcn-1 ( n4827 ) and abcf-3 ( n4927 ) mutants . Our genetic and biochemical data strongly suggest that GCN-1 and ABCF-3 physically interact in a complex in vivo to promote apoptosis . First , GCN-1 and ABCF-3 interacted in the yeast two-hybrid system . Second , GCN-1 co-immunoprecipitated with ABCF-3 from a total protein extract from C . elegans . Third , the absence of either the GCN-1 or ABCF-3 protein decreased the steady-state level of the other protein ( ABCF-3 or GCN-1 , respectively ) , indicating that an interaction between GCN-1 and ABCF-3 is likely important for the stability of both proteins . Fourth , gcn-1 ( n4827 ) abcf-3 ( n4927 ) double mutants were not enhanced in the defect in apoptosis compared to each single mutant . Although GCN-1 and ABCF-3 promote the deaths of most somatic cells , in gcn-1 or abcf-3 mutants only 12% or 13% of animals are defective in M4 sister cell death , respectively , and the cell-death defect of most other cells was observed only in a partial loss-of-function ced-3 mutant background , which is sensitized to weak defects in cell death . Furthermore , loss-of-function of gcn-1 and abcf-3 did not affect the deaths of germ cells under physiological conditions . By striking contrast , we found that GCN-1 and ABCF-3 play an essential role in germ-cell deaths induced by ionizing radiation . These results suggest that translational control by GCN-1 and ABCF-3 plays a more important role in germ-cell deaths induced by ionizing radiation than in somatic cell deaths . The hypothesis that translational control is particularly important for cell deaths induced by ionizing radiation is supported by a recent report that a mutation in RNA polymerase I ( rpoa-2 ) , which synthesizes ribosomal RNAs , causes a defect in germ-cell deaths induced by ionizing radiation [43] . Ionizing radiation causes DNA double-strand breaks , which lead to the progressive accumulation of mutations and chromosomal aberrations as damaged cells undergo division , resulting in apoptosis and the demise of genetically damaged cells . In C . elegans , ionizing radiation causes massive deaths of the germ cells during the late pachytene stage of oocyte development in adult gonads , resulting in the elimination of the damaged oocytes [39] . Germ-cell deaths induced by ionizing radiation specifically involve activation of the p53 homolog CEP-1 by the DNA damage response pathway and subsequent CEP-1- dependent transcriptional induction of the BH3-only gene egl-1 , which activates the cell-death execution pathway regulated by CED-9 [44] , [45] . How might GCN-1 and ABCF-3 interact with the known DNA-damage response and cell-death execution pathways in the regulation of germline cell deaths induced by ionizing radiation ? Our genetic results suggest that GCN-1 and ABCF-3 function independently of CED-9 , at least in the regulation of the death of the M4 sister cell . We suggest that as is the case for somatic cell deaths , GCN-1 and ABCF-3 function in a novel pathway independently of CED-9 in regulating the germ-cell deaths induced by ionizing radiation . Alternatively , if GCN-1 and ABCF-3 regulate germ-cell deaths induced by ionizing radiation via a mechanism different from that of somatic cell deaths , it is possible that GCN-1 and ABCF-3 act through egl-1 and its target ced-9 , since egl-1 is involved in somatic programmed cell deaths and germ-cell deaths induced by ionizing radiation but not in the stochastic germ-cell deaths that occur under physiological conditions . GCN-1 and ABCF-3 are conserved proteins from yeast to humans . The C . elegans GCN-1 protein has 43% and 53% similarities ( 23% and 32% identities ) to the homologs of S . cerevisiae and humans , respectively , and the C . elegans ABCF-3 proteins has 57% and 69% similarities ( 40% and 49% identities ) to the homologs of S . cerevisiae and humans , respectively ( Figure 1H ) . The yeast GCN-1 homolog Gcn1p and ABCF-3 homolog Gcn20p are required to maintain the basal level of the phosphorylation of eukaryotic initiation factor 2α ( eIF2α ) in the physiological condition and to increase the phosphorylation of eIF2α in response to amino-acid starvation . Gcn1p and Gcn20p activate the serine-threonine protein kinase Gcn2p , which phosphorylates an evolutionarily conserved serine residue of eIF2α . The phosphorylation of eIF2α results in both the inhibition of global translation and the translational activation of the GCN4 mRNA , which encodes a basic leucine zipper transcription factor . Translation of GCN4 mRNA is regulated by four short upstream open reading frames ( uORFs ) in the 5′ UTR with start codons that are out-of-frame with the main coding sequence and which generally reduce translation from the main reading frame [46] . We speculate that the mechanistic roles of C . elegans GCN-1 and ABCF-3 in translational control are conserved between yeast and C . elegans . First , the amino-acid sequences of GCN-1 and ABCF-3 proteins are conserved between yeast and C . elegans , particularly in functionally important domains ( Figure 1H ) . Second , the functions of GCN-1 and ABCF-3 can be substituted with those of S . cerevisiae GCN1 and GCN20 , respectively , for the promotion of M4 sister cell death . Third , like their yeast counterparts , C . elegans GCN-1 and ABCF-3 are required to maintain the basal level of phosphorylation of eIF2α and physically interact through an EF3-like domain-containing region of GCN-1 and an N-terminal ABCF-3 domain [43] . Fourth , like Gcn20p , the AAA domain ATPase activity of ABCF-3 is not required for its function [32] . Fifth , the atf-5 gene , the C . elegans homolog of S . cerevisiae GCN4 , has two upstream ORFs that have been shown to inhibit the translation of the atf-5 mRNA [47] . We have shown that in addition to gcn-1 and abcf-3 , ceh-34 , eya-1 , sptf-3 and pig-1 function in M4 sister cell death [15] , [17] . We previously reported that the Six family homeodomain protein CEH-34 and the Eyes absent homolog EYA-1 physically interact to directly drive expression of the pro-apoptotic BH3-only gene egl-1 in the M4 sister , leading to the death of the M4 sister ( Figure 5B ) [15] . We found that the SP1 family transcription factor SPTF-3 directly drives the transcription of the gene egl-1 , which encodes a BH3-only protein that promotes apoptosis via the CED-3 caspase-mediated canonical cell-death execution pathway [17] . SPTF-3 also directly drives the transcription of the AMPK-related gene pig-1 , which encodes a protein kinase that functions in a pathway in parallel to the CED-3-mediated canonical cell-death execution pathway . These interactions are shown in Figure 5B . Our analyses indicate that gcn-1 and abcf-3 likely function in a pathway that acts in parallel to those of pig-1 , ceh-34 and sptf-3 . These results are consistent with a model in which GCN-1 and ABCF-3 act independently of CED-9 to promote M4 sister cell death . In short , we propose that the regulatory network for the death of the M4 sister includes at least three different pathways involving translation , transcription and protein phosphorylation ( Figure 5B ) . Each gene in this network ( gcn-1 , abcf-3 , sptf-3 , pig-1 , egl-1 , ceh-34 and eya-1 ) has a human counterpart , some of which are implicated in human diseases , including developmental disorders and cancer . We anticipate that further analyses of this regulatory network will both reveal an evolutionarily conserved mechanism of apoptosis shared between C . elegans and humans and provide insights concerning how abnormalities in this apoptotic network can lead to human disease .
C . elegans strains were cultured at 20°C as described [48] . The N2 strain was used as the wild type . The following mutations , integrations and extrachromosomal arrays were used . LGI: sptf-3 ( n4850 ) , eya-1 ( ok654Δ ) , nIs177[Pceh-28::gfp , lin-15AB ( + ) ] , nIs180[Ptdc-1::gfp , lin-15AB ( + ) ] , zdIs5[Pmec-4::gfp , lin-15AB ( + ) ] . LGII: rol-1 ( e91 ) , gcn-2 ( ok871Δ ) , Y38E10A . 8 ( tm4094Δ ) . LGIII: ced-4 ( n1162 ) , ced-9 ( n2812 ) , gcn-1 ( n4827 , nc40Δ ) , abcf-3 ( n4927 , ok2237Δ ) , unc-45 ( r450 ) , dpy-18 ( e364 ) , nIs176[Pceh-28::gfp , lin-15AB ( + ) ] . LGIV: ced-3 ( n717 , n2427 , n2446 ) , pig-1 ( gm344Δ ) , nIs175[Pceh-28::gfp , lin-15AB ( + ) ] . LGV: egl-1 ( n1084 n3082 ) , ceh-34 ( n4796 ) , oyIs14[sra-6::gfp] . LGX: lin-15 ( n765 ) , pek-1 ( ok275Δ ) , nIs106[Plin-11::gfp , lin-15AB ( + ) ] , nIs429[Pphat-5::gfp , lin-15AB ( + ) ] , bcIs24[Ptph-1::gfp , lin-15AB ( + ) ] . Unmapped: nIs460[Pgcn-1::gfp] , nIs488[Pabcf-3::gfp] , nIs645 and nIs646[Pmec-7::gcn-1 cDNA , Pmec-7::abcf-3 cDNA , Pmec-3::mCherry , rol-6 ( su1006 ) ] , nIs648 and nIs649[Pmec-7::gcn-1 cDNA , Pmec-3::mCherry , rol-6 ( su1006 ) ] , nIs651 and nIs652[Pmec-7::abcf-3 cDNA , Pmec-3::mCherry , rol-6 ( su1006 ) ] . Extrachromosomal arrays: nEx1817 and nEx1818[Pgcn-1::gcn-1 cDNA::gcn-1 3′ UTR , Plin-44::gfp] , nEx1925 and nEx1926[abcf-3 ( + ) , Plin-44::gfp] , nEx1928 and nEx1929[abcf-3 K217M , Plin-44::gfp] , nEx1931 and nEx1932[abcf-3 K536M , Plin-44::gfp] , nEx1934 and nEx1935[abcf-3 K217M K536M , Plin-44::gfp] , nEx2223 and nEx2224[Pceh-34::eIF2α S49A , Plin-44::gfp] gcn-1 ( n4827 ) and abcf-3 ( n4927 ) were isolated from a genetic screen for mutations that cause an extra GFP-positive M4-like cell in animals carrying the Pceh-28::gfp transgene [15] . Mutagenesis was performed as described [48] . Mutagenized P0 animals were allowed to lay eggs , and 144 , 000 synchronized F2 animals were screened with a fluorescence-equipped dissecting microscope . Single nucleotide polymorphisms were used to map gcn-1 ( n4827 ) and abcf-3 ( n4927 ) to a 175 kb interval ( III: 2 , 044 , 521–2 , 220 , 200 ) and a 5 . 3 Mb interval ( III: 5 , 346 , 407–10 , 613 , 191 ) , respectively [49] . Whole-genome sequencing of gcn-1 ( n4827 ) mutants was performed using an Illumina/Solexa GAII , according to the instructions of the manufacture . DNA sequencing of the abcf-3 locus of abcf-3 ( n4927 ) mutants was performed using an Applied Biosystems 3130× . The programmed cell deaths of specific cells were scored at the indicated stages using the following strains , which express GFP in specific cells . A fluorescence-equipped compound microscope was used to score the programmed cell deaths . M4 sister cell death , nIs175 , nIs176 or nIs177 at the L1 stage . g1A sister cell death , nIs429 at the L1 stage . PVQ sister cell death , oyIs14 at the L4 stage [50] . NSM sister cell death , bcIs24 at the L1 stage [51] . RIM and RIC sister cell death , nIs180 at the L1 stage . Extra cells in the anterior pharynx were scored using a compound microscope equipped with Nomarski differential interference contrast optics . For physiological germ-cell deaths , germ-cell corpses in gonads of animals 24 hours after the fourth-larval stage were counted by direct observation using Nomarski optics . For ionizing radiation-induced germ-cell deaths , fourth-larval stage animals were exposed to 120 Gy of ionizing radiation , and germ-cell deaths were scored using acridine orange at 24 hours post-irradiation as described [38] . The transgenes Pceh-28::gfp , Ptph-1::gfp and sra-6::gfp are described [15] , [50] , [51] . The phat-5 promoter sequence in pGD48 was cloned in pPD122 . 56 to generate the Pphat-5::gfp transgene [52] . The Pflp-15::gfp transgene contained 2 . 4 kbp of the 5′ promoter of flp-15 in pPD122 . 56 . The Pgcy-37::gfp transgene contained 1 . 1 kbp of 5′ promoter of gcy-37 in pPD122 . 56 . The Ptdc-1::gfp transgene contained 4 . 5 kbp of 5′ promoter of tdc-1 in pPD121 . 83 . The Pgcn-1::gcn-1 cDNA::gcn-1 3′UTR transgene ( pTH gcn-1 cDNA ) contained 4 . 2 kbp of 5′ promoter of gcn-1 , a full-length gcn-1 cDNA and 1 . 0 kbp 3′ of the stop codon of gcn-1 . The 5′ promoter of gcn-1 , a full-length gcn-1 cDNA and the 3′ promoter of gcn-1 were generated by PCR and fused in pBluescript II using the In-Fusion cloning system ( Clontech ) . The abcf-3 ( + ) transgene contained 1 . 6 kbp of 5′ promoter , the coding region and 0 . 8 kbp 3′ of the stop codon of abcf-3 in pBluescript II . The QuickChange II XL Site-Directed Mutagenesis Kit ( Stratagene ) was used to generate transgenes of abcf-3 K217M , abcf-3 K536M and abcf-3 K217M K536M . gcn-1 cDNA corresponding to amino acids 1–2634 , 1–1760 , 1–880 , 880–2634 , 880–1760 or 1760–2634 of GCN-1 was cloned in pGBKT7 . abcf-3 cDNA corresponding to amino acids 1–712 , 1–512 , 1–202 , 202–712 , 512–712 or 202–512 was cloned in pGADT7 . The Pgcn-1::gfp transgene contained 4 . 2 kbp of 5′ promoter of gcn-1 in pPD122 . 56 . The Pabcf-3::gfp transgene contained 1 . 6 kbp of 5′ promoter of abcf-3 in pPD122 . 56 . The Pceh-34::eIF2α S49A transgene contained 3 . 8 kbp of 5′ promoter of ceh-34 and eIF2α with a replacement of serine 49 with alanine in pPD49 . 26 . For the Pmec-7::gcn-1 cDNA and Pmec-7::abcf-3 cDNA transgenes , full-length cDNA of gcn-1 and abcf-3 were cloned in pPD96 . 41 . Primer sequences used are available from the authors . Germline transformation was performed as described [53] . The gfp reporter transgene was injected at 50 µg/ml into lin-15 ( n765ts ) animals with 50 µg/ml of pL15EK as a coinjection marker [54] . To rescue the defect in M4 sister cell death , the transgenes pTH gcn-1 cDNA , abcf-3 ( + ) , abcf-3 K217M , abcf-3 K536M and abcf-3 K217M K536M described above were injected at 20 µg/ml into gcn-1 ( n4827 ) or abcf-3 ( n4927 ) animals with 50 µg/ml of Plin-44::gfp as a coinjection marker [55] . To establish transgenic lines carrying the Pceh-34::eIF2α S49A transgene , the Pceh-34::eIF2α S49A transgene was injected at 50 µg/ml into nIs175 animals with 50 µg/ml of Plin-44::gfp as a coinjection marker . The Pmec-7::gcn-1 cDNA and Pmec-7::abcf-3 cDNA transgenes were injected at 50 µg/ml , respectively , into zdIs5 animals with 50 µg/ml of pRF4[rol-6 ( su1006 ) ] and 20 µg/ml of the Pmec-3::mCherry transgene as coinjection markers . GAL4 fusion constructs were introduced into yeast strain PJ649A as described [56] . Single colonies were streaked and cultured for two days at 30°C on SD plates containing minimal supplements without tryptophan and leucine . Then yeast strains were streaked and cultured for three days at 30°C on SD plates containing minimal supplements without tryptophan , leucine and histidine to test yeast growth . Protein fragments corresponding to amino acids 753–857 of GCN-1 and 74–185 of ABCF-3 fused to glutathione S- transferase ( GST ) were expressed , purified using glutathione Sepharose 4B ( Amersham Biosciences ) and used to raise rabbit anti-GCN-1 or anti-ABCF-3 antibodies , respectively . Antisera were generated by Pocono Rabbit Farm and Laboratory . Specific antibodies were affinity-purified using identical GCN-1 or ABCF-3 protein fragments fused to maltose-binding protein ( MBP ) and coupled to Affigel 10 ( Bio-Rad ) . Protein extracts were prepared from nIs175 , gcn-1 ( n4827 ) ; nIs175 and abcf-3 ( n4927 ) ; nIs175 animals synchronized at the fourth larval stage as described [57] . 10 µg of total protein was loaded onto a 7 . 5% SDS PAGE gel and then transferred to nitrocellulose membranes . The membranes were probed with anti-GCN-1 or anti-ABCF-3 antibody . Immunocomplexes were detected using HRP-conjugated anti-rabbit IgG secondary antibodies ( Invitrogen ) followed by chemiluminescence ( Western Lightning ECL , PerkinElmer ) . To determine the level of phosphorylated eIF2α , protein extracts were prepared from nIs175 , gcn-1 ( n4827 ) ; nIs175 and abcf-3 ( n4927 ) ; nIs175 animals synchronized at the fourth larval stage as described [57] . 15 µg of total protein was loaded on a 10% SDS PAGE gel and then transferred to nitrocellulose membranes . The membranes were probed with anti-phospho-eIF2α ( Cell Signaling Technology ) and anti-eIF2α antibodies [28] . Immunocomplexes were detected as described above . For immunoprecipitation experiments , protein extracts were prepared from mixed-staged wild-type animals in TNE buffer containing 50 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 1 mM EDTA , 1% NP-40 , 5 mM β-mercaptoethanol , 10% glycerol . Protein extracts were mixed with either an affinity-purified anti-ABCF-3 antibody or a control IgG at 4°C for 2 hours . Immunocomplexes were recovered using Protein A Sepharose 4 Fast Flow ( GE Healthcare Life Sciences ) and washed with TNE buffer four times . The recovered immunocomplexes were subjected to western blot analysis using anti-GCN-1 or anti-ABCF-3 antibody . Fluorescence in situ hybridization was performed as described [58] . The gcn-1 and abcf-3 probes ( Biosearch Technologies , Inc ) were conjugated to the fluorophore Cy5 using the Amersham Cy5 Mono-reactive Dye pack ( GE Healthcare ) . DNA was visualized using 4′ , 6-diamidino-2-phenylindole ( DAPI ) . The probe sequences used are shown in Tables S3 and S4 . Figures 4D and H are maximum intensity projections of a Z-stack of images processed with the FFT Bandpass Filter operations in the image processing program Fiji . Oligonucleotides used for gcn-1 and abcf-3 FISH probe were described in Table S6 and S7 ) . For mRNA-seq [59] , total RNA was purified using an RNAeasy Mini kit ( Qiagen ) from synchronized L4 animals of wild-type animals and gcn-1 and abcf-3 mutants . The purified RNA was subjected to oligo ( dT ) selection , fragmentation and first- and double-strand synthesis with an Illumina Tru-Seq kit according to the manufacturer's instructions . DNA fragments longer than 30 bp were purified using SPRI-TE beads ( Beckmann Coulter ) according to the manufacturer's instructions . The purified DNA was end-repaired and single A bases were added for adaptor ligations . The adaptor-ligated DNA was then subjected to double SPRI-TE purification to select for 200 bp fragments . These fragments were enriched and barcoded by PCR for multiplexing . A final SPRI-TE purification was performed to purify the barcoded RNA-Seq libraries for Illumina DNA sequencing using HiSeq 2000 . RNA-seq data were aligned against the C . elegans reference genome ( ce10 ) using the Burrows-Wheeler Aligner ( BWA ) and Tophat . Ribosome profiling was performed as described [60] with modifications . Synchronized L4 wild type animals and gcn-1 and abcf-3 mutants were collected and washed with M9 buffer three times . Animals were homogenized using a dounce homogenizer in lysis buffer containing 20 mM Tris ( pH 7 . 5 ) , 150 mM NaCl , 5 mM MgSO4 , 1 mM DTT , 100 µg/ml cycloheximide , 1% Triton X-100 and 25 U/ml Turbo DNase ( Invitrogen ) and centrifuged at 20 , 000 g for 20 min at 4°C . The absorbance of the extract was measured at 260 nm . 40 absorbance units of extract were incubated with 300 units of RNase I at 25°C for an hour , and then 200 units of SUPERase In RNase Inhibitor ( Invitrogen ) were added . Digested extracts were loaded on 10–50% linear sucrose gradients containing 20 mM Tris ( pH 7 . 5 ) , 150 mM NaCl , 5 mM MgSO4 , 1 mM DTT and 100 µg/ml cycloheximide and centrifuged for three hours at 35 , 000 rpm at 4°C using a SW-40 rotor to isolate a monosome fraction . RNA from the monosome fraction was purified by phenol-chloroform extraction followed by miRNeasy Mini Kit ( Qiagen ) and separated using a 15% TBE-Urea gel ( BioRad ) to isolate ribosome-protected fragments ( RPFs ) . The RPFs were eluted from gels by incubating in RNA elution buffer containing 300 mM sodium acetate ( pH 5 . 5 ) , 1 mM EDTA and 0 . 25% SDS . RPFs were 3′ dephosphorylated with T4 polynucleotide kinase ( New England Labs ) and ligated to Universal miRNA Cloning Linker ( New England Labs ) using T4 RNA ligase 2 , truncated ( New England Labs ) according to the manufacturer's instructions . RPFs ligated with the linker were separated from an unligated linker using a 15% TBE-Urea gel ( BioRad ) and eluted from gels using RNA extraction buffer followed by phenol-chloroform extraction . RFPs were reverse-transcribed by Superscript III ( Invitrogen ) with a reverse transcription primer according to the manufacturer's instructions . The products of reverse transcripts ( RT ) were purified using a 15% TBE-Urea gel ( BioRad ) and eluted from a gel by incubating in DNA elution buffer containing 300 mM NaCl , 10 mM Tris ( pH 8 . 0 ) and 1 mM EDTA followed by phenol-chloroform extraction . The RT products were circularized by CircLigase ( Epicentre ) according to the manufacturer's instructions . About a quarter of the RT products were used in PCR reactions containing 1× Phusion HF buffer , 0 . 2 mM dNTP , 0 . 5 µm forward library primer , 0 . 5 µm reverse indexed primer and 0 . 02 units/µl Phusion polymerase ( New England Labs ) , and PCR was performed with a 30 second initial denaturation at 98°C , followed by 6 , 8 , 10 , 12 and 14 cycles of 98°C for 10 second , 65°C for 10 second and 72°C for 5 second . PCR products were separated using a 8% TBE gel ( BioRad ) and eluted from gels by incubating in DNA elution buffer followed by phenol-chloroform extraction . PCR products were suspended in 20 µl of 10 mM Tris ( pH 8 . 0 ) and sequenced by HiSeq 2000 . The adaptor sequences ( CTGTAGGCACCATC ) from 3′ end of the ribosome footprint reads were removed , then trimmed reads were mapped using BWA to distinguish the reads from ribosomal RNAs . About 60% of the reads were filtered out , and the remaining reads ( non-ribosomal ) were aligned to the C . elegans reference genome ( ce10 ) using BWA and Tophat . Because translational initiation is thought to be blocked rapidly by the stress animals encounter during harvesting , many ribosomes are stalled at the beginning of each transcript in the presence of cycloheximide , which prevents translation elongation [34] . Hence , high frequencies of reads at the beginning of each transcript might not correspond to high rates of translation . For this reason , the reads that mapped to the first 25 nucleotides of each transcript were not counted in evaluating gene expression in the Ribo-seq analyses .
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Apoptosis , also referred to as programmed cell death , is a crucial cellular process that eliminates unwanted cells during animal development and tissue homeostasis . Abnormal regulation of apoptosis can cause developmental defects and a variety of other human disorders , including cancer , neurodegenerative diseases and autoimmune diseases . Therefore , it is important to identify regulatory mechanisms that control apoptosis . Previous studies have demonstrated that the transcriptional induction of apoptotic genes can be crucial to initiating an apoptotic program . Less is known about translational controls of apoptosis . Here we report that the evolutionarily conserved C . elegans translational regulators GCN-1 and ABCF-3 promote apoptosis generally and act independently of the anti-apoptotic BCL-2 homolog CED-9 . GCN-1 and ABCF-3 physically interact and maintain the phosphorylation level of eukaryotic initiation factor 2α , suggesting that GCN-1 and ABCF-3 act together to regulate the initiation of translation . We propose that the translational regulators GCN-1 and ABCF-3 maternally contribute to the proper execution of the apoptotic program .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"model",
"organisms",
"genetics",
"biology",
"and",
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"sciences",
"research",
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"analysis",
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] |
2014
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The Translational Regulators GCN-1 and ABCF-3 Act Together to Promote Apoptosis in C. elegans
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In Sub-Saharan Africa , infectious diarrhea is a major cause of morbidity and mortality . A case-control study was conducted to identify the etiology of diarrhea and to describe its main epidemiologic risk factors among hospitalized children under five years old in Bangui , Central African Republic . All consecutive children under five years old hospitalized for diarrhea in the Pediatric Complex of Bangui for whom a parent’s written consent was provided were included . Controls matched by age , sex and neighborhood of residence of each case were included . For both cases and controls , demographic , socio-economic and anthropometric data were recorded . Stool samples were collected to identify enteropathogens at enrollment . Clinical examination data and blood samples were collected only for cases . A total of 333 cases and 333 controls was recruited between December 2011 and November 2013 . The mean age of cases was 12 . 9 months , and 56% were male . The mean delay between the onset of first symptoms and hospital admission was 3 . 7 days . Blood was detected in 5% of stool samples from cases . Cases were significantly more severely or moderately malnourished than controls . One of the sought-for pathogens was identified in 78% and 40% of cases and controls , respectively . Most attributable cases of hospitalized diarrhea were due to rotavirus , with an attributable fraction of 39% . Four other pathogens were associated with hospitalized diarrhea: Shigella/EIEC , Cryptosporidium parvum/hominis , astrovirus and norovirus with attributable fraction of 9% , 10% , 7% and 7% respectively . Giardia intestinalis was found in more controls than cases , with a protective fraction of 6% . Rotavirus , norovirus , astrovirus , Shigella/EIEC , Cryptosporidium parvum/hominis were found to be positively associated with severe diarrhea: while Giardia intestinalis was found negatively associated . Most attributable episodes of severe diarrhea were associated with rotavirus , highlighting the urgent need to introduce the rotavirus vaccine within the CAR’s Expanded Program on Immunization . The development of new medicines , vaccines and rapid diagnostic tests that can be conducted at the bedside should be high priority for low-resource countries .
In 2013 , 6 . 3 million children under the age of five years died , 578 , 000 of them from diarrheal diseases . Nearly half of these diarrhea-related deaths were reported from Sub-Saharan Africa [1] . The fourth Millennium Development Goal , established after the United Nations Millennium Summit in 2000 , seeks to decrease the mortality of children under five by two-thirds before 2015 [2] . Since 2000 , childhood mortality due to diarrhea has diminished by 6 . 5% every year , but this trend requires an acceleration to reach the 2030 objectives . In order to achieve this decline in childhood diarrheal mortality , the World Health Organization ( WHO ) published guidelines for the clinical management of childhood diarrhea [3] . These guidelines recommend using antibiotics only for bloody diarrhea , suspected cholera , or associated sepsis . They also encourage zinc supplementation and use of oral rehydration solution ( ORS ) to treat and prevent diarrhea . However , in practice , antibiotic treatments are overused , resulting in the emergence of antibiotic resistance: only 40% of the children with diarrhea receive the recommended zinc supplementation and ORS [4] . Nowhere are these problems of childhood diarrhea and shortfalls in its management more evident than in the poorest and most unstable countries of Sub-Saharan Africa . The Central African Republic ( CAR ) is a resource-limited country in equatorial Africa ( ranked 180/187 according to the Human Development Index in 2013 ) . Mortality among under five year-old children was 179/1000 in 2010 [5] . No high-quality epidemiological and biological data on severe childhood diarrhea , however , exist for CAR . Indeed , the consequences of decades of poverty , civil war , and economic and political crisis have complicated the management of severe childhood diarrhea . Moreover , a recent qualitative investigation in CAR revealed the complex home management of childhood diarrhea . Parents’ beliefs that diarrheal illness needs to be stopped immediately , that it requires medication , that they should avoid consulting primary health centers and minimize expenses were the most important reasons hampering effective home management of diarrhea [6] . The CAR therefore provides a strong case study in which to understand better the epidemiology and etiology of severe childhood diarrhea in the poorest and most unstable countries without functioning health care systems . Investigating childhood diarrhea in such a context can assist in managing this treatable pathology , by highlighting the most appropriate and adaptive public health interventions . The Global Enteric Multicenter Study ( GEMS ) study is a matched case-control study of moderate-to-severe diarrhea in children aged 0–59 months which aimed to estimate the pathogen-specific disease burden in populations from four sites in Africa and three in Asia . The GEMS showed that preventive strategies targeting five pathogens ( rotavirus , Shigella , ST-ETEC , Cryptosporidium , typical enteropathogenic E coli ) are likely to substantially reduce the burden of moderate-to-severe diarrhea . However , the public health interventions in very low income countries like the CAR , suffering from long-term instability , are different from relatively stable countries with higher income and better medical infrastructure . Our study , a matched case-control study of diarrhea among hospitalized children under five years was conducted at the Pediatric Complex ( PCB ) in the CAR’s capital city , Bangui . It was performed in collaboration with the Institut Pasteur de Paris ( IPP ) , the Institut Pasteur de Bangui ( IPB ) and the PCB . The study’s primary objective was to identify pathogens associated with diarrhea in hospitalized children under five years of age . Secondary objectives were i ) to describe the clinical symptoms of severe diarrhea among hospitalized children , ii ) to identify the risk factors associated with severe diarrhea ( anthropometric , socio-economic , environmental characteristics ) , iii ) to describe the management of diarrhea before and during hospitalization , and iv ) to describe the vital status of children with severe diarrhea during hospitalization and two months after discharge .
Our study was a matched case-control study conducted in Bangui , CAR from December 2011 to November 2013 at the PCB , the country’s sole public pediatric hospital . Cases were children under 60 months of age , hospitalized for diarrhea . Other inclusion criteria were residence in one of Bangui’s eight districts and a general health condition that would support blood and stool sampling . Exclusion criterion was being positive for human immunodeficiency virus ( HIV ) . Controls , identified from the community , were pair matched to the cases according to age ( ±2 months for infants ( 0–11 months ) , ±3 months for toddlers ( 12–23 months ) and ±6 months for children ( 24–59 months ) ) , sex and neighborhood . To be eligible , controls had to be in good general health , with no history of diarrhea or antibiotic use during the seven days before sampling . HIV status was not systematically tested in controls , but if parents spontaneously declared a child to be seropositive , that child was not included . Cases and controls could not be included more than once . The research protocol was approved by the Scientist Committee of the Sciences and Health University of Bangui , the CoRC ( Clinical Research Committee of Institut Pasteur ) , the CCTIRS ( Comité Consultatif sur le Traitement de l’Information en matière de Recherche dans le domaine de la santé ) and the CNIL ( Commission Nationale de l’Information et des Libertés ) in France . Written informed consent was obtained from all children’s parents or legal guardians for both cases and controls . This study was conducted according to the protocol and ethical principles with their origins in the Declaration of Helsinki . The project provided treatment and laboratory testing free of charge . The CN/CNLS ( Coordination Nationale du Comité National de Lutte contre le Sida ) , with financial support from the Global Fund , covered all costs for HIV treatment . To show an odds ratio of 2 characterizing the association between a given pathogen and severe diarrhea , with a pathogen prevalence of 5% , a power of 80% and a two-sided α = 0 . 05 , a sample size of 600 cases and 600 controls was necessary . The variables collected are defined in Table 1 . Data were double entered and analyzed using STATA: SE 13 . 1 ( Stata Corp Station , TX , USA ) . Continuous variables were expressed as mean ( ±SD ) or median [interquartile range] and discrete variable as percentage and 95% CI . Univariate analyses for continuous variables were performed using Student t-test or Mann-Whitney test when appropriate . For discrete variables , univariate analyses were performed using Chi-2 test or Fisher’s exact test . Tests were two-sided and a p-value<0 . 05 was considered significant . Comparison between cases and controls were made by univariate conditional logistic regression to take into account the matching of cases and controls . Pathogens potentially associated with severe diarrheas in univariate analysis with a p-value<0 . 25 were included in a backward conditional logistic regression , adjusted on the presence of other pathogens . Results are reported as adjusted OR ( aOR ) with 95% CI . The attributable fraction ( AF ) was calculated for pathogens with significant aOR with the following formula: aAF = P ( pathogens ) among cases* ( aOR-1 ) /aOR , that is , the proportion of severe diarrhea attributable to this specific pathogen . When the association was significantly negative with an aOR<1 , the protective fraction was calculated with the following formula: aPF = ( 1- aOR ) x p ( events ) among controls , that is , the proportion of severe diarrhea avoided by the presence of this specific pathogen . For cases , associations between socio-economic , anthropometric and clinical data with the use of ORS and antibiotics before and during hospitalization , and association with vital status were determined by univariate analysis . All variables associated with a p value<0 . 25 were included in a backward logistic regression . The final model includes only variables with a p-value<0 . 05 . Interactions were tested and the goodness-of-fit of the model was studied using the Hosmer-Lemeshow statistic .
General characteristics of cases are described in Table 2 . During the 24 months of the study , 428 consecutive cases were hospitalized for diarrhea . Twenty-two were tested positive for HIV and consequently excluded on that basis . No mothers refused their children’s participation in the study . Nine cases did not match any control . Sixty-four case-control pairs were wrongly matched: 3 for sex and 61 for age . Finally , 333 cases ( 78% ) and 333 ( 79% ) controls were analyzed ( Fig 1 ) . Fifty-six percent were male . The mean age at inclusion was 12 . 9 months ( ±9 . 8 ) . The distribution of age was as follows: 195 ( 59% ) between 0–11 months; 103 ( 31% ) between 12–23 months and 35 ( 10% ) between 24–59 months . The mean time since onset of diarrhea before cases were presented at the hospital was 3 . 7 ( ±1 . 8 ) days . Cases presented the following clinical symptoms: severe dehydration ( N = 239 , 72% ) ; hemodynamic shock ( N = 216 , 65% ) ; serious neurological injury ( N = 239 , 72% ) ; fever ( body temperature ≥38° ) ( N = 264 , 79% ) ; vomiting ( N = 267 , 80% ) ; and extra digestive signs ( N = 94 , 28% ) , including 69 cases of upper respiratory tract infection , 5 cases of pulmonary signs , 8 cases of digestive signs and 12 cases of other signs . 26 cases ( 8% ) were positive for malaria , all of them due to Plasmodium falciparum . The mean episodes of vomiting in the last 24 hours were 4 . 2 ( ±2 ) , and the mean number of stools during the last 24 hours was 6 . 1 ( ±2 ) . In 16 cases ( 5% ) and 56 cases ( 17% ) , macroscopic blood and mucous were found in the stools , respectively . Demographic characteristics of cases and controls are summarized in Table 2 . Cases and controls were well balanced for age and sex at baseline . Cases were significantly more severely or moderately malnourished than controls , 40% versus 14% , respectively , p<0 . 001 . Cases belonged to a higher socio-economic class , had more mothers who completed primary school and had more access to improved water than controls . No difference in diet was found between the two groups . The results are summarized in Table 3 . At least one of the sought-for pathogens was identified in 78% and 40% of cases and controls , respectively , p<0 . 001 . Mixed bacterial/viral infections were detected in 10% of cases and 4% of controls , p = 0 . 001 . Viruses were the most prevalent pathogens , detected in 55% of cases and 15% of controls , p<0 . 001 . Conversely , Giardia intestinalis was more frequent in controls ( 7 . 8% ) than in cases ( 0 . 9% ) , p<0 . 001 ) . The ipaH gene was detected in all 14 Shigella-positive culture specimens except one and in 67/651 ( 10% ) culture-negative specimens . In all , 50/333 ( 15% ) and 30/333 ( 9% ) samples were considered positive for the entity Shigella/EIEC in cases and controls , respectively . Presence of Shigella/EIEC was significantly associated with blood in the stool ( p<0 . 001 ) . Diarrhoeagenic E . coli were found in as many cases as controls . Of the diarrhoeagenic E . coli pathotypes detected , the most frequent was EAEC ( 5 . 7% of cases and 4 . 5% of controls ) followed by ETEC ( 4 . 8% of cases and 4 . 8% of controls ) . No significant difference between cases and controls for LT , ST , and LT/ST toxin-producing ETEC was found . Other pathotypes ( EPEC , ATEC , EIEC and STEC ) were found in less than 3% of the children . Cryptosporidium parvum/hominis was identified more frequently in cases than in controls ( 12 . 6% of cases and 2 . 7% of controls , p<0 . 001 ) . In multivariate analyses , when adjusted on the presence of other pathogens , five pathogens were positively associated with diarrhea: rotavirus , norovirus , astrovirus , Shigella/EIEC , Cryptosporidium parvum/hominis and one appeared negatively associated: Giardia intestinalis . The adjusted AFs ( aAF ) were 39% for rotavirus , 7% for both norovirus and astrovirus , 9% for Shigella/EIEC and 10% for Cryptosporidium parvum/hominis . The protective fraction for Giardia intestinalis was 6% , ( Table 3 ) . In cases , the prevalence of pathogens varied according to age categories ( Fig 2 ) . Globally , the prevalence of viruses and Cryptosporidium parvum/hominis in cases decreased with age , whereas they increased for Shigella/EIEC and Giardia intestinalis . In cases , viruses were found in 63% ( 122/195 ) of infants , 50% ( 52/103 ) of toddlers and 20% ( 7/35 ) of children; ( p<0 . 001 ) . Cryptosporidium parvum/hominis were found in 16% ( 32/195 ) of infants , 7% ( 7/103 ) of toddlers and 9% ( 3/35 ) of children , ( p = 0 . 04 ) . Shigella/EIEC were found in 9% ( 17/195 ) of infants , 22% ( 23/103 ) of toddlers and 29% ( 10/35 ) of children , ( p<0 . 001 ) . In controls , the percentage of virus was stable with age , and the proportion of Cryptosporidium parvum/hominis remained low . In contrast , the percentage of Giardia intestinalis was higher among controls than in cases and increased with age . Viruses were more frequently identified during the dry season , with 65% ( 115/179 ) of cases compared to 43% ( 66/154 ) in rainy season , p<0 . 001 . In contrast , Shigella/EIEC and Cryptosporidium parvum/hominis were mainly identified during the rainy season: for Shigella , 19% ( 30/154 ) cases in the rainy season and 11% ( 20/179 ) in the dry season ( p = 0 . 03 ) ; and for Cryptosporidium parvum/hominis , 16% ( 25/154 ) cases in the rainy season and 9% ( 17/179 ) in the dry season ( p = 0 . 06 ) ( Fig 3 ) . Before hospitalization , ORS and zinc supplementation were prescribed to 38% and 0 . 9% children , respectively . Antiparasitic treatments and antibiotics were administered to 44% and 34% of children , respectively . One on four children ( 25% ) received traditional treatments that consisted mainly in infusions or herbal decoctions ( 72% ) , herbal enemas ( 16% ) or fruit porridge ( 9% ) . During hospitalization , 99% of children received ORS , 87% intravenous rehydration , 70% antibiotics , 66% antiparasitic treatments , 55% zinc , and none received traditional treatments ( Fig 4 ) . The only factor identified as independently associated with the prescription of ORS before hospitalization was bloody stools . No factors were identified to be associated with the use of antibiotics before hospitalization or the use of any treatment during hospitalization . The mean duration of hospitalization was 4 . 8 ( ±2 . 8 ) days . Four percent of children ( 12/333 ) died during hospitalization and 1% ( 3/271; 50 missing values ) died during the two months after discharge . Among the 12 deaths , 5 were infants ( < 11 months ) , 5 were toddlers ( 12 to 23 months ) , and 2 were children ( 24–59 months ) . Three cases were positive for Plasmodium falciparum and probably died of severe malaria , 2 from severe anemia , 3 from septic shock , and 1 from small bowel obstruction . Finally , 3 died from metabolic acidosis secondary to hemodynamic or septic shock . In the multivariate analysis , the only factor found to be significantly associated with the vital status was the nutritional status at the time of hospitalization: 11% ( 4/38 ) of children with SAM ( WHZ < -3DS or MUAC < 115mm ) died during hospitalization , compared to 3% ( 8/293 ) for MAM or normally well-nourished children , ( p = 0 . 026 ) .
This study is the first case-control study conducted in CAR that provides the etiology and clinical outcome of children of less than five years that were hospitalized for diarrhea in Bangui . A large prospective study , the GEMS study , used a similar approach with matched case-control in children between 0 to 59 months suffering from moderate-to-severe diarrhea in seven countries , including four in Africa: The Gambia and Mali ( West Africa ) , Mozambique ( South Africa ) and Kenya ( East Africa ) [11] . Our study differs from GEMS by its recruitment . No country from Central Africa was included in the GEMS study . In addition , our study was conducted in a very low-income country , suffering from long-term instability , substantial poverty , and resource-poor primary health centers . Finally , the study also differs from the GEMS study in its inclusion criteria . In our study , children were included because they were hospitalized for diarrhea , whether or not they met the WHO criteria for severe dehydration [12] . This inclusion criterion , easier to apply in resource-poor settings , fits well with the WHO criteria for severe diarrhea . Indeed , an overwhelming majority of cases had severe dehydration ( 72% ) , more than 60% had signs of hemodynamic shock and almost 90% had to be intravenously rehydrated . In this way , our study complements the GEMS study . HIV positive children were excluded from the present study . Indeed , HIV ( co- ) infection increases the risk of severe diarrhea by impairing the immune system , making problematic the interpretation of results in the context of case/control comparison . Moreover , it was not possible to test the community controls for HIV without returning to provide individual announcement and counseling , which was not possible in our study . Even if the HIV status of the controls was unknown , we supposed that it was low because only 22 hospitalized cases were tested positive for HIV and the children selected as controls were in good health . Among the 406 HIV-negative cases , only 333 were included in our analysis because they were well-matched with the controls as shown in the flow chart . The main criterion for impaired matching was age . The majority of these cases were borderline , with only few days or weeks of difference between cases and controls . The addition of the wrongly matched children for age and sex did not modify the results . In the current analysis , we prefer to maintain the criteria that were defined in the protocol . At least one of the sought-for pathogens was identified in around 80% of children hospitalized for diarrhea , and approximately one in ten cases presented mixed bacterial/viral co-infections , consistent with findings in other African countries [13 , 14] . Five pathogens were significantly associated with severe hospitalized diarrhea , namely rotavirus , norovirus , astrovirus , Shigella/EIEC , Cryptosporidium hominis/parvum . Seasonality and age effects were also observed , consistent with other studies [9 , 15–17] . These results may assist clinicians diagnosing the causes of diarrhea . The frequency of Giardia intestinalis detection was lower in children with diarrhea ( 0 . 9% ) than in asymptomatic carriers ( 7 . 8% ) , a finding also in keeping with previous data [18 , 19] and supporting claims that this parasite is not a major cause of severe diarrhea . Further studies are needed to determine whether Giardia intestinalis protects against or is a consequence of diarrhea . As in GEMS [11] , most attributable episodes of diarrhea were associated with rotavirus ( 40% of the cases versus 3 . 3% in controls ) in concurrence with previous data from CAR [20 , 21] and other Sub-Saharan countries [15] . Most rotavirus infections occurred in the youngest children , consistent with the limited protection conferred by maternal antibodies during the first months of life and the effective immunity granted by repeated infections [22] . The incidence and severity of rotavirus infections has declined significantly in countries that have integrated the rotavirus vaccine into their routine childhood immunization policies [23] , highlighting the urgent need to introduce it within the CAR’s Expanded Program on Immunization [24] . The proportion of norovirus infections among cases and controls was slightly lower than the prevalence reported from high-mortality developing countries ( about 14% in cases versus 7% in controls ) [16] . Cryptosporidium , a major cause of chronic diarrhea in malnourished patients or those with positive HIV status [25] , was a significant pathogen in our study . Our findings on Cryptosporidium are consistent with those from GEMS [11] and indicate the high global burden of cryptosporidiosis among children in Africa , regardless of their HIV-status . These elements support the need to inform healthcare professionals about this pathogen and to develop practical , inexpensive kits for its detection in resource-poor settings . Shigella spp . are consistently reported as highly associated with diarrhea in case-control studies [11 , 26 , 27] . In our study , Shigella spp are the third most important pathogen to be associated with diarrhea , after rotavirus and Cryptosporidium . The proportion of Shigella/EIEC infections among cases and controls was 15% and 9% , respectively , indicating that the prevalence of asymptomatic shedders is higher than expected . However , comparing our findings with those of other studies is difficult , because the pathogen was largely detected using PCR . Traditionally , the current gold standard for Shigella species detection is culture , which is highly selective , but poorly sensitive due to inconsistent bacterial load , loss of bacterial viability during specimen transport and frequent antibiotic treatment before culture . In addition , the gene ipaH used for the diagnosis is also carried by EIEC , implying that PCR cannot differentiate between Shigellosis and EIEC . Nevertheless , it is well established that Shigella is much more prevalent and thus , probably represents most of the ipaH-associated organisms detected . Usually , PCR-based methods have a higher sensitivity compared to conventional culture methods , which improves the ability to detect pathogens in a stool sample . As individuals with diarrhea tend to have higher quantities of bacteria isolated from their stool than do those without diarrhea [28–30] , the Shigella-specific disease burden might be underestimated using qualitative PCR . A recent study showed that a cutpoint threshold of approximately 1 . 4 × 104 ipaH copies could be the new reference standard for the detection and diagnosis of shigellosis in children in low-income countries [31] . In case-control studies , diarrhoeagenic E . coli pathotypes show inconsistent association with diarrhea patients , whereas Salmonella enterica , Campylobacter and adenovirus are often found in similar proportions in patients with or without diarrhea , as we observed in our data [10 , 11 , 19 , 26] . This finding suggests that comparing prevalence between cases and controls may have little value for pathogens with frequent asymptomatic excretion [32 , 33] . As reported in previous studies [34 , 35] , acute malnutrition was associated with severe diarrhea and was shown to be a risk factor for death during hospitalization . This finding is of major concern , because in 2012 in CAR , 8% of the children under five years suffered from acute malnutrition and 39% of chronic malnutrition [36] . This situation has likely worsened because the country has experienced civil war since March 2013 . Our findings also shed light on the management of severe childhood diarrhea . Although the WHO recommends the use of antibiotics in children with bloody diarrhea ( 5% of cases in our study ) , suspected cholera , or associated sepsis , we found that 40% of children before their arrival and 70% during hospitalization received an antibiotic treatment . Combined with the widespread use of antiparasitic treatments before consultation ( 44% ) , this finding could partially account for the relatively low number of bacteria or parasites found among cases . However , the bias was minimized for Shigella species and Cryptosporidium parvum/hominis as they were detected using PCR . Furthermore , the uncontrolled consumption of antimicrobial agents is cause for concern in countries like the CAR with inadequate healthcare systems , because it favors the spread of antimicrobial resistance [37–39] . Antibiotic treatments were primarily pills obtained without prescription from street vendors , who offer also diagnosis and limited diagnostic and medical services [6] . These medicines are much less expensive than in pharmacies , but there are no oversights on the safety , appropriateness or duration of such treatments [6] . Despite guidelines that recommend the use of ORS and zinc supplementation for all children , only 40% of children received ORS and less than 1% took zinc before their hospitalization . Although the pre-hospitalization ORS finding is higher than that found in other studies ( ORS before hospitalization in 16% of cases according to UNICEF in CAR between 2008 and 2012 , 20% in Senegal ) , it is essential to continue education of mothers on the importance of rehydration and zinc in home management of diarrhea . The WHO also recommends exclusive breastfeeding for the first six months of life . Only 16% of children under six months old were exclusively breastfed , whereas others were exposed to putative pathogens in weaning foods or inadequate diversity of complementary foods . Our study has limitations . We could not complete the planned inclusion of 600 cases and 600 controls , mainly due to security problems in Bangui . Only 2 or 3 children were recruited per day during the study-period . This low recruitment can be explained by the cost associated with transport to the hospital , hospitalization , treatment and care . As previously described [6] , children afflicted with severe diarrhea have a complex therapeutic itinerary . On average , parents bring their diarrheic children to the hospital after three days of symptom , and after frequently receiving various street medicines or home remedies . These findings are confirmed by the socio-economic differences observed between cases and controls , the cases being “less poor” than the controls . It is likely that a certain number of children never go to the hospital because their parents cannot pay . Moreover , at a time where ORS was widely adopted in the community , it is not surprising that children with moderate diarrhea that predominates in Bangui were not hospitalized , and therefore were not cases included in our study . The high childhood mortality rate reported in our study ( 4% ) may be even higher for children with poor access to healthcare services . The choice of immunological methods to detect rotavirus , astrovirus , adenovirus and norovirus is questionable . Indeed , some more sensitive molecular methods have been used in other epidemiological studies on diarrhea [40 , 41] . However , the impact of using lower sensitivity methods on the interpretation of the results is likely to be low . The sporadic low level viral shedding is not necessarily clinically relevant and if detected using highly sensitive methods can complicate the interpretation of the results . As described above for Shigella species , high sensitivity of PCR-based methods could lead to an underestimation of virus-specific disease burden when comparing cases with controls . Nevertheless , quantitative methods would have been very useful to assess the relationship between virus shedding and clinical severity in our study [42] . The data reported here are particularly important , given the significance of childhood diarrhea in countries with inadequate healthcare systems and long-term instability , as well as the lack of high-quality data and the difficulty of carrying out such studies in these contexts . Rotavirus , norovirus , astrovirus , Shigella/EIEC , Cryptosporidium hominis/parvum and Giardia intestinalis were significantly associated with severe hospitalized diarrhea . Because rotavirus was the most common cause of severe diarrhea , the introduction of the rotavirus vaccine in CAR will certainly have a major impact on childhood diarrhea . Severe diarrhea requiring antibiotics was extremely rare among CAR children under five years old . The observed overuse of antibiotics poses a major risk for the emergence of resistance , particularly when new discoveries of antibiotics are almost non-existent and the risk of therapeutic impasse real . The development of new medicines , vaccines and new rapid diagnostic tests that can be conducted bed-side should be high priorities for low-resource countries , particularly those suffering from instability and poorly-functioning health systems , as well as for global health structures . Future studies measuring the impact of antibiotic overuse on the intestinal microbiome of children will help to shed light on the complex linkages between malnutrition , diarrhea and immunity .
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Infectious diarrhea is a major cause of illness and death among children under five years from low-income country . In order to identify infectious agents associated with diarrhea , we conducted a case-control study in the Pediatric Complex of Bangui , the sole public pediatric hospital from Central African Republic ( CAR ) . A total of 333 hospitalized children with diarrhea and 333 controls were included , controls being pair matched to the cases according to age , sex and neighborhood . At least one of the sought-for pathogens was identified in 80% of hospitalized children , and approximately one in ten cases presented mixed bacterial/viral co-infections . Five pathogens were positively associated with hospitalized diarrhea , namely rotavirus , norovirus , astrovirus , Shigella/EIEC and Cryptosporidium hominis/parvum . Giardia intestinalis was negatively associated with hospitalized diarrhea . A seasonality effect—viruses during the dry season , bacteria and parasites during the rainy season—but also an age effect , were observed , which may guide clinicians in the management of diarrhea . As rotavirus was the leading cause of severe diarrhea , the introduction of the rotavirus vaccine in CAR will certainly provide considerable direct health benefits in terms of reduced illness and deaths . New medicines , vaccines and rapid diagnostic tests that can be conducted bedside should be urgently developed for low-resource countries .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
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Etiology and Epidemiology of Diarrhea in Hospitalized Children from Low Income Country: A Matched Case-Control Study in Central African Republic
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Recently we identified multiple suramin-sensitivity genes with a genome wide screen in Trypanosoma brucei that includes the invariant surface glycoprotein ISG75 , the adaptin-1 ( AP-1 ) complex and two deubiquitylating enzymes ( DUBs ) orthologous to ScUbp15/HsHAUSP1 and pVHL-interacting DUB1 ( type I ) , designated TbUsp7 and TbVdu1 , respectively . Here we have examined the roles of these genes in trafficking of ISG75 , which appears key to suramin uptake . We found that , while AP-1 does not influence ISG75 abundance , knockdown of TbUsp7 or TbVdu1 leads to reduced ISG75 abundance . Silencing TbVdu1 also reduced ISG65 abundance . TbVdu1 is a component of an evolutionarily conserved ubiquitylation switch and responsible for rapid receptor modulation , suggesting similar regulation of ISGs in T . brucei . Unexpectedly , TbUsp7 knockdown also blocked endocytosis . To integrate these observations we analysed the impact of TbUsp7 and TbVdu1 knockdown on the global proteome using SILAC . For TbVdu1 , ISG65 and ISG75 are the only significantly modulated proteins , but for TbUsp7 a cohort of integral membrane proteins , including the acid phosphatase MBAP1 , that is required for endocytosis , and additional ISG-related proteins are down-regulated . Furthermore , we find increased expression of the ESAG6/7 transferrin receptor and ESAG5 , likely resulting from decreased endocytic activity . Therefore , multiple ubiquitylation pathways , with a complex interplay with trafficking pathways , control surface proteome expression in trypanosomes .
Trypanosoma brucei is the causative agent of human African trypanosomiasis ( HAT ) and nagana , and severely impacts both human health and economic prosperity in sub-Saharan Africa . HAT is subdivided into two forms; acute caused by the T . b . gambiense subspecies and chronic caused by T . b . rhodesiense . Currently , five drugs are available to treat HAT and , while deployment depends on disease stage and subspecies , adverse toxicity , complex administration regimes and emerging resistance all contribute to the need for new therapies and improved understanding of the mechanisms by which existing drugs act [1] . Recent appreciation of T . evansi and T . equiperdum as very closely related to T . brucei extends the impact of the African trypanosomes to much of Asia and Latin America [2] . Bloodstream-form trypanosomes exhibit a highly efficient endocytic system that enables rapid recycling of surface proteins , antibody clearance and nutrient uptake . This is reflected by the presence of the flagellar pocket , a defined membrane region at the flagellar base , dedicated to incoming and outgoing membrane traffic [3] . This organelle facilitates rapid uptake and recycling of variant surface glycoproteins ( VSGs ) , dimeric , glycosylphosphatidylinositol ( GPI ) anchored glycoproteins that dominate the cell-surface at this life cycle stage . The dense VSG surface coat , that has the ability to undergo antigenic variation by switching between immunologically distinct VSG variants , is recognized as the primary defence against both , innate and acquired immune response [4] . Intercalated with the VSG-coat are trans-membrane-domain ( TMD ) proteins . Invariant surface glycoprotein ( ISG ) families are amongst the most abundant TMD-proteins of bloodstream form T . brucei [5 , 6] , with ISG65 and ISG75 estimated at 70 , 000 and 50 , 000 copies per cell , respectively [7] . Significantly , both these type I TMD proteins are modified by ubiquitylation , with internalisation and degradation depending on ubiquitylation at specific cytoplasmic residues [7 , 8] . In higher eukaryotes , ubiquitylated proteins destined for the lysosome are regulated by the endosomal sorting complex required for transport ( ESCRT ) machinery , where ubiquitylated proteins are recognised and sorted into multivesicular bodies ( MVBs ) [9]; similar pathways probably operate in trypanosomes [10] . Recent studies linked ISG75 to the action of suramin [11] , the oldest trypanosome drug remaining in the pharmacopeia , but which is only useful in the clinic against early stage T . b . rhodesiense [12] . Using genome wide RNAi-target sequencing we identified a cohort of genes involved in sensitising trypanosomes to suramin [11] . This implicated , along with ISG75 , multiple proteins with roles and/or locations at the endocytic pathway , including a major facilitator superfamily transporter ( MFST ) [13] , two deubiquitylating enzymes ( DUBs ) orthologous to human Usp7 and Vdu1 , the AP-1 adaptin complex [14] , Golgi/lysosomal protein-1 ( GLP-1 ) [15] , the trypanosome ortholog of Vps5 [16] , cathepsin-L ( CatL ) [17 , 18] and the essential lysosomal protein p67 [19 , 20] . Specific knockdowns of several of the above , including the two DUBs , significantly increased suramin resistance ( [11] and S1 Fig ) . The absence of many central endocytic genes , e . g . clathrin , Rab5 and Rab7 , from the suramin sensitivity gene cohort likely arises from the severe lethality that knockdown of these genes elicits . AP-1 is involved in bi-directional clathrin-dependent transport between the trans-Golgi network ( TGN ) and endosomes in higher eukaryotes [21] , and while its role in T . brucei is less clear , AP-1 does participate in trafficking of lysosomal protein p67 in bloodstream form trypanosomes , potentially connecting these suramin-sensitivity genes [14 , 22] . Further , the absence of the AP-2 complex in T . brucei suggests that AP-1 likely assumes a more prominent endocytic role in African trypanosomes [23] . Significantly , no cargo-specific DUBs are known in trypanosomatids , but it is possible that ISG75 and the DUBs identified as suramin-sensitivity determinants are in some manner connected . Suramin most likely gains access to the cytoplasm via the MFST family of transporters , to interfere with additional cellular functions , including glycolysis [24 , 25] . Our current model is that suramin binds to ISG75 and is delivered via endocytosis to the lysosome , where CatL releases suramin from its binding partner [26] . However , definitive evidence for a direct interaction with ISG75 and a role for ISG75 as the suramin receptor remains to be obtained . Further , we have not integrated the role of the AP-1 complex into our model . We previously demonstrated that TbUsp7 knockdown specifically decreased ISG75 , but not ISG65 , expression levels . Given the similarities in architecture , trafficking , stage-specific expression and evolutionary histories , these findings suggest a complex mechanism underpinning ISG expression , and thus suramin sensitivity . To define the pathways affecting these processes , we examined the roles of TbUsp7 , TbVdu1 and TbAP-1 on ISG trafficking , combining knockdown , imaging , analysis of ISG turnover , as well as global proteome analysis . We find a complex interaction between ISGs and deubiquitylation pathways , as well as connections to additional surface proteins , indicating connectivity between ISG trafficking , endocytosis and nutrient acquisition . These data , in conjunction with our earlier findings [11] , also demonstrate that sensitivity of bloodstream-form trypanosomes to suramin is dependent on their uniquely active endocytic apparatus , and further confirms that exploitation of this pathway has significant potential for the development of novel therapeutics .
All four subunits of the trafficking adaptor , AP-1 , were identified in our screen for suramin-sensitivity . Given the known roles for this complex in post-Golgi trafficking in higher eukaryotes , we first asked if trafficking and stability of proteins located at the surface or within the endosomal system identified in the suramin screen are regulated by AP-1 [11] . Since knockdown of any AP-1 subunit destabilises the remaining proteins in the complex [14] , steady state levels of GLP-1 , CatL and MFST were assessed following AP-1γ knockdown [14] . RNAi strongly decreased AP-1γ protein levels ( ~90% ) , but no significant changes in steady state levels of GLP-1 were found and only a small increase in CatL level was seen ( Fig 1A ) . As we were unable to obtain reliable data for MFST12myc by Western blotting , we used immunofluorescence ( IF ) to monitor MFST localisation and copy number . The locations of GLP-1 , CatL and MFST were unaffected by AP-1 knockdown , with no significant differences in signal intensity between uninduced and induced cells , suggesting that neither trafficking nor expression levels of these proteins depends significantly on AP-1 . AP-1 knockdown also had no obvious effect on ISG65 and ISG75 localisation ( Fig 1C ) , steady state levels or turnover ( Fig 1A and 1D ) . We conclude that ISG65 and ISG75 are either targeted by an AP-1-independent mechanism or that a redundant pathway assumes the role of AP-1 in its absence . It is likely that the absence of a major effect on ISG reflects the specific roles of AP-1 . Significantly , another suramin sensitivity determinant , the lysosomal membrane protein p67 , depends on AP-1 for its lysosomal delivery . Evidence suggests that trafficking of p67 is via a distinct route from ISGs that does not involve transport through the flagellar pocket . Moreover , p67 trafficking is reliant on classical dileucine motifs within the cytoplasmic domain . Hence , the identification of the AP-1 complex as a suramin sensitivity determinant probably reflects a role in the trafficking of p67 or other lysosomal proteins required to maintain the lysosomal environment necessary for suramin action [11 , 14 , 20 , 22 , 27] . The suramin-sensitivity screen identified two deubiquitylating enzymes , encoded by Tb927 . 9 . 14470 and Tb927 . 11 . 12240 . In silico analysis indicated that both are evolutionarily conserved across eukaryotes , with the human orthologs being USP7 and VDU1 , respectively , and therefore we designated these gene products as TbUsp7 and TbVdu1 . Significantly , USP7 is involved in multiple pathways including cell cycle control , epigenetics and the immune response , and is the target of anti-oncogenesis drug screening efforts [28] . Conversely , VDU1 is predicted as membrane-associated and part of a complex ubiquitylation switch controlling expression of the HIF-1α transcription factor [29] . VDU1 exists in a complex with an Rbx/cullin/elongin E3 ubiquitin ligase that provides a rapid mechanism for downregulation of receptor tyrosine kinases upon ligand binding , and orthologs of all components of this E3 ubiquitin ligase are present in the T . brucei genome , suggesting that trypanosomes possess an analogous complex . Cells harbouring stem-loop RNAi constructs specific for either TbUsp7 or TbVdu1 were induced with tetracycline and their mRNA abundance assessed by qRT-PCR; both were silenced by over 60% ( Fig 2A ) . We previously observed growth defects for TbUsp7 under these conditions [11] , but in contrast , knockdown of TbVdu1 , a probable membrane-associated protein confined to the flagellar pocket region , did not impact proliferation ( S1 Fig ) . Furthermore , while knockdown of TbVdu1 did not induce morphological defects at the light microscopy level , knockdown of TbUsp7 led to enlargement of the flagellar pocket , the ‘BigEye’ phenotype originally observed for knockdown of clathrin , and consistent with the proliferative defect resulting from TbUsp7 RNAi ( Fig 2B and [30] ) . Quantification revealed that ~20% of cells in TbUsp7 knockdown cultures possess the BigEye morphology , constituting a 40-fold increase in BigEye frequency , compared with ~5-fold increase for TbVdu1 knockdown ( Fig 2C ) . As enlargement of the flagellar pocket suggests an endocytic block , we monitored endocytosis with fluorescent-ConA in TbUsp7 and TbVdu1 knockdown cells . ConA rapidly enters into early endosomes and is subsequently delivered to the lysosome in trypanosomes , a process unaffected by TbVdu1 knockdown ( S2 Fig ) . In contrast , TbUsp7 knockdown blocked ConA uptake in BigEye cells ( Figs 2D and S2 ) , with ConA remaining at the flagellar pocket over the course of the assay . TbUsp7 knockdown cells without the BigEye phenotype displayed ConA trafficking similar to uninduced cells , confirming that the BigEye morphology was indeed associated with blocked endocytosis . While the kinetics of appearance of the BigEye morphology was significantly slower than following clathrin knockdown , it was similar to knockdown of several other proteins acting within the endosomal apparatus , for example Rab5 [31] and TbCAP116 [32] , suggesting that modification of a critical component of the endosomal apparatus is impacted by TbUsp7 knockdown or that the protein has some direct role in endocytosis . We therefore asked whether TbUsp7 knockdown impacts clathrin itself , as Rab5 knockdown leads to a BigEye phenotype via decreased expression of clathrin [31] . However , steady state levels of clathrin were unaffected ( S3 Fig ) , suggesting another molecular target . ISG65 and ISG75 are both ubiquitylated in vivo , but the location where this modification occurs is unknown . To determine if ubiquitylation required entry to the cell interior or could occur at the cell surface , we performed RNAi knockdown of clathrin and monitored both steady state expression and the ubiquitylation status of ISG65 and ISG75 . Clathrin knockdown results in decreased intracellular ISG65 with a concomitant increase in steady state levels of ISG65 ( Fig 3A ) , consistent with a block in endocytosis and degradation [7] . By contrast , clathrin knockdown had no effect on steady state levels of ISG75 ( Fig 3A ) . Furthermore , while we did not observe a significant difference in the ubiquitylation profile of ISG65 in clathrin knockdown relative to uninduced cells , ISG75 consistently showed increased ubiquitylation ( Fig 3B ) . As endocytosis has been blocked , this indicates that ISG75 is likely ubiquitylated at the cell surface and prior to internalisation , but there is no similar evidence for ISG65 . Together , these data suggest that while ISG65 and ISG75 are both ubiquitylated , the modification likely occurs at different sub-cellular locations and by distinct mechanisms . We next assessed expression levels of ISG65 and ISG75 following knockdown of the two DUBs . TbUsp7 knockdown resulted in strongly decreased ISG75 expression ( to ~20% ) but not ISG65 , as previously shown [11] . Significantly , TbVdu1 knockdown led to decreases in both ISG75 ( to ~60% ) and ISG65 ( to ~35% ) ( Fig 4A ) . These observations were corroborated by immunofluorescence microscopy , where intracellular pools of ISG75 , but not ISG65 , were significantly diminished by TbUsp7 knockdown , while both ISG65 and ISG75 were reduced on TbVdu1 knockdown ( Fig 4B ) . Where a residual intracellular signal was observed in TbVdu1 knockdown cells , ISG65 and ISG75 remained localised to posterior endomembrane compartments , and hence were not significantly mislocalised . However , in cells exhibiting the BigEye phenotype following TbUsp7 knockdown , ISG65 and ISG75 staining accumulated at the enlarged flagellar pocket , presumably retained due to blocked endocytosis ( Fig 4B ) . Overall , these data suggest that while ISG65 and ISG75 are not mistargeted , decreased expression level represents the primary impact of TbUsp7 or TbVdu1 knockdown . We next examined degradation rates for ISG65 and ISG75 in DUB knockdowns by blocking protein synthesis with cycloheximide and measuring protein abundance by Western blotting . While ISG75 was significantly destabilised , ISG65 degradation was unaffected by TbUsp7 knockdown ( Fig 5A ) . In contrast , both ISG65 and ISG75 were turned over more rapidly in TbVdu1 knockdown cells ( Fig 5B ) . All of these observations indicate that the major mechanism underpinning lower ISG65 and ISG75 expression levels is decreased stability/increased turnover in DUB knockdown cells , consistent with an inhibitory effect of DUBs on ISG ubiquitylation levels . To determine if the impact of DUB knockdown on ISG expression was due to changes in synthesis or turnover , we initially quantified the respective mRNAs by qRT-PCR . While no significant differences were observed in transcript levels for either ISG when TbVdu1 was knocked down , we observed almost twice as much ISG75 mRNA in the presence of TbUsp7 knockdown compared with uninduced cells ( Fig 6A ) . This suggests the presence of a feedback mechanism able to upregulate ISG75 transcript levels in order to maintain copy number upon protein destabilisation . The lack of evidence for decreased ISG mRNA levels in either DUB knockdown , indicates that reduced transcription can be excluded as an explanation for the observed decrease in ISG protein level . To examine if decreased translation could account for lower ISG expression level following DUB knockdown , cells were pulse-labelled for one hour with 35S-methionine followed by immunoprecipitation of ISG65 and ISG75 . We observed a small effect on 35S-methionine incorporation into ISG65 following TbVdu1 depletion , but this was substantially less than the observed destabilisation . We saw more pronounced decreased 35S-methionine incorporation into ISG75 following TbUsp7 ( ~60% decrease ) or TbVdu1 ( ~30% decrease ) knockdown ( Fig 6B ) . While we cannot exclude that ISG75 biosynthesis is affected by DUB knockdown , given that the cells were pulse-labelled for 60 minutes and ISG75 is turned over rapidly following DUB knockdown ( T1/2 1 . 5 and 2 . 0 hours following TbUsp7 and TbVdu1 knockdown , respectively , Fig 5A and 5B ) , accelerated turnover likely accounts for the majority of the decrease in ISG75 labelling , as a substantial portion of the labelled ISG75 would have been degraded even during the 35S-pulse period . Taken together , these data indicate that the majority of observed changes in ISG65 and ISG75 expression following DUB depletion likely arise from accelerated turnover rather than biosynthesis , suggesting that the DUBs influence ISG modification directly . As knockdown of TbUsp7 and TbVdu1 has such a profound effect on ISG75 expression , we were unable to monitor the ubiquitylation status of ISG75 under these conditions , as even in wild type cells ubiquitylated adducts of ISG75 are a very minor fraction , and attempts to detect such adducts in TbUsp7 knockdowns were unsuccessful . As an alternative approach , we reasoned that if TbUsp7 was acting directly on ISG75 , then a reporter construct that bore the ubiquityation sites , but lacked the ISG75 ectopic domain would behave in a similar manner to ISG75 in TbUsp7 knockdown cells . TbUsp7 RNAi cells were transfected to overexpress BiPN-ISG75L , a reporter containing the BiP ATPase N-terminal domain ( BiPN ) fused to an HA-tag and followed by the trans-membrane and cytoplasmic domain of ISG75 [8] . As before , we observed an increased destabilisation of ISG75 both at steady state ( at time 0 h ) and over time following TbUsp7 knockdown , and also increased turnover of BiPN-ISG75L relative to uninduced cells ( Fig 7 ) . These data indicate that the cytoplasmic domain of ISG75 is sufficient for TbUsp7-mediated stabilisation , and suggests that modulation of ubiquitylation likely accounts for altered ISG75 expression levels . To extend our understanding of interactions between TbUbp7 and TbVdu1 and the parasite surface , we analysed the effect of DUB knockdown on the whole cell proteome using stable isotope-labeling by amino acids in culture ( SILAC , see methods ) . As maximal knockdown for both DUBs was achieved by 48 hours , we examined cells at 26 and 48 hours after induction for TbUsp7 , and at 48 hours for TbVdu1 knockdown . We were able to quantify 1769 , 1823 and 1879 distinct proteins from these analyses respectively , representing approximately 25% of the total cellular proteome . As the vast majority of proteins were unaltered , this indicates that the detected changes are specific and not the result of general toxicity arising from the knockdown . The impact of TbUsp7 and TbVdu1 on ISG65 and ISG75 determined by SILAC ( see Table 1 , Fig 8 ) was in perfect agreement with semi-quantitative Western blotting ( see above ) . TbUsp7 knockdown reduced ISG75 abundance to ~40% after 48 hours , whilst total ISG65 levels were unchanged . TbVdu1 knockdown decreased ISG75 and ISG65 to ~55% and ~65% after 48 hours , respectively . ISG75 paralogs were equally affected by the DUB knockdowns , and similarly the ISG65 paralogs were also decreased uniformly . Furthermore , SILAC analyses revealed an impact of TbVdu1 knockdown on additional proteins , including a distant ISG paralog , Tb927 . 5 . 630 , that was also decreased . Perturbation of the proteome by TbUsp7 knockdown was more extensive , necessitating a kinetic analysis . We considered as significant only those proteins where changes were seen at both sampled time points and also where the magnitude of that change increased between the 26 and 48 hour datasets . Significantly , the membrane-bound histidine acidic phosphatase MBAP1 decreased to 29% at 48 hours . This well-characterised trans-membrane protein possesses a topology similar to ISGs and is essential for both endocytosis and exocytosis [33] . Two predicted bitopic membrane proteins and a multi-spanning trans-membrane protein , all with unknown function , were decreased to a similar degree . Furthermore , the vesicle associated SNARE protein VAMP7B , which localises to endosomes ( Divya Venkatesh and MCF , in preparation ) and a TPR-repeat containing protein were also decreased . Finally , a small cohort of bloodstream stage-specific GPI-anchor containing proteins was significantly upregulated following TbUsp7 knockdown . The majority of these are VSG expression site-associated genes ( ESAGs ) . ESAG6 and ESAG7 , the transferrin receptor [34 , 35] increased two-fold ( Table 1 , Fig 8 ) . ESAG5 and to a much lesser extent ESAG2 also increased; these have recently been shown to be surface or endomembrane system proteins [6] . ESAG5 contains BPI ( bactericidal/permeability-increasing protein ) /LBP ( lipopolysaccharide-binding protein ) /PLUNC ( palate , lung and nasal epithelium clone ) -like domains [36] , and based on this architecture is proposed to bind lipid or lipopolysaccharide and have a signalling function [37] . Remarkably specific alterations to the trypanosome proteome result from DUB knockdown . These data explain the endocytosis defect resulting from TbUsp7 RNAi , as downregulation of MBAP1 is known to lead to an enlarged flagellar pocket . Given the topology of MBAP1 and the presence of several lysine residues within the short cytoplasmic region , we speculate that MBAP1 is likely ubiquitylated , and hence TbUsp7 may participate in controlling its expression . Increases to expression of ESAG6/7 probably arise from the endocytosis defect , as trypanosomes precisely regulate expression of the transferrin receptor to maintain iron homeostasis [38] .
Maintaining cell surface composition is a vital component of cellular homeostasis . For African trypanosomes this process is also essential to ensure integrity of the VSG monolayer , an aspect of surface biology critical for immune evasion and additional functions encompassing nutrient uptake and sensing . The importance of surface protein expression and trafficking in African trypanosomes is also underscored by the recent finding that ISG75 expression level and the endocytic apparatus are involved in suramin uptake and sensitivity . Understanding this process is fundamental both to revealing the mode of action of suramin and for the consideration of exploitation of this pathway for delivery of drugs into trypanosomes . Furthermore , the process is shown here as a component of mechanisms controlling turnover of surface proteins in trypanosomes . Several components of the mechanisms involved in setting expression levels of VSG , ISG65 , ISG75 and the ESAG6/7 transferrin receptor are known [38] ( reviewed [43] ) . For example , internalisation and turnover of ISG65 and ISG75 depends on ubiquitylation and sorting by the ESCRT system [7 , 10 , 8 , 27] , but the identity of the ubiquitin ligases and deubiquitylating enzymes has remained elusive . Significant divergence within the ubiquitylation system in trypanosomes compared to animals and fungi [10] has precluded facile functional assignments based on comparative genomics of DUB and ubiquitin ligase families . Furthermore , many proteins expressed at the trypanosome surface are lineage restricted and there is little information on expression level control or turnover [6] . The gene cohort involved in suramin sensitivity is restricted to proteins that are part of the surface proteome and/or endosomal system . The cohort contains AP-1 , a major player in protein targeting in many organisms . While roles for AP-1 in trafficking of p67 , the major lysosomal protein of trypanosomes are known [14 , 22] , no impact on expression or location of ISGs was observed here . The absence of canonical dileucine adaptin-binding motifs in the cytoplasmic domains of ISGs is also consistent with this finding , and suggests that AP-1 mediates an aspect of suramin-sensitivity distinct from ISG trafficking . It is possible that this is mediated via p67 itself , which was also identified as a suramin-sensitivity determinant and is probably directed to the lysosome via a route distinct to that for endocytosis of surface proteins . However , while evidence supports a role for AP-1 in p67 transport in insect stage cells , data based on proteolytic processing of p67 suggests this is not the case in bloodstream-form cells [44] . A small , but reproducible , upregulation of CatL under AP-1 knockdown was observed , suggesting that AP-1 is involved in CatL delivery to the lysosome and possibly also its maturation , but the mechanism remains unclear . These data do , however , demonstrate the presence of an ISG-independent , AP-1-dependent pathway that influences suramin sensitivity , suggesting that trafficking to the lysosome is an important factor . By contrast , TbUsp7 and TbVdu1 , two evolutionarily conserved DUBs , are critical to the control of ISG abundance and indicate the presence of a second , ISG-dependent pathway for suramin sensitivity . Our data suggests a model in which silencing of the respective DUB leads to increased targeting of specific ISG proteins into the degradative arm of the endocytic pathway ( Fig 9 ) . We propose that the most parsimonious interpretation is that TbUsp7 and TbVdu1 impact ISG cargo uptake by directly affecting ISG stability . This is supported by several lines of evidence; membership of the suramin-sensitivity gene cohort , demonstration of direct , and highly specific impact on ISG expression level , homology to mammalian DUBs , localisation of TbVdu1 to the endosomal region , the absence of a major impact on ISG biosynthesis and the ability to transfer knockdown sensitivity to the BiPN chimera . A direct demonstration of hyperubiquitylation has not been possible , as the expression level of the ISG75 protein itself is so greatly decreased in knockdown cells making detection of the small fraction of ubiquitylated adduct unreliable . None-the-less , all of these data support an intimate relationship between ISG turnover rates and TbUsp7 and TbVdu1 . Proteomics revealed highly specific and precise control of ISG75 and ISG65 by TbVdu1 . Silencing of TbUsp7 resulted in strong downregulation of MBAP1 , a membrane protein essential for both incoming and recycling membrane traffic [33] , and upregulation of several ESAG products . Due to the presence of a high confidence predicted ubiquitylation site at the cytoplasmic domain of MBAP1 ( Lys502 , 0 . 96 likelihood in UbPred [45] ) , it is tempting to speculate that MBAP1 steady state levels are also controlled by ubiquitylation . The observed accumulation of ESAGs , including the transferrin receptor , could result from a block to endocytosis following MBAP1 downregulation . However , upregulated proteins form a relatively small cohort , while the bulk surface proteome , including for example ISG64 , appear unaffected by TbUsp7 knockdown , suggesting that distinct mechanisms control expression of GPI-anchored and trans-membrane domain proteins . This also includes p67 , the abundance of which was unaltered by the DUB knockdowns in SILAC analysis ( S1 Table ) , suggesting very distinct mechanisms for controlling surface and lysosomal protein expression . Interestingly , a VSG-related ( VR ) protein , Tb927 . 7 . 180 , was significantly upregulated by TbVdu1 knockdown , which provides the first evidence that these VR proteins are expressed at the surface . TbUsp7 appears to be cytosolic [11] , while TbVdu1 is , similarly to its human ortholog , membrane associated and appears to be confined to the flagellar pocket/early endocytic compartments ( S1 Fig ) . Knockdown of neither DUB led to ISG mislocalisation , except in cells with an enlarged flagellar pocket , resulting from defective endocytosis and likely connected with decreased MBAP1 expression . Significantly , ISG65 and ISG75 demonstrate unique aspects in their trafficking and ubiquitylation , beyond differential sensitivity to TbUsp7 and TbVdu1 , including increased ubiquitylation of only ISG75 at the cell surface following inhibition of endocytosis , suggesting that ISG75 is primarily ubiquitylated at the plasma membrane , while ISG65 may be modified elsewhere . Overall , these data indicate that ISG65 and ISG75 have distinct trafficking and modification pathways , as has been suggested by other studies [46 , 47] , and which contribute to their differential impact on suramin sensitivity , despite being comparatively similar proteins . However , it is also likely that these two ISG families have distinct binding specificities , which are also relevant to accumulation of suramin . Blue-native PAGE indicates that both proteins likely exist as complexes , with ISG65 mainly present as a dimer , and ISG75 as dimers and higher order forms ( S4 Fig ) ; it is unclear how these biochemical differences connect to the distinct trafficking pathways . In common with other eukaryotes , E3 ubiquitin ligases are likely key players in ubiquitylation in trypanosomes , a system that arose in Archaea prior to eukaryogenesis [48] . There are a large number of RING and HECT family E3s encoded in the trypanosome genome , together with representatives of the Rbx/Cullin E3 ligases . DUBs function in a temporally and spatially distinct manner to remove ubiquitin modifications , and these two systems also modulate each other by direct interactions . Stabilisation of cognate E3 ubiquitin ligases by deubiquitylation is a known aspect of DUB function [49] and DUBs are themselves ubiquitylation substrates . The mammalian ortholog of TbVdu1 , is a downstream target for ubiquitylation by the Cullin-RING E3 ubiquitin ligase component of pVHL [29] , a complex that acts to control expression of many proteins , including the transcription factor HIF-1α and also PAR3 , which moderates endocytic pathways in mammalian cells and is associated with tumorigenesis [29] . The Rbx1 component of the equivalent complex in African trypanosomes has been targeted by RNAi and leads to defective kinetoplast replication in insect stages , but has little impact in the bloodstream form , while the relevant Cullin protein has no impact on replication in either life stage ( Federico Rojas , personal communication ) . Significantly , a likely DUB for this complex in trypanosomes , TbVdu1 is membrane-associated and targeted to the flagellar pocket/early endosomes; this localisation is consistent with the elevated ISG75 ubiquitylation seen upon the cessation of endocytosis following clathrin RNAi knockdown . These findings support the existence of a ubiquitylation switch in trypanosomes mediating ISG75 ( but not ISG65 ) expression levels . We have previously observed rather complex mechanisms underpinning ISG65 and ISG75 expression , such that very limited mutations within the cytoplasmic domain , altering three lysine codons to arginine , lead to significant differences in mRNA levels not reflected by changes in protein level [8] . Control of ISG75 here is very similar to that of type 2 iodothyronine deiodinase , which has a very short half-life but is stabilised by VDU1 in mammals [50] . In conclusion , we describe two pathways that contribute towards suramin sensitivity , an AP-1 dependant pathway and an ISG-dependant pathway , with a particular focus on the latter . Complex interactions between TbUsp7 , ISG75 and other membrane proteins , especially MBAP1 identifies sophisticated mechanisms coordinating surface protein expression and intracellular targeting in trypanosomes . Both pathways are required for suramin-sensitivity , and we speculate that the AP-1-dependent pathway is required to maintain lysosomal conditions required for suramin-sensitivity , whilst the ISG75 pathway is needed for delivery of suramin to that compartment . The presence of a conserved Rbx/Cullin-type ubiquitylation switch , as evidenced by TbVdu1 , indicates that control of ISG75 expression is likely critical to the parasite , although the normal physiological function of ISG75 remains to be determined . The essentiality of AP1 and the presence of multiple ISG75 paralogs also may explain the remarkable lack of emergence of suramin resistant strains in the wild . Finally , these data are further evidence of the high clinical value of endocytosis in the treatment of trypanosomiasis , adding suramin acquisition to a list already containing VSG recycling , antibody clearance , essentiality of many clathrin-associated proteins and N-myristoyltransferase inhibitor sensitivity [51] .
Bloodstream form ( BSF ) Molteno Institute Trypanosomal antigen type ( MITat ) 1 . 2 , derived from Lister strain 427 and expressing VSG221 , were cultured in HMI-9 complete medium ( HMI-9 supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , 100 U/ml penicillin , 100 U/ml streptomycin and 2 mM L-glutamine ) [52] at 37°C with 5% CO2 in a humid atmosphere , in non-adherent culture flasks with vented caps . 2T1 cells ( a variant of MITat 1 . 2 ) were maintained in HMI-9 complete medium in the presence of phleomycin ( 1 μg/ml ) and puromycin ( 1 μg/ml ) . Following transfection with stem-loop RNAi plasmids , 2T1 cells were maintained in phleomycin ( 1 μg/ml ) and hygromycin ( 2 . 5 μg/ml ) [53 , 54] . All TbUsp7 and TbVdu1 RNAi experiments were performed following 48 h induction with tetracycline ( 1 μg/ml ) . Cells harbouring the p2T7-AP-1γ RNAi construct were maintained in G418 ( 2 . 5 μg/ml ) and phleomycin ( 0 . 5 μg/ml ) as described previously [14] . Cells were maintained at densities between 105 and 2 . 5 x 106 cells/ml . Suramin EC50 was assessed following three days of TbVdu1 RNAi knockdown . Cells were plated at 2x103 cells/ml in 96-well plates in a two-fold dilution series of suramin , starting from 1 μM . After three days growth , 20 μl resazurin ( Sigma ) at 125 μg/ml in PBS was added to each well and incubated for a further six hours at 37°C . Fluorescence was determined using a plate reader ( Molecular Devices ) with the following settings: excitation , 530 nm; emission , 585 nm; filter cut off , 570 nm . Data were processd in Excel , and non-linear regression analysis carried out in GraphPad Prism . 1 x 108 cells were harvested at 800 x g for 10 min at 4°C and washed with ice-cold PBS and quick-frozen in dry ice for 1 min . RNA was purified using the RNeasy mini kit ( Qiagen ) according to the manufacturer’s instructions . RNA concentration was quantified using an ND-1000 spectrophotometer and Nanodrop software ( Nanodrop Technologies ) . cDNA synthesis and qRT-PCR reaction setup was performed as described previously [56] . qRT-PCR was performed using iQ-SYBRGreen Supermix on a MiniOpticon Real-Time PCR Detection System ( Bio-Rad ) and was quantified using Bio-Rad CFX Manager software ( Bio-Rad ) . The following primers were used for qRT-PCR: bTub-RTF ( 5’-CAAGATGGCTGTCACCTTCA-3’ ) , bTub-RTR ( 5’-GCCAGTGTACCAGTGCAAGA-3’ ) ; USP1 RTF ( 5’-GAGATGGCACCATCACTCCT-3’ ) , USP1 RTR ( 5’-GTGGGCAGCACCTCTAGAAC-3’ ) ; VDU1 RTF ( 5’-GTCGAAAGACGTGTGGGTTT-3’ ) , VDU1 RTR ( 5’-GGAGCGAGGGAAGAGAGATT-3’ ) ; ISG65-RTF ( 5’-GAGCATGTTGATAGAGGGATTG-3’ ) , ISG65-RTR ( 5’-CATTGCTGTTCTCTGATGTCTG-3’ ) ; ISG75-RTF ( 5’-GAGGGCAGCGAGGCCAAG-3’ ) , ISG75-RTR ( 5’-CTTCCTACGGCCCCTAATAAC-3’ ) . 3 x 107 bloodstream-form cells were harvested by centrifugation at 800 x g for 10 min at 4°C . Cells were resuspended in 100 ul of Amaxa Human T-cell Nucleofector solution ( VPA-1002 ) at 4°C , mixed with 10 ug ( in 5 ul ) of linearised plasmid DNA and transferred to electrocuvettes . Transfection was achieved using an Amaxa Nucleofector II with Program X-001 . Cells were then transferred to Tube A containing 30 ml of HMI-9 medium plus any appropriate antibiotic drug for parental cell growth . Serial dilution was performed by transferring 3 ml of cell suspension from Tube A into Tube B containing 27 ml of HMI-9 medium and repeated again by diluting 3 ml from Tube B into Tube C . 1 ml aliquots for each dilution were distributed between three 24-well plates and incubated at 37°C . After 6 h , HMI-9 containing antibiotic selection was added to the wells at the desired final concentration . Transformed cells were recovered on day 5–6 post-transfection . Samples for IF were prepared as previously described ( Leung et al . , 2008 ) . Antibodies were used at the following dilutions: mouse and rabbit anti-HA epitope IgG ( both from Santa Cruz Biotechnology Inc . ) at 1:1000 , mouse 9E10 anti-myc ( Sigma ) at 1:1000 , rabbit anti-GFP ( Life Technologies ) at 1:500 , rabbit anti-ISG65 and rabbit anti-ISG75 ( from P . Overath , Tubingen ) at 1:1000 , rabbit anti-CatL at 1:1000 ( from J . Bangs , Buffalo ) , mouse anti-GLP-1 at 1:1000 ( from D . Russell , Cornell ) . Secondary antibodies were used at the following dilutions: anti-rabbit Cy3 ( Sigma ) at 1:1000 . Coverslips were mounted using Vectashield mounting medium supplemented with 4’ , 6-diamidino-2-phenylindole ( DAPI ) ( Vector Laboratories , Inc . ) . The cells were examined on a Nikon Eclipse E600 epifluorescence microscope fitted with optically matched filter blocks and a Hamamatsu ORCA CCD camera . Digital Images were captured using Metamorph software ( Universal Imaging Corp . ) on a Windows XP computer ( Microsoft Inc . ) , and the raw images were processed using Adobe Photoshop CS3 ( Adobe Systems Inc . ) . Protein synthesis was blocked by the addition of cycloheximide ( 100 μg/ml ) and 1 x 107 cells were harvested at various time points by centrifugation at 800 x g for 10 min at 4°C . Cells were washed in ice-cold PBS , then resuspended in 1 x SDS sample buffer and incubated at 95°C for 10 min . Samples were subjected to protein electrophoresis . Whole cell lysates and hypotonic lysis fractions were prepared as previously described ( Leung et al , 2008 ) . Proteins were separated by electrophoresis on 12 . 5% SDS-polyacrylamide gels and then transferred to polyvinylidene difluoride ( PVDF ) membranes ( Immobilon; Millipore ) using a wet transfer tank ( Hoefer Instruments ) . Non-specific binding was blocked with Tris-buffered saline with 0 . 2% Tween-20 ( TBST ) supplemented with 5% freeze-dried milk and proteins were detected by Western immunoblotting . The PVDF membrane was then incubated in primary antibody diluted in TBST with 1% milk for 1 h at room temperature . Antibodies were used at the following dilutions: monoclonal anti-HA ( sc-7392 , Santa Cruz ) at 1:10 , 000 , mouse 9E10 monoclonal anti-myc ( Source Biosciences ) at 1:5 , 000 , rabbit polyclonal anti-GFP ( Life Technologies ) at 1:5 , 000 , rabbit polyclonal anti-ISG65 and anti-ISG75 both at 1:10 , 000 , KMX-1 anti-beta-tubulin at 1:2000 ( Millipore ) , rabbit anti-CatL at 1:1000 , mouse anti-GLP-1 at 1:1000 . Following three washes with TBST each for 10 min , the membrane was incubated in secondary antibody diluted in TBST with 1% milk for 1 h at room temperature . Commercial secondary anti-rabbit peroxidase-conjugated IgG ( A0545 , Sigma ) and anti-mouse peroxidase-conjugated IgG ( A9044 , Sigma ) were used both at 1:10 , 000 . Detection was by chemiluminescence with luminol ( Sigma ) on BioMaxMR film ( Kodak ) . Densitometry quantification was achieved using ImageJ software ( NIH ) . Blue Native PAGE followed by Western blotting experiments were carried out as described [57] . 1 x 107 cells were pelleted at 800 x g for 10 min at 4°C , washed twice in PBS and resuspended in 500 ul of Met/Cys-free RPMI-1640 medium supplemented with dialysed FBS followed by an incubation at 37°C for 1 h . Cells were pulse-labeled at 37°C for 1 h with EasyTag EXPRESS [35S] Protein Labeling Mix ( PerkinElmer ) at a specific activity of 200 uCi/ml ( 7 ul of 14 . 3 mCi/ml ) and then instantly cooled on ice . Cells were pelleted at 16 , 000 x g for 15 sec on a table top centrifuge , washed twice with ice-cold PBS and lysed by the addition of 100 ul of RIPA buffer [25 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , and Complete Mini Protease Inhibitor Cocktail ( Roche ) ] for 15 min on ice . Lysates were centrifuged for 30 s to remove nuclei and cell debris , and the supernatant transferred to a fresh tube . 13 μl of 10% SDS was added to the supernatant and incubated at 95°C for 5 min . Samples were diluted with 750 μl of dilution buffer ( 50 mM Tris-HCl pH 7 . 5 , 1 . 25% Triton X-100 , 190 mM NaCl , 6 mM EDTA , Complete Mini Protease Inhibitor Cocktail ) . 30 μl of Pansorbin ( Calbiochem ) ( pre-washed and resuspended in dilution buffer ) was added to the supernatant and incubated at 4°C for 1 h . Samples were centrifuged at 16 , 000 x g for 5 min and the supernatant transferred to a fresh tube . 5 μl of anti-HA antibody was added to each sample and incubated at 4°C overnight on a rotating device . Immune complexes were then isolated by the addition of 20 μl of Protein A-Sepharose on a rotating device for 1 h at room temperature . Subsequently , Sepharose beads were washed twice with wash buffer I ( 50 mM Tris pH 7 . 5 , 0 . 1% Triton X-100 , 0 . 02% SDS , 150 mM NaCl , 5 mM EDTA ) and twice with wash buffer II ( 50 mM Tris pH 7 . 5 , 0 . 02% Triton X-100 , 1 M NaCl ) . A further centrifugation step was performed for 15 s at 16 , 000 x g and the remaining supernatant was removed by pipetting . Samples were resuspended in 1 x SDS sample buffer and denatured at 95°C for 5 min and subjected to 12 . 5% SDS-PAGE . Gel was then immersed in destaining solution for 20 min , washed twice with distilled water and then soaked in 1 M sodium salicylate for a further 20 min . The gel was then dried on Whatman 3MM paper for 2 h at 60°C and exposed to autoradiographic film for 1 week . HMI11 for SILAC was prepared essentially as described in [58]: IMDM depleted of L-Arginine , L-Lysine ( Thermo ) and 10% dialysed ( 10 kDa molecular weight cutoff ) fetal bovine serum ( Dundee Cell Products ) was supplemented with 4 ug/ml folic acid , 110 μg/ml pyruvic acid , 39 μg/ml thymidine , 2 . 8 μg/ml bathocuproinedisulfonic acid , 182 μg/ml L-cysteine , 13 . 6 μg/ml hypoxanthine , 200 μM β-mercaptoethanol , 0 . 5 μg/ml Phleomycin and 2 . 5 μg/ml Hygromycin . Finally either normal L-Arginine and L-Lysine ( HMI11-R0K0 ) , or L-Arginine U−13C6 and L-Lysine 4 , 4 , 5 , 5-2H4 ( HMI11-R6K4 ) ( Cambridge Isotope Laboratories ) were added at 120 uM and 240 uM respectively . RNAi was induced by addition of 1 μg/ml tetracycline . At the indicated times , equal numbers of induced and uninduced cells , grown in the presence of HMI11-R0K0 or HMI11-R6K4 respectively , were mixed , harvested by centrifugation , washed twice with PBS containing Complete Mini Protease Inhibitor Cocktail ( Roche ) then resuspended in Laemmli buffer containing 1 mM dithiothreitol and stored at -80°C . TbUsp7 and TbVdu1 RNAi samples were generated in duplicate and triplicate , respectively , with each replicate representing a different clone . One label swap was performed in each set of replicates . The heavy isotope incorporation at steady state was determined from one gel slice ( 60–80 kDa ) of a control experiment omitting induction . Samples were sonicated and aliquots containing 5 x 106 cells were separated on a NuPAGE bis-tris 4–12% gradient polyacrylamide gel ( Invitrogen ) under reducing conditions . The sample lane was divided into eight slices that were excised from the Coomassie stained gel , destained , then subjected to tryptic digest and reductive alkylation . Liquid chromatography tandem mass spectrometry ( LC-MS/MS ) was performed by the Proteomic Facility at the University of Dundee . The eight fractions obtained from SDS-PAGE were subjected to LC-MS/MS on a UltiMate 3000 RSLCnano System ( Thermo Scientific ) coupled to a LTQ OrbiTrap Velos Pro ( Thermo Scientific ) and mass spectra analysed using MaxQuant version 1 . 5 [59] searching the T . brucei brucei 927 annotated protein database ( release 8 . 1 ) from TriTrypDB [60] . Minimum peptide length was set at six amino acids , isoleucine and leucine were considered indistinguishable and false discovery rates ( FDR ) of 0 . 01 were calculated at the levels of peptides , proteins and modification sites based on the number of hits against the reversed sequence database . SILAC ratios were calculated using only peptides that could be uniquely mapped to a given protein . If the identified peptide sequence set of one protein contained the peptide set of another protein , these two proteins were assigned to the same protein group .
|
The mechanisms by which pathogens interact with their environment are of major importance , both for fulfilling the basic needs of the parasite and understanding immune evasion . For African trypanosomes , the surface is dominated by the variant surface glycoprotein ( VSG ) , but recent data has demonstrated an important role for ubiquitylation in mediating turnover of invariant surface glycoproteins ( ISGs ) and maintaining ISG copy number independent of VSG . Further , ISG expression is required for suramin-sensitivity . Here we describe mechanisms mediating ISG turnover , uncovered using a screen for genes involved in sensitivity to suramin . These involve multiple aspects of the ubiquitylation machinery , and connect ISG turnover with additional surface proteins . Our data provide a first insight into the complexity of regulation of the ISG family , identifying further aspects to the control of a drug-sensitivity pathway in trypanosomes , and offering insights into metabolism of the parasite surface proteome .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Modulation of the Surface Proteome through Multiple Ubiquitylation Pathways in African Trypanosomes
|
Autophagy is a cellular process that is highly conserved among eukaryotes and permits the degradation of cellular material . Autophagy is involved in multiple survival-promoting processes . It not only facilitates the maintenance of cell homeostasis by degrading long-lived proteins and damaged organelles , but it also plays a role in cell differentiation and cell development . Equally important is its function for survival in stress-related conditions such as recycling of proteins and organelles during nutrient starvation . Protozoan parasites have complex life cycles and face dramatically changing environmental conditions; whether autophagy represents a critical coping mechanism throughout these changes remains poorly documented . To investigate this in Toxoplasma gondii , we have used TgAtg8 as an autophagosome marker and showed that autophagy and the associated cellular machinery are present and functional in the parasite . In extracellular T . gondii tachyzoites , autophagosomes were induced in response to amino acid starvation , but they could also be observed in culture during the normal intracellular development of the parasites . Moreover , we generated a conditional T . gondii mutant lacking the orthologue of Atg3 , a key autophagy protein . TgAtg3-depleted parasites were unable to regulate the conjugation of TgAtg8 to the autophagosomal membrane . The mutant parasites also exhibited a pronounced fragmentation of their mitochondrion and a drastic growth phenotype . Overall , our results show that TgAtg3-dependent autophagy might be regulating mitochondrial homeostasis during cell division and is essential for the normal development of T . gondii tachyzoites .
Proteolysis is very important to eukaryotic cells and occurs at a considerable constitutive rate . Base line degradation regulates the levels of numerous proteins and removes misfolded proteins . Mechanistically , this process can be separated into two main pathways: one pathway mediated by the proteasome and the other pathway by the lysosome [1] . The proteasome relies on the ubiquitin system for selection of target proteins and plays a major role in the rapid degradation of short-lived proteins as well as abnormal proteins . The lysosome , which represents the terminal compartment of the endosomal pathway , is a membrane-bound organelle that contains a diverse array of hydrolases for the degradation of plasma membrane proteins and endocytosed extracellular proteins . Lysosomes are also involved in bulk degradation of cytoplasmic components , such as long-lived cytosolic proteins and organelles , and this is achieved through a process called autophagy . Autophagy has been divided into several classes of pathways , such as macroautophagy , microautophagy , and chaperone-mediated autophagy , but macroautophagy has been studied most extensively and we will refer to this specific form of the process as “autophagy” in this manuscript for simplicity . Autophagy is evolutionarily conserved in eukaryotes from yeast to mammals and has important roles in various cellular functions [2] . The basal role of autophagy is in turnover and recycling of cellular constituents; these housekeeping functions include the elimination of defective proteins and organelles and the prevention of abnormal protein aggregates accumulation . Autophagy also plays an important role in organelles and proteins ( but also lipids ) recycling under nutrient starvation conditions , as a nutrient source for the cell . Finally , there are pleiotropic and more specialised roles for different eukaryotic cells ( in particular in mammals ) including cellular remodelling during differentiation and development , regulation of cell longevity and programmed cell death , elimination of invading pathogens and providing antigens to the immune system [2] , [3] . In the autophagic process , cytosolic components are sequestered in a double-membrane vesicle known as the autophagosome . The outer membrane of the autophagosome will then fuse with the lysosomal compartment to deliver the inner contents of the vesicle , which will be subsequently degraded . Although autophagy has been initially identified in mammalian cells , the characterisation of the molecular machinery involved in this cellular process has been mainly done in yeast . Due to the ease of genetic analyses in the yeast system , screening for mutants unable to survive nitrogen starvation , allowed the identification of more than thirty Atg genes involved in autophagy and the related cytoplasm to vacuole targeting pathway ( Cvt ) [4] . Of these , some are only present in one organism and others represented by orthologues in different eukaryotic cell types . Among all Atg proteins , Atg8 occupies a central position: it is essential to the process of autophagosome formation , especially for the membrane expansion step [5] , [6] and , possibly , the final membrane fusion steps [7] . The protein is present as a soluble form in the cytosol of eukaryotic cells , and gets recruited to the autophagosomal membrane upon induction of autophagy . Interestingly , the binding of Atg8 to the autophagosomal membrane involves two conjugation systems that resemble ubiquitin-conjugation systems [8] . Toxoplasma gondii is an obligate intracellular protozoan parasite that is virtually able to infect all species of warm-blooded animals [9] . T . gondii is a member of the phylum Apicomplexa , which also includes several other notable human pathogens such as Plasmodium and Cryptosporidium . The secretory pathway of T . gondii is highly polarised and includes several unique organelles devoted to the invasion of the host cell ( the micronemes , rhoptries and dense granules ) [10] . In addition the parasite harbours the apicoplast , a plastid-like organelle of endosymbiontic origin . The micropore , a cytostome-like structure formed by the invagination of the plasma membrane , can be seen laterally [11] , which suggests the existence of an endocytic pathway within the parasite , although it has not been clearly defined at the molecular level . Until very recently , the parasite was thought not to contain any morphological equivalent of “classic” lysosomes , as rhoptries appeared to be the only acidified organelles containing hydrolases in T . gondii [12] . Two recent reports have nevertheless identified an acidified vacuole bearing a cathepsin-like peptidase in Toxoplasma tachyzoites that could fulfil that role [13] , [14] , but its precise physiological role remains to be characterised . The autophagic pathway is even less well described in T . gondii , although it can reasonably be suspected to be involved in the survival of Apicomplexa , as well as for other parasites with complex life cycles . For example , the importance of autophagy for the development and virulence of protozoan parasites such as the trypanosomatids has been shown previously [15] , [16] and recent data have shown an implication of autophagy in the cellular remodelling of Plasmodium liver stages [17] . Here , we used the experimental opportunities afforded by Toxoplasma as a genetic model organism to investigate the autophagic machinery and assess its physiological role in the parasite .
Many of the molecular components involved in autophagy have been identified in the budding yeast Saccharomyces cerevisiae . We have thus used the known S . cerevisiae Atg protein sequences to identify T . gondii homologues through homology searches in the ToxoDB database ( [18] , Table S1 ) . The core machinery for the assembly of the pre-autophagosomal structure appears to be present in T . gondii . This includes the regulating kinase Atg1 , as well as proteins central to autophagosome formation itself , like Atg8 and its conjugating partners Atg7–Atg3 . In several other eukaryotes , Atg8 requires the activity of two conjugation systems to bind the autophagosomal membrane: Atg7–Atg3 on the one hand and Atg5–Atg12 on the other ( see [8] , [19] for a review ) , the latter being dispensable in vitro [20] . Interestingly , no clear orthologues of the members of the Atg5–Atg12 conjugation system were found in our homology search . These are also absent from the genomes of several other parasitic protozoa [21]–[23] . In Leishmania , distantly related proteins might have similar function [24] . However , the orthologues of these Leishmania proteins cannot be found in the T . gondii genome . Overall , this either suggests that the Atg5–Atg12 conjugation is not essential for autophagosome formation in T . gondii , or that Toxoplasma uses different ( possibly lineage-specific ) proteins to perform this task . In fact , a significant part of the yeast Atg proteins seems to be missing from the T . gondii genome . For instance , most of the Atg proteins involved in the Cvt pathway are not found in Toxoplasma . This may not be surprising as this pathway is likely fungi-specific , and is also largely lacking in other eukaryotic systems that are clearly able to perform autophagy [19] . The most reliable marker for autophagosomes is Atg8 ( or LC3 , its mammalian counterpart ) . This protein is essential to autophagosome formation , and stays associated with the autophagosomal membrane from the early membrane recruitment step until the fusion with the lysosomal compartment [25] . Hence , to further characterise the autophagic process , we raised a specific antibody against TgAtg8 ( TGME49_054120 , Table S1 ) . Using this antibody on tachyzoite lysates , we detected by Western blot analysis a major protein band consistent with the predicted molecular weight of 14 kDa ( Figure 1 ) . However , the rather weak signal that was observed suggested a low level of expression for endogenous TgAtg8 . To observe the dynamics of autophagosomes formation we then cloned TgAtg8 in fusion with the green fluorescent protein ( GFP ) gene to generate a T . gondii cell line that stably expresses GFP-TgAtg8 in addition to its endogenous copy . Stable clones of GFP-TgAtg8 transgenic parasites were obtained and showed no apparent difference in growth or morphology ( data not shown ) . When GFP-TgAtg8 parasites were assessed by Western blot with anti-TgAtg8 and anti-GFP antibodies , both detected a fusion protein at the expected molecular weight of 42 kDa ( Figure 1 ) . The strong tubulin promoter driving the expression of GFP-TgAtg8 showed an overexpression of ∼20 fold of the GFP-fused TgAtg8 compared with the native protein , which greatly facilitated its detection . We studied the localisation of the GFP-TgAtg8 fusion protein by fluorescence microscopy on living tachyzoites . In extracellular tachyzoites freshly released from host cells , the GFP-TgAtg8 signal was generally distributed throughout the cytoplasm and also occasionally found in one or more punctate vesicles of various sizes that could correspond to autophagic vesicles ( Figure 2A ) . Using the anti-TgAtg8 antibody , both on the parental cell line and on the GFP-TgAtg8-expressing parasites , we verified by immunofluorescence assay ( IFA ) that GFP-TgAtg8 and anti-TgAtg8 labelled identical cellular compartments ( Figure S1 ) . The GFP-TgAtg8 signal was convenient to follow; we thus conducted our subsequent microscopic observations on the GFP-TgAtg8 cell line . To establish whether the observed structures are autophagic vesicles , we performed starvation experiment on GFP-TgAtg8-expressing extracellular parasites . Free tachyzoites allow a more direct control of environmental conditions and nutrient access , compared with intracellular parasites sheltered within their host cells . Strong induction through starvation is a hallmark of autophagosomes in many eukaryotic systems . To induce starvation , we incubated GFP-TgAtg8-expressing extracellular tachyzoites for increasing intervals of time in an isotonic solution devoid of amino acids ( Hank's balanced salt solution , HBSS ) . We imaged the parasites and quantified our findings . We noticed that the number of parasites bearing GFP-labelled puncta increased over the incubation time ( increasing from 15±3% to 79±8% after 8 hours , Figure 2B ) as did the number of puncta per parasite ( Figure S2A ) . As a control , incubation of the tachyzoites in complete Dulbecco's modified Eagle medium ( DMEM , supplemented with 10% fetal bovine serum ) for similar time periods induced no appearance of these puncta ( 17±2% of parasites bearing GFP-labelled puncta versus 21±2% after 8 hours ) . These changes in the GFP-TgAtg8 signal were induced quickly and reached a plateau after ∼8 hours of starvation . The re-localisation of GFP-TgAtg8 from the cytosol to vesicular structures during starvation suggested that these puncta were indeed autophagosomal structures . The presence of punctate GFP-TgAtg8 signal in ∼20% of the extracellular tachyzoites at the start of the experiment might reflect basal autophagy , or a proportion of the population that have egressed early and are already in a relative nutrient stress condition . The majority of these extracellular tachyzoites showed a single relatively large vesicle , which appeared prominently localised in the anterior part of the parasite , in the Golgi apparatus/apicoplast region ( Figure S2B ) . Similarly , we performed microscopic observations on intracellular tachyzoites and found that they also occasionally displayed vesicular GFP-TgAtg8 in normal growth conditions ( Figure S2B ) . Additional vesicles were found to be localised in a non-polarised manner throughout the cytoplasm of the tachyzoites . Although mostly present as a punctate signal , the GFP-positive structures were found to display some heterogeneity as previously described for GFP-TgAtg8 in other eukaryotes [26] . We sought to morphologically characterise these autophagic vesicles . Tachyzoites , either freshly released or starved for 8 hours , were fixed and processed for electron microscopy analysis . Compared to the control parasites , the starved tachyzoites displayed a higher number of large cytoplasmic vacuoles ( Figure 3A , V ) . Additionally , in the starved parasites we identified several membrane-bound vesicles of about 300–900 nm in diameter containing cytoplasmic material or organelles . These structures were usually bounded by two membranes ( Figure 3 , Ap ) . These are commonly observed structural features of autophagosomes [27] . As an example of organelles that could be found in these vesicles , part of the unique mitochondrion of a tachyzoite was seen segregated inside a membranous compartment ( Figure 3A ) and co-labelling of GFP-TgAtg8-decorated autophagosomes and mitochondrion showed partial co-localisation ( Figure 3B ) , which is suggestive of mitophagy occurring in Toxoplasma . When the autophagosome has fused with the lysosomal compartment to deliver its content for degradation , it becomes an autolysosome , or autophagic vacuole . These degradative vacuoles containing partially degraded cytoplasmic material could also be identified in starved parasites ( Figure 3 , Av ) . Importantly , similar structures could be identified in a starved cell line expressing GFP-TgAtg8 , by immuno-electron microscopy with an anti-GFP antibody ( Figure 3C ) , providing a link to the observations we made by fluorescence microscopy . Our results thus show that nutrient starvation triggers the appearance of autophagosomes in T . gondii tachyzoites through the recruitment of autophagosomal marker TgAtg8 , indicating that autophagy is operating and functional in this parasite . The conjugation process of Atg8 to the autophagosomal membrane is fairly well understood at the molecular level in yeast and mammalian cells and resembles ubiquitination [8] . In order to bind the autophagosomal membrane , Atg8 must first undergo proteolytic maturation by the cysteine peptidase Atg4 [28] , [29] . This exposes a C-terminal glycine that is then conjugated to a phosphatidylethanolamine ( PE ) lipid moiety through the action of the Atg7–Atg3 system . Accordingly , most eukaryotic Atg8 orthologues described so far bear one or several amino acids after the C-terminal glycine . However , to our surprise , translation of the putative TgAtg8 gene as annotated by the ToxoDB genome database results in a protein that ends in a glycine in the absence of post-translational maturation . This feature was conserved in other predicted apicomplexan Atg8 proteins , with the exception of several Cryptosporidium species ( Figure S3 ) . To confirm this , we performed a 3′-RACE experiment using T . gondii cDNA as template and primers specific for Atg8 to map the stop codon . In all the clones that we obtained and sequenced , the penultimate codon was confirmed to be coding for a glycine ( data not shown ) . It is conceivable that this peculiar feature may result in constitutive lipidation of Atg8 in T . gondii and that maturation by the Atg4 peptidase may not be required . However , analysis of the T . gondii genome reveals the presence of a protein with some similarity to Atg4 from yeast ( Table S1 ) , and a conserved active site catalytic triad appears to be present . This peptidase , which appears to be expressed in T . gondii tachyzoites according to the proteomic data available in the ToxoDB database , could be involved in TgAtg8 recycling from the autophagosomal membrane , but this remains to be investigated . Atg8 and its lipidated form can be separated by SDS-PAGE in the presence of urea [25] . We thus used this technique to separate proteins from GFP-TgAtg8 lysates , followed by Western blot analysis with an anti-GFP antibody . In these conditions , the anti-GFP antibody revealed a major protein band with a molecular size consistent with the prediction for a GFP-TgAtg8 fusion protein ( 42 kDa ) and an additional faster migrating protein ( Figure 4A ) . We used the anti-Atg8 antibody on the parental cell line: we were also able to separate two isoforms of the native protein by urea SDS-PAGE at around 14 kDa ( Figure 4A ) . Lipidated Atg8 typically migrates more rapidly than the non-lipidated form [29] , thus the faster migrating protein possibly corresponds to the GFP-TgAtg8-PE form . To confirm that the faster migrating protein was indeed a membrane-associated form of GFP-TgAtg8 , we performed cell fractionation experiments that showed the faster migrating form was exclusively present in the 100 000 g insoluble pellet ( in contrast , the slower migrating protein was present in both fractions , but appeared to be enriched in the soluble fraction ) ( Figure 4B ) . Similar results were obtained on parental cells with the anti-TgAtg8 antibody ( Figure S4 ) . Moreover , only a treatment by a detergent ( DOC ) , but not by chemicals disrupting low energy bonds ( NaCl and urea ) , could lead to the complete solubilisation of TgAtg8 proteins present in the membrane fraction ( Figure 4C ) . This has been described for other Atg8s [30] and suggests a tight association of TgAtg8 to membranes . Metabolic labelling using 3H-ethanolamine ( as a precursor for PE ) and subsequent immunoprecipitation of TgAtg8 , revealed that TgAtg8 indeed incorporated the label , thus confirming that the membrane association was likely to be mediated by PE ( Figure 4D ) . Also , the abundance of this membrane-associated form increased with the duration of the starvation period ( Figure 4A ) . This confirmed that in T . gondii tachyzoites TgAtg8 becomes increasingly lipidated , and hence , potentially , more is recruited to the autophagosomal membranes during starvation ( as more autophagosomes are formed ) . It is to note that proportions of soluble versus membrane-associated forms of TgAtg8 are higher in the GFP-TgAtg8-expressing cell line , compared to what is generally observed for TgAtg8 in the parental cell line . One explanation is that while the global pool of GFP-TgAtg8 is higher in the GFP-TgAtg8 parasites , the number of autophagosomal vesicles harbouring the membrane-bound form is limited , leaving a significant part of GFP-TgAtg8 in the cytosol . We then constructed , by site-directed mutagenesis , a variant of GFP-TgAtg8 were we replaced the C-terminal glycine by an alanine . We expected that this should abolish the lipidation of the protein [31] . Tachyzoites were transfected with the GFP-TgAtg8-G/A construct and GFP-positive clones were selected . Autophagy was induced by starvation in amino acids-depleted medium as described above and the appearance of autophagosomes was monitored by fluorescence microscopy ( Figure 5A ) . While the proportion of autophagosome-bearing cells increased along time in the GFP-TgAtg8 control cell line , we did not observe the formation of GFP-labelled autophagosomes in the GFP-TgAtg8-G/A mutant . This finding suggests that the mutated protein is not recruited to autophagosomes ( Figure 5A and B ) , confirming the essential role of the glycine for the recruitment . This finding further validates the interpretation of the GFP-TgAtg8 positive vesicular compartment as autophagosomal . We also followed by Western blot the lipidation of GFP-TgAtg8-G/A following starvation . Consistent with the microscopy results , no lipidated form could be detected for GFP-TgAtg8-G/A by Western blot analysis following urea SDS-PAGE ( Figure 5C ) . Overall , in spite of a constitutively exposed C-terminal glycine , our results demonstrate that TgAtg8 exists both in soluble and membrane-associated forms in the tachyzoites . Using GFP-TgAtg8 as a marker we were able to demonstrate that autophagy occurs in extracellular parasites under starvation conditions . However , these are not necessarily reflecting physiological conditions and we wondered whether the parasite might encounter the need for this process during its normal intracellular development . After invasion of the host cell , T . gondii tachyzoites replicate inside a parasitophorous vacuole by a process called endodyogeny [32] . This process is composed of single gap phase ( G1 ) preceding a synthesis ( S ) phase , which is then followed by mitosis and cytokinesis through budding [33] . Daughter cells are assembled within the mother and , as they form , encapsulate most of the maternal cell contents , leaving only a small residual body behind . We hypothesized that some of the maternal material could be digested by autophagy to the benefit of the daughter cells . We thus sought to follow the presence of autophagy in intracellular parasites developing within their host cells in tissue culture . GFP-TgAtg8-expressing tachyzoites were used to synchronously infect host cells [34] and the proportion of intracellular tachyzoites displaying autophagic vesicles was measured as previously at different time points following infection . It is to note that intracellular parasites were found to display a less intense and a more diffuse GFP-TgAtg8 labelling than the autophagic vesicles induced by starvation , yet slightly more intense than the cytosolic background ( Figure 6B , compare 4h and 48 h timepoints ) . Similar structures were also seen in intracellular parasites expressing non functional GFP-TgAtg8-G/A and were partly co-localising with Golgi apparatus marker GRASP visualised with a red fluorescent protein ( RFP ) fusion ( Figure S5 ) , allowing us to rule out that they were autophagic vesicles . Taking this into account for our quantification experiments , it appeared that there was an initial slight increase in the proportion of cells displaying intensely labelled GFP-TgAtg8 autophagic vesicles , peaking at 2 hours post-invasion . However , such labelling generally decreased in following hours of intracellular development ( Figure 6A ) , where parasites could still be found to display one or several GFP-TgAtg8 autophagic vesicles , but the majority showed a rather homogenous cytosolic signal ( Figure 6B ) . Also , the GFP-TgAtg8 vesicular signal was usually not found at the residual body occasionally formed within the vacuole after parasite division ( only in ∼2 . 5% of the residual bodies ) ( Figure 6B , arrowed ) . To better follow the timing and localisation of the GFP-TgAtg8 signal , we used the inner membrane complex protein IMC1 [35] as a marker to track the progress of cytokinesis . This protein is a component of the subpellicular network that defines the periphery of both the mature tachyzoite and of the daughter cells developing with the mother cell . We imaged GFP-TgAtg8/IMC1-RFP co-expressing tachyzoites , and observed that the vesicular GFP-TgAtg8 signal was usually present in dividing cells ( Figure 6C ) . More precisely , live imaging observation of these parasites showed that autophagosomes were usually present until the cytokinesis process , but disappeared shortly after ( data not shown ) . Altogether , our data argue against a continuous autophagic activity in intracellular parasites , yet vesicle formations could occasionally be observed and transient autophagy seemed to occur . Autophagy is a multistep process that can be modulated by upstream kinase effectors and so can be interfered with using specific pharmacological agents . The “target of rapamycin” ( TOR ) kinase is a serine/threonine kinase which is central in regulating cellular growth by promoting anabolic processes and antagonising autophagy . Consequently , treatment with TOR inhibitor rapamycin mimics nutrient starvation and is known to increase the level of autophagy in several eukaryotes [36] . On the other hand , the class III phosphoinositide 3-kinase ( PI3K ) signalling cascade has been shown to promote autophagy , thus specifically inhibiting this kinase is thought to reduce autophagy [37] . Both a TOR kinase and a class III PI3K are predicted to be present in the T . gondii genome ( Table S1 ) ; we have thus sought to use specific kinase inhibitors to evaluate the biological significance of autophagy in Toxoplasma . We drug-treated GFP-TgAtg8-expressing extracellular parasites and evaluated the effect of treatment on autophagy . Increasing concentrations of rapamycin in the starvation medium resulted in higher proportions of GFP-TgAtg8 vesicles in extracellular parasites , suggesting an increased level of autophagic activity ( Figure S6A ) . However , the effective concentration was significantly higher than routinely observed in yeast or mammalian cells ( 5 µg/ml instead of 0 . 5 µg/ml ) . This suggests a modest effect of rapamycin on the modulation of T . gondii autophagy , like previously observed with the TOR kinase of plants such as rice , tobacco or Arabidopsis [38] and the parasitic protist Trypanosoma brucei [23] . We also evaluated the effects of class III PI3K on autophagy by incubating extracellular tachyzoites in the starvation solution in the presence of 3-methyladenine ( 3-MA ) or wortmannin , two known PI3K inhibitors . In these conditions , there were lesser proportions of GFP-TgAtg8 vesicles-positive cells amongst the tachyzoites treated with the two inhibitors ( particularly wortmannin ) , which suggested a role for the PI3K in promoting autophagy in Toxoplasma ( Figure S6B ) . However , again the concentrations of wortmannin and 3-MA we had to use were higher than those used for mammalian and yeast cells , similarly to plants were these PI3K inhibitors have to be used at greater concentrations to inhibit autophagy [39] , [40] . In conclusion , the use of inhibitors specific for kinases known to regulate autophagy in other eukaryotic systems allowed us to show that they generally follow the same trends in T . gondii ( inhibition of autophagy by the TOR kinase and activation by the class III PI3K ) , although the use of these drugs at relatively high concentrations can alter their specificity and would preclude a use on intracellular parasites to investigate the autophagic function . As we could not use kinase inhibitors to satisfyingly investigate the function of autophagy in Toxoplasma tachyzoites , we sought to genetically produce an autophagy-deficient cell line . To this end , we chose Atg3 as a target , a gene coding for a protein involved in the conjugation of Atg8 and essential for autophagy [41] . In other eukaryotes , Atg8 is activated by Atg7 to form an Atg8/Atg7 thioester intermediate and is then transferred to Atg3 to form an Atg8/Atg3 thioester intermediate , before being finally conjugated to the amino group of PE for binding to the autophagosome membrane . TgAtg3 ( TGME49_036110 , Table S1 ) was identified in the T . gondii genomic sequence based on protein sequence homology searches with eukaryotic orthologues and was found to have a conserved predicted active site region with , in particular , a conserved active site cysteine ( Figure S7 ) . T . gondii genomic database predicts a 397 amino acids-long TgAtg3 protein . Yet , when aligned to yeast or human orthologues , predicted TgAtg3 shows a N-terminal extension bearing a poly-serine motif ( Figure S7 ) . The corresponding putative mRNA region has a polypyrimidine tract ( promotes the assembly of the spliceosome ) , with a putative downstream AG splice acceptor site and a potential downstream start codon that would produce a shorter version of TgAtg3 , closer in size to other eukaryotic orthologues . The regions corresponding to the 3′ and 5′ untranslated regions ( UTRs ) of T . gondii TgAtg3 gene were cloned in a plasmid on either side of a selection marker gene to use for a knock-out strategy by gene replacement through a double recombination event . We tried to transfect the RHΔHX strain [42] and the RH-derived ΔKu80 strain , which is more amenable to targeted gene deletion [43] . Despite numerous independent attempts , we were unable to obtain clones in which TgAtg3 had been deleted ( data not shown ) . The inability to delete the TgAtg3 gene with these different strategies suggested an essential role for the corresponding protein in tachyzoites . We thus sought to produce a conditional null mutant cell line for TgAtg3 . We introduced an ectopic copy of TgAtg3 into the TAti tet-transactivator line by stable transformation [44] . The ectopic copy was placed under the control of the tetracycline-regulatable promoter 7tetOSag1 . To aid detection of the corresponding protein , the transgene was tagged with a sequence encoding an N-terminal c-myc epitope . We cloned both a long and short versions of putative TgAtg3 into the expression vector ( named imyc-lTgAtg3 and imyc-sTgAtg3 , respectively ) . After transfection of tachyzoites of the TAti cell line , clonal parasites were obtained . Both cell lines showed an anhydrotetracycline ( ATc ) -regulatable TgAtg3 expression by Western blot and by IFA with anti-myc antibody ( data not shown ) . IFA also demonstrated a cytosolic localisation for myc-tagged TgAtg3 ( long and short versions alike ) , which is compatible with a functional role in autophagy , where it should be conjugating cytosolic Atg8 to the autophagosomal membrane ( data not shown ) . To replace TgAtg3 , we used a cosmid-based gene disruption strategy , allowing the use of large flanking regions to increase the chance to recombine at the appropriate locus [45] . A single cosmid clone was available for the TgAtg3 locus ( TOXOU62 ) . In this cosmid the TgAtg3 open reading frame was replaced with a chloramphenicol acetyl transferase marker by recombineering ( Figure 7A ) . Parasites expressing a regulatable copy of TgAtg3 were transfected with the resulting cosmid and stable clones were isolated by chloramphenicol selection . Clones were tested by PCR for 5′ and 3′ disruption of the TgAtg3 locus , and presence of the resistance cassette ( Figure 7B ) . We note that we successfully obtained a null mutant clone in the imyc-sTgAtg3 background , but not in the cell line expressing the longer version of TgAtg3 . This suggests that this short transcript likely encodes the functional protein . Further analysis by Southern blot confirmed the correct integration of the cosmid-derived cassette and the disruption of native TgAtg3 locus ( Figure 7C ) . Conditional mutant clone displayed a tight regulation of TgAtg3 expression by ATc , as shown by Western blot and IFA with anti-myc antibody ( Figure 8A and B ) : Western blot analysis showed that a 2 days treatment with ATc was reducing almost completely the expression of the protein and that a 4 days treatment led to undetectable levels of the extra copy . We next assessed whether the Atg8-conjugation function of Atg3 was conserved in T . gondii . To do so , we used the conditional mutant to deplete intracellular tachyzoites of TgAtg3 by a 2 days treatment with ATc , we then isolated extracellular parasites and triggered autophagy by exposing them to starvation medium for increasing intervals of time as described before . Parasites extracts were prepared and analysed by urea SDS-PAGE to visualise the lipid-conjugated form of TgAtg8 . As described above , in the presence of TgAtg3 we observed an increase in the TgAtg8 lipidated form , as autophagy was induced ( Figure 8C ) . In contrast , when TgAtg3 was repressed by ATc treatment: i ) a significant proportion of the slower migrating form was present even before autophagy was induced and ii ) the lipidated form was not upregulated by starvation ( Figure 8C ) . This shows that reducing the levels of TgAtg3 leads to a reduced capacity in TgAtg8-conjugation to the autophagosomes and thus likely impairs the putative autophagic function in the parasite . We examined the effect of TgAtg3 depletion on parasite growth using plaque assays . The conditional TgAtg3 null mutant formed markedly smaller plaques when repression of the imyc-sTgAtg3 copy was induced by ATc ( Figure 9A and B ) . Longer repression periods increased the effect on growth , as no plaque was visible with mutant parasites pre-incubated with ATc for 4 days prior to the start of the plaque assay ( Figure 9A ) . No viable parasite could be detected after three passages following mechanical release , in the presence of ATc ( data not shown ) , providing additional support for a critical function of TgAtg3 . Growth was also assessed at shorter times following invasion . Mutant parasites that were untreated or pre-incubated with ATc for 4 days were allowed to invade host cells . Cultures were kept in the presence of ATc and fixed 24 or 48 hours later and numbers of parasites per vacuole were counted in both samples . TgAtg3-depleted parasites showed a considerable delay in growth compared to controls , and they did not progress through cell division as they accumulated vacuoles with mostly one or two parasites ( Figure 9C ) . As the intracellular development of tachyzoites appeared to be affected by TgAtg3 depletion , we sought to investigate whether this was associated with specific morphological defects . We performed IFA using a battery of antibodies recognising specific subcellular structures . These included secretory organelles such as the rhoptries , micronemes and dense granules , the IMC , the apicoplast and the mitochondrion ( Figure S8 and data not shown ) . The organelle that stood out in these comprehensive analyses as being particularly affected by the lack of TgAtg3 was the mitochondrion . T . gondii tachyzoites typically have a single mitochondrion , which forms a reticulated network extending through most of the parasite ( Figure 10 , top , bottom ) . Strikingly upon depletion of TgAtg3 , we observed the loss of the mitochondrial network as judged by staining for two independent mitochondrial marker proteins: F1 beta ATPase and HSP28 ( Figure 10 ) . In parasites that were grown for 2 days in the presence of ATc , the mitochondrion appeared highly fragmented or entirely absent . We note that a significant amount of staining was now found in the residual body . This structure is , located at the center of the parasitophorous vacuole and is thought to represent the residua of mother cells left behind by the emerging daughters ( Figure 10 ) . We confirmed this phenotype in transgenic parasites pre-incubated for longer periods with ATc , but it was not generally seen in TgAtg3-expressing control parasites exposed to ATc ( Figure 10 ) . To independently assess the morphology and the functional status of the mitochondrion in this mutant we performed labelling experiments with Mitotracker Red . This cationic dye accumulates in the mitochondrion depending on an intact membrane potential . We noted a profound loss of staining consistent with a concomitant loss of mitochondrial membrane potential and/or loss of the organelle ( Figure S9 ) . No obvious defect could be detected for the apicoplast or the secretory organelles ( Figure S8 ) . We therefore assume that this phenotype reflects a specific role of Atg3 in mitochondrial maintenance rather than a general loss of cell structure due to necrosis and cell death . However , more subtle effects might be beyond the limit of resolution of fluorescence microscopy . Therefore we sought to analyse the morphology of TgAtg3-depleted parasites by electron microscopy . Electron microscopy confirmed that these tachyzoites possessed morphologically normal secretory organelles ( rhoptries , micronemes and dense granules ) and apicoplast . We examined parasites grown in the presence of ATc for up to 4 days and found no apparent detriment to these organelles over this period ( Figure 11 and data not shown ) . In contrast , electron microscopy revealed numerous mitochondrial defects ranging from alteration of cristae , to organelles including large collapsed membranous structures next to vestigial cristae that were the only recognisable feature of the mitochondrion ( Figure 11 ) . Altered mitochondrial material was also occasionally present in the residua found in the vacuolar space ( not shown ) . In conclusion , loss of TgAtg3 impedes the intracellular development of parasites and the most visible effect at the subcellular level is a dramatic collapse of the parasite mitochondrion .
Autophagy plays a role in organelle and protein turnover that is important for cellular homeostasis , adaptation to starvation and overall cellular development of eukaryotic cells . Yet , it was to date poorly characterised in apicomplexan parasites . Autophagy is dependent on the formation of the autophagosome , the vesicular structure in which the material to be degraded will be incorporated . In the present study we have used a molecular marker for autophagosomes , TgAtg8 , to detect these structures in T . gondii . We have identified structures that show the morphological hallmarks of autophagosomes and can be formed and induced by starving extracellular tachyzoites; more importantly , we find that autophagosomes are also formed during normal development and that their presence is most pronounced at a particular point in the parasite division cycle . Our genome mining efforts to uncover autophagy-related genes in T . gondii and other Apicomplexa have revealed a core of the autophagosome formation machinery in these parasites . We identified genes coding for proteins involved in the pre-autophagosome formation , the Atg8 conjugation system for autophagosome elongation , as well as upstream regulating kinases . However , several proteins previously described to regulate the formation of autophagosomes in other eukaryotic systems appear to be missing . Examples of these are the Atg5–Atg12 conjugation system for Atg8 , or partners of TOR-regulated Atg1 kinase ( i . e . Atg13 , Atg17 ) . This suggests that Apicomplexa possess a simpler system for autophagy that lacks some of the complex layers of regulations observed in mammalian cells or yeast [46] or , alternatively , that the regulation of autophagy in Apicomplexa involves parasite-specific proteins that remain to be discovered . Along this line , our use of inhibitors to interfere in T . gondii with known upstream autophagy-regulating kinases ( inhibiting mTOR kinase and activating class III PI3K ) has shown that , although following the same trends as their other eukaryotic counterparts , high concentrations of inhibitors were needed to act upon autophagy , suggesting differences in the kinase-dependent regulatory network . The structure of the Atg8 protein in T . gondii and many other Apicomplexa may be consistent with this apparent differential regulation . In yeast and mammals , Atg8 first needs to be processed by the Atg4 cysteine peptidase in order to bind to the nascent autophagosome membrane . This exposes a C-terminal glycine residue that will , in turn , be conjugated to a lipid by the Atg7–Atg3 complex . Yet , most apicomplexan Atg8 orthologues already constitutively bear a C-terminal glycine , which could suggest that autophagosome formation is constitutive in Apicomplexa . However , our experiments suggest some level of regulation of TgAtg8 binding to the autophagosomes , and this could occur by a mechanism independent of C-terminal proteolysis . Firstly , using urea PAGE , we could separate the non-lipidated from the lipidated isoform of GFP-TgAtg8 and native TgAtg8 , and further showed that the proportions of the membrane-bound form were increasing in autophagy-inducing conditions . Secondly , microscopic observation of GFP-fused or native TgAtg8 showed dual vesicular and cytosolic localisations of the protein . Lastly , using ectopic expression of GFP-TgAtg8 mutants , we demonstrated that the C-terminal glycine was essential for conjugation . Overall , our data suggest that autophagy in T . gondii is more complex than it appears at first glance and could have original ways of regulation compared with other eukaryotic systems studied so far . As they invade their host cell , T . gondii tachyzoites establish themselves into a parasitophorous vacuole , which constitutes a niche that offers protection and nutrients and thus allows for efficient parasite multiplication ( see [47] for a review ) . Spatial reorganisation of host organelles and cytoskeleton around the parasitophorous vacuole is observed within minutes following entry and it almost certainly plays a role in parasite nutrient acquisition . It is thus unlikely that the parasite , once intracellular , is experiencing starvation . We have produced a TgAtg3 conditional mutant that appears unable to regulate the conjugation of TgAtg8 and thus is likely deficient in autophagy . TgAtg3-depleted parasites invade host cells , and divide once or twice . Therefore TgAtg3-dependent autophagy does not seem to be essential in early phase of infection; however parasites cease to grow rapidly , indicating an important role for continued intracellular development . As for extracellular tachyzoites , once they have egressed their host cell , their fate is highly dependent on their motility and their ability to quickly invade a neighbouring cell or tissue , and although little is known about the exact environmental conditions encountered within its vertebrate host , autophagy could also be promoting the ability of T . gondii to survive during the journey of the parasite within the host tissues and organs . Autophagy can occur in response to cellular stresses such as starvation , but there is growing evidence for more specific needs for autophagy to maintain cell homeostasis during normal growth and development . This can be directed towards specific organelles such as the mitochondrion ( mitophagy ) or the peroxisomes ( pexophagy ) [48] . This type of selective autophagy is used by cells to dispose of damaged organelles , or to clear the cell from these organelles when undergoing differentiation . In that context , we thought that there could be a targeted role for autophagy in the intracellular development of Toxoplasma . Some of the organelles of the mother cell are made redundant by their de novo formation in the daughters ( i . e . rhoptries , micronemes ) and we hypothesized that these might be degraded and recycled through autophagy . Interestingly , the use of PI3K inhibitor 3-MA on intracellular tachyzoites has been recently shown to affect parasite division , particularly daughter bud formation [49] . Also , morphological observations of 3-MA-treated parasites showed considerable retention of cellular material in residual body-like structures during daughter cell formation . It is to note that the authors showed that wortmannin did not produce similar effects , although in our hands wortmannin was a more potent inhibitor of autophagy than 3-MA on extracellular parasites , however wortmannin is notoriously unstable in culture over long periods of time and could have been degraded in the 20 hours long incubation used in their protocol , thus preventing lasting effects [50] . When depleting the parasites of TgAtg3 , no accumulation of organelles or morphological alteration of de novo synthesized organelles was observed , suggesting that TgAtg3-dependent autophagy is not required for the recycling of secretory organelles from the mother . Nevertheless , TgAtg3 depletion has a significant cellular phenotype: the structural and functional alteration of the single mitochondrion present in the tachyzoites . Is the break up of the mitochondrion that we observe in this mutant a cause or a consequence of the growth arrest of the tachyzoites ? Although mitochondria are generally considered the powerhouse of the cell , many intracellular parasites rely heavily on glycolysis for energy production and do not require oxidative phosphorylation . The relative importance of the mitochondrion for energy generation in Apicomplexa is disputed , although the presence of a mitochondrial membrane potential ( detected with Mitotracker labelling for instance ) suggests it is at least partly functioning . However the other , often overlooked , metabolisms possibly hosted by the mitochondrion or in close interaction with the neighbouring apicoplast ( i . e . redox metabolism , heme synthesis ) are essential for the survival of the tachyzoites . On the other hand , the mitochondrion plays an active role in mediating apoptosis-like cell death , even in unicellular eukaryotes such as yeast [51] , but this area remains largely unexplored in Toxoplasma . When treating intracellular tachyzoites for up to two days with dihydrofolate reductase inhibitor pyrimethamine [52] , we could observe that cells having already lost their overall morphology , their normal micronemal distribution and displaying a fragmented nuclear content , still retained a reticulated mitochondrial signal ( Figure S10 ) , indicating that mitochondrial fragmentation is not necessarily an early event in the course of tachyzoite cell death . Moreover , when TgAtg3-depleted parasites ( i . e . after 4 days of ATc treatment ) were left to invade hot cells , parasites with a fragmented mitochondrion were found intracellularly after short invasion times ( 15 minutes ) , demonstrating that , in spite of the perturbation of mitochondrial homeostasis , these tachyzoites had retained their invasive potential ( data not shown ) . Along with this , a recent work has been illustrating that glycolysis , but not mitochondrion-associated oxidative phosphorylation , was the major contributor to ATP production and was necessary to maintain the invasive potential of extracellular tachyzoites in carbon-depleted medium [53] . Mitochondrial dynamics and mitochondria turnover are linked . In yeast , but also mammalian cells , mitochondria undergo fusion/fission events during the cell cycle , for exchange of metabolites or DNA [54] . During these events , “maintenance” mitophagy is used to eliminate damaged mitochondrion and is intimately linked with fission events [55] , which lead to perturbation of mitochondrial membrane potential . This way , depolarised mitochondria are eliminated and autophagy contributes to normal mitochondria homeostasis [56] . The replication and dynamics of T . gondii mitochondrion have been poorly studied; it is not known how the organelle elongates and to which extend fusion/fission events occur in the parasite . A recent report [57] has shown that the replication of the organelle was tightly coupled with the cell division cycle and that the mitochondrion was elongating after daughter scaffold formation , before entering the daughter cells at a very late stage . The appearance of the autophagosomal structures we have observed in Toxoplasma tachyzoites seems to be within the timeframe of mitochondrion replication/division . Consistent with this , recent cell-cycle microarray data [58] searchable through ToxoDB release 6 . 2 ( www . toxodb . org ) have revealed that , although TgAtg8 levels seemed to vary little during the cell cycle , autophagosome-conjugating partners TgAtg3 and TgAtg7 had similar profiles and were predominantly expressed towards the end of the G1 phase . This is compatible with an increase in autophagosomal activity after this part of the cell cycle , with a peak during the end of the S phase and the mitosis . Our microscopic observations also suggest the occurrence of mitophagy in the parasites . It thus seems that maintaining mitochondrial homeostasis during the biogenesis of this organelle in tachyzoites necessitates TgAtg3-dependent autophagy , and that loss of this autophagic function could lead to mitochondrion break up , loss of membrane potential and cell death . Interestingly , a recent report [59] has shown that a mammalian Atg3 null mutant also displays increased levels of fragmented mitochondria , and that Atg3 regulates mitochondrial homeostasis through an association with autophagy protein Atg12 ( which appears to be absent from T . gondii's genome ) . Another recent study has suggested that cell death induced by starvation in autophagy-defective yeast mutants was caused by a dysfunction of the mitochondria [60]; more precisely , it showed that autophagy mutants accumulated high levels of reactive oxygen species and experienced defects in their respiratory functions . In summary , our study shows that autophagy is present and functioning in the apicomplexan parasite T . gondii and that protein TgAtg3 , through its role in TgAtg8 conjugation to the autophagosome , is likely essential for autophagy . Moreover , its function is crucial for maintaining mitochondrial homeostasis and for parasite growth . Many questions remain unanswered regarding the physiological roles of autophagy for T . gondii and Apicomplexa in general , or the machinery they use for this particular cellular function . For instance , the drastic phenotype we have observed with TgAtg3-depleted parasites is not exclusive and autophagy could still have a role in regulating parasite organelles other than the mitochondrion in specific physiological conditions . It is for instance possible that autophagy plays a role for organelle clearance during conversion from a parasitic stage to another . In related Apicomplexa Plasmodium , sporozoite to trophozoite conversion in the liver involves quite an extensive clearance of superfluous organelles and shape remodelling , in which autophagy is possibly playing a role [17] . Infectious stages of T . gondii , sporozoites , tachyzoites , and bradyzoites , are rather similar ultrastructurally but differ in certain organelles ( for instance tachyzoites have more dense granules than bradyzoites , but have less micronemes and amylopectin granules ) as well as their metabolic state and could be dependent on autophagy for differentiation and cell remodelling . Also , the role of autophagy during oocyst sporulation should be investigated . Again , in vivo studies using the TgAtg3 conditional mutant we have generated could reveal interesting features . Also , one important question concerns the degradation and recycling of autophagocytosed material in Toxoplasma . Indeed , once completed , the double-membrane autophagosome is transported to a hydrolases-containing compartment and the outer membrane of the vesicle fuses with this compartment for degradation of its content . Typically , this lytic compartment is the lysosome in mammalian cells ( or the vacuole in yeast ) , but so far the presence of such a compartment has remained elusive in T . gondii tachyzoites . Recent findings have nonetheless identified a compartment that seems to bear several characteristics of a bona fide late endosomal/lysosomal compartment ( i . e . acidified , containing a peptidase ) [13] , [14] , however this compartment is changing both in aspect and contents during the tachyzoite cell cycle , which renders it difficult to grasp . Moreover , the interactions between this compartment and autophagosomes are supposedly transient , so the interplay between the two would require dynamic studies on live cells . The repertoire of hydrolases present in the lytic compartment of tachyzoites and the ones particularly involved in the degradation of autophagic material also remain to be characterised . Our discovery that autophagy protein TgAtg3 is essential for intracellular development of the parasite opens a new area for looking into possible parasitic drug targets , especially given the apparent peculiarities in the regulation of the parasite autophagic machinery compared with its host counterpart and the fact that this machinery contains enzymes ( kinases , peptidases ) for which inhibitors could be screened .
This study was conducted according to European Union guidelines for the handling of laboratory animals and the immunisation protocol for antibody production in rabbits was conducted at the CRBM animal house ( Montpellier ) and approved by the Committee on the Ethics of Animal Experiments ( Languedoc-Roussillon , Montpellier ) ( Permit Number: D34-172-4 , delivered on 20/09/2009 ) . Tachyzoites of the RHΔHX strain , deleted for hypoxanthine guanine phosphoribosyl transferase [42] or ΔKu80 strain [43] and derived transgenic parasites generated in this study , were propagated in vitro under standard procedures by serial passage in human foreskin fibroblasts ( HFF ) monolayers in Dulbecco's modified Eagle medium ( DMEM , with 4500 mg/l D-Glucose , sodium pyruvate , Invitrogen ) supplemented with 10% fetal bovine serum and 2 mM L-glutamine . Total RNA was isolated from T . gondii tachyzoites using the RNAqueous kit ( Ambion ) , according to the manufacturer's instructions . cDNA was synthetized from the isolated RNAs by reverse transcription using random hexamers and the SuperScript II kit ( Invitrogen ) or using the SMART RACE cDNA Amplification Kit ( Clontech Laboratories ) . DNA corresponding to T . gondii Atg8 orthologue ( TGME49_054120 , http://toxodb . org ) was obtained by PCR from cDNA with primers ML303/ML304 ( see Table S2 for primer sequences ) bearing the PstI and PacI restriction sites , respectively . The fragment was cloned into the pTGFP vector [61] to bear the pGFP-TgAtg8 plasmid for expression of Atg8 in Toxoplasma with the green fluorescent protein ( GFP ) fused at its N-terminus . C-terminal glycine mutant version of the GFP-TgAtg8 construct was obtained by site-directed mutagenesis with the QuikChange mutagenesis kit ( Stratagene ) , with primers ML308/ML309 to yield plasmid pGFP-TgAtg8-G/A with the C-terminal glycine mutated to an alanine . All constructs were checked by sequencing . To generate stable transformants , 5×107 extracellular tachyzoites of the of the RHΔHX strain were transfected and selected as previously described [42] . GFP-TgTgAtg8-expressing parasites were obtained by electroporation of 100 µg of plasmids for the expression of GFP-TgTgAtg8 or its mutated version . After overnight growth , transfectants were selected with 25 µg/ml mycophenolic acid and 50 µg/ml xanthine for three passages , before cloning by limiting dilution under drug selection . After expanding the clones , GFP-expressing parasites were selected by observation with a fluorescent microscope . A DNA sequence corresponding to the full TgAtg8 protein was obtained by PCR from T . gondii RH tachyzoites cDNA with primers ML697 and ML698 . It was then cloned into pGEX-4T-3 ( GE healthcare ) and the construct was transformed into E . coli BL21 cells to produce a recombinant protein with an N-terminal glutathione-S transferase tag , which was used to immunise a rabbit . The antibody was subsequently used at 1/500 for Western blot or IFA . Two approaches were used for knock-out of autophagy-related genes by double homologous recombination events . First , a plasmid bearing 5′ and 3′ untranslated regions ( UTR ) of the gene of interest flanking a selection cassette was generated . 5′ and 3′ UTR were obtained by PCR . They were cloned on either side of the chloramphenicol acetyl transferase ( CAT ) gene in the pTub5/CAT vector , serving as a selection marker [62] . Primer pairs used for PCR amplification were ML358/ML355 ML339/ML340 for 5′ and 3′UTR , respectively , of TgAtg3 . Second , cosmid with larger flanking regions to increase the frequency of homologous replacement was generated . Cosmid recombineering was performed as described previously [45] . Briefly , a cosmid overlapping TgAtg3 ( TOXOU62 ) was recombineered with a cassette bearing a selection marker and obtained from plasmid template pH3CG by PCR , with primers ML537/ML538 . DNA constructs for gene replacement were transfected into either RHΔHX or ΔKu80 strains and selected with the appropriate antibiotic . For conditional knock-out strategy , an ectopic copy of TgAtg3 was introduced under the dependence of a SAG1 promoter . Two alternative start codons ( see Figure S5 ) were tried for the constructs , corresponding to a long ( amplified with ML454/ML456 ) or shorter ( ML455/ML456 ) version of the TgAtg3 . Constructs were introduced into the TAti tet-transactivator cell line by stable transformation [44] . For repression of the expression of the extra-copy , anhydrotetracycline ( Clontech ) was put at 1 . 5 µg/ml in the culture medium for two to four days . Correct disruption of the TgAtg3 locus was verified by PCR using primers ML595/ML654 ( PCR1 ) , ML386/ML387 ( PCR2 ) , ML595/ML596 ( PCR3 ) . Primers ML650/ML651 and ML648/ML649 were used to generate the 5′ and 3′ probes , respectively , used for Southern blot analysis . To induce autophagy , extracellular tachyzoites were put in starvation conditions . Extracellular parasites were obtained from freshly lysed HFFs , sedimented by centrifugation and washed twice in Hank's Balanced Salt Solution ( HBSS ) before being resuspended in pre-warmed HBSS and incubated at 37°C for up to 16 h . Autophagy was occasionally modulated by incubation of the cells with several effectors: PI3K inhibitors Wortmannin and 3-methyladenine ( Sigma ) at 10 µM and 10 mM , respectively; TOR kinase inhibitor rapamycin ( Santa Cruz ) , at concentrations up to 5 µg/ml . Autophagosomes were quantified in live or paraformaldehyde-fixed GFP-TgAtg8 expressing parasites , by microscopic observation and counting of the punctate GFP signals . At least 200 cells were counted in each experimental set . Alternatively , the presence of the lipidated , autophagomal membrane-associated , form of GFP-TgAtg8 was assessed by Western blotting with anti-GFP antibody after separation by urea SDS-polyacrylamide gel electrophoresis ( see below ) . Parasites extracts were normalised on counts of viable parasites ( by trypan blue assay ) at the end of the incubation time . 3 . 107 GFP-TgAtg8 tachyzoites starved for 8 hours in HBSS were solubilised in 1 ml of Tris HCl 50 mM pH 7 . 5 and sonicated twice for 30 seconds . Cellular debris were removed by centrifugation at 500 g for 10 minutes . The supernatant was submitted to an ultracentrifugation at 100 000 g for 30 minutes to yield a membrane-enriched high speed pellet and high speed supernatant soluble fractions , respectively . The supernatant fraction was TCA-precipitated and extracts were resuspended in SDS-PAGE loading buffer prior to Western blot analysis . Alternatively , the high speed pellet was further extracted by 1 M NaCl , 2 M urea or 1% deoxycholate ( DOC ) for 4 hours at 4°C and submitted to another ultracentrifugation to yield a pellet and supernatant fraction . Western blots were performed as described previously [63] , with the modification that urea was included at a concentration of 6 M in the SDS-polyacrylamide gel to separate lipidated and non-lipidated forms of GFP-TgAtg8 . The primary antibodies used for detection and their respective dilutions were: anti-GFP monoclonal mouse antibody ( Roche ) at 1/500 , and anti-ROP5 [64] at 1/1000 as a loading control . One 75 cm2 flask of HFF was grown for 24 hours with 0 . 1% fetal bovine serum , in the presence of 120 µCi of [1–3H] Ethan-1-ol-2-amine hydrochloride ( GE Healthcare ) . HFFs were then infected for 24 hours with 5 . 107 GFP-TgAtg8 parasites; HFF layer was scrapped and parasites were syringed out . Isolated parasites were washed once in HBSS and incubated for 8 hours in 10 ml of HBSS , still in the presence of 120 µCi of labelled ethanolamine . They were washed once in HBSS and the pellet was resuspended in 1 . 5 ml of lysis buffer ( PBS with 1% Nonidet 40 ( NP40 ) , 0 . 5% DOC and 0 . 1% SDS with a protease inhibitors cocktail ( Roche ) ) and incubated at 4°C for 1 hour . The lysate was centrifuged for 20 min at 15 000 g and the supernatant was collected for subsequent immunoprecipitation . Protein A-sepharose beads ( Sigma ) were prepared by putting together 50 µl of polyclonal rabbit anti-TgAtg8 antibody with 20 µl of beads for 1 hour . They were then washed 3 times in PBS to eliminate unbound antibodies . 1 . 5 ml of radiolabeled lysate was incubated with protein A-bound anti-TgAtg8 antibody overnight at 4°C under gentle agitation , and then washed five times with lysis buffer . Remaining buffer was discarded and the beads were resuspended in 20 µl of SDS-PAGE loading buffer and boiled for 5 minutes before analysis by urea SDS-PAGE . The gel was treated with Amplify ( GE healthcare ) , dried and used for fluorography . As a control , 5 . 107 parasites were treated in a similar way except that no radioactive ethanolamine was used during growth and starvation and urea SDS-PAGE , followed by Western blot analysis with anti-GFP antibody , were used after immunoprecipitation . IFAs were performed either on extracellular tachyzoites recovered from freshly lysed HFF , or intracellular parasites at their various stages of development . They were fixed in 4% ( w/v ) paraformaldehyde in PBS and processed for immunofluorescent labelling as described previously [63] , with the modification that the extracellular parasites were made to adhere onto poly-L-lysine slides for 20 minutes prior to processing for immunofluorescent labelling . The following antibodies were used at 1/1000 dilution unless mentioned: anti-mitochondrial F1 beta ATPase ( P . Bradley , unpublished ) , anti-mitochondrial HSP28 [65] , anti-acyl carrier protein [66] , anti-MIC3 [67] , anti-ROP5 [64] , anti-c-myc at 1/250 ( Santa Cruz ) . For co-labelling with fluorescent markers in live cells , constructs allowing the expression of IMC1 fused to the Tomato variant of RFP ( B . Striepen , unpublished ) and GRASP-RFP [57] were transfected in GFP-TgAtg8-expressing tachyzoites . Fluorescent labelling of the mitochondrion was performed on extracellular parasites using MitoTracker Red CMXRos ( Invitrogen ) at 50 nM for 30 minutes at 37°C . Tachyzoites were then washed extensively in HBSS , fixed in 4% ( w/v ) paraformaldehyde in PBS and adhered onto poly-L-lysine slides before microscopic observation . For labelling of the mitochondrion in intracellular parasites , MitoTracker Red CMXRos was used at 500 nM for 45 minutes at 37°C and chased for 15 minutes 37°C before cells were processed for immunolabelling and microscopic observation . Slides were mounted with Immumount ( Calbiochem ) and observed either with a Leica DMRA2 microscope , and images acquired with a MicromaxYHS1300 camera ( Princeton Instruments ) using the Metamorph software ( Molecular Devices ) or with an Axiovert/200M Zeiss inverted microscope equipped with an Axiocam MRm CCD camera ( Zeiss ) driven by the Axiovision software . Image acquisition was performed on workstations of the Montpellier RIO Imaging facility . Parasite pellets were fixed for 2 hours with 2 . 5% glutaraldehyde in 0 . 1 M Na cacodylate buffer pH7 . 2 , washed in buffer , post fixed in 1% OsO4 in the same buffer for 2 hours . After dehydration with graded ethanol series followed by propylene oxide , they were embedded in Epon . Ultrathin sections were prepared with a Leica ultracut E microtome , stained with Uranyl acetate and lead citrate and observed with a JEOL 1200E electron microscope . Immuno-electron microscopy on ultrathin cryosections was performed as described elsewhere [68] using anti-GFP antibodies on GFP-TgAtg8 transfected parasites . Confluent monolayers of HFF grown in 6-well plates were infected with ∼50 tachyzoites per well and incubated for 6–7 days at 37°C . They were then fixed in cold methanol for 20 minutes and stained with Giemsa stain . Images were obtained with an Olympus MVX10 macro zoom microscope equipped with an Olympus XC50 camera . Plaque area measurements were performed with CellA software ( Olympus ) . TgAtg8 ( TGME49_054120 , http://toxodb . org ) ; TgAtg3 ( TGME49_036110 , http://toxodb . org )
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Autophagy is a catabolic process involved in maintaining cellular homeostasis in eukaryotic cells , while coping with their changing environmental conditions . Mechanistically , it is also a process of considerable complexity involving multiple protein factors and implying numerous protein-protein and protein-membrane interactions . The cellular material to be degraded by autophagy is contained in a membrane-bound compartment called the autophagosome . We have characterised the formation of autophagosomes in the protozoan parasite Toxoplasma gondii by following the relocalisation of autophagosome-bound TgAtg8 . Thus , exploiting GFP-TgAtg8 as a marker , we showed that it is a process that is regulated and can be induced artificially by amino acid starvation . Autophagic vesicles were also observed in normally dividing intracellular parasites . Depleting Toxoplasma of the TgAtg3 autophagy protein led to an impairment of TgAtg8 conjugation to the autophagosomal membrane and , at the cellular level , to a fragmentation of the single mitochondrion of the parasite and to a severe growth arrest . We have thus found that TgAtg3-dependent autophagy is essential for normal intracellular development of T . gondii tachyzoites .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"parastic",
"protozoans",
"toxoplasma",
"gondii",
"protozoology",
"biology",
"microbiology"
] |
2011
|
Autophagy Protein Atg3 is Essential for Maintaining Mitochondrial Integrity and for Normal Intracellular Development of Toxoplasma gondii Tachyzoites
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The behavioral effects of psychomotor stimulants such as amphetamine ( AMPH ) arise from their ability to elicit increases in extracellular dopamine ( DA ) . These AMPH-induced increases are achieved by DA transporter ( DAT ) -mediated transmitter efflux . Recently , we have shown that AMPH self-administration is reduced in rats that have been depleted of insulin with the diabetogenic agent streptozotocin ( STZ ) . In vitro studies suggest that hypoinsulinemia may regulate the actions of AMPH by inhibiting the insulin downstream effectors phosphotidylinositol 3-kinase ( PI3K ) and protein kinase B ( PKB , or Akt ) , which we have previously shown are able to fine-tune DAT cell-surface expression . Here , we demonstrate that striatal Akt function , as well as DAT cell-surface expression , are significantly reduced by STZ . In addition , our data show that the release of DA , determined by high-speed chronoamperometry ( HSCA ) in the striatum , in response to AMPH , is severely impaired in these insulin-deficient rats . Importantly , selective inhibition of PI3K with LY294002 within the striatum results in a profound reduction in the subsequent potential for AMPH to evoke DA efflux . Consistent with our biochemical and in vivo electrochemical data , findings from functional magnetic resonance imaging experiments reveal that the ability of AMPH to elicit positive blood oxygen level–dependent signal changes in the striatum is significantly blunted in STZ-treated rats . Finally , local infusion of insulin into the striatum of STZ-treated animals significantly recovers the ability of AMPH to stimulate DA release as measured by high-speed chronoamperometry . The present studies establish that PI3K signaling regulates the neurochemical actions of AMPH-like psychomotor stimulants . These data suggest that insulin signaling pathways may represent a novel mechanism for regulating DA transmission , one which may be targeted for the treatment of AMPH abuse and potentially other dopaminergic disorders .
Virtually all major classes of abused drugs share an ability to enhance dopamine ( DA ) transmission throughout midbrain reward centers [1 , 2] . Once DA is released into the synapse , the DA transporter ( DAT ) is the primary mechanism for clearing the transmitter from the extracellular space , particularly within the striatum [3–5] . DAT is a target of multiple psychomotor stimulants including cocaine , methamphetamine and amphetamine ( AMPH ) [6] . Dysregulation of DAT function has been implicated in a wide variety of neuropsychiatric pathologies , including attention-deficit hyperactivity disorder , depression and bipolar disorder [1 , 7] . DA clearance is dynamically modulated by several signaling pathways [8–10] . Importantly , recent studies suggest a unique role for insulin and insulin-like growth factors ( e . g . , IGF1 and IGF2 ) in this modulation [11–14] . Insulin receptors ( IRs ) and receptors for IGF1–2 are found on DAT-expressing midbrain DA neurons [15–18] . Insulin and IGF1–2 receptors function as receptor tyrosine kinases ( RTKs ) , which have been shown to regulate the activity of a variety of neurotransmitter transporters [19–22] . Additionally , RTKs are known to stimulate phosphotidylinositol 3-kinase ( PI3K ) signaling , which in turn activates protein kinase B ( PKB ) , also known as Akt [23 , 24] . Akt is a central player in insulin and growth factor signaling and a regulator of several cellular functions including cell growth and apoptosis [25] . Recently , the PI3K/Akt signaling pathway has been shown to regulate DA clearance [11] and has been implicated in cocaine sensitization [26] , alcohol tolerance [27] and opioid dependence [28] . The mechanism underlying the regulation of DA clearance by PI3K seems to rely on DAT trafficking , as Garcia et al . [13] and Wei et al . [29] recently demonstrated that Akt activity is critical for sustaining human DAT ( hDAT ) membrane expression and function . In vivo evidence supporting insulin and PI3K signaling pathways in the control of DA clearance comes from Patterson et al . [30] , who demonstrated that in rats , hypoinsulinemia induced by food deprivation decreases the maximum velocity [Vmax] for DA uptake ( with no significant change in the affinity constant [Km] for DA ) , as determined by rotating disk voltammetry on striatal suspensions . Consistently , the uptake of DA , as determined ex vivo by using striatal synaptosomes and in vivo by high-speed chronoamperometry ( HSCA ) , is severely reduced in rats previously depleted of insulin with the diabetogenic agent streptozotocin ( STZ ) [14] . AMPH-like stimulants are actively transported by catecholamine carriers such as DAT [6] . As substrates , AMPHs not only competitively inhibit DA reuptake and thereby increase synaptic DA , but also promote reversal of transport , resulting in efflux of DA via the DAT [6] . This efflux results in an increase in extracellular DA and is believed to be of major importance for the psychomotor stimulant properties of AMPHs [6] . Because insulin and PI3K signaling have been shown to fine-tune DAT cell surface expression [13 , 29] , it is possible that inhibition of PI3K signaling in vivo , by reducing DAT cell surface expression , inhibits AMPH-induced DA efflux and , hence , its behavioral effects . The ablation of pancreatic β cells by STZ in rats is a model of insulin depletion , and as such , we hypothesized that PI3K signaling in the brain , as well as DAT cell surface expression and possibly DAT-mediated behavioral effects of AMPH , would be reduced following STZ pretreatment . In support of our hypothesis are studies showing that , insulin-depleted , diabetic rodents have significantly reduced basal locomotor activity [14 , 31 , 32] and are resistant to the motor stimulant properties of AMPH and other related psychomotor stimulants [31 , 33 , 34] . Likewise , the reinforcing potential of AMPH , as determined by the daily maintenance of intravenous AMPH self-administration , is significantly blunted in the STZ model of hypoinsulinemia [12] . Therefore , in light of these data , it is possible that the regulation of AMPH-induced DA efflux—promoted by insulin and PI3K signaling—is mediated by changes in DAT cell surface expression . Here we show that pharmacological manipulation of the PI3K signaling pathway caused by hypoinsulinemic conditions or selective pharmacological inhibition/activation of PI3K dramatically regulates the ability of AMPH to evoke DAT-mediated DA release in the striatum , as determined by HSCA . Consistently , in hypoinsulinemic rats we observed a blunting of AMPH-evoked striatal activation measured by functional magnetic resonance imaging ( fMRI ) . We couple these findings with biochemical data showing that PI3K/Akt signaling is reduced under hypoinsulinemic conditions , as is the cell surface distribution of the DAT within striatum . Collectively , these data support the novel concept that insulin signaling—possibly through PI3K and/or Akt—plays a critical role in DA homeostasis by regulating DA clearance and the increases in extracellular DA induced by AMPH-like psychomotor stimulants .
PI3K signaling , which is stimulated by activation of IRs and other RTKs [23] , plays a critical role in the maintenance of DA clearance and DAT cell surface expression [11 , 13 , 21 , 35] . Therefore , it is conceivable that PI3K signaling and ultimately Akt , by fine-tuning DAT expression at the plasma membrane [13 , 29] , regulate the ability of AMPH to cause DAT-mediated DA efflux . To test this hypothesis , we first altered PI3K signaling in vivo by depleting circulating plasma levels of insulin , a potent hormonal activator of the PI3K/Akt pathway [23 , 24] , using the antibiotic STZ [36] . We administered a single dose of STZ ( 65 mg/kg ) by tail vein injection at least 7 d prior to experiments . This regimen led to a significant increase in blood glucose levels: 532 ± 39 mg/dl ( STZ-treated rats ) versus 108 ± 21 mg/dl ( untreated controls ) ( p < 0 . 001 , Students t-test; n = 11–12 rats ) . Radioimmunoassay data from our laboratory suggest that STZ reduces striatal levels of insulin by at least 50% ( M . Shiota , Vanderbilt Diabetes Center , unpublished data ) . Importantly , in striatum—a region that contains abundant DATs [37–39] and IRs [15 , 17 , 18] and that participates in the reward pathway [1 , 2]—STZ treatment inhibited Akt activity . To assess Akt activity in these studies , we measured its ability to phosphorylate in vitro GSK3α [29] . As seen in Figure 1 , STZ treatment reduces basal Akt activity , reflected by a decreased phosphorylation of GSK3α with respect to untreated controls . In three independent experiments , STZ treatment in rats led to a 44 ± 16% decrease in Akt activity measured from striatal synaptosomes ( Figure 1B ) , suggesting that the STZ treatment significantly downregulates basal PI3K signaling in striatum . To verify whether inhibition of PI3K signaling induced by STZ treatment correlates with changes in AMPH-induced DA efflux , we used HSCA to measure the release and clearance kinetics of striatal DA in vivo [14 , 40] . One week after STZ treatment ( blood glucose: 495 ± 31 mg/dl [STZ-treated rats] versus 115 ± 5 mg/dl [saline-treated controls] , p < 0 . 001 , Students t-test; n = 6 ) , HSCA recordings were carried out . In saline-treated rats , ejection of AMPH ( 400 μM/125 nl ) caused a robust release of DA that was rapidly cleared from the extracellular space ( Figure 2A ) . In contrast , AMPH elicited significantly less DA release in STZ-treated rats , and the released DA was cleared more slowly in these animals ( Figure 2A ) . The slope of the rising portion of the DA signal indicates the rate of DAT-mediated DA efflux , which is primarily dependent on the affinity and turnover rate of DA and is independent of DA content [14 , 40] . Analysis of the rising phase of the trace revealed that DA efflux rates in STZ-treated rats were severely attenuated compared with those of saline-treated control rats ( Figure 2B ) . STZ-treated rats also had a significantly lower amount of released DA ( Figure 2C ) . Furthermore , STZ-treated animals also displayed significant deficits in DAT-mediated DA clearance , indicated by the reduced slope of the descending phase of the DA signal compared to saline-treated rats ( Figure 2D ) . These data suggest that under hypoinsulinemic conditions , in which PI3K signaling is diminished , the ability of AMPH to cause DA efflux is impaired , possibly by decreasing DAT function . It is possible that factors other than PI3K signaling ( e . g . , altered blood glucose levels ) might contribute to the blunted AMPH-induced DA release caused by STZ treatment . To address this concern , we selectively inhibited PI3K activity within the striatum of naive rats using LY294002 and then recorded AMPH-induced DA efflux in this region using HSCA . Figure 3 shows the effect of LY294002 pretreatment on AMPH-induced DA release . LY294002 ( 1 mM/125 nl ) or vehicle ( artificial cerebrospinal fluid [aCSF] in DMSO ) were infused into the striatum by way of a calibrated micropipette positioned adjacent to the recording electrode . AMPH ( 400 μM/125 nl ) was infused 0 , 45 and 90 min later . The inhibition of PI3K led to a significant reduction in the ability of AMPH infusions to induce DA efflux 45 and 90 min after treatment ( Figure 3 ) . The precise concentration of LY294002 or AMPH that reaches the recording site is unknown , because it depends on diffusion through the extracellular matrix [41] . However , it has been estimated that there is at least a 10-fold dilution in drug concentration when ejected from a micropipette at a distance of 300 μm from the recording electrode [42] , which is the separation distance that was used in the current studies . Additional studies from our laboratory suggest that a 10- to 200-fold dilution in drug concentration occurs by the time it diffuses to the recording electrode [43] . Thus , a barrel concentration of 400 μM AMPH or 1 mM LY294002 when pressure-ejected into brain would yield concentrations at the recording electrode of approximately 2–40 μM or 5–100 μM , respectively . Previous studies have shown that these concentrations of AMPH are consistent with those reported in brain after systemic administration of a behaviorally effective dose of AMPH and its derivatives [44 , 45] . Furthermore , the concentrations of LY294002 are similar to those that are able to regulate cocaine sensitization [26] . DAT is dynamically regulated at the plasma membrane by a number of intracellular signals [9 , 10 , 46 , 47] , and recent data have also shown that transporter levels can be regulated by DA [48] , pseudosubstrate stimulants such as AMPH [48 , 49] , and inhibitors of DAT function such as cocaine [50] . To evaluate whether the reduction in AMPH-induced DA efflux caused by hypoinsulinemic conditions is promoted by a decrease in DAT cell surface expression , we evaluated DAT levels at the plasma membrane in striatal synaptosomes from rats made hypoinsulinemic with STZ [13] . As shown in Figure 4 , chronic depletion of insulin results in a significant ( >40% ) decrease in the level of biotinylated , membrane-associated DAT within synaptosomes , indicating that DAT cell surface expression was significantly reduced in hypoinsulinemic rats . These findings , together with our electrochemical data ( Figures 2 and 3 ) , support the hypothesis that the reduction in AMPH-induced DA efflux caused by STZ treatment is a consequence of a reduction in DAT levels on the plasma membrane and are consistent with the previously reported blunted behavioral properties of AMPH under hypoinsulinemic conditions [12 , 33 , 34 , 51] . To further explore noninvasively the effect of STZ , hypoinsulinemia and downregulation of the PI3K signaling on AMPH-induced DA efflux , we used blood oxygenation level–dependent ( BOLD ) fMRI , which is sensitive to fluctuations in blood/hemoglobin oxygenation that closely reflects changes in neuronal activity [52 , 53] . In recent years , fMRI has proven useful in the study of the neural and pharmacological properties of psychostimulants within small laboratory animals [54–60] . Notably , when examined in rodents , BOLD responses to AMPH are linearly correlated with AMPH-induced changes in extracellular DA levels within the striatum [54 , 55] . In the present study , the BOLD responsiveness of the DAT- and IR-rich striatum to AMPH stimulation in normal and hypoinsulinemic rats was measured at 9 . 4 T using T2*-weighted multi-slice gradient echo imaging . Figure 5 shows that STZ-pretreated rats displayed a marked reduction in striatal activation in response to an acute exposure to AMPH ( 3 mg/kg , intraperitoneal [i . p . ] ) . Figure 5A depicts representative BOLD activation maps from untreated control versus STZ-treated animals , each co-registered to high-resolution anatomic templates acquired in the same animals . Compared to untreated control rats given acute saline , those receiving AMPH exhibited significant BOLD activation in the dorsolateral striatum . However , this response was absent in rats rendered hypoinsulinemic by STZ treatment . To quantify the effects of hypoinsulinemia on the striatal BOLD signal we performed region-of-interest ( ROI ) analysis of dorsolateral portions of this structure , which is predominantly innervated by the substantia nigra compacta and where t-maps indicated strong AMPH-evoked BOLD activation that was sensitive to insulin depletion . Figure 5B–5D summarizes the results of this analysis across groups of subjects ( n = 5–6 per treatment group ) . When compared to treatment with saline , AMPH-treated animals exhibited a strong BOLD signal increase above baseline in the dorsolateral striatum . In contrast , there was no significant AMPH-induced BOLD signal change from baseline in STZ-pretreated , insulin-depleted rats ( Figure 5B ) . In Figure 5C , post-injection traces from animals within each of the four treatment conditions described in Figure 5A and 5B were integrated and compared using one-way analysis of variance ( ANOVA ) : F3 , 31 = 3 . 30 , p < 0 . 05 . Multiple comparisons between group pairs were conducted post hoc using the Newman-Keuls test: p < 0 . 05 compared to *Baseline , +Saline and #Untreated Control . ROI analysis of the ventral striatum ( nucleus accumbens , NAc ) , which is innervated by the ventral tegmental area ( VTA ) , revealed that the BOLD response to AMPH challenge did not significantly differ between STZ-treated and control animals ( unpublished data ) . Likewise , prelimbic and cingulate cortices , also innervated by the VTA and where the norepinephrine transporter is the predominant carrier supporting DA inactivation [61] , failed to show significant differential AMPH-induced BOLD responses after STZ ( unpublished data ) . To further elucidate the links between the PI3K signaling pathway , DAT function and AMPH action , we activated the PI3K pathway pharmacologically within the striatum of STZ-treated , hypoinsulinemic animals by locally infusing insulin just before the delivery of a brief AMPH pulse in this region . One week after depleting insulin with STZ treatment , local ( striatal ) application of exogenous insulin ( 10μM/100 nl ) 2 min before AMPH infusion ( 400 μM/125 nl ) almost fully restored to control levels the rate and the amount of AMPH-evoked DA release ( Figure 6A and 6B ) , as well as the rate of DAT-mediated clearance ( Figure 6C ) . These data further support our hypothesis that PI3K signaling is crucial for AMPH to stimulate DA efflux .
In recent years , the PI3K/Akt signaling pathway has been heavily implicated in the development , progression and maintenance of drug dependence [26–28] . Regulation of DAT plasma membrane expression ( and subsequently of extracellular DA ) by PI3K signaling is emerging as an important mechanism linking neurotransmitter transporter function to psychomotor stimulant abuse [11 , 13] . Profound adaptations within the neuronal dopaminergic system occur in experimentally induced diabetic mice [51] . Compared to controls , STZ-treated hypoinsulinemic rats display a marked reduction in striatal DA clearance [14 , 30] and are resistant to the behavioral effects of AMPH [12 , 33 , 34 , 51] . In experimentally induced diabetic rats ( i . e . , alloxan-treated ) , AMPH administered acutely is less effective at producing anorexia and stereotyped behavior and at increasing locomotor activity; subsequent administration of insulin reverses this attenuated sensitivity to AMPH [33] . Importantly , Galici et al . [12] showed that there is a selective decrease in AMPH self-administration in diabetic rats , consistent with data showing that dopamine uptake is decreased in hypoinsulinemic rats [14 , 30] . Considering that the striatum is highly enriched in insulin [17 , 62] and IRs [15 , 17 , 18] , as well as in DAT [37–39] , these studies strongly support a role for the neuronal PI3K pathway in regulating DAT activity and extracellular DA levels , as well as in the actions of AMPH . The link between the PI3K pathway and the actions of AMPH is further fortified by recent studies from our laboratory as well as others , showing that prolonged exposure to AMPH ex vivo and in vivo inhibits PI3K signaling , as measured by Akt activity in striatum [29 , 63] . Akt is a protein kinase that is immediately downstream of PI3K , and Akt activity has been shown to be essential for insulin modulation of transporter function in striatal synaptosomes and human DAT-expressing cells [11 , 13] . Indeed , insulin signaling increases DA uptake capacity and cell surface expression [11 , 13] . In contrast , in vitro inhibition of either PI3K or Akt causes a decrease in DA uptake capacity and a redistribution of DAT away from the plasma membrane [11 , 13] . Here we demonstrate in vivo that hypoinsulinemia and pharmacological inhibition of PI3K signaling reduces the ability of AMPH to evoke DA efflux in the striatum . The reduction in DA efflux determined by HSCA in the current studies may result either from a decreased DAT plasma membrane expression , as suggested by in vitro [11 , 13] and ex vivo [14 , 30] studies , from a diminished DA content [64] , or from both . Our data suggest that it is unlikely that the reduced DA efflux is a consequence of changes in tissue DA content . This is because the analysis of the rising phase of the HSCA traces revealed that the rate of AMPH-induced DA efflux in STZ-treated rats was severely attenuated compared with that of saline-treated control rats ( Figure 2B ) . In fact , the slope of the rising portion of the DA signal represents the rate of DAT-mediated DA efflux , which is primarily dependent on the affinity and turnover rate of DA and is independent of DA content [14 , 40] . In addition , DA clearance as measured by HSCA that is independent of DA content but dependent on DAT number at the plasma membrane is reduced in STZ-treated animals [14] . STZ treatment does not significantly influence total DAT number or DA affinity [12 , 14] . Thus , the current findings suggest that a reduction in insulin signaling leads to a decrease in DAT function , a notion supported by the previous study from Owens et al . [14] showing that AMPH-naive , STZ-treated rats exhibit significantly less DA uptake in striatum as determined in vivo with HSCA and ex vivo in synaptosomes . Collectively these data support the hypothesis that hypoinsulinemia , by downregulation of PI3K signaling ( see Figure 3 ) , significantly reduces AMPH-induced DA efflux because of reduced DAT plasma membrane expression . In support of the current in vivo electrochemical and ex vivo biochemical findings , STZ treatment was also found to inhibit the ability of AMPH to induce a BOLD response in the dorsal striatum . The current study did not reveal significant differences in insulin-dependent , AMPH-induced BOLD signal fluctuations in the NAc ( unpublished data ) . Possibly , this was due to the limited radio frequency penetration of the surface coil used in this study into more ventral brain areas such as the NAc . Importantly , others have reported decreases in AMPH-induced DA release in NAc dialysates collected in freely moving rats [65] . Because hyperglycemia has been shown to not significantly influence BOLD signals [66] , our data suggest that blunting of the AMPH-induced BOLD response in the DAT-dense striatum of insulin-depleted rats ( Figure 5 ) is not due to STZ-mediated metabolic abnormalities . Importantly , in the striatum , the AMPH-induced BOLD response has been shown to correlate with striatal extracellular DA levels [54 , 55] and , consequently , with DAT-mediated reverse transport of DA . These data further support our hypothesis that STZ treatment , by decreasing PI3K signaling in striatum , downregulates AMPH-induced DA efflux measured by HSCA ( Figures 2 , 3 and 6 ) and fMRI ( Figure 5 ) . Our results are consistent with in vitro studies demonstrating that the blockade of insulin signaling decreases the number of active DATs on the plasma membrane [13] as we currently demonstrate in DAT cell surface biotinylation studies from striatal preparations ( Figure 4 ) . These data support the hypothesis that the attenuated rate of AMPH-induced striatal DA efflux in hypoinsulinemic rats results from a DAT trafficking phenomenon [14] . Conceivably , in STZ-treated animals , insulin stimulation of PI3K signaling should restore DA clearance and AMPH-induced DA efflux . Figure 6 shows that local application of insulin in STZ-treated rats almost completely restores AMPH-stimulated DA efflux . We demonstrate here that PI3K signaling regulates the pharmacological actions of drugs ( e . g . , AMPH ) that act on dopaminergic systems . Importantly , our data show that hypoinsulinemia reduces basal PI3K signaling and impairs the ability of AMPH to increase extracellular DA levels . Therefore , PI3K signaling may provide a new cellular target for the development of novel treatments of AMPH abuse and regulation of dopaminergic tone .
All procedures were approved by the Vanderbilt University Medical Center and the University of Texas Health Science Center at San Antonio Institutional Animal Care and Use Committees and were conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals . For all experiments , male Sprague–Dawley ( Harlan , Indianapolis , Indiana , United States ) rats ( 275–350 g ) served as experimental subjects . STZ is an antibiotic that destroys the insulin-secreting β cells of the pancreas [36] and has previously been used to induce chronic hypoinsulinemia in rats by our laboratories [12 , 14] . STZ ( Sigma-Aldrich; http://www . sigmaaldrich . com ) was freshly dissolved in ice-cold 100 mM citrate saline ( pH 4 . 5 ) for all studies . Rats received STZ ( 50 mg/kg , i . p . for HSCA studies; 65 mg/kg into the tail vein for fMRI studies ) and were returned to their home cages for at least 7 d . Blood glucose was measured with a glucometer ( Advantage Accu-Chek , Roche Diagnostics; http://www . roche . com ) before STZ and just before an experiment . Animals were considered hypoinsulinemic when their glucose levels exceeded 300 mg/dl . Preparation of synaptosomes was performed as described previously [11 , 12 , 14] . Rats were killed by decapitation , their brains were removed and their striata were rapidly dissected on a plastic dish placed on ice . Tissue was homogenized in ice-cold Krebs-Ringer buffer ( 125 mM NaCl , 1 . 2 mM KCl , 1 . 2 mM MgSO4 , 1 . 2 mM CaCl2 , 22 mM NaHCO3 , 1 mM NaH2PO4 , 10 mM glucose , pH 7 . 4 ) containing 0 . 32 M sucrose using a glass-Teflon homogenizer . Homogenates were centrifuged at 1 , 000g for 10 min at 4 °C , and the resulting supernatants were centrifuged at 16 , 000g for 25 min at 4 °C . P2 pellets were then placed on ice and resuspended immediately prior to experiments . Akt activity assays were performed as described previously [29] . Striatal synaptosomes were lysed for 45 min at 4°C in a buffer containing 20mM Tris ( pH 7 . 5 ) , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1% Triton X-100 , 2 . 5 mM sodium pyrophosphate , 1 mM β-glycerolphosphate , 1 mM Na3VO4 , 1 μg/ml leupeptin and 1 mM PMSF . Lysed proteins ( ∼400 μg; BioRad DC Protein Assay Kit; http://www . biorad . com ) underwent immunoprecipitation with an Akt-specific monoclonal antibody as part of a commercially available Akt activity assay kit ( BioVision; http://www . biovision . com ) . Activity of the immunoprecipitated Akt was determined in vitro with the addition of recombinant GSK3α as the kinase substrate; the resulting phosphorylated GSK3α ( pGSK3α ) was determined by immunoblotting ( see below ) using phosphospecific antibodies to GSK3α ( Ser 21 , diluted 1:1000 ) , provided in the Akt activity assay kit . Biotinylation studies were performed as described previously [13 , 48 , 49] with modification . Striatal synaptosomes were washed twice with warm Krebs–Ringer bicarbonate ( KRB ) buffer ( containing 145 mM NaCl , 2 . 7 mM KCl , 1 . 2 mM KH2PO4 , 1 . 2 mM CaCl2 , 1 . 0 mM MgCl2 , 10 mM glucose , 0 . 255 mM ascorbic acid , and 24 . 9 mM NaHCO3 ) and then incubated in the same buffer for 1 h at 37 °C . The reaction was stopped in ice and the samples were washed with phosphate-buffered saline ( PBS ) containing 0 . 1 mM CaCl2 and 1 mM MgCl2 ( PBS-Ca-Mg ) and incubated with EzLink Sulfo-NHS-SS-Biotin ( 2 . 0 mg/ml in PBS-Ca-Mg; Pierce Chemical; http://www . piercenet . com ) on ice for 30 min . The reaction was quenched by washing twice with 4 °C PBS-Ca-Mg containing 100 mM glycine ( PBS-Ca-Mg-glycine ) followed by an incubation with PBS-Ca-Mg-glycine for 15 min on ice . Synaptosomes were then washed twice with cold PBS-Ca-Mg before lysis with 1 ml of radioimmunoprecipitation assay ( RIPAE ) buffer ( 10 mM Tris pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 0 . 1% SDS , 1% sodium deoxycholate and 1% Triton X-100 ) containing protease inhibitors ( 0 . 5 mM phenylmethylsulfonyl fluoride , 5 μg/ml leupeptin and 5 μg/ml pepstatin ) for 1 h 30 min on ice with agitation . Lysates were then centrifuged at 14 , 000g for 30 min at 4 °C . After isolation of supernatants , biotinylated proteins were separated by incubation with 90 μl ImmunoPure immobilized streptavidin beads ( Pierce ) for 1 h at room temperature with agitation . Beads were washed three times with RIPAE buffer; biotinylated proteins were then eluted in 50 μl of 2X SDS-PAGE sample loading buffer at room temperature . Total cell lysates ( ∼300 μg protein ) and the biotinylated ( cell surface ) fraction ( ∼10% of total; 30 μg protein ) underwent immunodetection for DAT as described below . Determination of biotinylated DAT immunoreactivity was conducted with some modification according to previously described methods [13 , 29] . Briefly , synaptosomal lysates were separated by SDS-PAGE , and resolved proteins were transferred to polyvinylidene difluoride ( PVDF ) membranes ( BioRad ) , which were incubated for 1–2 h in blocking buffer ( 5% dry milk and 0 . 1% Tween 20 in Tris-buffered saline ) . To quantify biotinylated ( surface ) DAT , immunoblots were incubated with mouse monoclonal primary antibodies to the N terminus of the rat DAT ( antibody 16 , 1:1000 , [67] ) , generously provided by Roxanne Vaughan ( University of North Dakota School of Medicine and Health Sciences , Grand Forks , North Dakota , United States ) . All proteins were detected using HRP-conjugated goat anti-mouse secondary antibodies ( 1:5000; Santa Cruz Biotechnology; http://www . scbt . com ) . After chemiluminescent visualization ( ECL-Plus; Amersham; http://www . amersham . com ) on Hyperfilm ECL film ( Amersham ) , protein band densities were quantified ( Scion Image; http://www . scioncorp . com ) and normalized to the appropriate total protein amount . Immunoblotting experiments were performed in triplicate , analyzed ( GraphPad v4 . 0; http://www . graphpad . com ) and reported as mean ± standard error of the mean . HSCA was conducted using the FAST-12 system ( Quanteon; http://www . quanteon . cc ) as previously described with some modification [14] . Recording electrode/micropipette assemblies were constructed using a single carbon-fiber ( 30 μm diameter; Specialty Materials; http://www . specmaterials . com ) , which was sealed inside fused silica tubing ( Schott , North America; http://www . schott . com ) . The exposed tip of the carbon fiber ( 150 μm in length ) was coated with 5% Nafion ( Aldrich Chemical Co . ; http://www . sigmaaldrich . com; 3–4 coats baked at 200 °C for 5 min per coat ) to provide a 1000-fold selectivity of DA over its metabolite dihydroxyphenylacetic acid ( DOPAC ) . Under these conditions , microelectrodes displayed linear amperometric responses to 0 . 5–10 μM DA during in vitro calibration in 100 mM phosphate-buffered saline ( pH 7 . 4 ) . Animals were anesthetized with injections of urethane ( 850 mg/kg , i . p . ) and α-chloralose ( 85 mg/kg , i . p . ) , fitted with an endotracheal tube to facilitate breathing , and placed into a stereotaxic frame ( David Kopf Instruments; http://www . kopfinstruments . com ) . To locally deliver test compounds ( see below ) close to the recording site , a glass single or multi-barrel micropipette ( FHC; http://www . fh-co . com ) was positioned adjacent to the microelectrode using sticky wax ( Moyco; http://www . moycotech . com ) . The center-to-center distance between the microelectrode and the micropipette ejector was 300 μm . For experiments in Figure 2 , the micropipette was filled with AMPH ( 400 μM; Sigma ) or its vehicle ( PBS ) . The study in Figure 3 used a multibarrel configuration in which barrels contained AMPH ( 400 μM ) or vehicle ( aCSF ) and additional barrels contained LY294002 ( 1 mM; Sigma ) or its vehicle ( aCSF in DMSO ) . For experiments in Figure 6 , one barrel contained AMPH ( 400 μM ) and an adjacent barrel contained insulin ( 10 μM; Sigma ) ; a third barrel contained aCSF , the vehicle for both AMPH and insulin . The electrode/micropipette assembly was lowered into the striatum at the following coordinates ( in mm from bregma [68] ) : A/P , +1 . 5; M/L , ±2 . 2; D/V , −3 . 5 to −5 . 5 . The application of drug solutions was accomplished using a Picospritzer II ( General Valve Corporation; http://www . parker . com ) in an ejection volume of 100–150 nl ( 5–25 psi for 0 . 25–3 s ) . After ejection of test agents , there is an estimated 10–200-fold dilution caused by diffusion through the extracellular matrix to reach a concentration of 2–40 μM ( AMPH ) , 5–100 μM ( LY294002 ) or 0 . 05–1 μM ( insulin ) at the recording electrode [43] . To record the efflux and clearance of DA at the active electrode , oxidation potentials—consisting of 100-ms pulses of 550 mV , each separated by a 1-s interval during which the resting potential was maintained at 0 mV—were applied with respect to an Ag/AgCl reference electrode implanted into the contralateral superficial cortex . Oxidation and reduction currents were digitally integrated during the last 80 ms of each 100-ms voltage pulse . For each recording session , DA was identified by its reduction/oxidation current ratio: 0 . 55–0 . 80 . At the conclusion of each experiment , an electrolytic lesion was made to mark the placement of the recording electrode tip . Rats were then decapitated while still anesthetized , and their brains were removed , frozen on dry ice , and stored at −80°C until sectioned ( 20 μm ) for histological verification of electrode location within the striatum . HSCA data were analyzed with GraphPad Prism using three signal parameters ( see Figure 2A for exemplary trace ) : ( i ) the DA efflux rate ( in nM/s ) , which is the change in DA oxidation current evoked by AMPH application as a function of time; ( ii ) the maximal signal amplitude of the released DA ( in μM ) ; and ( iii ) the DA clearance rate ( in nM/s ) , defined as the slope of the linear portion of the current decay curve , i . e . , from 20−60% of maximal signal amplitude . Under isoflurane anesthesia , rats were implanted with femoral artery and i . p . catheters , tracheotomized and artificially ventilated with a 30:70% O2:N2O mixture . Rats were paralyzed with a bolus infusion of pancuronium bromide ( 2 mg/kg; Sigma ) dissolved in isotonic saline ( 1 ml/kg , i . p . ) , and the concentration of isoflurane was reduced to 0 . 88% . Ventilation parameters were adjusted ( respiratory rate = 48–52 breaths/min; inspiration volume 14–18 cm H2O ) to maintain stable blood gases , which were sampled from the arterial catheter immediately before and after the completion of MRI scans . Mean arterial gas values obtained from all 23 rats used in fMRI studies were: pH = 7 . 36 ± 0 . 06 , pCO2 = 37 . 7 ± 6 . 8 mm Hg , pO2 = 140 . 5 ± 20 . 6 mm Hg . A respiration pillow sensor ( SA Instruments; http://www . i4sa . com ) was positioned underneath the animal's abdomen . Core body temperature and heart rate were monitored during imaging studies using a rectal probe and subdermal electrocardiograph ( ECG ) electrodes implanted into the forepaws . Temperature , ECG and respiratory data were collected and analyzed using an MR-compatible monitoring system ( SAM-PC , SA Instruments ) . To minimize motion artifacts , rats were positioned within a custom-built plexiglass stereotaxic platform and fixed in place with Teflon ear bars and an adjustable incisor bar . Attached to the platform was a socket holding a 20-mm dual transmit-receive radio frequency surface coil ( Varian Instruments; http://www . varianinc . com ) lowered to 1 mm above the scalp . The platform was then placed inside a 9 . 4 T , 21-cm bore Varian Inova superconducting magnet equipped with actively shielded gradients of 40 G/cm and peak rise times of 135 μs . The MRI system was controlled by a Varian console interfaced with a Sun Microsystems computer running VnmrJ 6 . 1D software ( Varian ) . Nine contiguous coronal slices , serving as within-subject high-resolution anatomic templates , were acquired using conventional gradient echo multi-slice ( GEMS ) imaging . Seventy-two functional image volumes , spatially aligned with the anatomic templates , were then continuously acquired for 30 min using the following GEMS parameters: TR/TE = 220/12 ms; flip angle = 20°; NEX = 2; slice thickness = 1 mm; in-plane voxel resolution = 0 . 47 × 0 . 47 mm; matrix = 64 × 64; FOV = 30 × 30 mm; acquisition time = 25 . 6 s per excitation . After a 15-min baseline period , AMPH dissolved in isotonic saline was administered as a bolus i . p . infusion ( 3 mg/ml/kg; 20–30 s ) ; image acquisition continued uninterrupted for 15 min after the infusion . Analysis of fMRI data was conducted in MATLAB ( v7 . 0 . 4; The MathWorks; http://www . mathworks . com ) as described previously , with some modification [69] . Data were first analyzed to generate statistical parametric activation maps based on the Student's t-test . t values were computed for each image voxel by comparing the baseline signal to the post-injection signal . For each subject , colorized t values from each image voxel were registered to a high-resolution anatomic template obtained in the same subject . ROI analyses were conducted over the dorsolateral striatum , the ventral striatum ( NAc ) and prelimbic/cingulate cortices based on 1-mm-thick coronal slices spanning 1–2 mm anterior to bregma [68] . For each ROI , fMRI time series data underwent baseline drift correction with a linear detrending function; high-frequency noise was suppressed with a low-pass Butterworth filter . Pixel intensities from each image voxel in the ROIs were converted to percent signal changes from baseline ( % ΔS/So ) , averaged across left and right hemispheres , integrated and analyzed by one-way ANOVA followed by a Newman-Keuls post test .
|
Abuse of psychostimulants such as amphetamine remains a serious public health concern . Amphetamines mediate their behavioral effects by stimulating dopaminergic signaling throughout reward circuits of the brain . This property of amphetamine relies on its actions at the dopamine transporter ( DAT ) , a presynaptic plasma membrane protein that is responsible for the reuptake of extracellular dopamine . Recently , we and others have revealed the novel ability of insulin signaling pathways in the brain to regulate DAT function as well as the cellular and behavioral actions of amphetamine . Here we used a model of Type I diabetes in rats to uncover how insulin signaling regulates DAT-mediated amphetamine effects . We show that by depleting insulin , or through selective inhibition of insulin signaling , we can severely attenuate amphetamine-induced dopamine release and impair DAT function . Our findings demonstrate in vivo the novel ability of insulin signaling to dynamically influence the neuronal effects of amphetamine-like psychostimulants . Therefore , the insulin signaling pathway , through its unique regulation of brain dopamine , may be targeted for the treatment of amphetamine abuse .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neurological",
"disorders",
"physiology",
"radiology",
"and",
"medical",
"imaging",
"neuroscience"
] |
2007
|
Hypoinsulinemia Regulates Amphetamine-Induced Reverse Transport of Dopamine
|
Organisms respond to environmental changes by adapting the expression of key genes . However , such transcriptional reprogramming requires time and energy , and may also leave the organism ill-adapted when the original environment returns . Here , we study the dynamics of transcriptional reprogramming and fitness in the model eukaryote Saccharomyces cerevisiae in response to changing carbon environments . Population and single-cell analyses reveal that some wild yeast strains rapidly and uniformly adapt gene expression and growth to changing carbon sources , whereas other strains respond more slowly , resulting in long periods of slow growth ( the so-called “lag phase” ) and large differences between individual cells within the population . We exploit this natural heterogeneity to evolve a set of mutants that demonstrate how the frequency and duration of changes in carbon source can favor different carbon catabolite repression strategies . At one end of this spectrum are “specialist” strategies that display high rates of growth in stable environments , with more stringent catabolite repression and slower transcriptional reprogramming . The other mutants display less stringent catabolite repression , resulting in leaky expression of genes that are not required for growth in glucose . This “generalist” strategy reduces fitness in glucose , but allows faster transcriptional reprogramming and shorter lag phases when the cells need to shift to alternative carbon sources . Whole-genome sequencing of these mutants reveals that mutations in key regulatory genes such as HXK2 and STD1 adjust the regulation and transcriptional noise of metabolic genes , with some mutations leading to alternative gene regulatory strategies that allow “stochastic sensing” of the environment . Together , our study unmasks how variable and stable environments favor distinct strategies of transcriptional reprogramming and growth .
A stable environment generally favors organisms that are well-adapted to that specific niche [1]–[3] . However , in many cases , adaptation to one environment comes at costs to fitness in alternative niches [1] , [4]–[9] . Aside from the fitness tradeoffs , adaptation through mutation is relatively slow . Thus to deal with certain recurring environmental changes , many organisms have evolved the capacity to change gene expression in response to the environment , reducing the need for genetic adaptation . Microbial nutrient uptake and metabolism is a prime example of how organisms use transcriptional regulation to optimize fitness in variable environments . Because the expression of nonnecessary metabolic routes and genes is costly [3] , [10] , [11] , microbes often use catabolite repression mechanisms to preferentially consume nutrients that afford high growth rates . This way , nonpreferred nutrient genes are only expressed when other , more preferred nutrients have been depleted . The sensing and signaling cascades required for carbon catabolite repression in the yeast Saccharomyces cerevisiae are particularly well-studied and serve as a model for similar systems in higher eukaryotes [12]–[15] . Glucose acts as a primary signal , triggering a regulatory cascade that results in repression of the consumption of alternative carbon sources , such as maltose , galactose , or ethanol . The main mechanism by which glucose regulates transcription is via the Ras/protein kinase A ( PKA ) signal transduction pathway . Other effectors include Snf1 , the yeast homologue of mammalian AMP-activated PK , and Rgt1 . Both of these proteins effect catabolite repression by triggering the transcriptional rewiring of a small subset of genes , many of which are directly involved in the uptake and metabolism of alternative carbon sources [12]–[14] , [16] . Like other gene regulation programs , catabolite repression reduces the fitness cost associated with unnecessary gene expression while preserving the possibility of growth in environments with different nutrients . However , the ability to adapt to changing environments appears to be intrinsically opposed to obtaining maximal fitness in stable environments [6] , [17]–[20] . This is partly because maintaining a regulatory mechanism requires energy and provides little benefit in stable environments [1] , [21]–[25] . Moreover , in variable environments , transcriptional reprogramming also requires energy and time . This is clearly manifested when microbes switch from growth on one carbon source to another . During such a switch , cells show temporarily reduced growth rates ( a so-called “lag phase” ) because transcription is not yet adapted to the new environment [1] , [26] , [27] . As a consequence , an organism's fitness in a given environment depends not only on the maximal reproductive rates in that particular niche , but also on the speed with which gene expression can adapt to the new conditions [1] , [8] , [27]–[29] . Because maintaining and operating an environmental sensing system is not always beneficial , it has been suggested that some environments might favor simpler strategies . For example , certain variable and unpredictable environments might stimulate strategies where a clonal population uses random ( stochastic ) switching between phenotypically different states . Such so-called “bet-hedging” strategies can generate phenotypic diversity independently of present environmental conditions . If this diversity correlates with the frequency of environmental uncertainty , this will be an “evolutionarily stable” strategy that ensures that some portion of the population is always adapted to future conditions . This strategy reduces the duration of the lag phase and also avoids fitness costs associated with maintaining an environmental sensor [1] , [19] , [30]–[33] . Other studies have proposed that microbes can evolve mixed gene regulation strategies that combine sensing with stochastic switching . Such “stochastic sensing” strategies use clues about future environmental changes to induce anticipatory transcriptional changes in a portion of individuals within the population [1] , [2] , [29] , [34]–[38] . Whereas the molecular cascades underlying gene regulation have been extensively studied , the natural diversity and fitness costs and benefits of different gene regulation strategies have received less attention . This is in part because accurately measuring fitness across different environments is challenging . Because of the exponential nature of population growth , the long-term expected fitness of an organism is determined by its geometric mean growth rate ( GMR ) across every environment it encounters , weighted by the frequency and duration with which these environments occur [1] , [4]–[6] , [27] , [39] , [40] . Hence , even short periods of low fitness may have a significant effect on the long-term performance of an organism . “Generalist” strategies affording similar but more modest fitness levels across different environments can therefore result in a higher geometric mean fitness , even if maximal fitness in certain preferred environments is reduced [2] , [41]–[43] . In this study , we use a combination of population- and single-cell-level measurements of the model eukaryote S . cerevisiae to explore how different environments shape fitness and transcriptional regulatory strategies . More specifically , we measure fitness as cells grow in environments with a stable supply of glucose compared to environments where cells need to transition from one carbon source to another . We find that different natural yeast strains show large differences in the speed with which they are able to adapt gene expression and growth to changes in carbon sources . Using experimental evolution , we demonstrate that growing a strain that shows slow transcriptional reprogramming in a variable environment frequently results in mutations in key regulatory genes such as HXK2 . These mutations give rise to phenotypic “generalists” that thrive well in variable environments , with short lag phases , less stringent catabolite repression , and faster transcriptional reprogramming—at the expense of maximal growth rates ( MaxRs ) in a stable glucose environment . Many of these generalist isolates implement a transcriptional regulatory strategy mediated by “stochastic sensing” of alternative carbon sources , allowing cultures to maintain consistent fitness across different environments . Alternatively , the same selection regime can favor specialist mutants of an opposite character , which display tight catabolite repression and slow adaptation to new environments ( long lag phases ) , but higher growth rates in stable glucose conditions . An experimentally validated mathematical model reveals how alternative regimes of variable carbon environments will favor one carbon catabolite repression strategy over another . Together , our results reveal that the speed with which genes are induced and repressed in response to environmental signals is a highly variable and evolvable trait . Our study moreover illustrates how distinct strategies of transcriptional reprogramming shape fitness in constant or variable environments .
To investigate how the lag phase can influence fitness , we compared the growth behavior of 18 different S . cerevisiae strains in stable and variable environments . These included two commonly used laboratory strains ( S288c and SK1 ) as well as 16 genetically diverse strains described by Liti et al . ( 2009 ) . We used bulk population-level growth measurements in an automated plate reader ( see Materials and Methods ) to measure fitness in four different environments , including one stable condition with abundant glucose and three variable environments where populations gradually run out of glucose and thus need to switch to a different carbon source to continue growth . To obtain a stable “high glucose” ( HG ) condition , we supplemented the growth medium with 3% glucose , a condition that allowed relatively constant growth rates until cells entered stationary phase . At the other extreme we supplemented growth media with only 0 . 5% glucose ( low glucose , LG ) , a condition that allows cells to first grow quickly by fermenting glucose , and then reprogram their metabolic genes to switch to respiratory growth on the ethanol accumulated during the fermentation phase ( Figure 1 and Figure S1 ) . Two other variable conditions included supplementation of LG with either maltose or galactose , two nonpreferred fermentable carbon sources whose metabolism is repressed in the presence of glucose ( Figure 1 and Figure S1 ) . As expected , many yeast strains showed a clear lag phase when grown in media that induce an adaptation to new carbon sources ( called a “diauxic shift” ) ( Figure 1 and Figure S1 ) . The shift can be detected by three characteristic changes in growth rate: a deceleration in growth speed as glucose is depleted , a brief phase where the growth rate reaches a ( local ) minimum , and subsequent re-acceleration to adapted growth on the alternative carbon source ( Figure 1A , B and Figure S1A ) . The lag phase was especially pronounced in media with LG alone , and more subtle or in some cases completely absent in LG + galactose and LG + maltose . However , even when the growth deceleration during the diauxic shift is very pronounced , it is not trivial to accurately quantify the lag phase because populations rarely arrest growth completely , and because the start and end of the phase cannot be clearly defined . Furthermore , the deceleration and local minimal phases appeared to be affected by the presence of an alternative carbon source ( Figure S1A ) , and this effect was highly variable between strain backgrounds . For example , S288c growing in LG + maltose initially decelerates faster than the LG condition , but subsequently shows significantly higher growth rates across the rest of the experiment . By contrast , the LG + galactose sample has a higher rate of growth throughout the deceleration phase but decelerates later on in the curve as the culture adapts fully to galactose consumption ( Figure 1C , D and Figure S1A ) . Moreover , these differences are strain-dependent . Strain Y55 , for example , shows a more pronounced decrease in growth speed during the diauxic shift in LG + galactose compared to LG alone ( Figure S1A ) . The results above show that it is difficult to quantify the population-level lag phase by simply measuring its duration . In order to quantify the rate with which strains are able to adapt to variable environments , we therefore use a simple metric that summarizes the overall growth speed ( or fitness ) of a population as it transitions through a diauxic shift . This GMR is the weighted geometric mean of growth rate values across the shift in carbon conditions and represents the average growth rate of the strain across a specific interval . We chose to calculate the GMR for the interval between O . D . 0 . 15 and O . D . 0 . 75 , which comprise the complete shift from one carbon source to another ( see Materials and Methods and Figure 1A , B ) . The GMR can approach but never exceed the MaxR achieved by the culture while it was growing on the preferred carbon source glucose . Hence , the longer that a population of cells grows in stable glucose conditions , the closer its GMR approaches the culture's MaxR . In mixed media , by contrast , the GMR can be considerably lower than MaxR because of the decline in growth speed as cultures transition to growth in the nonpreferred carbon sources . Therefore , the ratio of the GMR in stable versus variable conditions is a measure for how efficiently the cells can transition between the different conditions , which in turn largely depends on the severity of the lag phase ( see also Materials and Methods ) . Examining the GMR across growth in stable ( HG ) and variable ( LG , LG + maltose and LG + galactose ) conditions allowed us to quantify how the carbon environment affected the overall fitness of the different strains . As expected , for all strains , the highest GMR was found in stable ( HG ) conditions , whereas the LG condition resulted in the lowest GMR ( Dataset S1 ) . Remarkably , however , the difference between these bounds was highly variable: whereas some strains only showed a 10% reduction in GMR in LG conditions compared to HG conditions , others showed a 70% reduction in GMR . A similar although less pronounced variation between strains was also observed for LG + galactose and LG + maltose media ( Figure S1B and Dataset S1 ) . Together , these results indicate that the diauxic shift from glucose to a less preferred carbon source leads to a wide array of growth behaviors , ranging from highly variable growth rates in different media to almost constant growth rates throughout the shift in carbon source . The decrease in fitness during diauxic shift depends both on the yeast strain and the carbon source . In general , the transition from glucose to ethanol ( LG medium ) induced the strongest decrease in growth rate ( and thus also GMR ) , whereas the glucose to maltose transition ( LG + maltose medium ) caused the smallest reduction compared to stable HG conditions ( Figure S1B ) . To obtain a more detailed picture of the decrease in fitness during the lag phase as cells transition from one carbon source to another , we turned to single-cell measurements . The cells were first grown in glucose , harvested , and then immediately transferred to maltose-containing media . The duration of the lag phases of individual cells after this sudden transfer to maltose was measured using automated time-lapse microscopy ( Materials and Methods and Movies S1 and S2 ) . We recorded the lag phases of individual cells as the time it took a cell to begin dividing after transfer from glucose to maltose-containing medium ( i . e . , the first appearance of a cell bud or the resumption of bud growth ) . The results of these experiments indicate that the 18 yeast strains showed a striking diversity in the average duration of the lag phase ( Figure 1G ) . Interestingly , even within a population of a given strain , we often observed a large diversity in lag duration among different individual cells . Moreover , in some cases , not all cells seemed to survive the shift in carbon source . The degree of intraclonal variability in lag duration ( measured by the standard deviation of single-cell lag phases of a population of cells of a given strain ) and fraction of cells surviving within 20 h of recording was significantly related to the average lag phase , an observation that held true for all strains and mutants we examined in this study ( Figure S1C–F ) . Strikingly , the average duration ( and thereby heterogeneity ) of the single-cell lag phase of a given yeast strain was also highly anticorrelated with the fitness ( GMR ) measured in population-level experiments where strains often displayed pronounced lag phases , such as in LG and LG + galactose conditions . More generally , cells showing long lag phases as measured by single-cell microscopy also showed higher variation in fitness ( standard deviation and coefficient of variation in GMR ) between different growth media ( HG , LG , LG + Gal , LG + Mal; see Figure 1H ) . Further statistical analysis using proportional hazard regression ( see Text S1 ) confirmed this correlation ( Dataset S1B ) . Taken together , these analyses indicate that the efficiency with which populations are able to shift between carbon sources varies significantly between different strains , and is correlated with the average lag phase measured during sudden shifts in carbon source . Specifically , strains that show large differences in fitness between stable ( HG ) and variable ( LG , LG + Maltose and LG + Galactose ) environments also show long lag phases . The results from the previous section support that lag phases measured for single cells in sudden glucose to maltose shifts are correlated with lag phases measured by monitoring population-level growth in conditions where glucose is more gradually depleted ( Figure 1H and Dataset S1 ) . Interestingly , although sudden transitions from glucose to maltose media often led to long and heterogeneous lag phases , most cultures growing in LG + maltose mixed media displayed modest lag phases , suggesting that the yeast cells were able to maintain higher overall growth rates if the transition between glucose and maltose was more gradual . Exploring the molecular details of this shift from glucose to maltose proved to be an ideal model because only three genes are required for maltose consumption , making it a simple system to study . To grow on maltose , yeast cells need to express a maltose transporter ( MalT ) , a maltase ( MalS ) , and a regulator ( MalR ) that induces the genes in the presence of maltose via positive feedback regulation [44] . To characterize this phenomenon in further depth , we chose to work with the laboratory strain S288c because it is genetically tractable and displays a clear lag phase in glucose-to-maltose transitions . It seemed likely that carbon catabolite-mediated repression of the MAL genes was a key factor contributing to the long lag phases in sudden glucose to maltose shifts . Using fusions of the MAL proteins with fluorescent reporters , we observed , as expected , that expression of MAL genes is repressed in the presence of glucose , and induced in the presence of maltose after glucose is depleted ( Figure S2 ) . Moreover , constitutive overexpression of the MAL genes resulted in a much shorter lag phase ( logrank Chi2 = 591 , p<1×10−16 ) that was comparable to some of the natural strains with short lag phases , suggesting that a long lag phase is due to the slow de-repression of MAL genes ( Figure S2B ) . Taken together , these results suggest that the duration of the lag phase is determined at least in part by the gene expression state of cells upon transition to the new carbon source . In sudden shifts from glucose to maltose , lag phases appear to be longer and more heterogeneous due to the time required to activate the MAL genes , whereas in mixed LG + maltose medium , strains can prepare for maltose fermentation before glucose is completely depleted ( Figure 1C , D and Figure S2 ) . Therefore , the different lag phase behaviors are likely to be mediated by a variable extent of carbon catabolite repression across strains and conditions . At first sight , it may seem suboptimal for strains to have long lag phases instead of rapidly adapting to a new environment . However , long lag phases might be adaptive under certain conditions [1] . For example , a long lag phase caused by carbon catabolite repression could potentially allow cells to more rapidly resume growth should preferred carbon sources like glucose return to the environment . To test this , we transferred a population of glucose-repressed cells to maltose , waited until half of the population had committed to maltose growth , as indicated by expression of a MalS-YeCitrine fluorescent reporter construct . We then transferred these cultures to glucose medium and measured the initial MAL fluorescence state of individual cells and subsequently tracked these cells' growth rates in glucose using time lapse microscopy ( Figure 2 and Figure S2D , E ) . Compared to isogenic sister cells that had not yet escaped the lag phase , cells that already had activated their MAL genes showed lower growth rates in glucose for at least two cell divisions , showing that commitment to maltose growth came at a fitness cost when glucose reappeared . The large distribution in lag duration between isogenic cells in a population may therefore serve as a way to distribute the costs and benefits of commitment to nonpreferred nutrients across individuals within the population . Strains with tight catabolite repression , with longer and more heterogeneous distribution of single-cell lag times , appear to implement this strategy to a greater extent than strains with shorter and more homogeneous lag time distributions . The above results indicate that the lag phase ( and thus the speed of transcriptional reprogramming ) has significant genetic determinants: there is a wide variation in lag duration between genetically distant yeast strains , and further we can engineer shorter lag phases with a reverse-genetics approach . Moreover , because it appeared that long lag phases could themselves be beneficial when glucose returns frequently to the environment ( Figure 2 ) , we reasoned that this trait should be subject to natural selection . To test this , we cycled the strain S288c between glucose and maltose to generate conditions of strong selective pressure . In the first leg of the cycle , to maintain selection on MaxR in preferred nutrients , cells were allowed ∼10 generations of exponential growth in stable glucose conditions . Then , in the second half of the cycle we selected for short lag phases by transferring these cultures into maltose-containing medium , allowing for ∼5 more generations of growth . In a first experiment , 12 populations expressed a constitutively transcribed YeCitrine marker to facilitate later analysis in competition experiments . After six cycles or ∼90 generations , 11 out of 12 evolving populations showed shorter lag phases when compared to the ancestral strain ( Figure S3A and Movies S3 , S4 , S5 ) . At the end of the experiment we isolated individual cells from each population . We found that clones isolated from different cultures varied widely in their single-cell lag profiles ( Figure 3A ) , but that the profiles of individual isolates within each evolved population were similar ( Dataset S2 ) , suggesting that a single evolved phenotype had come to dominate each independently growing culture . The average single-cell lags of the isolated mutants range from as short as 5 h , comparable to the shortest lag phases observed in the collection of different strains reported in Figure 1 , to clones with lags of longer duration than the ancestral strain ( Dataset S2 ) . The Malthusian fitness under conditions mimicking selection for each of three short-lagged isolates from independent populations was ∼1 . 35–1 . 4-fold higher than the ancestral strain ( Figure 3B ) . To investigate how reproducible this result was , and to unravel the underlying genetic and molecular mechanisms that allowed these strains to increase fitness , we repeated the evolution experiment , however this time using 12 populations of cells bearing MALT-YeCitrine and MALS-mCherry constructs to allow measurement of MAL gene expression . After eight cycles or ∼120 generations , all 12 populations in this experiment showed shorter lag phases ( Figure S3B ) . We first carried out extensive growth rate analyses on 36 isolates ( three clones from each of the 12 populations ) . Similarly to what we observed in the previous experiment , the majority of the mutants isolated after cycling populations between glucose and maltose medium showed shortened lag phases ( Figure S3B , C ) . Using population-level growth measurements similar to those reported in Figure 1 , we found that these evolved strains had smaller differences in fitness ( GMR ) between different growth environments than the parental strain , leading to overall higher fitness across the shift in carbon sources relative to the ancestral strain ( Figure 4 ) . Interestingly , despite the fact that selection was only based on glucose-to-maltose cycles , the mutants also showed dramatic improvement in fitness in media containing LG alone or LG + galactose , conditions where the ancestral strain showed a pronounced lag phase ( Figures 4 and S4 ) . For example , several mutants no longer have any lag phase in galactose-containing media , maintaining steady rates of growth throughout the curve with no local growth rate minimum ( Figures 4B and S4 and Dataset S2 ) . This reduction in lag phase leads to a 1 . 2–1 . 4-fold higher fitness ( GMR ) during the diauxic shift . Likewise , several isolates have a GMR increase in LG conditions of up to 1 . 3-fold , an increase in fitness mediated by increased rates of growth throughout the deceleration , local minimum , and re-acceleration phases of the lag phase ( Figure 4B , C and Figure S4 ) . In LG + maltose conditions , where the ancestral strain showed a relatively limited lag phase ( Figure 1C , D ) , we found that the evolved strains' fitness also showed a modest increase due to a further reduction of the lag phase . Taken together , compared to the ancestor , the majority of evolved isolates developed a low degree of fitness variability ( i . e . , similar fitness levels in stable HG and variable conditions ) and short lag durations that are similar to some of the strains measured in Figure 1 ( Figure S4D and Datasets S1 and S2 ) . Intriguingly , however , in addition to the isolates with increased fitness during transition between carbon sources , clones from a few populations showed an increase in lag duration compared to the parental strain ( Figure 4D and Figure S4C , D ) . These isolates appear to have evolved higher MaxRs in stable glucose conditions at the cost of even more pronounced lag phases than the ancestral strain . When the average fitness ( GMR ) is plotted against the average MaxR , it becomes clear that isolates generally evolved following two different paths: the evolution of shorter lag phases and increased fitness during carbon transitions at the expense of MaxRs in stable glucose conditions , or the evolution of faster growth in stable conditions at the expense of longer lag phases in variable environments ( Figure 4D and Dataset S2 ) . To test which mutations might have given rise to short lag phases and increased fitness during carbon transitions in the evolved populations , we sequenced the genomes of four evolved isolates showing shorter lag phases . Three mutants of the shortest lag phase length carried mutations in the glucose sensor HXK2 , a gene that encodes a protein with multiple genetically dissociable roles in glucose sensing . Specifically , in addition to phosphorylating glucose for entry into glycolysis , Hxk2p plays a signaling role in the SNF1 and Ras/PKA pathway , and further can itself translocate into the nucleus to repress certain nonpreferred carbon catabolite genes [45] . High throughput studies have shown that clean deletions of this gene lead to reduced fitness in YPD media ( a condition akin to our HG media , YPD contains 2% glucose ) relative to the WT , and increased fitness in diauxic shift from 0 . 1% glucose to ethanol and glycerol [46] . Another study demonstrated that deletion of this gene leads to genome-wide disruption of transcription , with significant gene ontological ( GO ) enrichment for genes involved with respiration and nonpreferred carbon metabolism [47] . Another isolate ( Isolate 3 ) showing longer lag phase duration carried a mutation in the STD1 gene , encoding a protein that interacts with glucose sensors Snf3 and Rgt2 to regulate RGT1-mediated repression of nonpreferred carbon source genes . Like HXK2 , STD1 is also involved in glucose-induced repression of alternative metabolic pathways [48] . Interestingly , however , deletion of this gene typically leads to higher rates of growth [46] , [49] and a disruption of gene regulation that is anticorrelated with that of a strain in which HKX2 has been disrupted [47] . Sequencing of HXK2 and STD1 in the 36 different evolved isolates revealed that HXK2 was mutated at different positions in all but one of the different populations that showed shorter lag phases , whereas STD1 was mutated only once ( Figure 5A and Datasets S3 and S4 ) . Isolates bearing HXK2 mutations had significantly shorter single-cell lag phases in sudden glucose to maltose shifts than other isolates ( 12 . 72±2 . 85 versus 4 . 60±0 . 77 h for non-HXK2mut and HXK2mut strains , respectively ) and were 26 times more likely to resume growth after a transition from glucose to maltose ( Cox hazard coefficient = 3 . 26 , p<10−10 ) . Moreover isolates with HXK2 mutations had significantly reduced population-level MaxR and increased GMR across the four carbon source environments ( p<0 . 001 ) . To confirm that these mutations were sufficient to confer comparable growth strategies to the evolved strains , we introduced the mutated STD1 allele and two of the HXK2 alleles into the ancestral S288c genome ( Materials and Methods ) . The mutations phenocopied the behavior of the evolved isolates ( Figure 5B , C ) . Furthermore , reverting the mutation back to WT in the evolved clones had the opposite effect , restoring wild-type growth patterns and fitness . Control strains bearing the same dominant marker but that did not incorporate the intended allele all behaved as the parental strain ( Figure 5B , C and Datasets S3 and S4 ) . Taken together , these findings reveal how simple mutations in carbon sensing pathways can tune the length of the lag phase in both gradually and suddenly changing environments . To determine the molecular mechanisms giving rise to the altered growth characteristics in evolved isolates , we examined whether the mutants display altered MAL gene regulation . Flow cytometry measuring the fluorescence of the MALT-YeCitrine and MALS-mCherry reporter constructs revealed that many short-lagged mutants showed reduced catabolite repression of the MAL genes ( indicated by “leaky” MAL gene expression in glucose ) , possibly explaining why they have shorter lag phases in sudden glucose-to-maltose transitions ( Figure 6A ) . Furthermore , the degree of leaky background expression correlated with high fitness ( GMR ) in variable carbon environments and was inversely correlated with the MaxR and lag-phase length ( Figure S5A and Dataset S2 ) . Although the MAL genes are not necessary per se for growth in alternative carbon sources like ethanol or galactose , this correlation indicates that the leaky expression of the MAL genetic reporter relates more generally to a partial loss of glucose catabolite repression . Most interestingly , in contrast to the leaky expression we observed in glucose media alone , we found that when maltose was added to glucose media , some HXK2 mutants expressed their MALT and MALS genes to a great extent ( Figure 6A and Figure S5 ) . Deleting the MAL activator protein MalR ( Figure S5D ) did not affect leaky background expression but did relieve high levels of MAL gene expression in maltose and glucose-containing media , indicating that the MAL genes were being induced by the presence of maltose despite the glucose present in the media . There was significant variability in magnitude between isolates , and further between isogenic cells in the same population ( Figure 6A and Dataset S2 ) . However , within single cells , the average MalT and MalS signals were expressed to similar extents , implying that the entire MAL system was activated ( Dataset S2 ) . The different HXK2 mutants show a wide range of MAL expression levels and expression noise , suggesting that the different mutations have distinct effects on catabolite repression . We reasoned that this heterogeneous MAL expression could serve as a model to elucidate how evolved isolates maintain steady fitness levels in more gradual diauxic shifts between glucose and maltose . To address this , we used both population- and single-cell analyses to track MAL gene activity ( using fluorescent reporter constructs ) and growth . Figure S5B and S5C and Movies S6 , S7 , S8 show the result of a time-lapse microscopy experiment of one isolate's cells growing in the presence of glucose and maltose . This experiment suggested that cells in the population switch stochastically between MAL-active and MAL-repressed states . Moreover , flow cytometry of a population of cells indicated that in the absence of glucose repression , similar to other positive feedback-driven networks in microbes [11] , [30] , [50] , the MAL genes of these mutants show hysteresis ( history dependence ) . That is , cultures that are inoculated into maltose/glucose media with repressed MAL genes display fewer induced cells than populations inoculated with MAL genes induced , even after ∼10 generations of growth ( Figure 6B ) . We found the extent of this hysteresis was widely variable across the different HXK2 mutants , with wide variability in the rates at which populations switch from induced to uninduced ( ON to OFF ) and vice versa ( Figures 6B and S6 and S7 and Dataset S2 ) . The per-generation switching rate from ON to OFF and OFF to ON for HXK2 isolates depended on the initial carbon source ( maltose or glucose ) and the strain's genotype ( two-way ANOVA F = 27 , p<0 . 001; see Figure S7 ) . The magnitude of expression of MAL protein for these strains exceeds the generation time , indicating that newborn cells inherit and propagate the MAL activity state from their respective mother cell ( Figures 6B and S5 , S6 , S7 and Movies S6 , S7 , S8 ) . These results imply that the different HXK2 alleles led to varying rates of switching between MAL induced and repressed states . The variability in MAL gene regulation has a profound effect on growth rate ( Figure S8 ) . For example , the magnitude of population-level MAL gene expression in maltose/glucose medium is anticorrelated with the population-level growth rate ( Figure S8A ) , suggesting that the expression of MAL genes in medium containing glucose comes at a significant fitness cost . This cost is dependent upon an intact MAL activator , which drives MAL gene expression via positive feedback regulation ( Figure S8B ) [44] . Further single-cell analyses of one isolate confirm that genetically identical cells with transcriptionally active MAL genes grow at slower rates compared to cells that keep their MAL genes inactive ( Figure S8C ) . Although induced cells suffer a fitness defect as long as glucose is present , they show a much reduced or even absent lag phase when glucose is no longer available , increasing their fitness during this transition phase ( Figure S8D , E ) . In diauxic shift conditions , this bimodal gene expression state leads to a diversified growth strategy that distributes the costs and benefits of expressing the genes involved in alternative carbon source metabolism across individuals , allowing the population to maintain steady fitness levels in both stable and variable environments . The isolates resulting from our evolution experiment and subsequent population- and single-cell-level analyses indicate that cultures can generally evolve in two directions . In one case , a “glucose-specialist” phenotype emerges , with faster adapted growth rates in glucose together with long lag phases upon a switch to a different carbon source . Alternatively , generalist phenotypes evolve with shorter lag phases in maltose , at an apparent cost to MaxRs in glucose . Furthermore , increased leaky expression of the MAL genes in short-lagged mutants is anticorrelated with MaxR in glucose , and positively correlated with reduced lag phases and fitness variability during diauxic shift ( Figure S5A ) . This further suggests that a molecular cost is paid in order for cells to be prepared for sudden environmental changes ( Figures 6 and S5 , S6 , S7 , S8 ) . We reasoned that the predominant growth strategy would be shaped by both ( 1 ) how often the culture must switch from stable glucose environments to alternative carbon sources and ( 2 ) the relative duration of these environments [1] , [27] , [34] . To test this , we first developed a stochastic model of the growth characteristics of population-level behavior based upon a culture of single cells escaping from the lag phase . In the model , each cell of a given strain's population is assigned a time tau corresponding to the point at which the cell will begin growth in maltose . The distribution of tau for a given strain in maltose is equivalent to the cumulative lag time distribution measurements ( as reported in Figure 3A ) . After the time exceeds tau , the cell begins growth at the strain's specific growth rate in maltose . In glucose environments , we simulated that cells would grow without a lag phase at the measured MaxR . At any given time , the sum of the growing and nongrowing cells equals the total population growth as it escapes from the lag phase ( Materials and Methods ) . The model allows prediction of the clonal interference patterns that would result between competing strains with different lag characteristics across various maltose-to-glucose switching regimes . For example , the heat map in Figure 7A illustrates how Isolate 1 , a “carbon source generalist” HXK2 mutant with short lag phases , would compare in direct head-to-head competition with Isolate 6 , a “glucose-specialist” strain with ∼28% faster growth rate in glucose compared to Isolate 1 but with considerably longer and more heterogeneous lag phases ( as shown in Dataset S2 ) . The model predicts that an environment that consists of a single shift from glucose to maltose will result in Isolate 1 growing rapidly to high relative frequencies in the population due to its very short lag phase . However , during prolonged growth in glucose , Isolate 1 grows more slowly and thus is rapidly outcompeted . Importantly , certain regimes of maltose-to-glucose shifts are predicted to result in a stable abundance of each isolate relative to the other ( green region in Figure 7A ) . We tested the model in a set of experiments where two strains with different lag characteristics were competed in different carbon switching regimes . In a first set of experiments ( Figure 7B ) , we measured Isolate 1 and Isolate 6's abundance relative to one another in single shifts from glucose to maltose , with varying duration of growth in glucose and in maltose . The cultures were grown initially in glucose so that they would have a lag phase upon switching to maltose , or alternatively carry on at steady-state growth rates in glucose . The results confirm the trend suggested by the model: under regimes with longer periods of maltose growth , Isolate 1 shows the highest competitive fitness; conversely , with increasing time in glucose , the fast-growing Isolate 6 performs better . Second , to examine if the model could predict strain performance over multiple cycles of glucose-to-maltose shifts , we carried the experiment forward for another two cycles for the cultures growing under the 4 h∶20 h , 8 h∶16 h , and 12 h∶12 h maltose-to-glucose switching regimes ( Figure 7C ) . The results confirm that different regimes of environmental change result in drastic changes in the frequency of the two competing genotypes within the total population . For example , in the top panel of Figure 7C , when the period of time in glucose exceeds the time in maltose , Isolate 6 comes to dominate because the benefits of faster growth rates in glucose outweigh those of shorter lag phases in maltose . By contrast , as the length of time in maltose increases , the short-lagged Isolate 1 outperforms the slower switching strain despite lower rates of growth in glucose ( Figure 7C , bottom panel ) . Note that the competitor populations depicted in the middle panel remain at relatively equal frequencies when the culture undergoes cycles of 8 h of growth in maltose followed by 16 h of growth in glucose . Interestingly , in the 12 h∶12 h regime , we observed that Isolate 1 grew ∼10% more slowly over the glucose leg of the cycle compared to our measurements of the same strain in steady-state conditions ( Dataset S8 ) —thus growing ∼40% more slowly than the Isolate 6 competitor during glucose growth . Across the entire 24-h period of the glucose to maltose cycles , we measured that this slower rate of growth in glucose reduces the average growth rate of this strain by about 5% ( Dataset S8 ) . The reduced rate of growth in glucose indicates that Isolate 1 paid a considerable cost upon reintroduction to the glucose-containing environment . This result further supports conclusions from the experiment reported in Figures 2 and S8 where commitment to maltose resulted in slower growth rates for individual cells . Taken together , these results demonstrate that the duration and frequency of carbon environments shape the fitness of two of the archetypal phenotypes recovered from the evolution experiments . Environmental regimes with long periods of growth in glucose relative to the duration of time in maltose will favor the growth of the specialist phenotype , with stringent catabolite repression and high growth rates in glucose , but long lag phases upon switching to a different carbon source . By contrast , more frequent shifts of carbon source and longer periods of growth in maltose will favor the growth of strains with less stringent catabolite repression , resulting in short , homogeneous lag phases and slow growth in glucose .
When Monod ( 1941 ) [26] first described the diauxic shift and the corresponding lag phase , he was frustrated by why a culture would reach such slow growth rates despite the abundance of alternative carbon sources . Monod realized that the lag phase was due to the time required for transcriptional reprogramming , which in turn propelled research into the molecular mechanisms underlying gene regulation . However , surprisingly little attention was given to how this transcriptional reprogramming affects growth rate . Our experiments show that the speed with which metabolic genes are reprogrammed in the face of changes in carbon availability is a highly variable and evolvable trait . Strains can maintain high fitness in stable glucose conditions by tightly repressing the costly expression of genes needed for growth in alternative carbon sources . However , this results in slow transcriptional reprogramming when glucose is depleted , leading to reduced fitness during the adaptation to a less-preferred carbon source . By contrast , less stringent catabolite repression and “stochastic sensing” strategies come at a fitness cost in stable conditions , but allow quick and more uniform adaptation , which in turn can lead to higher fitness during the transition phase . It is tempting to speculate that feral yeasts are faced both with relatively stable as well as variable carbon supplies , and are therefore subjected to pressure that is similar to the selection in our directed evolution experiments . Moreover , the mutants that we isolated after experimental evolution closely resembles the diversity found in natural isolates , both regarding differences between strains as well as differences between single cells within populations ( Figures 1H , S3 , and S4D ) . Furthermore , we observe that mutants isolated after selection in variable environments appear to be constrained by opposing selection for maximal reproductive fitness in stable glucose environments versus adaptability in variable environments . These results are consistent with theories of phenotypic tradeoffs associated with optimality at specific tasks versus the capacity to adapt to new environments [4] , [6] , [9] , [17]–[20] . It is notable , however , that we do not observe the same level of direct tradeoffs between MaxR and lag phase across the strains reported in Figure 1 as we do in the evolved isolates . Given the allelic diversity of these strains , such a result is not unexpected . For example , previous evolution experiments have observed that common phenotypic tradeoffs , such as that between growth rate and carrying capacity , become undetectable with increasing genotypic distance [7] . We therefore speculate that single mutations that give rise to tradeoffs in the evolved isolates could potentially be compensated by additional mutations that might increase overall fitness or that other evolutionary paths are followed in more complex natural environments [51] . The natural strains and evolved mutants with short lag phases show similar differences in lag duration and fitness variability . More specifically , strains with short lag phases show similar fitness levels across different environments , a phenotype referred to in the ecological literature as a “generalist” strategy to indicate that these organisms thrive equally well in different environments . It is also worth noting that the HXK2 and STD1 mutants showed increased fitness both in gradually changing conditions ( LG , LG + maltose , and LG + galactose ) where the preferred carbon source is gradually depleted , as well as in conditions where the cells experience sudden changes in carbon availability ( Figure 4 and Dataset S2 ) . In ecological theory , both of these “fine-grained” and “temporally fluctuating” environments are predicted to favor generalist growth strategies , with reduced variability in fitness across different environments [1] , [4]–[6] , [27] , [39] , [40] . However , in contrast to our results , most experimental evolution studies tend only to find niche specialization ( i . e . , higher rates of growth on one carbon source or the other ) [52] , [53] . It seems likely that these studies uncovered specialist phenotypes because the selective pressure to improve adapted growth speeds on particular carbon sources was greater than the pressure to reduce lag phases or increase switching rates between carbon sources [28] , [54] . The mathematical model and experiments reported in Figure 7 provide further insight how these strategies are shaped and favored by the environment . These analyses illustrate how different regimes of environmental change favor the growth of either specialist strains with stringent catabolite repression or alternatively generalist strains with leaky or even stochastic expression of genes involved in metabolism of different carbohydrates . More broadly , these results confirm theoretical analyses and experimental reports that while stable preferred environments will promote slow switching rates , frequently changing environments can favor the growth of phenotypes with high adaptability , even at the cost of slower rates of growth in preferred environments [1] , [8] , [27] . Interestingly , apart from revealing differences in growth strategies between genetically different yeast strains , our results also illustrate how genetically identical cells within a population can also show different growth behaviors that can help to optimize a strain's fitness in variable conditions . Specifically , the results show that an organism can maintain competitiveness by allowing only a fraction of the population to adapt to a new environment . This subpopulation of cells contributes some progeny to exponential growth while keeping others dormant ( Figures 1G , 3A , and S3 ) . Although they contribute no progeny in the new environment , these uninduced cells appear to have an advantage upon reintroduction of the previous environment ( Figure 2 ) , increasing overall mean growth rates ( Figure 7 ) . Such growth strategies have commonly been investigated for the model of seed germination in annual plants , a system that in many ways bears semblance to our model system of the lag phase [42] , [55] , [56] . Heterogeneity within isogenic populations bears further similarities to other microbial systems that lead to variable physiological states between isogenic individuals in the same population . For example , heat-shock resistance [19] , the variable timing of meiosis [57] , or the switch-like commitment to mating [58] in yeasts likely underlie a cost-benefit strategy that increases fitness in the long term . Indeed , these systems are costly to implement [19] , [57] , [59] but offer fitness advantages in stressful environments . However , despite receiving much attention in the literature , few studies have systematically quantified how heterogeneous individual-level behavior can affect evolutionary outcomes [27] , [33] , [55] , [56] . Our evolution experiments demonstrated the flexibility of growth strategies and also revealed that simple genetic switches in catabolite repression can regulate lag duration ( Figures 4–6 ) . Specifically , HXK2 mutations appear to increase fitness in lag phases in glucose-to-maltose shifts in part by allowing leaky expression of MAL genes ( Figure S5 ) . The repression of other genes involved in alternative carbohydrate metabolism is also likely relieved because the fitness of the mutants also improved in mixed carbon conditions different from those used in the selection scheme . This conclusion is supported by whole-genome gene expression profiling of a targeted hxk2 deletion [47] , where genes involved with respiration and alternative carbon source utilization are significantly up-regulated . Although null mutations in HXK2 are known to relieve catabolite repression in S288c , the potential for more subtle mutations in this key regulator to allow “tuning” of switching rates between alternative carbon sources has not been explored extensively [45] , [52] , [53] , [60] , [61] . Furthermore , in the case of the STD1 allele , we observe reduced growth rates , which is the opposite effect to that usually observed in high-throughput studies , where null STD1 alleles grow with higher fitness than the WT S288c strain [46] , [49] . Taken together , this aspect of our results suggests that regulators such as HXK2 sit at an apical position in the regulation of cellular physiology , allowing adaptive reprogramming of cellular fitness strategies in times of environmental change . Interestingly , some of the short-lagged isolates show a high degree of heterogeneity in MAL expression within a population ( Figures 6 and S5 ) . Specifically , in medium containing both glucose and maltose , some HXK2 mutants exhibit a striking multimodal state , where MAL genes in individual cells are expressed to varying extents ranging from repressed to induced . This behavior emerges because the rate of switching between ON and OFF is slower than the generation time , allowing newly budded cells to inherit their MAL expression state from the mother cell ( Figures S6 and S7 ) . This epigenetic behavior is due to the structure of the MAL genetic circuit , which induces via positive feedback ( Figures S5D and S8B ) [11] , [30] , [44] , [50] . Furthermore , it appears that distinct HXK2 mutations can set different “energy barriers” for transitions between induced and uninduced states ( Figures 6A , B , S6 , and S7 ) . Although such “stochastic switching” networks have been reverse engineered ( for example , [27] ) , and shortened lag phases observed in natural selection [28] , [54] , no studies have found that mutations in global regulators can give rise to such a wide array of diversified gene regulation strategies . The simple genetic architecture of the MAL system has allowed us to closely examine the costs and benefits of different levels of catabolite repression and the outcomes of stochastic gene regulation [3] , [10] , [11] , [20] , [62] , [63] . The stochastic nature of the transition between MAL activation and repression results in diversified growth behavior that appears to be a bet-hedging strategy . However , maltose must be present to induce the positive feedback necessary for the high levels of MAL gene expression shown in Figures 6 and S5 , S6 , S7 , and thus this environmental dependence does not satisfy the most stringent criteria for bet-hedging [33] . Even in the case of stable , constant glucose environments , the low leaky expression of costly nutrient assimilation genes could be viewed as a mechanism of “stochastic sensing , ” a term first used by Perkins and Swain ( 2009 ) [37] to describe predictive microbial networks [35] , [36] . More recently , Arnoldini et al . ( 2012 ) [34] demonstrated analytically that combinations of sensing and stochastic switching strategies are evolutionarily stable when environments provide partially reliable signals about future events . Given that such positive feedback-driven circuits are widespread in microbes , it is likely that nutrient assimilation pathways act as basic sensing tools to maintain long-term fitness in changing environments , without the need for complex sensing and signaling systems [1] , [21] , [27] , [29] , [30] , [34] , [35] , [37] , [64] , [65] . Taken together , our results show that individual-level heterogeneity in gene regulation and growth has strong genetic determinants . The speed of metabolic reprogramming in the face of environmental change is a highly regulated trait , and populations can implement catabolite regulatory strategies that fall between traditional sensing/signaling cascades and stochastic switching mechanisms . Specifically , stringent catabolite repression seems favorable in relatively stable environments , whereas less stringent regulation , or even stochastic sensing strategies can increase fitness in variable conditions where cells often need to switch their metabolism . We speculate that similar principles and emergent ( epi ) genetic switches likely also contribute to other gene regulation systems , including in human diseases involving clonal growth , such as microbial pathogenesis and cancer .
Standard protocols were used for routine S . cerevisiae strain propagation [66] . A specially engineered maltose-prototrophic S288c strain , bearing a functional MAL regulator allele ( MAL63 ) in place of MAL13 on chromosome VII [67] , was engineered to have a low petite frequency by rescuing a frameshift mutation in SAL1 to reduce the high petite frequency that occurs after extended growth on glucose ( Text S1 and [68] ) . Other feral strains were part of the SGRP collection [69] . Special attention was given to standardization of pregrowth conditions , in particular to avoid cells that would experience carbon depletion prior to transfer to maltose for lag phase measurements . Specifically , this entails keeping cultures at low densities throughout the experiments . Moreover , where appropriate , care was taken to measure steady-state conditions ( where the growth speed of the population was stable ) . Please refer to specific experimental details provided in Text S1 for the precise conditions for each experiment . Cells from a turbid culture grown in YPD for 14 h were inoculated to a final density of 1×105 ( haploid S288c ) cells per ml in 150 microliters of YP media containing the appropriate carbon source and allowed to grow in the Bioscreen C ( Growthcurves USA ) at 30°C and continuous medium-amplitude shaking until stationary phase . All media for growth rate measurements were prepared starting from the same batch of double concentrated YP medium ( 20 g/l yeast extract , 40 g/l bacterial peptone ) , which was supplemented with an equal volume of filter-sterilized sugar solutions to generate 1× YP medium containing the required mixture of carbon sources to obtain HG ( 30 g/l glucose ) , LG ( 5 g/l glucose ) , LG + Gal ( 5 g/l glucose and 25 g/l galactose ) , or LG + Mal ( 5 g/l glucose and 50 g/l maltose ) . We found that variation in osmolarity is a significant factor affecting the lag phase , and we therefore supplemented LG medium with 0 . 14 molar sorbitol to match the osmolarity of the HG , LG + Mal , and LG + Gal media . All media were divided into smaller batches that were kept frozen until the day of use . All growth measurements represent the averages of at least three biological replicates . In general , growth measurements were highly reproducible , with standard errors generally below 5% of the measured growth rates . Standard errors are reported in detail in Datasets S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 . Growth of populations of S . cerevisiae were measured by OD600 readings every 15 min in the Bioscreen C automated OD meter at 30°C with constant medium amplitude shaking . This plate reader uses 100-well microcultivation of microbial cultures covered by a heated lid to prevent evaporation . R and MS Excel software were used for all data analyses . Briefly , all growth curves were smoothed using R's smooth . spline function , and then the first derivative of the log-transformed smoothed data was plotted as a function of population size between 0 . 15 and 0 . 75 OD600 units , corresponding to 1 . 0×107 to 5×107 haploid S288c cells per ml ( see Datasets S6 and S7 ) . These values are linearly correlated with cell density , and further correspond to lowest and highest OD at which we were able to obtain reproducible measurements , and further capture most of the active growth phase of the cultures before other noncarbon growth resources become depleted . Reported maximum growth rates were calculated as the slope of a linear regression model to ln-transformed OD600 values between 0 . 15 and 0 . 3 A . U . To calculate GMR , we used R's splinefun function to determine the amount of time t that the culture spent between interpolated OD600 values of 0 . 15 and 0 . 75; the GMR is thus equal to ln ( 0 . 75/0 . 15 ) /t . This interpolation approach significantly reduces the coefficient of variation between biological replicates ( error ) compared to a simpler approach that would only use the raw OD measurements . These statistics were further analyzed to determine relative variability across the different environments ( see Datasets S1 and S2 ) . Note that the GMR is an average growth rate that could be calculated for many different initial and final population densities . Here , we use the GMR to reflect the average growth rate across the linear range of our spectrophotometer ( i . e . , between O . D . 0 . 15 and 0 . 75 ) . This is a relatively wide interval that comprises the initially high growth rate in glucose , the lag phase , and the resumption of growth on the new carbon source ( Figure 1A , B; Materials and Methods ) . This interval is easy to standardize and yielded a high reproducibility ( Datasets S1 and S2 ) . However , to explore the role that the lag phase played in the GMR , we also calculated average growth rates over other more narrow windows centered on the middle of the lag phase ( defined as the point where the culture reaches a minimal growth rate ) . These measures correlate strongly with the GMR . For example , the narrowest possible window—the minimal growth rate ( reported in Datasets S1 ) —explained 55% of the variance in GMR for the SGRP strains that had lag phases ( Dataset S1 , R2 = 0 . 55 , p<0 . 001 ) . Computing the average growth rate across a wider custom-drawn window encompassing 0 . 1 to 1 doublings prior to and 0 . 1 doublings after the minimal growth rate explained from 66% to 79% of the variance in GMR ( p<0 . 001 ) . In summary , the GMR in between OD 0 . 15 and 0 . 75 seems to be largely influenced by the actual lag phase . Moreover , calculating the GMR across more narrow intervals around the center of the lag phase is much more complex , is not always possible for all growth conditions where strains do not always show a local minimal growth rate , and yields similar results . Further details and example raw data and analyses are available in Text S1 . We devised a system that traps cells between a coverslip and an agar pad containing media necessary for growth . This allowed continuous monitoring of cellular growth for long periods of time using an inverted automated Nikon TiE fluorescence microscope placed in a temperature-controlled incubator . A 60× , 1 . 40 NA oil immersion lens was used to monitor up to 120 XY positions per experiment , and lag measurements were made using the microscope's automated Z-plane focusing . Lag phases for a given strain become longer as the culture grows for longer periods of time in glucose ( Figure S2A ) . Thus , for analysis of lag phases , we varied the length of time that cells were grown in glucose to regulate the severity of single-cell lag phases . Apart from the results reported in Figure S2A , we either grew populations in glucose for 6 h ( reported in Figure 1 ) or 20 h ( reported in Figure 3—the length of time cells grew in glucose during the evolution experiment ) . After growth in this glucose environment , we transferred cells by two brief ( 2 min at 1 , 250×g ) centrifugations and resuspension in maltose-containing media . Cells were then transferred to the custom growth chamber ( see Text S1 ) and transferred to the microscope for analysis . Under the microscope , brightfield imaging proceeded every 15 min . After the experiment , cell budding events were scored manually as the hour at which the first morphological change leading to a new bud occurred , or when an already existing bud began to grow . Examiners were blind to experimental conditions at the time of scoring , and separate investigators independently replicated results of preliminary analyses . For statistical analysis of single-cell lag data , we used survival analysis: log-rank tests for pairwise comparisons between different WT strains or between ancestral strains and mutants , and a Cox proportional hazards test for datasets for which we had covariate information ( see Text S1 ) . For these latter analyses , single covariates were used ( 1 degree of freedom ) , and the most significant predictors were then used in paired analyses using other covariates ( 2 degrees of freedom ) . The majority of single-cell lag variance was explained by single covariates that reflected relative fitness ( GMR ) or fitness variability ( Dataset S1 ) . For doubling time measurements presented in Figure 2 , an initial fluorescence image was acquired , followed by brightfield imaging every 5 min . Mother cell doubling times were recorded as the time t taken for a cell to complete two cell divisions ( equal to t/2 ) . Brightfield images reported in Figures S5 and S8 were acquired every 5 min , with fluorescence images in the mCherry field every hour . Growth rate measurements of microcolonies reported in Figure S8C account for the fold change in area of microcolonies between 3 and 6 h after recording began ( equal to Δ ( ln ( area ) ) /3 h ) [19] . To measure the relative MAL expression reported in Figure 6 and Dataset S2 , we pregrew cultures in maltose media ( Figure 6A ) or either maltose or glucose media ( Figure 6B ) and diluted them as exponentially growing cultures into a mixture of 2% glucose + 5% maltose in YP media to a final population density of 1–2 , 000 cells/ml and allowed them to grow for 20 h for final cell densities between 1–5×106 cells per ml . Cultures were then centrifuged to concentrate cells , and these were frozen at −80°C in 1× phosphate buffered saline ( Sigma-Aldrich no P5493 ) in 25% glycerol until flow cytometric analysis . The low densities at which these cultures grew did not measurably affect glucose concentrations ( unpublished data ) . For gene expression measurements , cell samples were thawed on ice until analysis on a BD Bioscience Influx flow cytometer . mCherry signal detection used a 561 nm laser coupled to a 610/20 nm detector and YeCitrine signal detection used a 488 nm laser coupled to a 580/30 nm detector . R's flowCore package was used to first filter out ∼70% of events using a filter ( curv2Filt ) that selected the highest-density regions in side- and forward-scatter dimensions . mCherry and YeCitrine intensities for each filtered sample were stored as binned fluorescent measurements and summary statistics . For each evolution experiment , the methods established by Lenski were largely followed [70] . All growth for the experimental evolution experiment was in 5 ml of YP media containing either 10% glucose or 20% maltose at 30°C on a rotating wheel . The protocol was followed for two founding S288c strains derived from the modified S288c strain ( see above ) : AN296 constitutively expressed a YeCitrine marker [71] , and AN148 contained fusion constructs of MAL11-YeCitrine ( encoding a MalT ) and MAL12-mCherry ( encoding a MalS ) . Populations of ∼30 , 000 initial cells were grown exponentially for 20 h in 5 ml glucose YP to reach population sizes of ∼1–5×107 , and then cultures were centrifuged and resuspended in 20% maltose YP and then put back on the wheel for another 3 d until the populations reached high densities ( ∼5×108 cells/ml , with a final population size of ∼2 . 5×109 ) . After each round of selection in maltose , we froze an aliquot at −80 for future analysis and resurrection . At the end of the selection experiments , we resurrected individual clones for analysis by diluting the populations to single colonies , and then restreaking random single colonies again to single colonies before phenotypic characterization and long-term storage at −80°C in glycerol . For the experiment reported in Figure 3B , we pregrew constitutively YeCitrine-labeled query strains ( isolates 1 , 3 , and 4 ) in sextuplicate and mixed these 1∶1 with an mCherry fluorescently labeled S288c reference strain ( AN74; see strain list in Dataset S4 ) exactly as in the selection protocol in 20% maltose YP media for 24 h . OD600s were determined and query cultures were mixed at a 1∶1 ratio between reference and query strains . Samples were frozen for initial ratio measurements , and initial population densities determined by diluting cells down such that 100–200 single colonies would grow in 2 d time on solid YPD plates . Thereafter , the exact protocol from the selection procedure was then followed: a 20-h growth in glucose , followed by 2 d of growth to high turbidity in maltose media . At the end , samples were frozen in 1× PBS + 25% glycerol for later analysis , and final population densities determined by plating as before . For flow cytometry analysis , see below . We calculated the Malthusian growth rate w of the query as:where N is the total population size determined by plating . Likewise w ( reference ) was calculated for the fluorescently labeled reference . The w ( query ) /w ( reference ) was taken as the fitness of the query strain . This value divided by the ancestral strain's fitness ( calculated identically against the same reference ) gives the fitness of the query strain . All fitness or relative growth rate measurements are the ratio of the query strain's fitness relative to the reference strain divided by the control query strain's fitness relative to the reference strain . For a given culture of cells , the population total Ntotal is equal to the sum of each ith growing and nongrowing cell . Within the population , there are J phenotypes ( or strains ) and each cell belongs to the jth strain or phenotype such that Ntotalj is the number of cells of the given phenotype or strain . At any given time , the proportion of cells in the jth phenotype is: Each strain j in environment k has a specific growth rate equal to μj , k . The model assumes specifically that cells entering into glucose from maltose do not have lag phases . Cells that have no lag phase in environment k grow at steady-state growth rates; thus , at time t the total population size of strain j is determined by: with total population size . The model assumes specifically that cells going from glucose to maltose do have a lag phase . In a lag phase within environment k , cells of strain j have lag phase durations tau equal to a vector of lag phase durations drawn from experimentally determined distributions dist ( τj , k ) . In the lag phase , cells of strain j only begin growth at steady-state growth rate μ ( j , k ) when t = τj , k ( cellij ) . Each celli , j has an initial population size equal to 1 . Thus , in the lag phase the population size N for the jth strain at time t is the sum of growth of all cells i . And the total populations size N at time t is the sum of all j competitors . The model was implemented as a stochastic simulation in R ( functions and scripts available upon request ) . Time was iterated in glucose and maltose environments in 0 . 1-h increments . A total of 50 , 000 cells were chosen as initial population sizes for maltose lag phase simulations , and each cell was given a lag time tau and a status ( 1 or 0 ) that indicated whether this cell would begin growth within 24 h ( the period of time for which we had data about lag time distributions for these strains ) . A normal distribution of tau was chosen for Isolate 1 , and a uniform distribution for Isolate 6 . After computing lag phase growth as a function of time in maltose , growth in the glucose environment was computed for each time point in maltose . The resulting matrices of population sizes for the two competitors were used to compute the proportion of each strain in the total population . In the text and in Figure 7 , p ( i ) is the only parameter reported . In Figure 7A , the plot reflects the initial conditions of the experiment pictured in Figure 7B and 7C , where the average initial proportion of Isolate 1 relative to Isolate 6 for the six independently competing populations was 0 . 53317 and the cells had been in maltose for 1 h prior to time point 0 . See Dataset S8 for the exact parameters used in the model . To begin the experiment where strains were competed in various carbon switching regimes , six independent replicate populations of Isolate 1 , a strain constitutively expressing YeCitrine , and Isolate 6 , a MALT-YeCitrine and MALS-mCherry strain , were inoculated from turbid YPD overnight cultures into glucose media and allowed to grow for 20 h until they had reached population densities of <5×106 cells per ml , such that both cultures would have lag phases upon transfer to maltose . The cultures were washed into maltose media , OD600 values measured , and then mixed 1∶1 and either kept in maltose or were transferred to glucose to generate the results shown in Figure 7B . This initial time point also served as the beginning of the glucose-to-maltose cycling populations shown in Figure 7C . Growth was at maximum 5×106 cells per ml , and minimal population sizes for long periods of glucose growth were ∼20 , 000 cells . At each time point , cells were centrifuged and frozen at −80°C in 1× PBS + 25% glycerol for later analysis at the flow cytometer . We used the same glucose and maltose YP media and volumes in this experiment as in the selection procedure . For analysis of competitions , 50 , 000 single-cell events were acquired by a BD Biosciences Influx flow cytometer . We used a 561 nm laser coupled to a 610/20 nm detector for mCherry , and for YeCitrine signal detection , we used a 488 nm laser coupled to a 580/30 nm detector . We used R's Flowcore package to identify subpopulations of unlabeled , YeCitrine-labeled , or mCherry-labeled cells using polygonal gates . We used control cultures of each competitor growing on its own in identical conditions to those of the competition to determine the fraction of events that were incorrectly determined to be one competitor when in fact they were from the other . In general these error rates were below 1/5 , 000 events . Selected samples were whole-genome sequenced using Illumina HiSeq 2000 with 500 bp inserted library . Quality assessment of resulted short reads was performed using FASTX-Toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/index . html ) . After removing the low-quality reads ( below Q30 ) and adaptors , pair-end reads were then mapped onto the reference S . cerevisiae genome ( S288C , version genebank64 ) using Burrows–Wheeler Alignment [72] . Default settings were used except the maximum edit distance was set to 0 . 01 ( −n 0 . 01 ) . The MarkDuplicates command in Picard ( http://picard . sourceforge . net/ ) was used to remove the reads that mapped to the same positions in the reference genome ( PCR duplications ) . Consensus single-nucleotide variations ( SNPs ) and small insertions and deletions ( Indels ) were called for each chromosome using SAMtools and GATK [73] , [74] . Default settings were used , except the maximum read depth in SAMtools was set to 150× ( −D 150 ) . The generated SNPs and Indels were then filtered to minimize the false positive mutation calls . First , SNPs and Indels lying in low complexity sequences ( such as telomeric , subtelomeric , transposon , repeat regions , etc . ) were filtered out . Second , mutations with a total read depth below 20× were discarded . Third , SNPs and Indels with a quality score below 30 were removed . Fourth , mutation calls were only kept when at least 80% of the reads were positive for the SNP sites . Only the SNPs/Indels that were verified by both GATK and SAMtools were kept as confident sites . The lists of SNPs/Indels were then annotated by in-house Perl scripts with the yeastgenome database [75] . CNV-seq [76] was used to identify consecutive regions along the chromosome that show abnormal log2-ratios , which indicated the potential copy number variation ( CNV ) . Only regions larger that 1 Kb were considered as CNV regions . Mutations identified by whole genome sequencing that lead to nonsynonymous or frame-shifted protein products in HXK2 and STD1 were first confirmed with dye-terminator Sanger sequencing . To test whether the mutations caused the observed phenotypes , we first integrated a dominant ( KANMX ) marker downstream of the ancestral and evolved alleles . This marker , including the upstream coding region containing the mutated or WT allele , was used as a template for PCR , which was then transformed via homologous recombination into the corresponding loci in the evolved or ancestral clones . The mutations were subsequently confirmed using Sanger sequencing , and the phenotypes of the genetically transformed strains was compared to that of strains bearing the same KANMX marker at the same locus , but lacking the mutation .
|
When microbes grow in a mixture of different nutrients , they repress the metabolism of nonpreferred nutrients such as complex carbohydrates until preferred nutrients , like glucose , are depleted . While this “catabolite repression” allows cells to use the most efficient nutrients first , it also comes at a cost because the switch to nonpreferred nutrients requires the de-repression of specific genes , and during this transition cells must temporarily stop dividing . Naively , one might expect that cells would activate the genes needed to resume growth in the new environment as quickly as possible . However , we find that the length of the growth lag that occurs when yeast cells are switched from the preferred carbon source glucose to alternative nutrients like maltose , galactose , or ethanol differs between wild yeast strains . By repeatedly alternating a slow-switching strain between glucose and maltose , we obtained mutants that show shortened lag phases . Although these variants can switch rapidly between carbon sources , they show reduced growth rates in environments where glucose is available continuously . Further analysis revealed that mutations in genes like HXK2 cause variations in the degree of catabolite repression , with some mutants showing leaky or stochastic maltose gene expression . Together , these results reveal how different gene regulation strategies can affect fitness in variable or stable environments .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"evolutionary",
"ecology",
"microbial",
"mutation",
"model",
"organisms",
"microbial",
"evolution",
"gene",
"expression",
"genetics",
"yeast",
"and",
"fungal",
"models",
"biology",
"microbiology",
"evolutionary",
"biology",
"microbial",
"growth",
"and",
"development",
"saccharomyces",
"cerevisiae",
"evolutionary",
"genetics",
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"ecology"
] |
2014
|
Different Levels of Catabolite Repression Optimize Growth in Stable and Variable Environments
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Optimal decision-making is based on integrating information from several dimensions of decisional space ( e . g . , reward expectation , cost estimation , effort exertion ) . Despite considerable empirical and theoretical efforts , the computational and neural bases of such multidimensional integration have remained largely elusive . Here we propose that the current theoretical stalemate may be broken by considering the computational properties of a cortical-subcortical circuit involving the dorsal anterior cingulate cortex ( dACC ) and the brainstem neuromodulatory nuclei: ventral tegmental area ( VTA ) and locus coeruleus ( LC ) . From this perspective , the dACC optimizes decisions about stimuli and actions , and using the same computational machinery , it also modulates cortical functions ( meta-learning ) , via neuromodulatory control ( VTA and LC ) . We implemented this theory in a novel neuro-computational model–the Reinforcement Meta Learner ( RML ) . We outline how the RML captures critical empirical findings from an unprecedented range of theoretical domains , and parsimoniously integrates various previous proposals on dACC functioning .
In the next two subsections of the Introduction we describe qualitatively the computational principles of the RML ( The RML: General description ) and the main novelties introduced by the model ( The RML: Innovations ) . In the subsequent Results section , we describe the experimental paradigms we used to test the RML and the results , together with domain-specific discussion paragraphs . Next , in the domain-general Discussion section we broadly frame and connect the results , comparing our model with other models from recent literature ( Relationships to Other Models ) . We also propose future experimental paradigm to test RML predictions ( Experimental Predictions ) , including possible applications to translational research , and we describe some limitations of our work ( Limitations ) . Finally , in the Methods section we provide the full mathematical description of the RML . At the basis of our model is the idea that a macrocircuit involving dACC-VTA-LC represents a core computational unit for optimizing both behaviour and internal states that modulate behaviour itself ( meta-learning ) . Fig 1 represents an overview of the RML architecture . The RML dynamics is based on two inter-related loops connecting four computational modules: dACCBoost , dACCAct , VTA , and LC . An external loop represents the interaction between the dACC modules and the environment , while an internal loop covers the interaction between the dACC modules and the brainstem nuclei ( VTA and LC; orange and red bidirectional arrows in Fig 1 ) . This double loop structure is aimed at optimizing performance ( i . e . , maximizing reward ) while minimizing two different types of costs: the costs of motor actions ( external loop; e . g . the metabolic cost of climbing a stair ) , and the boosting costs of neuromodulators release ( internal loop; e . g . the cost of neurotransmitters depletion ) . Connectivity and functional studies corroborate the hypothesis underlying this architecture , because they show that there is an anatomical overlap between the midfrontal sub-region related to the meta-learning processes discussed above , and the midfrontal sub-region maximally connected with both LC and VTA nuclei [13 , 18–21] , both located within the dACC area . In the RML , the dACC plays the role of a performance monitoring system , which compares expectations about environmental states and executed actions with environmental outcomes ( cf . [22] ) . Discrepancies between expectations and outcomes generate prediction error ( PE ) signals ( Figure G in S2 File ) , which are used to update the expectations themselves [3] . This monitoring process is at the basis of two dACC modules , and in substantial agreement with the experimental literature ( see [3] for a review ) . One dACC module ( dACCAct in Fig 1 ) receives environmental states and selects actions directed toward the external environment ( part of the external loop ) . Although value-based action selection involves also other subcortical and cortical structures ( e . g . the dorsolateral prefrontal cortex , DLPFC ) , here we frame both value estimation and action selection within the dACC for both modeling parsimony and because also the MFC , with its motor components , plays an important role in action selection ( see [3] for a review ) . A second dACC module ( dACCBoost in Fig 1 ) receives environmental states and consequently modulates ( that is , boosts ) the release of catecholamines from the brainstem nuclei LC and VTA ( part of the internal loop ) . Catecholamines , in turn , control the internal dynamics of the dACC in real time ( i . e . while the RML is interacting with the environment ) , by modulating the magnitude of reward signals ( by VTA module ) and the amount of effort ( by LC module ) that the RML exerts to execute a task . Although the dACCBoost module is the main responsible for catecholaminergic modulation , the dACCAct module , too , is in recurrent interaction with the brainstem nuclei , providing the VTA with a reward prediction signal . The latter is used by the VTA to compute non-primary rewards , which are sent back to the dACCAct ( like in a TD-learning algorithm; [23] ) allowing the system to learn complex tasks without the immediate availability of primary rewards ( higher-order conditioning ) . Importantly , both dACC modules have dynamic learning rates ( λ ) , ensuring that knowledge is updated only when there are relevant environmental changes ( volatility ) . Learning rate adaptation emerges from the interaction between the LC and both dACC modules . Each dACC module feeds the LC with reward prediction and PE signals , while the LC analyzes these “raw data” from the cortex ( approximating a Bayesian learner ) , estimating volatility and adjusting the modules’ learning rate as a consequence . Finally , the RML can be connected to other neural models ( e . g . a visuo-spatial working memory model , see Simulation 2c ) . This allows the effort-related signal from the LC to modulate processing in other brain areas for performance optimization ( Fig 1 , orange arrows; see Methods for details ) . In this section we briefly introduce the main theoretical novelties of the RML . For a more detailed analysis we address the reader to the Discussion section , where we also relate the RML in detail to previous models , describe explicit experimental predictions that derive from the model , and speculate on the potential application of the RML to translational research . The RML is an autonomous agent able to near-optimally adapt to a diverse range of environments and tasks , with no need of task-specific parameters setting: across all the reported simulations the RML autonomously controlled its internal dynamics as a function of the environmental challenges , with no offline parameters optimization or human intervention ( i . e . one parameter set was used for all the simulations ) . From here , four major novelties can be identified .
Adaptive control of learning rate is a fundamental aspect of cognition . Humans can solve the tradeoff between stability and plasticity in a ( near ) Bayesian fashion [4 , 29] , distinguishing between variability due to noise versus variability due to actual changes of the environment; thus they can increase the learning rate only when volatility is detected [30 , 31] . At the neural level , a currently unexplained dissociation exists between dACC and LC activity , recorded during decision-making tasks where uncertainty due to noise and uncertainty due to volatility were systematically manipulated . The LC activity ( and thus NE release ) has been shown to track specifically volatility [30 , 32 , 33] , while the results about the dACC role in volatility estimation are less consistent . Indeed , while in the seminal study by Behrens et al . [4] , the dACC was hypothesized to track volatility , more recent study suggested that dACC activity in volatile environments are driven rather by PE coding , rather than specifically by volatility estimation [21] . These empirical findings seem to attribute different roles to LC and dACC in uncertainty coding , without providing a computational rationale for their functional specialization . In this simulation , we will investigate to what extent the model accounts for human adaptive control of learning rate at both behavioural and neural levels , and whether it can explain the dACC/LC dissociation . A long list of experimental results indicates that DA and NE neuromodulators are not only crucial for learning environmental regularities , but also for exerting cognitive control [37–41] . Although these mechanisms have been widely studied , little is known about how the brainstem catecholamine output is controlled to maximize performance [31 , 42 , 43] , and how the dACC is involved in such a process . In this section , we describe how the dACCBoost module learns to regulate LC and VTA activity to control effort exertion , at both cognitive and physical level [19 , 44 , 45] . In Simulation 2a , we test the cortical-subcortical dynamics in experimental paradigms involving decision-making in physically effortful tasks , where cost/benefit trade off must be optimized [46–48] . In Simulation 2b , we show how the LC can provide a NE signal to external neural modules to optimize cognitive effort [19 , 20] allocation and thus behavioural performance in a visuo-spatial working memory ( WM ) task . In both simulations , we also test the RML dynamics and behaviour after cortical and subcortical lesions . Deciding how much effort to invest to obtain a reward is crucial for human and non-human animals . Animals can choose high effort-high reward options when reward is sufficiently high [46 , 47] . The impairment of the mesolimbic DA system strongly disrupts such decision-making [46 , 47] . Besides the VTA , experimental data indicate also the dACC as having a pivotal role in decision-making in this domain [19 , 20 , 48–50] ( see also[51] for a review ) . In this simulation , we show how cortical-subcortical interactions between the dACC , VTA and LC can drive optimal decision-making when effortful choices leading to large rewards compete with low effort choices leading to smaller rewards . We thus test whether the RML can account for both behavioral and physiological experimental data from humans and nonhuman animals . Moreover , we test whether simulated ACC lesion or DA depletion can replicate the disruption of optimal decision-making , and , finally , how effective behaviour can be restored . Simulation results will be compared with behavioural data from rodents ( [47] , see also Simulation 2a in S1 File ) , and with physiological data from nonhuman primates [35] and humans [44] . Rodent data from Walton et al . [47] were chosen for comparison to study how the cost-benefit trade-off could be affected by ACC damage and by DA lesion and how behavioural performance could be partially recovered with environmental intervention ( Simulation 2b ) . We express the caveat that DA depletion studies in the literature we cited ( [46 , 47] , to compare with RML performance ) either deplete DA systemically , or are focused more on the mesolimbic-accumbens path than on DA afferents to the medial prefrontal cortex . Our assumption that mesolimbic DA lesion affects dACC functioning is neurophysiologically sound , because functional and anatomical connectivity indicates strong nucleus accumbens ( NAc ) —dACC connectivity [12 , 13 , 52 , 53] , probably contributing to convey reward-related information to the dACC . For this reason , lesioning the NAc may also disrupt the information flow from VTA to the dACC . Moreover , our simulations lead to the experimental prediction that DA lesion to dACC generates effects similar to mesolimbic DA lesions . In DA lesioned subjects , the preference for HR option can be restored by removing the difference in effort between the two options [47] , that is , by removing the critical trade-off between costs and benefits . In Simulation 2b , we show how the RML can recover a preference toward HR options , as demonstrated empirically in experimental paradigms used in rats . We focused specifically on recovery after DA lesion . Our choice was aimed at investigating the consequences of DA lesion at cortical-subcortical level and how these can be modulated by the environment , to open a view on future translational scenarios about DA-related neuropsychiatric disorders . We elaborate on the latter topic in the Experimental Predictions section . NE neuromodulation also plays a crucial role in WM , improving signal-to-noise ratio by gain modulation mediated by α2-A adrenoceptors [37 , 56] . A low level of NE transmission leads to WM impairment [57 , 58] . At the same time , as described above , NE is a major biological marker of effort exertion [35 , 59] . Besides NE release by the LC , experimental findings showed that also dACC activity increases as a function of effort in WM tasks [19 , 20 , 60] . Here we show that the same machinery that allows optimal physical effort exertion ( Simulation 2a ) may be responsible for optimal catecholamine management to control the activity of other brain areas , thus rooting physical and cognitive effort exertion in a common decision-making mechanism . This is possible because the design of the RML allows easy interfacing with external modules ( Fig 1 and Methods ) . Animal behavior in the real world is seldom motivated by conditioned stimuli directly leading to primary rewards . Instead , behavior is guided by higher-order conditioning , bridging the gap between reward and behavior . However , a unifying account explaining behavioral results and underlying neurophysiological dynamics of higher-order conditioning is currently lacking . First , at the behavioral level , literature suggests a sharp distinction between higher-order conditioning in classical versus instrumental paradigms . Indeed , although it is possible to train animals to execute complex chains of actions to obtain a reward ( instrumental higher-order conditioning , [62] ) , it is impossible to install a third- or higher-order level of classical conditioning ( i . e . when no action is required to get a reward [63] ) . Although the discrepancy has been well known for decades , its reason has not been resolved . Second , a number of models have considered how TD signals can support conditioning and learning more generally [64 , 65] . However , no model addressing DA temporal dynamics also simulated higher-order conditioning at behavioural level . Here we use the RML to provide a unified theory to account for learning in classical and instrumental conditioning . We show how the RML can closely simulate the DA shifting in classical conditioning ( Simulation S2 and Fig F in S2 File ) . We also describe how the VTA-dACC interaction allows the model to emancipate itself from primary rewards ( higher-order conditioning ) . Finally , we investigate how the synergy between the VTA-dACCBoost and LC-dACCBoost ( the catecholamines boosting dynamics ) is necessary for obtaining higher-order instrumental conditioning and how this process could be considered one of the foundations of intrinsic motivation . This provides a mechanistic theory on why higher-order conditioning is possible only in instrumental and not in classical conditioning . As VTA can vigorously respond to conditioned stimuli , it is natural to wonder whether a conditioned stimulus can work as a reward itself , allowing to build a chain of progressively higher-order conditioning ( i . e . not directly dependent on primary reward ) . However , for unknown reasons , classical higher-order conditioning is probably impossible to obtain in animal paradigms [63 , 66] . We thus investigate what happens in the model in such a paradigm . Differently from classical conditioning paradigms , animal learning studies report that in instrumental conditioning it is possible to train complex action chains using conditioned stimuli ( environmental cues ) as reward proxies , delivering primary reward only at the end of the task [62] .
The flexibility of RML , and the explicit neurophysiological hypotheses on which it is based , allow several experimental predictions . In this paper we aimed at presenting the general potential and the theoretical value of the RML , comparing , in a qualitative fashion , the results from our simulations with experimental data from many different domains . A larger use of quantitative approaches to test the experimental predictions derivable from the RML ( e . g . model-based data analysis ) will be necessary in future work . Here we list some potential experiments deriving from RML predictions . The first three are sufficiently specific to potentially falsify the model ( at least in its neurophysiological interpretation ) , the others are currently formulated as working hypotheses . First , the RML architecture suggests that PE signals are generated by the dACC and then converge toward the brainstem nuclei . This hypothesis implies that dACC lesion disrupts DA dynamics in higher-order conditioning , with a consequent impairment in higher-order instrumental conditioning; further , dACC lesion should disrupt LC dynamics related to learning rate control , with a consequent impairment of behavioural flexibility optimization . A second prediction concerns the mechanisms subtending higher-order conditioning and the difference between classical and instrumental paradigms . In the RML , higher-order conditioning is possible only when the agent plays an active role in learning ( i . e . , instrumental conditioning ) . We predict that hijacking the dACC decision of boosting catecholamines ( e . g . , via optogenetic intervention ) would make possible higher-order conditioning in classical conditioning paradigms ( ref . simulations 3a-b ) . Third , the DA-lesioned RML shows stronger dACC activation during an easy task ( without effort ) in the presence of a high reward ( see Simulation 2a , Fig 4B ) . This finding can be interpreted as a compensatory phenomenon allowing to avoid apathy ( i . e . refusal to engage in the task ) if a small effort can make available a big reward . This is an explicit experimental prediction that could be tested both in animal paradigms and in mesolimbic DA impaired humans [76] , or in patients with Parkinson’s disease on and off medication [51] , therefore providing also possible translational implications . Fourth , as shown above , the model provides a promising platform for investigating the pathogenesis of several psychiatric disorders . In a previous computational work , we proposed how motivational and decision-making problems in attention-deficit/hyperactivity disorder ( ADHD ) could originate from disrupted DA signals to the dACC [77] . In the current paper , we also simulated a deficit related to cognitive effort ( Simulation 2c ) in case of DA deficit . Together , these findings suggest how DA deficit can cause both motivational and cognitive impairment in ADHD , with an explicit prediction on how DA deficit can impair also NE dynamics [78] in ADHD . This prediction could be tested by measuring performance and LC activation during decision-making or working memory tasks , while specifically modulating DA transmission in both patients ( via pharmacological manipulation ) and RML . Fifth , another clinical application concerns a recent theory on autism spectrum disorder ( ASD ) pathogenesis . Recent studies [79 , 80] proposed that a substantial number of ASD symptoms could be explained by dysfunctional control of learning rate and overestimation of environment volatility . This qualitative hypothesis could be easily implemented and explored quantitatively by altering meta-learning mechanisms in the RML leading to chronically high learning rate and LC activation . The RML framework has three main limitations . First , in the RML DA plays a role only in learning . As with any other neuromodulator , experimental results suggest a less clear-cut picture , with DA being involved also in performance directly ( e . g . attention and WM via DLPFC modulation ) [39 , 81–83] . The goal of our simplified characterization of DA function was to elucidate how the two neuromodulators can influence each other for learning ( DA ) and performance ( NE ) . Moreover , other theories stress the importance of direct ( and hierarchically organized ) interaction between the medial prefrontal cortex and the DLPFC in cognitive control [84] and WM function [68] . From this perspective , reduced DA signal to the dACC could directly disrupt the dACC-DLPFC interaction , impairing cognitive control and WM without the involvement of the NE modulation . dACC-DLPFC interaction is a neglected aspect in our model that should be investigated in future works ( see next section ) . The second limitation is the separation of the LC functions of learning rate modulation ( λ ) and cognitive control exertion . The cost of this separation between these two functions is outweighed by stable approximate optimal control of learning rate and catecholamines boosting policy . It must be stressed that the ACCBoost module receives the LC signal λ related to learning rate in any case , making the boosting policy adaptive to environmental changes . Third , the RML reacts to environmental changes by learning rate modulation , while human and nonhuman primates can use specific events that occurred ( episodic control [85] ) , to trigger policy change for adapting to novel situations . There is also converging evidence that primate dACC ( and most likely its homologous area in rats ) is critical to perform this type of higher-order inference ( see [7] for a short review ) , and that LC bursts could work as circuit breakers to reset ongoing neural representations and trigger behavioural adaptation driven by episodic control [86] . The lack of contribution by episodic knowledge in behavioural optimization is clearly a limitation of our model , especially if we consider that episodic control can also optimize motivational signals to modulate cognitive effort [84] . We believe that these two adaptive processes ( i . e . learning rate control and episodic control ) are complementary and run in parallel and that their integration ( a possibly arbitration on influencing behaviour ) should receive future theoretical investigation . The RML shows how meta-learning involving three interconnected neuro-cognitive domains can account for the flexibility of the mammalian brain . However , our model is not meant to cover all aspects of meta-learning . Many other decision-making dimensions may be optimized by meta-learned too . One obvious candidate is the stochasticity ( temperature ) of the decision process [87] , which arbitrates the exploration/exploitation trade-off . We recently proposed that this parameter is similarly meta-learned trading off effort costs versus rewards [6] . It must be noted that experimental findings indicated a link between LC activation and the arbitration on exploration/exploitation trade-off [88 , 89] , suggesting that the same mechanism used for learning rate optimization could be extended also to this domain . Other aspects from the classical RL modeling framework include discounting rate or eligibility traces [90]; future work should investigate the computational and biological underpinnings of their optimization . Moreover , considering the strong empirical evidence attributing to the dACC a prominent role in foraging ( e . g . [91] ) , future work should focus on how the RML can also face this class of problems , where it is studied not only how mammals optimize choices within a task , but also how they decide when it is convenient to switch to another task , to maximize reward in the long run . Given the exceptionally extended dACC connectivity [12] , other brain areas are likely relevant for the implementation of decision making in more complex settings . For example , we only considered model-free dynamics in RL and decision-making . However , both humans and nonhuman animals can rely also on complex environment models to improve learning and decision making ( e . g . spatial maps for navigation or declarative rules about environment features ) . In this respect , future work should particularly focus on dACC-DLPFC-hippocampus interactions [92 , 93] , in order to investigate how environment models can modulate reward expectations , how the nervous system can represent and learn decision tree navigation [94] and how reward expectations can modulate goal-directed DLPFC representations [84] . Another anatomo-functional aspect that could be investigated concerns the anatomical segregation of the twofold dACC function we described here ( dACCAct and dACCBoost ) . Although we remain agnostic about this question , it would be interesting to investigate whether the neural units performing these two types of decision-making operations are overlapping , intermixed , or even segregated in different dACC sectors . Finally , the RML can work in continuous time and in the presence of noise . These features are crucial to make a model survive outside the simplified environment of trial-level simulations , and allow simulating behaviour in the real world , like , for example , in robotic platforms . RML embodiment into robotic platforms could be useful for both neuroscience and robotics . Indeed , testing our model outside the simplified environment of computer simulations could reveal model weaknesses that are otherwise hidden . Moreover , closing the loop between decision-making , body and environment [95] is important to have a complete theory on the biological and computational basis of decision-making . At the same time , the RML could suggest new perspectives on natural-like flexibility in machine learning , helping , for example , in optimizing plasticity as a function of environmental changes .
RML architecture was implemented in two versions: a discrete model ( simulating inter-trial dynamics ) and a dynamical model ( a dynamical system simulating also intra-trial dynamics ) . Both implementations share the same architecture displayed in Fig 1 , and follow the same computational principles . All the results reported above were obtained with the dynamical model . Here we introduce the mathematical form of the discrete model , which provides a clearer and more compact RML description . All the simulations ( with exception of Simulation 2c , which requires intra-trial dynamics ) were replicated with the discrete model ( Figures S9-S12 in S2 File ) , demonstrating that the computational principles founding the RML are independent from specific implementations . We used a single set of parameters across all simulations both for the discrete model ( Table 1 ) and for the dynamical model ( Table A in S1 File ) . Parameters were hand-tuned to ensure acceptable performance in a simple 2-armed bandit task and second-order conditioning task . The mathematical description of the dynamical model can be found in the S1 File . We designed the model such that communication with the external environment is based on 9 channels ( Fig 9A ) . Six channels represent environmental states ( s ) and RML actions ( a ) ( 3 states and 3 actions ) . The first two actions are aimed at changing the environmental state ( e . g . turning right or left ) , while the 3rd action means “Stay” , i . e . refusing to engage in the task . There are two other input channels , one dedicated to reward from environment ( RW ) and the other to signal costs of motor actions ( C ) . Finally , there is one output channel conveying norepinephrine ( NE ) signals to other brain areas . The RML is scalable by design , i . e . there is no theoretical limit to the number of state/action channels , and neither the number of parameters nor their values changes as a function of task type/complexity .
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A major challenge for all organisms is selecting optimal behaviour to obtain resources while minimizing energetic and other expenses . Evolution provided mammals with exceptional decision-making capabilities to face this challenge . Even though neuroscientists have identified a heterogeneous and distributed set of brain structures to be involved , a comprehensive theory about the biological and computational basis of such decision-making is yet to be formulated . We propose that the interaction between the medial prefrontal cortex ( a part of the frontal lobes ) and the subcortical nuclei releasing catecholaminergic neuromodulators will be key to such a theory . We argue that this interaction allows both the selection of optimal behaviour and , more importantly , the optimal modulation of the very brain circuits that drive such behavioral selection ( i . e . , meta-learning ) . We implemented this theory in a novel neuro-computational model , the Reinforcement Meta-Learner ( RML ) . By means of computer simulations we showed that the RML provides a biological and computational account for a set of neuroscientific data with unprecedented scope , thereby suggesting a critical mechanism of decision-making in the mammalian brain .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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2018
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Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
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Influenza A virus usurps host signaling factors to regulate its replication . One example is mTOR , a cellular regulator of protein synthesis , growth and motility . While the role of mTORC1 in viral infection has been studied , the mechanisms that induce mTORC1 activation and the substrates regulated by mTORC1 during influenza virus infection have not been established . In addition , the role of mTORC2 during influenza virus infection remains unknown . Here we show that mTORC2 and PDPK1 differentially phosphorylate AKT upon influenza virus infection . PDPK1-mediated phoshorylation of AKT at a distinct site is required for mTORC1 activation by influenza virus . On the other hand , the viral NS1 protein promotes phosphorylation of AKT at a different site via mTORC2 , which is an activity dispensable for mTORC1 stimulation but known to regulate apoptosis . Influenza virus HA protein and down-regulation of the mTORC1 inhibitor REDD1 by the virus M2 protein promote mTORC1 activity . Systematic phosphoproteomics analysis performed in cells lacking the mTORC2 component Rictor in the absence or presence of Torin , an inhibitor of both mTORC1 and mTORC2 , revealed mTORC1-dependent substrates regulated during infection . Members of pathways that regulate mTORC1 or are regulated by mTORC1 were identified , including constituents of the translation machinery that once activated can promote translation . mTORC1 activation supports viral protein expression and replication . As mTORC1 activation is optimal midway through the virus life cycle , the observed effects on viral protein expression likely support the late stages of influenza virus replication when infected cells undergo significant stress .
Influenza virus is a major human pathogen for which there are few treatment options . To search for novel potential therapeutic targets while systematically investigating viral-host interactions , comprehensive proteomics screens [1 , 2] and various genome-wide screens were performed in influenza virus infected cells [3] . In two screens , the host kinase mechanistic target of rapamycin ( mTOR ) was identified as a protein that promotes influenza virus infection [4 , 5] . Indeed , we and others found that H1N1 influenza viruses activate mTORC1 [5 , 6] , and influenza virus replication is reduced when mTOR is inhibited [4] . Additionally , we showed that mTORC1 inhibition by chemical induction of REDD1 , a known mTORC1 inhibitor , reduced influenza virus replication [5] . Collectively , these data highlight the importance of mTORC1 for efficient influenza virus replication . mTOR is a highly conserved serine/threonine kinase that resides in two functionally distinct multi-protein complexes termed mTOR complex 1 and 2 ( mTORC1 and mTORC2 ) , which are defined by association with the proteins Raptor or Rictor , respectively . mTORC1 regulates cellular protein synthesis , growth and proliferation in response to nutrients , such as amino acids and growth factors . Amino acid stimulation promotes mTORC1 recruitment to lysosomes , which results in mTORC1 activation [7] . This process requires amino acid-sensing by the lysosomal vacuolar H+-adenosine triphosphatase ATPase ( v-ATPase ) and its interaction with the Ragulator complex of scaffold proteins [8] . The Ragulator then recruits the Rag GTPases , which brings mTORC1 in close proximity to its small GTPase activator Rheb ( Ras homolog enriched in brain ) [7] . Growth factors activate mTORC1 through a signaling axis involving phosphoinositide 3-kinase ( PI3K ) and AKT ( Fig 1A ) . Once activated by phosphorylation , AKT inhibits Tuberous Sclerosis Complex 1/2 ( TSC1/2 ) , which is a GTPase-activating protein ( GAP ) that acts on Rheb , thereby stimulating mTORC1 . REDD1 inhibits mTORC1 activation in a TSC1/2-dependent manner [9] . Activated mTORC1 phosphorylates downstream substrates to elicit cellular responses . p70 S6 kinase 1 ( S6K ) and 4E-BP1 are key mTORC1 substrates that regulate protein translation . S6K phosphorylation stimulates translation initiation and elongation through its substrate ribosomal protein S6 . Phosphorylation of 4E-BP1 results in loss of eIF4E binding so that eIF4E can help facilitate cap-dependent translation initiation [7] . The proteins Rictor and SIN1 distinguish mTORC2 from Raptor-containing mTORC1 . mTORC2 regulates the cytoskeleton , cell survival and has a more recently identified role in translation [10–13] . The activation and functions of mTORC2 are not as well characterized as mTORC1 . Recent studies indicate that phosphatidylinositol-3 , 4 , 5-triphosphate ( PIP3 ) activates mTORC2 in response to growth factors or insulin [14 , 15] . mTORC2 substrates include AGC kinases AKT , PKC-α and SGK1 . Notably , mTORC2 phosphorylates AKT at serine 473 ( S473 ) , a growth stimuli responsive site that promotes cell survival [13] . mTORC1 and mTORC2 signaling are often enhanced during oncogenic transformation , and both are being considered as targets for cancer therapeutics [7] . The influenza virus non-structural protein 1 ( NS1 ) has been shown to interact with the p85β subunit of PI3K , which results in activation of AKT S473 [16 , 17] ( Fig 1A ) . AKT activation by influenza virus is thought to suppress apoptosis as PI3K/AKT signaling is well known to promote cell survival , and NS1 has anti-apoptotic functions [18–21] . However , NS1 proteins , especially from avian strains , were shown to have pro-apoptotic functions [22–25] . Despite the binding of NS1 to PI3K , it is not entirely clear whether this interaction contributes to apoptotic regulation [26] . It has also been hypothesized that the anti-apoptotic function of NS1 would contribute to the early stages of the virus life cycle whereas the pro-apoptotic role would be important at late stages of the infection cycle [27] . PI3K activation stimulates 3-phosphoinositide dependent protein kinase-1 ( PDPK1 , also known as PDK1 ) -mediated phosphorylation of AKT at threonine 308 ( T308 ) [28] , whereas mTORC2 and other kinases are responsible for AKT phosphorylation at S473 [29] . Notably , TANK binding kinase 1 ( TBK1 ) phosphorylates both the T308 and S473 sites on AKT [30 , 31] . However , the relationship between AKT and mTOR activation during influenza virus infection has not been established . Here we sought to understand the players and mechanisms involved in mTORC1 activation during influenza virus infection and identify the mTORC1-dependent substrates and pathways activated during infection .
Influenza virus infection promotes AKT phosphorylation at amino acid S473 [16–18 , 32] , but the kinase involved has not been identified , and phosphorylation at the T308 site has not been examined . To first determine whether AKT is required for mTORC1 activation , wild-type cells and cells lacking two of the three AKT isoforms AKT1 and AKT2 were infected . Influenza A virus strain A/WSN/1933 ( WSN ) induced AKT phosphorylation at S473 and T308 sites as well as mTORC1 activation in wild-type cells , as evidenced by phosphorylation of its substrate S6K on threonine 389 ( p-S6K ) and the multiple forms of the 4E-BP1 protein . However , mTORC1 activity was strikingly reduced in the absence of AKT1/2 ( Figs 1B and S1A–S1D ) . Thus , we show that functional AKT is critical for proper induction of mTORC1 by influenza virus . The influenza virus NS1 protein binds PI3K to induce AKT S473 phosphorylation by an uncharacterized kinase [26] ( Fig 1A ) . AKT is fully activated when both T308 and S473 residues are phosphorylated [33] . PI3K activation results in T308 phosphorylation of AKT by PDPK1 [28] . Alternatively , AKT is phosphorylated at T308 and S473 by TBK1 [31] independently of PI3K and PDPK1 [30] . mTORC2 is one of several kinases that phosphorylates AKT at S473 [13] . To assess whether mTORC2 is required for influenza virus to activate AKT , we examined AKT activity in the absence of mTORC2 using mouse embryonic fibroblasts ( MEF ) expressing or lacking Rictor , a component required for mTORC2 complex formation and function [7] . In addition , we have knocked down Rictor in A549 cells . In both conditions , infection of Rictor expressing cells with WSN stimulated AKT phosphorylation at both T308 and S473 sites ( Fig 1C , S1E–S1G Fig ) . In contrast , AKT phosphorylation at S473 was absent or severely reduced in WSN infected Rictor-/- MEFs and in A549 cells where Rictor was knockdown , respectively ( Fig 1C , S1E and S1G Fig ) . These results indicate that influenza virus promotes mTORC2-mediated phosphorylation of AKT at S473 . Stimulation of AKT T308 phosphorylation still occurred in Rictor-/- MEFs to a similar degree as in Rictor+/+ cells when normalized to total AKT levels ( Fig 1C and S1F Fig ) . We noticed higher basal levels of p-AKT ( T308 ) in Rictor-/- MEF as compared to Rictor+/+ cells ( Fig 1C ) but this was not the case in A549 cells with transient knockdown of Rictor versus control cells ( S1G Fig ) . Nevertheless , influenza virus infection stimulated mTORC1 to a similar extent in both Rictor-/- MEF and A549 cells depleted of Rictor compared to their respective controls , as evidenced by increased p-S6K levels ( Fig 1C and S1G–S1J Fig ) . S6K phosphorylation at T389 was normalized to total S6K levels and showed that Rictor-/- MEF has slightly lower levels of total S6K than Rictor+/+ cells but the degree of activation is similar to wild-type cells as mentioned above ( Fig 1C , S1H–S1I Fig ) . In sum , mTORC1 was activated despite the absence or severe reduction of p-AKT ( S473 ) , indicating that full AKT activation is not required for influenza virus to trigger mTORC1 . Since Rictor , as a member of the mTORC2 complex , is known to phosphorylate p-AKT ( S473 ) in uninfected cells and we observed a lack or striking decrease of AKT phosphorylation at this site in Rictor-/- infected MEFs ( Fig 1C , S1E and S1G Fig ) , these findings indicate that influenza virus usurps mTORC2 to promote phosphorylation of AKT at S473 . This phosphorylation event presumably occurs through NS1-mediated PI3K activation , which is a process that is known to regulate apoptosis [26] . Altogether , mTORC1 stimulation by influenza virus can occur independently of mTORC2-mediated activation of AKT through phosphorylation of the S473 site . To ascertain the kinase responsible for phosphorylation on residue T308 of AKT during infection and its potential relationship with mTORC1 activation , we first infected wild-type and Tbk1-/- cells with WSN in the absence or presence of BX795 , an inhibitor of PDPK1 [34] , TBK1 and IKKε [35] . mTORC1 activation by influenza virus still occurred in Tbk1-/- cells and was down-regulated in the presence of BX795 ( Fig 1D ) . These results indicated that TBK1 was not involved in influenza virus-mediated induction of mTORC1 activity and that another kinase inhibited by BX795 played a role in stimulating mTORC1 . As expected , viral protein levels were higher in Tbk1-/- cells than in Tbk1+/+ cells , as Tbk1 knockout cells are more permissive to infection . mTORC1 activation was also higher in Tbk1-/- cells than in Tbk1+/+ cells , suggesting that virus replication may activate mTORC1 , a point that will be addressed below . Next , we tested cells that lack PDPK1 to assess if it had a role in mTORC1 activation by influenza virus . We observed that p-AKT ( T308 ) phosphorylation was largely reduced in Pdpk1-/- cells whereas p-AKT ( S473 ) was slightly decreased after normalization to total AKT ( Figs 1E , S1K and S1L ) . mTORC1 activation was inhibited in the absence of PDPK1 during infection ( Fig 1E and S1M Fig ) . Taken together these findings and the results from Rictor knockout cells that activate mTORC1 to the same extent as Rictor wild-type cells in the presence of p-AKT ( 308 ) but in the absence of p-AKT ( S473 ) , these findings point to the AKT T308 phosphorylation site as important to stimulate mTORC1 activity during infection ( Fig 1B–1E ) . It is possible that in Pdpk1-/- cells phosphorylation of both T308 and S473 sites are required for full AKT activation , as has been shown with growth factor stimuli [33]; therefore , these AKT phosphorylation sites may influence the phosphorylation status of each other in certain conditions . Altogether , mTORC2 and PDPK1 are the kinases responsible for AKT S473 and T308 phosphorylation , respectively , during infection . AKT is required for influenza virus to activate mTORC1 and AKT phosphorylation at T308 appears to be the key to trigger mTORC1 during infection , indicating that site-specific phosphorylation of AKT directs its downstream functions . We demonstrated that influenza virus stimulates mTORC1 signaling through activation of AKT via the T308 site ( Fig 1B–1E ) . Therefore , we sought to elucidate how AKT activation by influenza virus results in mTORC1 activity . We first examined the role of the viral NS1 protein , which is a multifunctional virulence factor that is synthesized during replication and usurps several host signaling pathways . NS1 promotes AKT S473 phosphorylation through its interaction with PI3K [26] . To determine whether NS1 is necessary for mTORC1 activation , we tested viruses lacking NS1 ( WSNΔNS1 and PR8ΔNS1 ) . NS1-deficient viruses have replication defects in cells with functional type I interferon ( IFN ) signaling; therefore , Vero cells were used which lack type I IFN genes [36–38] , and thus allow comparable replication of wild-type and ΔNS1 viruses . mTORC1 was similarly activated by both wild-type and ΔNS1 viruses , demonstrating that influenza virus does not require NS1 to activate mTORC1 ( Fig 2A and 2B ) . The results suggested that low levels of AKT T308 phosphorylation induced by WSNΔNS1 was sufficient to activate mTORC1 ( Fig 2A , 2B and S2A Fig ) , whereas AKT S473 phosphorylation was severely reduced and likely not required ( Fig 2A , 2B and S2B Fig ) . These findings are corroborated by infection of Akt1/2+/+ and Akt1/2-/- cells with WSN or WSNΔNS1 , which show that AKT is required for WSNΔNS1 induction of mTORC1 activity ( Fig 2C ) and that , as in Vero cells , WSNΔNS1 infected Akt1/2+/+ cells promoted mTORC1 activity with low levels of p-AKT ( T308 ) and in the absence or barely detectable levels of p-AKT ( S473 ) ( Fig 2D ) . Therefore , mTORC1 activation by influenza virus appears to be independent of AKT S473 phosphorylation and NS1 . To clarify our understanding of viral factors involved in mTORC1 activation , we first set out to better characterize mTORC1 signal induction during influenza virus infection . To understand the kinetics of mTORC1 activation during infection , S6K phosphorylation was examined at multiple times post-infection . mTORC1 activation was evident at 4 hours post-infection and increased over time ( S3A Fig ) . To ensure that influenza virus activates mTORC1 in non-transformed cells , mTORC1 activation was assessed in primary MEFs and human bronchial epithelial cells ( HBECs ) . Indeed , influenza virus activated mTORC1 in primary MEFs ( S3B Fig ) and HBECs ( S3C Fig ) . We and others have previously shown that mTORC1 is activated by WSN [5 , 6] and A/TX/36/1991 H1N1 influenza viruses [5] . To determine whether a more distantly-related strain of influenza virus could stimulate mTORC1 , cells were infected with the recently emerged H7N9 strain A/Shanghai/1/2013 ( Sh/1 ) . Sh/1 activated mTORC1 ( S3D Fig ) . We also tested a recombinant strain that contains HA and NA segments from A/PR/8/1934 ( PR8 ) and the other 6 segments from A/Shanghai/1/2013 ( rSh/1 ) [39] . rSh/1 activated mTORC1 ( S3E Fig ) . Moreover , to understand whether mTORC1 activation was a general effect of viral gene expression , we examined whether another negative-sense RNA virus vesicular stomatitis virus ( VSV ) was capable of activating mTORC1 . VSV did not activate mTORC1 at 6 hours post-infection despite high levels of M protein expression ( S3F Fig ) . It was previously observed that mTORC1 was activated during VSV infection in MEFs at 16 hours post-infection [40] , so it appears that the kinetics of mTORC1 activation by VSV differ and might be cell type-dependent . Altogether , these results show that different strains of influenza A viruses stimulate mTORC1 signaling and that mTORC1 can be activated by influenza virus in several cell lines , highlighting the importance of mTORC1 during influenza virus infection . To further elucidate the mechanism behind influenza virus-mediated mTORC1 activation , we set out to identify viral and cellular factors that might contribute to mTORC1 signaling . We first tested the M2 protein for a role in mTORC1 activation as it is implicated in regulating macroautophagy ( referred to herein as autophagy ) [41] . Influenza virus M2 protein induces autophagy early in infection but then inhibits autophagosome-lysosome fusion , which is necessary to degrade contents [41] . Autophagy inhibition can lead to mTORC1 activation as they are antagonistic processes [7] . To decipher whether M2 restriction of autolysosomal degradation can induce mTORC1 activity , we infected cells with a viral mutant strain that is deficient in M2 expression ( PR8:WSNDeficientM2 ) and the wild-type strain ( PR8:WSN ) . The mutant virus is propagated in an M2 expressing cell line and therefore contains M2 in virions , which is necessary for entry , but is unable to synthesize new M2 protein [41] . Both wild-type and mutant virus strains activated mTORC1 similarly , despite the lack of autophagy in PR8:WSNDeficientM2 infected cells as determined by LC3-I to LC3-II conversion ( S4A Fig ) . Therefore , mTORC1 stimulation by influenza virus is independent of M2 effects on autophagic processing . We further evaluated a possible role of autophagy in mTORC1 induction as mTORC1 can be reactivated in cells that are serum starved for 6 or more hours , which requires initial autophagy induction that is eventually reduced due to autolysosome degradation [42] . Autophagy is induced early during influenza virus infection [43] , but discontinuation of autophagy could promote mTORC1 activation . To establish whether autophagy is required for mTORC1 activation during influenza virus infection , we infected cells depleted of or lacking major proteins involved in autophagosome formation , Atg5 and Atg7 . Depletion of Atg5 or Atg7 did not alter mTORC1 activation by influenza virus ( S4B Fig ) . Furthermore , mTORC1 was still activated by influenza virus in Atg5-/- cells ( S4C Fig ) . Thus , induction and termination of autophagy does not contribute to mTORC1 activation during influenza virus infection . Overall , we demonstrated that NS1 and autophagy are dispensable for influenza virus to stimulate mTORC1 signaling . To investigate other factors and processes necessary for mTORC1 activation by influenza virus , we determined what stages of the virus replication cycle were critical for mTORC1 stimulation . We first assessed whether influenza virus replication was a requirement . WSN was UV-inactivated , which was confirmed by plaque assay ( S4D Fig ) . UV-inactivated influenza virus was unable to activate mTORC1 ( Fig 2E ) , illustrating that influenza virus replication is likely required to stimulate mTORC1 . Thus , we conclude that stimulation of mTORC1 by influenza virus is not mediated by virus-induced processes prior to viral fusion , such as binding to cellular receptors and endocytosis , and likely requires active viral replication . To further evaluate the effect of viral replication on mTORC1 activation , we tested whether mTORC1 activity was increased in cells deficient in immune signaling or effector molecules that allow enhanced virus replication compared to wild-type cells . Additionally , we sought to determine whether key innate immune proteins involved in type I IFN induction contribute to mTORC1 activation since IFN can stimulate mTORC1 [44] . To this end , MEFs deficient in MAVS and IFITM3 were utilized . MAVS is the key adaptor molecule of the RIG-I-like receptor signaling pathway , which responds to viral RNA detection and induces IFN expression in virally-infected cells [45] . Deletion of MAVS reduces innate immune responses to viral infection , which results in increased virus replication . IFITM3 is an IFN-stimulated gene that restricts influenza virus replication [46] by preventing virus escape from endosomes during entry [47] . Influenza virus protein levels and mTORC1 activation were increased in both Mavs-/- and Ifitm3-/- MEFs compared to their wild-type counterparts ( Fig 2F and 2G ) , suggesting that virus replication amplifies mTORC1 signaling . Moreover , these data provide additional evidence that mTORC1 activation by influenza virus is not a result of the type I IFN response to viral infection , in agreement with our results showing mTORC1 activation by influenza virus in Vero cells , which lack type I IFN genes ( Fig 2A and 2B ) . In addition , no mTORC1 activation was detected upon stimulation of cells with poly ( I:C ) , which led to interferon expression ( S4E–S4G Fig ) . To investigate the potential role of additional viral proteins on mTORC1 activation , an RNA interference ( RNAi ) approach was taken . Small-interfering RNAs ( siRNAs ) against individual viral mRNAs were employed to reduce specific viral protein levels followed by assessment of mTORC1 activation . Viral siRNAs were specific to their targets with the exception of M2 , which reduced both M1 and M2 ( Fig 3A ) since they are generated from the same RNA . Depletion of either M1 or M2 did not impact mTORC1 activity at 6h post-infection , although high levels of M2 , which occur late in infection , can activate mTORC1 as addressed below . As expected , significant reductions in viral protein expression were observed when the viral polymerase complex was depleted using siRNAs against NP , PA , PB1 or PB2 ( Fig 3A ) . Depleting the viral polymerase complex prevented mTORC1 activation likely by reducing viral replication . Indeed , when an influenza virus replication simulation system was applied to test mTORC1 activation in the presence of only NP , the polymerase complex and a mini-genome encoding negative-sense luciferase inserted in the NP cRNA promoter [48] , mTORC1 was not activated ( Fig 3B ) , suggesting that additional viral components are necessary to stimulate mTORC1 . However , we did observe that knock down of HA or NA during infection reduced mTORC1 activation , although expression of other viral proteins was detectable at 6 hours post-infection ( Fig 3A ) . Knock down of HA decreased mTORC1 activity more effectively than NA knock down . These data were corroborated by expression of HA alone , which induced AKT T308 phosphorylation and mTORC1 activation , while expression of NA alone did not promote mTORC1 activation to a significant degree ( Fig 3C ) . Taken together , the viral protein HA promotes mTORC1 activation during infection . We then investigated how the AKT-TSC1/2-Rheb axis might be regulated during infection to prompt mTORC1 signaling by assessing REDD1 , which converges on and inhibits the pathway . Under stress conditions , TSC2 activity is induced by REDD1 , and therefore , REDD1 is inhibitory to mTORC1 [7] . REDD1 was originally described to promote TSC2 stability and activity by sequestering the TSC1/2 inhibitory protein 14-3-3 [49] . However , a recent study revealed that REDD1 promotes AKT T308 dephosphorylation to prevent mTORC1 signaling [50] . We previously identified REDD1 as an antiviral factor and showed that REDD1 protein levels decrease as viral infection progresses [5] . Thus , we analyzed how REDD1 influences AKT-mTORC1 signaling during influenza virus infection . We assessed AKT and mTORC1 activation simultaneously with REDD1 protein levels during viral infection and found that mTORC1 activity was strongest ( as compared to the mock counterpart for each time point normalized to total S6K levels ) when REDD1 levels were greatly diminished and AKT T308 phosphorylation was strongest by 7h post-infection ( Fig 4A and S5 Fig ) . AKT S473 activation increased up to 5h post-infection but decreased at later time points ( Fig 4A and S5 Fig ) . Influenza virus infection reduced REDD1 mRNA levels at and beyond 5 hours post-infection ( Fig 4B ) , which likely contributed to the decrease in protein levels ( Fig 4A and S5 Fig ) . REDD1 protein levels were also reduced in mock infected cells at later times post-infection , though not as great as in infected cells , which was likely a result of REDD1 having a short half-life ( ~5 minutes ) [51 , 52] and the experiments being performed in serum-free media ( Fig 4A and S5 Fig ) . mTORC1 activation was also assessed by p-4E-BP1 , which shows the expected phosphorylation pattern of activation peaking at 6h post-infection ( Fig 4A and S5 Fig ) . AKT T308 phosphorylation preceded REDD1 down-regulation by influenza virus suggesting that AKT activation is the initial stimulus for mTORC1 and that influenza virus-induced reduction of REDD1 amplifies and/or maintains mTORC1 signaling throughout infection . To investigate what triggers down-regulation of REDD1 at the mRNA and protein levels during infection , we knocked down influenza virus proteins and assessed REDD1 protein levels . As expected , REDD1 protein levels decreased upon infection but dramatically increased upon knockdown of the viral NP protein , which prevents virus replication and viral protein expression , as NP functions with the virus polymerase ( Fig 4C ) . These results suggest that REDD1 levels are up-regulated upon viral entry and/or during primary transcription and are down-regulated after these early processes . This is consistent with our previous results [5] and with Fig 4A in which REDD1 levels increase early in infection and are down-regulated at later stages of infection . Depletion of HA , NA , M1 and PA did not alter REDD1 protein levels . However , depletion of M2 increased REDD1 protein levels ( Fig 4C ) . Since both NP and M2 knockdown up-regulated REDD1 protein levels , we analyzed REDD1 mRNA levels upon expression of NP alone or in combination with the viral polymerase as well as upon M2 expression alone . We found that neither NP nor components of the viral polymerase complex altered REDD1 mRNA levels ( Fig 4D ) . However , M2 expression alone decreased REDD1 mRNA levels and induced mTORC1 activation as noted by increased levels of p-S6K and change in mobility of p-4E-BP1 ( Fig 4E and 4F ) . Together , these findings suggest that the REDD1 down-regulation during infection is likely mediated by the viral M2 protein and that the observed NP effect on REDD1 protein levels is due to inhibition of virus replication that prevented expression of M2 protein . We have not noticed an impact of M2 on mTORC1 activation before or at 6h post-infection ( Fig 3A ) , which is mediated by the HA protein . This is consistent with an effect of M2 at late stages of infection when REDD1 down-regulation is prominent , after 7h , and high levels of M2 protein would be present to activate mTORC1 and to further support virus replication . To determine whether increased levels of REDD1 can reduce AKT T308 phosphorylation during infection as was previously observed under certain basal conditions [50] , we employed a tetracycline-inducible REDD1 cell line , U2OS-REDD1 . High levels of REDD1 did not reduce AKT T308 phosphorylation in infected cells ( Fig 4G ) . Therefore , REDD1 does not act on AKT to implement its mTORC1 inhibitory effects during influenza virus infection . We also determined whether high levels of REDD1 could prevent influenza virus from activating mTORC1 . Indeed , REDD1 over-expression inhibited mTORC1 signaling in infected cells ( Fig 4G ) , indicating that mTORC1 is stimulated when REDD1 is down-regulated by influenza virus . Overall , we reveal that influenza virus reverses REDD1 inhibition of mTORC1 downstream of AKT to enhance mTORC1 signaling . mTORC1 and mTORC2 signaling are enhanced during influenza virus infection ( Fig 1 ) , and both can influence translation [53] . mTORC1 promotes translation of 7-methylguanosine capped mRNAs [7] , which would likely affect influenza virus protein translation as its mRNAs possess 7-methylguanosine caps acquired from host mRNAs . mTORC2 co-translationally stabilizes proteins via phosphorylation by associating with ribosomes , but few substrates have been identified thus far [53] . Therefore , we evaluated whether mTORC1 and mTORC2 can both impact influenza virus protein expression and replication . To investigate the role of mTOR on influenza virus protein expression , infected A549 cells were treated with non-toxic concentrations of the potent mTOR inhibitor Torin1 ( S6A Fig ) , which targets both mTORC1 and mTORC2 [54] . Viral protein levels were reduced and viral mRNA levels decreased to a lesser extent in the presence of Torin1 at 10 hours post-infection ( Fig 5A and S6B Fig ) , indicating that mTOR activity is important for optimal influenza virus mRNA and protein production . Therefore , we assessed the effect of Torin1 on the replication of WSN ( Fig 5B ) and rSh/1 ( S6C Fig ) . As predicted , replication of both strains was reduced when mTOR was inhibited with Torin1 . In addition , inhibition of mTORC1 by rapamycin also impaired virus replication ( Fig 5C ) . Torin1 does not distinguish between mTOR associated with mTORC1 or mTORC2 [54] , and the inhibitor rapamycin inhibits mTORC1 but does not completely abrogate mTORC1 activity [54 , 55] , and can inhibit mTORC2 with prolonged treatment [56] . Since mTORC1 and mTORC2 can both regulate translation [53] and are both inhibited by Torin1 [54] , we tested whether Torin1-mediated reduction of viral proteins involved inhibition of both complexes by using MEFs lacking Rictor to disrupt mTORC2 . If Torin1 inhibition of viral protein expression was dependent on mTORC2 , then Torin1 treatment in Rictor-/- MEFs would not reduce viral protein production as compared to Rictor+/+ MEFs . However , we observed reduced viral protein levels in both Rictor+/+ and Rictor-/- MEFs treated with Torin1 ( Fig 5D ) . Thus , influenza virus mainly relies on mTORC1 , not mTORC2 , for viral protein expression . However , mTORC2 may influence viral protein expression at different times post-infection than what was assessed here . Therefore , we assessed viral replication in Rictor-/- cells compared to Rictor+/+ cells and we found no striking difference in virus replication ( Fig 5E ) . Similarly , knock down of Rictor in A549 cells did not substantially affect virus replication as compared to control cells ( S6D Fig ) . These findings together with the results above indicate PDPK1-mediated AKT phosphorylation at T308 leads to mTORC1 activation to regulate viral protein expression and replication . To identify what host processes are affected by influenza virus-induced mTORC1 signaling , we identified the landscape of mTORC1-dependent substrates during infection using an unbiased systematic phosphoproteomics approach in cells lacking functional mTORC2 treated with or without the mTOR inhibitor Torin1 to distinguish mTORC1 from mTORC2 substrates . Rictor-/- MEFs were infected with WSN in the absence or presence of Torin1 treatment , lysed 7 h post-infection and subjected to phosphopeptide enrichment followed by mass spectrometric analyses . We set out to identify mTORC1 substrates by comparing WSN infected Rictor-/- MEFs treated with DMSO or Torin1 . Rictor-/- MEFs lack functional mTORC2 , and therefore , Torin1 treatment will only inhibit mTORC1 signaling in these cells . We identified 90 phosphorylation sites that were altered +/- 1 . 5-fold or greater in the presence and absence of mTORC1 signaling ( Fig 6A ) . Torin1 efficiently inhibited mTORC1 in Rictor-/- MEFs ( Fig 6B ) . Although we did not identify p-S6K ( T389 ) or p-4E-BP1 ( T37/46 ) sites due to low peptide abundance , the S6K substrate eIF4B ( S422 ) [57] and another previously identified mTORC1 substrate , eIF4G1 ( S1189 ) , were found ( Fig 6A and 6C ) . Both eIF4G1 ( S1189 ) and eIF4B ( S422 ) have been identified in an mTORC1 substrate screen [58] and we now show that these translation factors are mTORC1 substrates regulated during influenza virus infection . When comparing uninfected DMSO- and Torin-treated Rictor-/- cells , nearly 16% of phosphorylation sites that we identified are previously defined mTORC1-dependent sites ( Fig 6A and S1 Table ) . In addition to eIF4G1 ( S1189 ) and eIF4B ( S422 ) phosphorylation changes in infected cells , LARP1 ( S743 ) phosphorylation was also altered . LARP1 was previously shown to interact with mTORC1 [59 , 60] and stabilizes its mRNA [61] . Next , to examine whether any of the mTORC1-dependent substrates were previously identified as important host proteins in multiple genome-wide genetic screens from influenza virus infected cells , we assessed overlap between our phosphoproteomics hits and previous screen hits [4 , 46 , 62–66] . We found that 13 out of 72 ( 18% ) of the proteins we identified as mTORC1 substrates during infection overlapped with proteins that alter influenza virus infection [3] . Since many proteins from the multiple influenza virus siRNA screens were IFN regulated , we also examined whether any of the mTORC1 substrates that we identified were IFN regulated proteins . Our phosphoproteomics analysis show that 10 mTORC1-dependent phosphorylation sites are present in proteins that are regulated by IFN . Since Torin inhibits virus replication , some of the regulation shown here could be due to diminished replication . However , the mTORC1-mediated changes in phosphorylation during infection in mTORC1 substrates identified in the absence of infection likely reflect the true role of mTORC1 as regulator of infection . To determine the cellular processes altered by these mTORC1-dependent phosphorylation events , we assessed the hits by Gene Set Enrichment Analysis to find commonly altered pathways . We identified pathways that regulate mTORC1 signaling and/or are regulated by mTORC1 such as the insulin receptor pathway and the PI3K cascade , which revealed significantly altered protein phosphorylation during infection ( Fig 6D ) . mTORC1 mediated signaling was also identified , which included members of the translation machinery . mRNA processing was also affected; in fact , mTORC1 was previously shown to modulate mRNA splicing through activated S6K interactions with the exon junction complex protein SKAR , which enhances spliced mRNA translation [67] . Notably , proteins aligning with the FAS signaling pathway had altered phosphorylation during influenza virus infection suggesting that mTORC1 has a role in regulating Fas-mediated apoptotic cell death during influenza virus infection . Indeed , Fas and FasL are upregulated during influenza virus infection [68–70] , and the resulting apoptosis can promote viral replication [68 , 71] . Overall , our findings implicate mTORC1 as a central player in the regulation of influenza virus infection by altering the phosphorylation state of members of key signaling pathways .
The regulation of mTOR by influenza virus has not been previously characterized , although mTOR promotes influenza virus replication [4 , 5] . mTOR is central to protein production and cell survival , and therefore , it is understandable that influenza virus would exploit mTOR to regulate cellular processes for its advantage . The data presented herein have revealed several new findings regarding manipulation of mTOR signaling by influenza virus , which are summarized in Fig 7 . We discovered that mTORC2 and PDPK1 are required during influenza virus infection to phosphorylate AKT , while AKT is required for influenza virus to induce mTORC1 signaling . PDPK1-mediated phosphorylation of AKT T308 is necessary for influenza virus to activate mTORC1 , whereas mTORC2-mediated phosphorylation of AKT S473 is not required for mTORC1 activation . mTORC2 may contribute to the regulation of apoptosis by the viral NS1 protein during infection since previous reports have linked AKT S473 phosphorylation to NS1 and apoptosis [26] . Therefore , AKT is differentially phosphorylated by influenza virus to yield different outcomes related to viral protein expression and cell death . We observed that mTORC1 activation by influenza virus required virus replication and promoted infection . The viral glycoprotein HA promotes mTORC1 activation while NS1 is dispensable . To this end , it has been shown that membrane accumulation of HA activates ERK signaling through protein kinase Cα to promote nuclear export of the viral genome [72] . In addition , HA expression can activate NF-κB via oxidative radicals and induction of IκB activity [73 , 74] , which likely contributes to the regulation of immunity and inflammation during infection . Whether mTORC1 activation by HA contributes to these process remains to be determined and it would be an interesting topic for future investigation . Furthermore , we showed that influenza virus M2 protein down-regulates the mTORC1 inhibitor REDD1 , a process that impacts the lates stages of infection . Since the viral M2 protein is a proton channel , M2-mediated mTORC1 activation might be analogous to the cellular v-ATPase proton channel that induces mTORC1 activation upon amino acid sensing and recruitment of scaffold proteins and regulators of mTORC1 [8] . REDD1 protein levels increase during viral entry and/or primary viral transcription and are then down-regulated at late stages of infection . The transient early increase in REDD1 levels likely represents an antiviral response , as REDD1 is a host defense factor [5] . REDD1 prevented induction of mTORC1 signaling by influenza virus . Inhibition of mTORC1 signaling by REDD1 during influenza virus infection was independent of AKT T308 dephosphorylation . Therefore , REDD1 acts downstream of AKT to exert its negative effects on mTORC1 signaling during influenza virus infection . It is likely that REDD1 targets the TSC1/2 complex possibly through promoting TSC2 activity , as previously described [49] . REDD1 has also been shown to induce dephosphorylation of AKT T308 to repress mTORC1 signaling in the absence of infection [50] . Perhaps REDD1 has the ability to down-regulate the mTOR pathway at several nodes , which would vary depending on the cellular environment or stimuli . The impact of mTORC1 on influenza virus infected cells was also demonstrated with the identification of mTORC1-dependent substrates that were regulated during infection using a systematic phosphoproteomics approach . In addition to known mTORC1 substrates , we have also identified multiple mTORC1 substrates that were not previously revealed in an mTORC1 substrate screen using insulin stimulus [58] , which could indicate that influenza virus modulates mTORC1 differently than insulin signaling . However , the differences could also be a result of the technical differences between the experiments . For example , the prior screen used TSC2+/+ and -/- MEFs in which they inhibited mTORC1 using rapamycin and Ku-0063794 , they performed Stable Isotope Labeling with Amino acids in Cell culture ( SILAC ) to distinguish peptides from the differing conditions and their method of phosphopeptide enrichment was different than the one we used [58] . Rps6ka3 , known as RSK2 , is a known regulator of mTORC1 signaling [75] , which is downstream in the MEK/ERK pathway that can stimulate mTORC1 [76] . Here , it appears that RSK2 is an mTORC1 substrate during influenza virus infection , which suggests that mTORC1 may regulate MEK/ERK signaling . This may be advantageous for influenza virus as MEK/ERK signaling is critical for viral replication [77] . We also observed that three members of the Fas signaling pathway had altered phosphorylation during influenza virus infection indicating that mTORC1 may influence Fas-mediated apoptosis during infection . FasL expression and apoptosis enhance influenza virus replication [68 , 71] and promote influenza virus pathogenicity in mice [78] . In addition , we identified key translation factors , eIF4B ( S422 ) and eIF4G1 ( S1189 ) , whose phosphorylation are regulated during infection in a mTORC1-dependent manner . Phosphorylation of eIF4B at S422 activates translation initiation upon amino acid refeeding via mTORC1 [79] . The mTORC1-mediated regulation of key constituents of the translation machinery during infection shown here is in agreement with its activity in promoting viral protein expression and viral replication . Taken together , these results indicate that mTORC1 signaling supports viral replication through regulation of translation and /or through Fas-mediated signaling and apoptotic responses to infection . mTORC1 regulates protein translation and other cellular processes , such as autophagy and lipid biosynthesis [7] . Since these processes can influence influenza virus replication , trafficking of viral RNA/proteins and/or virion biogenesis , they may be useful for developing therapeutics and vaccines . Targeting host proteins required for viral replication is an alternative therapeutic strategy to avoid the rapid development of drug resistant viruses . Furthermore , promoting mTORC1 activation to enhance virus replication could have commercial implications for processes requiring robust virus replication , such as vaccine production . In sum , the activation of the mTORC1 pathway by influenza viruses midway through the virus replication cycle likely promotes mechanisms that antagonize cellular stress responses , such as translational shutoff and/or proper regulation of apoptosis to ensure an optimal environment for viral gene expression and progeny virion generation at later stages of the virus replication cycle .
Human lung adenocarcinoma epithelial cells ( A549 , ATCC ) , Madin-Darby canine kidney cells ( MDCK , ATCC ) , African green monkey kidney epithelial cells ( Vero , ATCC ) , mouse embryonic fibroblasts ( MEFs , already-existing collection from our laboratory [5] ) , human bone osteosarcoma cells with inducible expression of REDD1 ( U2OS-REDD1 , obtained from James Brugarolas , UT Southwestern ) [80] , human colon carcinoma cells ( HCT116 Pdpk+/+ and -/- and Akt+/+ and -/- , obtained from Bert Vogelstein , Johns Hopkins University ) [81] , and MDCK cells stably expressing influenza virus HA or M2 protein were cultured in DMEM containing 10% FBS ( Atlas Biologicals or Sigma ) and penicillin-streptomycin ( Gibco ) . In the case of M2 expressing MDCK cells , 5 μM amantadine was added in the culture medium but removed 2 h prior to harvesting the cells . The human bronchiolar epithelial cells ( HBEC30KT , a kind gift from Michael White , UT Southwestern ) were grown in Keratinocyte-SFM supplemented with human recombinant epidermal growth factor ( Invitrogen ) , bovine pituitary extract ( Invitrogen ) and penicillin-streptomycin . Primary wild-type MEFs were isolated from 129P2/OlaHsd mouse embryos ( day 14 ) by mincing and trypsinization ( after removal of the head ) , and cells were plated and used for experiments prior to senescence ( approximately 8–10 passages ) . Rictor+/+ and -/- were kindly provided by Mark Magnuson [82] . HCT116 Pdpk1+/+ and -/- and Akt+/+ and -/- were graciously shared by Bert Vogelstein [81] . Tbk1+/+ and -/- MEFs were kindly provided by Tak Mak [83] . Mavs+/+ and -/- MEFs were generated as previously described [84] . Immortalized Ifitm3+/+ and -/- MEFs were generated from day 15 embryos according to published protocols [85] . Atg5+/+ and -/- MEFs were kindly provided by Beth Levine ( UT Southwestern Medical Center , TX ) . Cells were maintained at 37°C with 5% CO2 . Cells tested negative for mycoplasma . For tetracycline induction of REDD1 in U2OS-REDD1 cells , cells were pre-treated for 2 h with 1 μg/mL of tetracycline and throughout the infection . Chemical reagents include: Torin1 ( Tocris , in DMSO ) , HEPES ( Gibco ) , urea ( Sigma ) , PhoSTOP phosphatase inhibitor cocktail tablets ( Roche ) , Complete EDTA-free protease inhibitor tablets ( Roche ) , Trichloroacetic acid solution ( Sigma ) , DMSO ( Sigma ) , BX795 ( gift from Michael White , UT Southwestern ) and Tetracycline ( USB Corporation ) . Antibodies used in western blots to detect viral proteins were generated against Influenza A virions ( Meridian Life Science B65141G ) ( recognize HA , NP , M1 , low level NA ) , HA ( Genetex GTX127357 ) , NP ( Abcam ab20343 ) , NA ( GeneTex GTX125974 ) , PA ( GeneTex GTX 118991 ) , PB1 ( Santa Cruz sc-17601 ) , PB2 ( Santa Cruz sc-17603 ) , M2 ( clone 14C2 , Thermo MA1-082 ) , NS1 ( generated by García-Sastre laboratory ) and VSV M protein generated by our laboratory in collaboration with Cocalico Biologicals against full-length VSV M . Antibodies from Cell Signaling Technologies for western blot analysis were against phospho-S6K ( T389 ) ( #9234 ) , S6K ( #9202 ) , phospho-4E-BP1 ( T37/46 ) ( #2855 ) , 4E-BP1 ( #9644 ) , phospho-Akt ( S473 , T308 ) ( #4060 , #9275 ) , Akt1 ( #2967 ) , TBK1 ( #3504 ) , PDK1 ( #5662 ) and p-eIF4B ( S422 ) ( #3591 ) . Additional antibodies used for western blot analysis were against Rictor ( Millipore 05–1471 ) , IFITM3 ( R&D Systems AF3377 ) , MAVS ( generated by Z . Chen laboratory ) , β-actin ( Sigma A5441 ) , REDD1 ( Novus Biologicals NBP1-22966 ) , ATG5 ( Novus Biologicals NB110-53818 ) , ATG7 ( Sigma A2856 ) , and LC3 ( Novus Biologicals NB100-2220 ) . Horseradish peroxidase ( Hrp ) -conjugated secondary antibodies used were donkey anti-rabbit and sheep anti-mouse ( GE Healthcare NA934V and NA931V , respectively ) , as well as donkey anti-goat ( Jackson Immunoresearch 705–035003 ) . NA was expressed from a pCAGGS-NA plasmid . All virus work was performed in strict accordance with CDC guidelines for biosafety level 2 and 3 agents: BSL2 ( A/WSN/1933 , A/PR/8/1934 and rSh/1 ) and BSL3 ( A/Shanghai/1/2013 ) . All Sh/1 ( A/Shanghai/1/2013 , H7N9 ) virus work was performed in the BSL3 laboratory of the Icahn School of Medicine at Mount Sinai , NY . Virus strains used in these studies were VSV-GFP and the following influenza viruses A/WSN/1933 ( WSN ) , A/PR/8/1934 ( PR8 ) , A/Shanghai/1/2013 ( Sh/1 ) , recombinant A/Shanghai/1/2013 ( rSh/1 , "rSh/1 ( 6+2 ) ": recombinant virus containing A/Shanghai/1/2013 segments 1 , 2 , 3 , 5 , 7 and 8 and A/PR/8/1934 segments 4 and 6 ) [39] , PR8:WSN and PR8:WSNDeficientM2 [41] . Influenza viruses were propagated in chicken embryonated eggs or MDCK cells . A/WSN/1933 and A/PR/8/1934 viruses lacking NS1 were propagated as previously described [86 , 87] . To amplify stocks in MDCKs , cells were infected at an MOI of 0 . 01–0 . 001 in infection media: EMEM ( ATCC , 30–2003 ) , 10 mM HEPES ( Gibco ) , 0 . 125% BSA ( Gibco ) , 0 . 5 ug/mL TPCK trypsin ( Worthington Biomedical Corporation ) . After 1 hour at 37°C , cells were washed and overlaid with infection media . Once cytopathic effect was evident around 48–72 hours post-infection , supernatants were harvested , centrifuged at 1 , 000 x g for 10 minutes , aliquoted and stored at -80°C . For high MOI infection experiments ( MOI of 2 ) , adherent cells were serum starved overnight in infection media . EMEM infection media ( see above ) was used for A549 , HBEC , U2OS-REDD1 , Vero cells and primary , Atg5+/+/-/- and Ifitm3+/+/-/- MEFs . DMEM infection media ( DMEM substituted for EMEM in standard infection media ) was used for Rictor+/+/-/- , Mavs+/+/-/- as well as Tbk1+/+/-/- MEFs and HCT116 Pdpk+/+/-/- and Akt+/+/-/- to maintain cell viability . Rictor-/- MEFs were plated at a density of 25% more than Rictor+/+ MEFs for each experiment to standardize the number of cells so that the same amount were being infected , as determined by cell counting prior to the infection . Nearly confluent cells in 12- or 24-well plates were mock infected with media only or infected with 150–200 μL virus in infection media for 1 hour at 37°C . After 1 hour , 350–800 μL of infection media was added and cells were incubated at 37°C prior to harvest . For Torin1 treated cells at a high MOI , cells were infected for 1 h at 37°C , after which the infection media was removed and media containing 250 nM Torin1 or 0 . 1% DMSO was added for the additional time indicated in the legend . Viral titer analyses by plaque assays were performed as described previously [88] and as indicated in the figure legends . For western blot analysis , cells were washed with PBS and harvested in 2X sample buffer ( 125mM Tris HCl pH 6 . 8 , 20% glycerol , 4% SDS ) , boiled 10 minutes and subjected to western blot analysis . Sample loading was standardized by Bradford assay ( Biorad DC Protein Assay Kit ) . Western blot quantification was performed using ImageJ64 . Images were converted to grayscale , rectangles were drawn around the lane of bands of interest and a profile plot was generated for each lane of bands . Each band was separated using the line tool , and the density of each band was determined by the area under the peak using the wand tool . Phosphorylated protein bands were compared to the total non-phosphorylated protein bands . Values were normalized to uninfected controls as indicated . Cell viability assays ( CellTiter-Glo , Promega ) were performed to assess cell health . In Fig 5B , the MTT assay [Cell Proliferation Kit I ( MTT ) , Roche ) was used to determine cell survival , following the manufacturer’s protocol . For statistical analyses from three independent experiments , an unpaired , two-tailed t-test was performed . A normal distribution can be assumed for all populations ( p>0 . 05 ) . To UV inactivate WSN , virus was exposed 10 cm from the UV lamp in the laminar flow hood for 7 minutes . The amount of virus inactivated was the same amount as non-inactivated virus used in each experiment for an MOI of 2 PFU/cell . Inactivation was confirmed by plaque assay , and a hemagglutination ( HA ) assay was performed following the VIRAPUR HA Assay Protocol to ensure UV-inactivated virus was present and still able to fuse with turkey red blood cells ( LAMPIRE Biological Products ) . U2OS or A549 cells were forward transfected in duplicates in 12 well plates with 450 ng pcDNA3-NP , 283 . 3 ng pcDNA3-PA , 283 . 3 ng pcDNA3-PB1 and 283 . 3 ng pcDNA3-PB2 +/- 200 ng spLuc using 3 . 2 μL/well Mirus TransIT-X2 transfection reagent ( MIR6004 ) following manufacturer's protocol . For the experiment in Fig 3B , cells were serum starved overnight the following day . On day two post-transfection , the cells receiving FBS were stimulated with 10% FBS for 2 hours . The cells were harvested at 48 hours post-transfection in 2X sample buffer for western blot analysis or 1X Reporter Lysis Buffer ( RLB , Promega , #E3971 ) to proceed with luciferase analysis . The Promega Luciferase Assay System ( #E4030 ) was used to measure luciferase in each sample . Cells were suspended in 100 μL RLB , and 20 μL were mixed with 100 μL Luciferase Assay Reagent ( Promega ) following manufacturer's protocol . Luciferase was read on a BMG Labtech PHERAstar , and data were graphed after normalizing the reads to the pcDNA3-NP , -PA , -PB1 and -PB2 ΔspLuc wells to 1 . For influenza virus mRNA-specific siRNAs , A549 cells were reverse transfected using RNAiMAX ( Lipofectamine , Life Technologies ) with 50 nM of a pool of 3 mRNA-specific siRNAs for approximately 36 h , during which cells were serum starved overnight prior to infection the next day as outlined above . Cell viability analysis ( CellTiter-Glo , Promega ) was performed to ensure that the siRNA concentrations were not toxic . Influenza virus mRNA-specific siRNAs were generated by Thermo Scientific to target mRNAs from the WSN strain ( siGENOME modifications ) . siRNA sequences are as follows: siRNA Universal Negative Control #1 and #2 ( Sigma SIC001 and SIC002 , proprietary sequences ) siGENOME Non-Targeting ( pooled for use in experiments ) : Influenza virus ( WSN ) -specific ( Table 1 ) : Cells were washed with PBS , harvested in TRIZOL ( Invitrogen ) and subjected to RNA extraction using Direct-zol RNA Miniprep Kit ( Zymo Research ) . For each sample , 500 ng of RNA were used to generate cDNA by reverse transcription using the iScript cDNA Synthesis Kit ( Bio-Rad ) following manufacturer's protocol . cDNA was diluted 1:3 , mixed with primers ( 300 nM total per reaction ) and Roche 480 SYBR Green I Master real-time PCR reagents following manufacturer's protocol and subjected to quantitative real-time PCR using the Roche LightCycler 480 . Real-time PCR was performed at 95°C for 5 min , 40 cycles of 95°C for 15 sec , 60°C for 15 sec and 72°C for 18 sec . A melting curve cycle was performed from 65°C to 95°C for quality assurance . Primers ( Eurofins ) used were REDD1 ( forward: 5'- GACAGCAGCAACAGTGGCTTCG -3' , reverse: 5'- GCTGCATCAGGTTGGCACAC -3' ) ; 18S rRNA ( forward: 5'- GTAACCCGTTGAACCCCATT -3' , reverse: 5'- CCATCCAATCGGTAGTAGCG -3' ) ; Minigenome RNA ( firefly luciferase RNA ) ( forward: 5’-gaggttccatctgcaggta-3’ , reverse: 5’-ccggtatccagatccacaac-3’ ) . Primers to quantify viral mRNAs were previously described [88] . For statistical analyses , an unpaired , two-tailed t-test was performed . A normal distribution can be assumed for all populations ( p>0 . 05 ) . Peptides were dissolved in solvent A ( 0 . 1% FA ( formic acid , Fluka ) in 2% ACN ) and directly loaded onto a reversed-phase pre-column ( Acclaim PepMap 100 , Thermo Scientific ) . Peptide separation was performed using a reversed-phase analytical column ( Acclaim PepMap RSLC , Thermo Scientific ) with a linear gradient of 4–22% solvent B ( 0 . 1% FA in 98% ACN ) for 50 min , 22–35% solvent B for 12 min , 35–85% solvent B for 4 min and holding at 85% for the last 4 min at a constant flow rate of 300 nl/min on an EASY-nLC 1000 UPLC system . The resulting peptides were analyzed by Q ExactiveTM Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer ( ThermoFisher Scientific ) . The peptides were subjected to NSI source followed by tandem mass spectrometry ( MS/MS ) in Q ExactiveTM Plus coupled online to the UPLC . Intact peptides were detected in the Orbitrap at a resolution of 70 , 000 . Peptides were selected for MS/MS using NCE setting as 28; ion fragments were detected in the Orbitrap at a resolution of 17 , 500 . A data-dependent procedure that alternated between one MS scan followed by 20 MS/MS scans was applied for the top 20 precursor ions above a threshold ion count of 5 . 0E3 in the MS survey scan with 15 . 0s dynamic exclusion . The electrospray voltage applied was 2 . 0 kV . Automatic gain control ( AGC ) was used to prevent overfilling of the ion trap; 5E4 ions were accumulated for generation of MS/MS spectra . For MS scans , the m/z scan range was 350 to 1800 . Fixed first mass was set as 100 m/z . The resulting MS/MS data was processed using MaxQuant with integrated Andromeda search engine ( v . 1 . 4 . 1 . 2 ) . Tandem mass spectra were searched against SwissProt_Mouse database concatenated with reverse decoy database . Trypsin/P was specified as cleavage enzyme allowing up to 2 missing cleavages , 5 modifications per peptide and 5 charges . Mass error was set to 10 ppm for precursor ions and 0 . 02 Da for fragment ions . Carbamidomethylation on Cys was specified as fixed modification and oxidation on Met , phosphorylation on Ser , Thr , Tyr and acetylation on protein N-terminal were specified as variable modifications . False discovery rate ( FDR ) thresholds for protein , peptide and modification site were specified at 1% . Minimum peptide length was set at 7 . For quantification method , iTRAQ-8 plex was selected . All the other parameters in MaxQuant were set to default values . The site localization probability was set as > 0 . 5 . The resulting raw data can be found in S2 Table . Protein phosphorylation site ratios were generated from the LC-MS/MS values by comparing WSN-infected + DMSO ( control ) -treated Rictor-/- MEFs to WSN-infected + Torin1-treated Rictor-/- MEFs ( mTORC1 substrates ) . The fold-change for each phosphorylation site between groups was calculated . Protein sites that showed +/- 1 . 5-fold change or more in phosphorylation are shown ( Fig 6A and S1 Table ) . The dataset was then subjected to Gene Set Enrichment Analysis ( GSEA ) ( http://software . broadinstitute . org/gsea/index . jsp ) to reveal the top pathways that showed the best enrichment scores . Interferon regulated genes were selected based on the database http://www . interferome . org [89] . The study using embryonated chicken was carried out in strict accordance with recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The use of embryonated chicken eggs before hatching is not considered animal use . Embryonated eggs were purchased from Charles River Laboratories , inoculated with influenza viruses at day 10 , incubated at 37°C for 2 days , and then incubated at 4°C overnight before allantoid fluid harvesting .
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Drug-resistant influenza viruses commonly arise due to frequent genetic changes and current antiviral drugs are not highly efficient . These underscore the need for new antiviral therapies effective against influenza viruses . Understanding how influenza virus uses cellular proteins for infection can potentially identify novel targets for pharmacological intervention . Influenza virus modulates cellular pathways to promote its replication and avoid immune restriction . Here we reveal the interplay between the cellular protein mTOR , which functions in two distinct protein complexes , and influenza virus infection . mTOR complex 1 ( mTORC1 ) is activated during influenza virus infection through a cascade of specific modifications , or phosphorylation events , and by reducing the levels of another cellular protein termed REDD1 , which is an mTORC1 inhibitor . Activation of mTORC1 results in additional phosphorylation events that together promote viral protein expression and replication . On the other hand , mTOR complex 2 ( mTORC2 ) phosphorylates AKT at a specific site during infection , which is a process mediated by the viral NS1 protein that is known to regulate viral-mediated cell death . Since these effects occur midway through the virus life cycle in the infected cell , mTORC1 and mTORC2 activation are likely important to regulate the cellular environment in order to facilitate the late stages of viral infection .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
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] |
2017
|
Influenza virus differentially activates mTORC1 and mTORC2 signaling to maximize late stage replication
|
Noise in the expression of a gene produces fluctuations in the concentration of the gene product . These fluctuations can interfere with optimal function or can be exploited to generate beneficial diversity between cells; gene expression noise is therefore expected to be subject to evolutionary pressure . Shifts between modes of high and low rates of transcription initiation at a promoter appear to contribute to this noise both in eukaryotes and prokaryotes . However , models invoked for eukaryotic promoter noise such as stable activation scaffolds or persistent nucleosome alterations seem unlikely to apply to prokaryotic promoters . We consider the relative importance of the steps required for transcription initiation . The 3-step transcription initiation model of McClure is extended into a mathematical model that can be used to predict consequences of additional promoter properties . We show in principle that the transcriptional bursting observed at an E . coli promoter by Golding et al . ( 2005 ) can be explained by stimulation of initiation by the negative supercoiling behind a transcribing RNA polymerase ( RNAP ) or by the formation of moribund or dead-end RNAP-promoter complexes . Both mechanisms are tunable by the alteration of promoter kinetics and therefore allow the optimization of promoter mediated noise .
Cellular processes involve stochastic reactions between limited numbers of molecules , and therefore are subject to random noise . The existence of noise in the intracellular concentration of various species has been highlighted in a number of natural and engineered genetic circuits [1]–[6] , which has been coupled with an increasing focus on the theory of how noise might be controlled or exploited by the cell . Gene expression is perhaps the most important stochastic process in the cell . Transcription involves the production of small numbers of mRNAs , which are then translated multiple times , creating and amplifying noise in protein concentrations . Therefore , the probability distribution underlying the timing of transcription initiation is important for understanding cellular dynamics . A distribution where initiations are evenly spaced will result in less noise and a more uniform cell population . In contrast , a highly variable rate of initiation will produce large fluctuations that can lead to heterogeneous behavior across populations of genetically identical cells . This variability is important to allow populations of unicellular organisms to cope with variable environments [1] , [5] . Another example is the spontaneous induction of ‘non-inducible’ prophages such as P2 [7] , where stochastic flipping of a genetic switch allows a low rate of transition from lysogeny into lytic development . Noise in transcriptional initiation also has implications for transcriptional interference between convergent promoters [8] . Bertrand [9] and colleagues have developed a system where an mRNA containing multiple MS2 binding sites can be visualized by the binding of MS2-GFP fusion proteins to the mRNA . Golding and colleagues [10] placed such an mRNA under the control of the Plac/ara promoter in E . coli and could thereby detect production of individual mRNAs . When the promoter was induced , transcription was observed to occur in an unexpectedly irregular fashion , with bursts of transcription separated by long periods of inactivity . This phenomenon was called transcriptional bursting . The bursts of activity ( on-periods ) lasted an exponentially distributed amount of time , with a mean of 6 minutes at 22°C . During an on period a geometrically distributed number of transcripts are produced in rapid succession , with a mean of 2 . 2 transcripts per on-period . The long periods without transcription ( off-periods ) were also exponentially distributed , with a mean of 37 minutes . Golding et al . also report that similar behavior is seen with the PRM promoter of phage lambda . Golding et al . [10] showed that this behavior was inconsistent with transcription occurring as a Poisson process . Here we consider the McClure model of transcription initiation [11]–[13] , a more general model of transcription initiation , and show that it is still unable to reproduce the transcriptional bursting observed by Golding et al . We then consider current hypotheses for the mechanism of transcriptional bursting and find them wanting . Finally we propose two novel hypotheses for the mechanism behind transcriptional bursting , demonstrating that they are able to explain the results of Golding et al .
Golding et al . showed that their results were not consistent with transcription initiation being a single Poisson process . By considering the McClure model of transcription initiation ( Figure 1A ) we show that initiation as a single Poisson process is a special case where only one step is rate limiting , and that while the more general case is not a single Poisson process it is still unable to fit the results of Golding et al . In prokaryotes , the initiation of transcription requires the binding of an RNAP to the promoter , the isomerisation of the RNAP through several intermediate forms , rounds of abortive initiation and then finally release from the promoter . Here we consider the McClure model of transcription [11]–[13] ( Figure 1A ) , where transcription initiation requires three steps: RNA polymerase ( RNAP ) binding to the promoter to form a closed complex , followed by isomerisation of the closed complex to an open complex in which the DNA at the promoter is melted , and the escape of the open complex to form an RNAP complex engaged in elongation of the transcript . The closed complex is assumed to be in rapid equilibrium with free RNAP , while isomerisation and escape are treated as being slower and irreversible . This model is a simplified but useful version of the full kinetics of initiation . The kinetics of each elementary reaction in initiation determines the final distribution of transcription initiation . Transcription is often treated as a Poisson process , i . e . the probability of initiation at a given moment is a constant , which results in an exponential distribution of times between transcripts . Golding et al . were able to show through several methods that the distribution of transcription initiation was non-Poisson . However , the exponential distribution is a special case where there is only one rate limiting step in the initiation of transcription . For the analytical analysis of the McClure model , we make the assumption that the rates of binding kb and unbinding ku of the closed complex are relatively fast , and therefore that there are only two kinetically significant steps , isomerisation of the closed complex to an open complex , and promoter escape by the open complex . We assume that each step is elementary , i . e . that it can be approximated as a single chemical reaction . We also ignore the effect of self-occlusion , where an RNAP prevents further initiation at the promoter until it has transcribed far enough to no longer occlude the promoter ( 50 bp ) , as the time needed to transcribe this distance ( 1–4 seconds ) is negligible compared to the time between initiations in the Golding et al . experiments . The average time needed to complete the first step , to , is therefore to = ( 1+K ) /O , where K = ku/kb is the equilibrium constant of dissociation for the closed complex and O is the rate of transition from closed to open complex . The inverse of the rate of the open to elongating transition ( E ) gives the average time needed for the second kinetically important step , tE ( Figure 1A ) . The average time taken for initiation ( and therefore the time gap between initiations , 〈Δt〉 , with 〈…〉 indicating the average ) is the sum of two exponentially distributed random variables , 〈Δt〉 = 〈τO+τE〉 . The probability distribution of time gaps between initiations is given by ( 1 ) for tO≠tE . For tO = tE = t , we get ( 2 ) In the case where one step is much slower than the other ( Class I ) , there is only one rate-limiting step in initiation and the distribution of Dt approaches a single exponential with mean tL = max ( tO , tE ) ( Equation 1; Figure 1B ) , i . e . it approaches a single Poisson process . Here , the data points in Figure 1B ) have been obtained by simulating the model of the promoter in Figure 1A ) using the Gillespie algorithm [14] , which stochastically determines the next reaction to occur and the time interval between reactions based on the given rates . The other extreme , where tO = tE ( Class II ) , is shown in Figure 1C . In Class II , the chance of rapid successive firings faster than the average ( Dt<<tO+tE ) is smaller than for a Class I promoter , as for a Class II promoter a low Dt requires both the isomerisation and the escape to productive transcription to occur in rapid succession , whereas for a Class I promoter a low Dt requires the rapid occurrence of the rate limiting step only . As a consequence the distribution in Class II shows a peak at non-zero Dt . Promoter models that specify more kinetically significant reaction intermediates produce more extreme versions of the Class II distribution , with a larger peak centered around 〈Δt〉 , resulting in more regular firing intervals . The Class I type promoter shows the most fluctuation in Dt , and the effect of adding more kinetically significant intermediate steps is to reduce the amount of variability in Dt . Therefore neither the standard model nor models that take into account more intermediates can reproduce the bunched activity observed by Golding et al . [10] , which show greater fluctuations in Dt than a Poisson process . In order to reproduce the bunched activity , it is necessary to consider a model with a branched pathway , where the system can go into either an active state or an inactive state with a switching mechanism between them . Here we consider several hypotheses for the mechanism of transcriptional bursting and argue that they are unlikely to be correct . The promoter used by Golding et al . [10] , Plac/ara , can be repressed about 70-fold by the lac repressor and activated about 30 fold by AraC [15] . Therefore , a simple hypothesis put forward by Golding et al . is that the silent periods are periods where the lac repressor is bound to the promoter , and the bursts are periods of activity when the promoter is free . However , the mean duration of off-periods is 37 min while on periods are only 6 min in duration , despite the fact that the promoter has been fully induced by 1 mM IPTG . It seems impossible for the lac repressor to remain bound to the DNA for 37 minutes under these conditions; especially considering that 1 mM IPTG derepresses the lac promoter in less than 5 sec [16] . A similar idea is that the off-periods represent periods where AraC is not bound to the promoter [10] . To make this feasible the on rate for AraC in an E . coli cell would have to be exceedingly small given the large off periods . This is unreasonable in view of the high association rate for AraC to other operators [17] . Presumably association rate is diffusion limited , meaning that it would take one AraC molecule less than a minute to bind to the operator [18] . In conclusion we find it unlikely that binding AraC is sufficient to produce bunched activity . Another hypothesis put forward by Golding et al . is that RNAP might be able to re-initiate after termination , aided by the retention of sigma factor during transcription [19] . Presumably the RNAP would have to be positioned to rebind to the same promoter after termination for re-initiation to occur with any reliability , and it is not clear how this would be caused . One possibility is that a transcription factor might remain in contact with both the RNAP and the promoter via a DNA loop . This would render the promoter unavailable during transcription , which has some support from the data in that the lengths of the observed on-periods were approximately equal to the number of initiations multiplied by the time taken to transcribe the reporter mRNA for both Plac/ara and PRM ( Golding , private communication ) , which would be expected if transcription does not occur simultaneously . However , this data is somewhat anecdotal , and stands in contradiction to the simultaneous transcription observed with electron microscopy [20] . Also , this mechanism requires binding of a closed complex to the DNA to be the rate limiting step that causes the 37 minute long off-period , and we consider it unlikely that simple recognition of the promoter by RNAP would take this long , especially given that closed complex formation is often thought to be a rapid equilibrium process . Multiple RNAP can cooperate to overcome pause sites [21] . It might therefore be possible that the burst is due to multiple RNAP building up at a pause site and overcoming it together . However , this would require the RNAP to pause for a length of time on the same scale as the off-period; such an extreme pause is unlikely given that even the strongest pauses measured in vitro only last for around one minute . Bursting could also result if there were distinct regions of high and low transcriptional activity within bacteria , akin to the idea of transcription factories in eukaryotes , and the promoter moved in and out of these regions on a slow time scale [22] , [23] . Although this is an interesting possibility , not enough is known to evaluate such a mechanism in bacteria in much detail . Fluctuations in the availability of free RNAP within the cell could contribute to variable initiation rates but it is difficult to see how such severe and long-lasting fluctuations capable of producing extended periods of complete inactivity could occur in cells where ∼3000 RNAPs [24] produce >105 RNAs per generation . There is both theoretical [25] and experimental evidence [26] , [27] that an elongating RNAP can increase the negative supercoiling of the DNA behind it . Promoters can be very sensitive to supercoiling; for example , in vitro the activity of the LacP promoter increases by more than a factor of 10 when the super-coilings is changed from zero to −0 . 065 ( which is the average supercoiling of DNA in E . coli ) [26] . We therefore consider it a possibility that the bursts of transcription might be caused by a transcribing RNAP assisting the recruitment of further RNAP via the wake of supercoiling left behind it . In principle one could argue that perturbed supercoil states could relax quickly in a plasmid [25] like the one used by Golding et al . , but it has been demonstrated that a promoter can induce huge changes in supercoiling of a plasmid [28] . Consider a promoter where open complex formation is a rate limiting step that is assisted by negative supercoiling . To model this , we assume that the negative supercoiling assists this step to the extent that it is no longer rate limiting . We parameterize this effect of supercoiling into a single number q , the probability that supercoiling left in the wake of a prior RNAP allows a subsequent RNAP to rapidly form an open complex before the supercoiling is relaxed ( Figure 2A ) . This then creates two possible behaviors at the promoter . If the promoter is in the supercoiled state , open complex formation is enhanced to the point where it is not rate limiting , and transcription events occur at rate E and are exponentially distributed . If the promoter is not in the supercoiled state , then open complex formation is very much slower and now rate limiting; transcriptional events are still exponentially distributed but now with the much lower rate O . This creates the long periods of inactivity associated with off periods ( Figure 3A ) and holds when O≪E , and gives a distribution ( 3 ) ( shown in Figure 3B ) . The supercoiling need not persist for the full length of the on-period , or for the length of time between two initiations . In the scheme we present here , it is only required that the supercoiling persists long enough to allow an open complex to form rapidly . The final escape step is assumed to be neutral with respect to supercoiling and hence as soon as an open complex has formed at the promoter the supercoiling can be relaxed without interrupting the on-period . This assumption can be varied without changing the general behavior of the model . If the supercoiling is relaxed before an open complex is formed , the promoter has switched to an off-period where initiation occurs at a much slower rate . The parameter q determines the size of the on-periods , as after each initiation there is a probability q that another open complex will be recruited and the on-period will continue , or a probability 1-q that an off-period will start . Therefore , the probability of getting a burst of 〈Δn〉 initiations is proportional to qDn−1 . In this model a promoter is in the on-state when it is in the supercoiled state or when it has an open complex . Table 1 gives equations relating model parameters to the average 〈Δn〉 , 〈ton〉 , and 〈toff〉 ( Derivations are given in Text S1 ) . This mechanism can reproduce the observations of Golding et al . [10] with the parameters tO = 37 [min] , tE = 29 [min] and q = 0 . 545 . We simulated the recruitment model using the Gillespie algorithm [14] . It gives the expected shape for the P ( Dt ) distribution ( Figure 3B ) and matches the distribution of Dn measured by Golding et al . ( 3C ) and also the distributions of on and off-periods measured by Golding et al . ( 3D ) . In these plots the on-periods are defined as being the time intervals when there is rapid successive initiation ( Figure 3A ) , following the procedure in Golding et al . [10]; the detailed definition is given in the Materials and Methods section . Another possibility is that the off periods are due to the formation of long-lived non-productive initiation complexes at the promoter [29]–[31] . These non-productive complexes have been observed in vitro and may be arrested backtracked complexes or complexes that cannot exit the abortive initiation state into productive elongation . In both cases initiation can be made more efficient by the GreA/B RNAP-binding factors [29] , [30] . The random formation of such ‘dead-end’ complexes could block the promoter for extended periods of time , causing productive transcription to be confined to those times when the promoter is free . For the promoter lPR the lifetime of these complexes was found to be in the order of 10–20 minutes under in-vitro conditions , thus dead-end complexes can last long enough to cause the observed off-periods [31] . For the analytical treatment of this model we call the probability that a promoter bound complex will undergo a productive initiation Q , and the probability that the promoter bound complex enters a moribund state is therefore 1- Q . We assume that removal of the moribund complexes is a Poisson process with a rate d , which gives 〈toff〉 = τdead/Q with tdead = 1/d , which allows for the fact that a single off-period can be caused by multiple subsequent moribund complexes ( Table 1 ) . Here we consider a promoter to be in the off-period if it is occupied by dead-end complexes; otherwise it is on . The derivations of on- and off-times are given in Text S1 . The dead-end complex mechanism is also capable of causing the behavior observed by Golding et al . The data of Golding et al . are reproduced with Q = 0 . 545 , tdead = 20 [min] , and tO+tE = 2 . 9 [min] . Figure 3E shows the distribution P ( Dt ) with these parameters obtained by the simulation using the Gillespie algorithm [14] . It has been confirmed that the distributions of Dn , ton , and toff are reproduced as well as the recruitment model ( data not shown ) . The formation of dead-end complexes is favored by low temperatures at the lac UV5 promoter [32] . If this were also the case for the Plac/ara promoter , it could be part of the explanation for why the Plac/ara promoter is so weak in the conditions used by Golding et al . ( 22°C ) when it is reported to be a strong promoter elsewhere [15] . However , the activity of the promoter observed by Golding et al . at 37°C is still rather low compared the previously reported estimate [15] . This could be associated with the fact that there is almost no activation of the promoter caused by AraC/arabinose under their experimental conditions ( see Figure 1E in Golding et al . ) . Another possibility could be the presence of an unknown terminator , which would imply that the number of complete transcripts represents only a fraction of the transcription initiation events . One of observations made by Golding et al . that was used as evidence for transcriptional bursting was that the Fano factor for the distribution of number of transcripts N , ν = 〈 ( N−〈N〉 ) 2〉/〈N〉 , was approximately 4 for the Plac/ara promoter at 37°C , rather than 1 predicted for Poisson transcription . The Fano factor is a measure of noise; higher values indicating a more noisy process . When the on-periods are much shorter than the off-periods , the Fano factor n is linked to the burst size Dn as ν≈〈Δn〉 . If the on-time is sizable , on the other hand , 〈Δn〉 needs to be much larger to give the same n . By considering a population of cells where transcripts are degraded with rate g , we can relate n to model parameters . Figure 4 shows how n varies with model parameters for each model while keeping 〈N〉 = 10 obtained by analytical calculations ( The detailed calculations are in the Text S1 . ) . In the recruitment case the Fano factor is larger for smaller a and larger q , i . e . , when the open complex formation is the rate limiting step and once a firing has occurred further recruitment occurs successively . In the dead-end model the Fano factor is larger for smaller b = ( tO+tE ) /tdead and larger Q , which occurs when moribund persist for long periods of time , but transcription during the on periods is rapid and occurs many times before another off period occurs . One should note that the Fano factor can be changed depending on parameters for a given 〈N〉; This means that the noise can be tuned for a given promoter strength under either model , which can allow the promoter noise to evolve to reflect a level that provides the best fitness for the cell .
The recruitment model implies a number of predictions that can be tested . In particular , promoters with bunched transcription initiation will be highly sensitive to negative supercoiling of the DNA . And conversely , promoters that are insensitive to supercoiling will have transcription events which are separated by more regular time intervals . For promoters that are sensitive to supercoiling , one could selectively shorten the long off periods by introducing a second nearby promoter . One option is to add a divergent promoter that might be able to donate its negative supercoil wake . Such a construct was investigated by Opel et al . [27] , who reported that a second promoter could indeed increase the activity of a supercoiling sensitive promoter in the ilvYC operon . This predicts that if a similar experiment was done with the Plac/ara promoter , then reduced off periods would be observed . Another prediction is that for promoters with bunched activity the isomerisation step is rate limiting . Thus the fraction of time spent in open complex is small compared to the time between transcription initiations . One might be able to show an inverse correlation between the noisiness of a promoter and the occupancy of the promoter by open complexes using potassium permanganate DNA footprinting [35] . The dead-end mechanism implies that the promoter is mostly occluded by an RNAP with an open transcription bubble . This could be identified permanganate footprinting [35] . The availability of GreA/B could affect the rate of removal of the dead-end complex , d [29] , [30] . Overexpression of GreA/B could increase d and reduce off-periods , while longer off-periods , due to lower d , could be observed in greA/B mutants . It is possible that the dead-end complexes could be removed by a collision with an RNAP transcribing from a second promoter in a fashion similar to the removal of an open complex by transcriptional interference [36] . The off-times of a promoter could therefore in principle be shortened by using other RNAP's initiated from another promoter that transcribes across the promoter in question . If a promoter spent a substantial fraction of the time occupied by a dead-end complex , it could be strongly activated by tandem or even convergent promoters , which would be a novel twist on the usually repressive effect of transcriptional interference . If d is reduced in Table 1 , the “off-times” could be reduced by a factor set by the ratio of the strength of the two promoters , and the promoter activity could increase . Thus , if Plac/ara activity is affected by dead-end complex formation , then placing a weak divergent promoter upstream should not increase Plac/ara activity but placing this promoter in a convergent orientation may activate Plac/ara . The sensitivity of a promoter to supercoiling mediated recruitment or dead-end complex formation provides additional avenues for control of overall promoter strength , either by evolution or by regulatory factors . DNA supercoiling can increase or decrease promoter activity both in vitro [26] and in vivo [37] in a promoter specific manner . Supercoiling can affect RNAP binding to the promoter and open complex formation in vitro and presumably can affect other steps as well . RNAP recruitment induced by the supercoiling created by an elongating transcription complex may contribute significantly to the activity of certain promoters . We expect that , except for very active promoters , rapid dissipation of the supercoil wake would make inhibition of a supercoiling-repressed promoter by this mechanism unlikely . Stimulation by the departing elongating complex should similarly only apply to the early steps in initiation . Thus only promoters whose early steps are rate-limiting and can be enhanced by supercoiling should be stimulated by this mechanism . The reduction of promoter activity by the formation of dead-end complexes is potentially very strong . The effect increases with the probability of forming such a complex ( 1-Q ) and with the lifetime of the complex ( 1/d ) , parameters which could be determined both by the promoter sequence and by the availability of factors such as GreA/B that may remove the complex [29] , [30] . This mechanism would seem to be an inefficient way to set the strength of a promoter , as it would sequester an RNAP . However , it would allow regulation by transcription factors that change the fraction of RNAPs that enter into dead-end complexes or that stabilized the dead-end complex . As a consequence , genes which are silenced through this mechanism will have relatively high fluctuations in expression level , and thereby some cells can explore advantages afforded by relatively high expressions , even when most cells are kept at near zero expression . Bunched activity for a near silenced promoter could , for example , be important in the pathway for the spontaneous induction of lysogeny for some temperate phages , like P2 . High noise in protein levels can also be obtained at the translation level . If a single mRNA molecule is rapidly translated many times the result is a burst of protein production . Therefore transcriptional bursting is not strictly required for protein production to occur in bursts . However , transcriptional bursting might allow for additional modes of regulation by transcription factors or other proteins that influence the state of the DNA around the promoter site . It may also complement bursts of protein production produced by rapid translation by removing constraints placed on burst size by the upper limits of mRNA translation rate . Dynamics and the interplay between timescales presents an open , and until recently , quite unexplored part of molecular biology . The present analysis suggests a new mechanism for in vivo regulation , where long silent timescales emerge as the result of some particularly large rate limiting step in the promoter . These steps are open for new levels of regulation by transcription factors , which naturally will be most effective when they influence the rate limiting step of transcription initiation [38] .
To calculate the activity of a promoter we first calculate the probability that the promoter will be occupied by closed ( h ) and open ( q ) complexes using steady state conditions . The total activity of the promoter is given by F = Eq for the standard model and the recruitment model , and F = QEq for the dead-end model . Details of the calculation are found in the Text S1 . The time between subsequent initiations is calculated by considering the time needed for each step as described in the Text S1 . For class I there is only one step and the distribution is a simple exponential . For class II there is two steps . If these steps take an average time of to and tE , the total waiting time between events is distributed with ( 4 ) giving eq . ( 1 ) in the main text for tO≠tE . For one t much greater than the other , this distribution degenerates into a simple exponential . For tO = tE , eq . ( 4 ) gives eq . ( 2 ) in the main text . For the recruitment model , the intervals between initiations are partitioned between the supercoiling assisted or unassisted outcomes , with a partitioning ratio given by q . Details are in the Text S1 . For the dead-end model the distribution is similarly partitioned between the two distributions with a partition ratio given by Q . Details are in the Text S1 . In the Text S1 we also show how to calculate the distribution of “on” and “off” times from q or Q . Finally , we calculate the Fano factor ν = 〈 ( N−〈N〉 ) 2〉/〈N〉 by using generating functions as described in the Text S1 . We distinguish “on-periods” and “off-periods” in the simulation data following the procedure used by Golding et al . [10] . They analyzed the experimentally obtained time series of fluorescent signal manually . The system is considered to be in “off-period” when the signal does not change for a while , and otherwise it is in “on-period” . The specific time resolution to detect an “off-period” was not given , but the shortest off-time measured was around 6 [min] ( Golding , private communication ) ; in other words , transcription events separated by less than 6 [min] were considered to be in the same “on-period” . During an on-period , the number of messages transcribed , Dn≥1 , and the duration ton were recorded; the time to transcribe one message D was 2 . 5 [min] [10] , which corresponds to the on-time for Dn = 1 case . Considering this protocol used by Golding et al . [10] , we defined Δn , ton , and the duration of the off-time toff out of the time series of firings from our model ( Figure 3A ) as follows: ( i ) When firings are separated by more than τc = 6 [min]+Δ = 8 . 5[min] , the promoter is in an off period . ( iii ) Otherwise , if successive firings are separated by an interval less than τc , the gene is considered to be on until we observe an interval greater than τc . This defines the on-time ton , and we count the number of transcripts per on-time Δn .
|
Noise in gene expression is important for phenotypic variation among genetically identical cells . The gene expression will be particularly sensitive to noise in transcription initiation . Transcription initiation from a given promoter involves multiple steps , each of which could be rate limiting . In this paper we discuss how transcription initiation could come in bursts , separated by long periods where the promoter is inactive . Our results are compared to recent data of Golding et al . ( 2005 ) , which suggest that transcriptions from some prokaryotic promoters occur in a highly irregular burst-like fashion . We show that the observed bursting could be caused by one of two alternate mechanisms . One possibility is that changes in supercoiling induced by previous RNA polymerase can help a subsequent RNAP to enter directly into open complex . Another possibility is that an RNAP at the promoter sometimes forms a dead-end complex , and thereby occludes the promoter for a sizeable amount of time .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry/transcription",
"and",
"translation",
"biophysics/transcription",
"and",
"translation",
"biophysics/theory",
"and",
"simulation",
"computational",
"biology/transcriptional",
"regulation"
] |
2008
|
The Generation of Promoter-Mediated Transcriptional Noise in Bacteria
|
Bacterial whole genome sequencing offers the prospect of rapid and high precision investigation of infectious disease outbreaks . Close genetic relationships between microorganisms isolated from different infected cases suggest transmission is a strong possibility , whereas transmission between cases with genetically distinct bacterial isolates can be excluded . However , undetected mixed infections—infection with ≥2 unrelated strains of the same species where only one is sequenced—potentially impairs exclusion of transmission with certainty , and may therefore limit the utility of this technique . We investigated the problem by developing a computationally efficient method for detecting mixed infection without the need for resource-intensive independent sequencing of multiple bacterial colonies . Given the relatively low density of single nucleotide polymorphisms within bacterial sequence data , direct reconstruction of mixed infection haplotypes from current short-read sequence data is not consistently possible . We therefore use a two-step maximum likelihood-based approach , assuming each sample contains up to two infecting strains . We jointly estimate the proportion of the infection arising from the dominant and minor strains , and the sequence divergence between these strains . In cases where mixed infection is confirmed , the dominant and minor haplotypes are then matched to a database of previously sequenced local isolates . We demonstrate the performance of our algorithm with in silico and in vitro mixed infection experiments , and apply it to transmission of an important healthcare-associated pathogen , Clostridium difficile . Using hospital ward movement data in a previously described stochastic transmission model , 15 pairs of cases enriched for likely transmission events associated with mixed infection were selected . Our method identified four previously undetected mixed infections , and a previously undetected transmission event , but no direct transmission between the pairs of cases under investigation . These results demonstrate that mixed infections can be detected without additional sequencing effort , and this will be important in assessing the extent of cryptic transmission in our hospitals .
Whole genome sequencing ( WGS ) offers the prospect of high precision investigation of infectious disease outbreaks [1] , [2] . Close genetic relationships between organisms isolated from different infected cases suggest transmission is a strong possibility , whereas transmission between cases with genetically distinct isolates can be excluded . WGS has been successfully applied to several high profile national outbreaks , in particular the Escherichia coli outbreak in Germany [3]–[5] , and cholera outbreak in Haiti [6] . The advent of rapid benchtop sequencing technology allows WGS to be applied in clinically relevant timescales to local outbreaks , for example those caused by the important healthcare-associated pathogens Clostridium difficile and MRSA [7] , [8] . The increased resolution offered by WGS allows isolates apparently identical by traditional genotyping methods to be distinguished [7] , [9] . Fast availability of this precise information on person-to-person transmission to individual healthcare practitioners and institutions is likely to transform the practice of routine infection control [1] , [7] . However , potentially undetected mixed infections—infection with two or more unrelated strains of the same species—means that transmission cannot be excluded with complete certainty [10] . This is because if a mixed infection is present in a transmission donor or recipient and only one isolate sampled from each , it is possible the sequenced isolates may differ even though an identical strain is present in both cases . In this scenario , transmission would be incorrectly excluded , exposing a potentially serious weakness of the technique . Even so , sequencing single colonies is common practice in microbiology , and the protocol has underpinned the majority of bacterial WGS studies to date [3]–[8] ( but see [11] , [12] ) . Traditional approaches to investigating mixed infection are expensive because they involve separate sub-culture of multiple bacterial colonies , a process in which multiple individual colonies are transferred to a separate culture plate and re-incubated [10] . Because this approach is cost and labour intensive , it is not used in routine clinical laboratories or in large-scale transmission studies . As WGS in bacteria typically yields generous depth of coverage ( measured by the number of reads mapping to any particular site in the sequenced genome [1] , [13] ) , interrogation of these reads offers the prospect of detecting mixed infection by sequencing an aggregate of colonies at the same cost as sequencing an individual colony . In this approach , the short reads produced by next generation sequencers would be mapped to a reference genome using a standard method [13] . The composition of bases mapping to any given nucleotide position can then be analysed to detect evidence of multiple strains . Whereas bacterial genomes should normally be haploid , a pattern of bases that resembles a heterozygous base call in a diploid genome is symptomatic of mixed infection [14] . This idea has been used to detect viral genetic diversity within individual hosts [15] , [16] . In viral sequences , the density of single nucleotide polymorphisms ( SNPs ) may be sufficient to allow common SNPs to be identified between overlapping reads and haplotypes to be reconstructed [15] . Clearly this ability is dependent on the within-host viral diversity , the sequencing technology and depth of sequencing coverage . In contrast , the density of SNPs between sequences in potential mixed bacterial infections is much lower; for example , in the major hospital-associated bacterial pathogen C . difficile , there may be 100–10000 SNPs over a total genome of 4 . 3 million base pairs , which corresponds to just 1 SNP in 400–40000 base pairs [17] . At this density , SNPs are sufficiently sparse that complete haplotype reconstruction is not possible from current short-read sequencing with read lengths of the order of 100 base pairs . Many if not all SNPs are likely to lack adjacent variants closer than the maximum read length , making it impossible to associate these reads with the correct haplotype . The one exception to this is the scenario in which the haplotypes make up markedly different proportions of the sample . The major healthcare-associated infection , C . difficile [18] , provides an important example of where undetected mixed bacterial infection may affect estimates of transmission between cases . C . difficile causes substantial morbidity and mortality , and is the focus of costly prevention efforts in healthcare systems worldwide [19] . Although it is generally believed that C . difficile is predominantly nosocomially acquired [18] , a recent study found that <25% of C . difficile infections in Oxfordshire , UK , over a 2 . 5 year period could be linked to a previous case with the same strain type via hospital ward contact [20] , suggesting a substantial unsampled reservoir for human infections . However a potential limitation of this study was that only one strain was sequence typed per case and therefore mixed infections could in theory comprise some or much of the unsampled reservoir . C . difficile mixed infection rates of ∼7–13% have been consistently described over the last decade [10] , [21]–[24] , but their significance in transmission has never been investigated . We have therefore developed a method for detecting mixed infection from bacterial WGS data that exploits frequency differences between the dominant and minor strain making up the sample and compares putative base calls to a database of known sequences to assist in determining the haplotypes present . We demonstrate our algorithm performs well in in silico and in vitro mixed infection experiments and apply it to quantify the extent of transmission arising from mixed C . difficile infections in order to determine its relevance to routine hospital outbreak investigations .
We developed a maximum likelihood based method to detect mixed infection in bacterial WGS data , based on high quality base counts at sites known to vary on the basis of available previously sequenced isolates . The algorithm can be applied to any set of variable sites within a genome . As the stochastic transmission model above had suggested potential mixed ST infections , we initially investigated variable sites within the MLST loci as these are sufficient to demonstrate if mixed ST infection is present . We then investigated sites across the whole genome that are known to vary within an individual ST , allowing us to determine the precise identity of mixed infection strains . To calibrate the mixed infection estimator all reads from 100 whole genome sequences derived from a single colony , and thus expected not to be mixed , were initially analysed investigating the 150 variable sites within the MLST loci for evidence of mixed ST infection . The ST previously obtained by PCR was recovered using our method on all occasions and accounted for a median of 100% ( interquartile range [IQR] 99 . 9–100% , range 93 . 5–100% ) of the sample based a median ( IQR ) read depth of 80 ( 67–93 ) ( Figure S1 ) . The divergence between the dominant and minor haplotypes was estimated at a median 0 SNPs ( IQR 0 – 0 SNPs , range 0–15 SNPs ) . A likelihood ratio statistic was used to compare the maximum likelihood obtained under the mixed infection model , with the likelihood of the data without mixed infection . For each sample in the calibration set we calculated the deviance ( −2 times the log likelihood ratio ) and used the quantiles of the distribution to set a threshold for calling mixed infection of ≥19 . 4 in order to achieve a 5% false-positive rate . This empirical approach to choosing the significance threshold avoids making unrealistic assumptions about the statistical distribution of the deviance under the null hypothesis of single infection . Simulated mixed infections were generated to test the ability of our method to detect mixed infections , and the constituent strains . Reads obtained from the unmixed samples above were mixed in silico to create 10000 simulated mixed ST infections with median ( IQR ) read depth 78 ( 67–90 ) and mixture proportions from 0 . 5 to 0 . 95 . The known input mixture proportion was estimated with a root mean square error , RMSE , of 0 . 086 ( see Figure S2a for the distribution of estimated mixed proportions across the 10 input proportions ) . Accurate mixture proportion estimates were obtained even when the simulated sequence divergence was as low as 3 SNPs between dominant and minor sequences . Mixture proportions closer to 1 were associated with a smaller variance and RMSE . The divergence between sequences at the MLST loci was estimated with a RMSE of 0 . 079 , consistently across varying mixture proportions ( Figure S2b ) . Having already set the specificity of the algorithm to 95% with the empirical calibration procedure above , we found that the sensitivity of the algorithm in this dataset for detection of simulated mixed infections was 99 . 0% . The two input STs were recovered as the most likely pair on 9704/10000 occasions , 9151 with the correct ordering of the dominant and minor STs ( Figure S3 ) . As expected , the minor ST was less likely to be recovered when it made up a smaller proportion of the overall sequence . Recovery of neither input ST was associated with mixture proportions near 0 . 5 and relatively low divergence between input sequences ( median input divergence 0 . 033 where neither ST recovered versus 0 . 073 in all other samples , Kruskal–Wallis p<0 . 001 ) . To confirm the performance of the estimator in vitro , DNA extracted from single colonies was mixed in known proportions prior to sequencing . Thirty-six mixed ST infections were simulated: DNA from 12 single ST infections was mixed with DNA from 12 different single ST infections , at 3 different mixture proportions – 50/50% , 70/30% and 90/10% . Using our method and whole genome data the input pair of dominant and minor haplotypes was obtained as the most likely on all occasions and the mixture proportion and divergence estimated with RMSEs of 0 . 032 and 0 . 002 respectively ( Figure 2 , supplementary table S1a ) . In order to demonstrate our method is also able to detect mixed infections where the two infecting strains are of the same ST , but differ at a whole genome level , we also simulated mixed infections of the same ST . A database of previously sequenced Oxfordshire isolates[30] was used to determine the variable sites within each ST across the rest of the whole genome . These variable sites were analysed for evidence of within-ST mixed infection , using the same algorithm as for mixed ST infection , determining the most likely dominant and minor sequences from the unique whole genome sequences within each ST in the database . Fifteen within-ST mixed infections were generated with DNA from 5 pairs of isolates sharing the same ST ( STs 1 , 3 , 8 , 14 , 46 ) , but with differing whole genome sequences , at 3 different mixture proportions ( 50/50% , 70/30% and 90/10% ) . The correct dominant and minor sequences were obtained on all occasions ( Table S1b ) . Mixture proportions and between sequence divergence were accurately estimated with a single exception where the within sequence divergence was over-estimated in a 90/10% mix ( Figure 2 ) . Accurate estimation of mixture proportions was possible even in a mixed infection where the samples differed only by a single site , with estimated mixture proportions of 0 . 50 , 0 . 70 and 0 . 92 for input values of 0 . 50 , 0 . 70 and 0 . 90 . The median ( IQR ) read depths for these simulations were 82 ( 72–91 ) . Having confirmed the accurate performance of our method , we then applied it to the 15 potential mixed infection transmissions described above where transmission was highly plausible based on hospital contacts but the STs obtained from sequencing single colonies differed ( Figure 1 ) . When we aligned sequence data from the 26 samples , the Burrows Wheeler Aligner , BWA [28] , outperformed Stampy [27] because a number of samples also included sequence from non-C . difficile anaerobic bacteria . DNA for sequencing was obtained from an area of confluent growth on primary culture plates , and despite the use of selective agar and individual colonies resembling C . difficile , other similar antibiotic-resistant anaerobic gut bacteria were detected in some samples ( by extracting 16S ribosomal RNA genes using BLAST [31] from de novo assemblies [32] of the sequences and comparison with the Ribosomal Database Project [33] ) . Sequence reads from these other species do not map or map poorly to the reference genome , therefore the percentage of reads mapped with Stampy to the CD630 reference ranged from 11 . 0%–95 . 4% , with 15/26 samples having <60% of reads mapped . As Stampy is designed to perform well with relatively large sequence variation relative to the reference , in the more contaminated samples markedly divergent reads were mapped to the MLST loci . These reads must have arisen from other species as such divergence would not be expected within the highly conserved housekeeping genes of the MLST loci within C difficile . These divergent reads were interpreted by our algorithm as mixed infection , such that a clear relationship was seen between samples estimated to contain mixed infection based on Stampy mapping and those with low percentages of reads mapped to the reference ( figure 3a ) . We therefore remapped all samples with BWA to increase the penalties associated with insertions and deletions relative to the reference such that only reads arising from C . difficile would map to the MLST loci . This allowed assessment of the proportion of mixed infection across all 26 samples ( figure 3b ) . Reductions in read depth were modest with BWA compared to Stampy ( overall median ( IQR ) read depth was 21 ( 15–80 ) with Stampy across 26 samples , versus 18 ( 13–75 ) with BWA ) . Using our method with whole genome data we found 2 of 26 cases ( 8% of cases , 95% confidence interval , CI , 1–25% ) had evidence of mixed ST infection , coincidently in the same transmission model pair , pair 13 ( see Figure 1 ) . The estimated dominant ST matched the original ST from MLST PCR in all 26 cases . The putative donor with a mixed ST infection ( donor A with a ST1 dominant infection ) had a minor ST46 infection that accounted for 3% ( 95%CI 2–4% ) of the sample . However , this did not concord with the dominant ST17 infection found in the putative recipient ( recipient A ) . This recipient in turn had a minor ST recovered , ST1 , with sample frequency 8% ( 95%CI 6–10% ) , which was compatible with acquisition from donor A . Therefore one of the donor-recipient ST matches predicted by the stochastic transmission model on the basis of shared time and space in the hospital but ruled out by single colony sequencing appeared to be explained by mixed infection ( Figure 4 ) . To scrutinize in more detail whether the WGS data were compatible with transmission of the dominant ST1 infection in donor A to recipient A as a minor infection , we exploited a panel of 45 unique Oxfordshire ST1 genomes to assist in whole genome prediction of the dominant and minor haplotypes in both cases ( Figure 4a ) . Informally , our method compared recipient A's minor ST1 sequence to all 45 ST1 whole genome sequences in our database ( which included an ST1 genome sequenced from a single colony from donor A ) using a total of 79 ST1-specific SNPs across the whole genome . The most likely recipient A minor sequence ( posterior probability = 0 . 9997 ) was from another patient ( donor B ) , and differed by 8 SNPs scattered throughout the genome from the sequence found in donor A ( Figure 4c ) . In fact , donor B represents a substantially more plausible donor than donor A identified by the stochastic transmission model on the basis of epidemiological data alone , because the short-term rate of evolution in C . difficile has been estimated at ∼1 SNP/genome/year [12] , [30] . Donor B was also epidemiologically linked to recipient A , albeit less strongly than donor A . Recipient A was diagnosed on day 77 of a 93-day admission on a surgical ward . Donor B was diagnosed 63 days earlier and spent 34 days after diagnosis on the same ward as the recipient , and was also readmitted for 2 days to the same ward , 6 days before the recipient's diagnosis ( Figure 4b ) . Not only does this reiterate the power of WGS for differentiating potential transmission donors that appear identical on the basis of low-resolution genotyping alone , it also demonstrates that our method is able to extend the approach to mixed infections and identify the source of the minor strain . We did not find strong evidence for onward transmission from the minor sequence in the mixed infection in recipient A . A single further case ( Figure 4b , c , recipient B ) with the identical sequence was identified , but the patient had not shared time or space in hospital with donor B or recipient A prior to diagnosis . Given the relatively high prevalence of ST1 and its relatively low diversity even at the whole genome level , indirect transmission via community contact or an undiagnosed third party is the most likely explanation . Additionally , only two descendant sequences ( Figure 4 , recipient C , recipient D ) were identified from the phylogenetic tree of all Oxfordshire ST1s ( Figure 4c ) . Both patients shared time on the same ward with donor B , but not with the mixed infection case ( recipient A ) after this case's diagnosis . Therefore donor B may have been the source of onward transmissions , but probably not the mixed infection recipient A . Having found evidence of mixed infections with differing STs , we applied our method to search for previously undetected mixed infections of the same ST in the 24 putative donors and recipients without evidence of mixed-ST infection ( Table 1 ) . Five samples contained evidence of mixed infection according to our method . In three cases the divergence estimated between dominant and minor sequences was consistent with levels of within host diversity observed in serially sampled patients where up to 2 SNPs were expected between samples taken on the same day ( 95% prediction interval ) [30] . As such these cases might not have arisen from two transmission events , but from evolution within a host of the same strain . Discounting these 3 cases , we therefore identified 2 mixed infections of the same ST , to add to the 2 mixed infection cases identified with different STs . The two within-ST mixed infection cases had an estimated divergence between the dominant and minor sequences that differed substantially from the best matching sequences in the database , 542 SNPs and 56 SNPs compared to best matches in the database of 1022 SNPs and ≤4 SNPs respectively ( Table 1 ) . This suggests the true minor sequence was not present in the database , highlighting our method works best with an established database of local sequences , but is able to identify when novel sequences arise .
We describe a new approach for detecting mixed infection from bacterial whole genome sequence data with low SNP density , utilizing a computationally efficient algorithm that we show performs well in in silico and in vitro simulations . This offers the prospect of screening for mixed infection in transmission studies and routine outbreak surveillance without labour and cost-intensive individual sub-culture of multiple colony picks and the expense of typing or sequencing these isolates separately . We demonstrate the utility of the approach , which is generalizable to any bacterial pathogen/loci for which a database of known sequences exists , using both WGS and MLST in C . difficile . Our new approach revealed a number of biologically meaningful findings . In our sample of clinical cases significantly enriched for the possibility of mixed ST infection due to the high prior probability of transmission based on epidemiological data but without matching sequence types , we found only 2/26 ( 8% ) cases had evidence of a mixed ST infection . This is consistent with previous estimates for mixed genotype infections of ∼7–13% [10] , [21]–[24] , although we might have expected to find a higher prevalence had mixed infection genuinely been contributing to transmission . However , these previously undetected mixed infection events could not account for transmission between the 15 putative donor-recipient pairs sequenced . The fact that these pairs were highly selected based on hospital exposure suggests that mixed infection is unlikely to explain a large proportion of the ∼75% CDI cases which cannot be linked to a previous case based on hospital ward exposure [20] . However , by interrogating a larger database of >1200 genomes [30] representing potential donors that were previously sequenced from single colonies , we did find an example of transmission leading to mixed infection . Using the extra resolution afforded by whole genome data we were able to refine our estimate of the likely donor . This revealed a previously undetected transmission event , reflecting additional transmission from the donor , and another transmission event on the ward in question . The significance of the infection for the recipient is unclear; but as ST1 is a virulent strain , it is possible that whilst it only accounted for the minority of the C . difficile sequenced it may nevertheless have been the cause of the patient's illness . Therefore , use of our method in outbreak investigation demonstrably offers the ability to detect additional transmission as well as robust determination that true transmission events are not being missed . We were also able to detect mixed infections where both infections shared the same sequence type in 2/24 ( 8% ) cases without mixed ST infections and detect likely within host variants in 3 further cases . In order to capture the full diversity of C . difficile present on the primary culture plate a sweep was taken across all the growth . In around half the samples this resulted in contamination of the sequenced reads with other bacterial species , despite morphological appearances consistent with C . difficile . This necessitated use of a restrictive mapping algorithm ( BWA ) favouring mapping reads closely related to the reference . Differences in the proportion of mixed infections estimated by the same algorithm from these two mapping methods highlight the impact of such choices on inferences made from whole genome data . The principle advantage of the primary culture sweep is the ability to capture the full diversity present on the plate , rather than selecting a limited number of colonies for sub-culture . However if contamination with other bacterial species is a concern , one possible refinement still enabling an assessment of mixed infection , without expensive sequencing of multiple single picks , might be to sample multiple individual colonies from the primary culture plate , and sub-culture these together on a single plate prior to sequencing . Paradoxically such approaches may actually increase sensitivity even if only relatively modest numbers of colonies are sampled ( e . g . 10–20 colonies ) . This is because high levels of contamination result in reduced read depths for a given sequencing effort , as evidenced by the lower median depth achieved in samples with <60% of reads mapped , 14 , compared to 77 in samples with ≥60% of reads mapped . However , further sub-culture does risk increasing any biases introduced by differential growth of strains on culture media , relative to their original frequency within the host . The sequencing process itself can also potentially introduce read frequency biases , however this did not appear to have a significant impact in the in vitro simulations performed . Although our method assumes mixtures contain only 2 sequences , it still should detect the presence of mixed infection where there is a dominant sequence and several minor sequences . We would expect the estimated minor sequence would be a hybrid of the true minor sequences . This might be apparent where the minor sequence did not match a known sequence , or where >2 nucleotides were found at a single SNP site . A possible hybrid minor sequence could then prompt further more detailed investigation including sub-culture of individual isolates . In the case of C . difficile , mixed infection with more than 2 genotypes is reported , but the majority of mixed infections are with 2 genotypes [10] , [23] . When comparing bacterial sequences , SNPs are often sparsely distributed throughout the genome , making it likely that a mixture with several minor sequences would still only contain biallelic SNPs . Therefore instead of the two-stage approach of estimating the mixture proportions followed by haplotype matching we demonstrate , any future approach to detect mixtures of more than 2 bacterial sequences would have to jointly estimate mixture proportions and haplotypes . Such an approach would still have to make use of a library of known haplotypes , given the limited numbers of SNPs relative to read lengths . As the current approach also depends on access to a database of known haplotypes this emphasises the benefits of read archives which could enable sequence data generated by different researchers to be incorporated into such a database . Future availability of long read sequencing , including “strand sequencing” with no theoretical read length limit [13] , may allow approaches taken in viral sequencing to be applied using SNPs identified at the ends of overlapping sequence fragments to reconstruct haplotypes [15] , [16] or may simplify the identification of mixed infection to identifying individual genomes sequenced in a single read . However until then , next-generation whole genome sequencing offers the potential for high-throughput , labour- and cost-effective screening for mixed infection , and such approaches should become the standard when investigating transmission and potential outbreaks .
This study was approved by the Berkshire Research Ethics Committee ( 10/H0505/83 ) and the National Information Governance Board ( 8-05 ( e ) /2010 ) without requiring individual patient consent . Selective culture for C . difficile was undertaken following an alcohol-shock on modified Brazier's cycloserine-cefoxitin-egg yolk agar . Plates were incubated anaerobically at 37°C for up to 7 days following the method of Griffiths et al [26] . DNA was extracted directly from a sweep taken across each primary culture plate to capture the complete genetic diversity present: a 5 µl loop was passed through an area of confluent growth , and the loopful of growth then suspended in saline prior to DNA extraction with a commercial kit ( QuickGene , Fujifilm , Tokyo , Japan ) . All growth was morphologically consistent with C . difficile , exhibited a characteristic odour and fluoresced under ultraviolet light . Extracted DNA underwent whole genome sequencing using the Illumina HiSeq 2000 platform ( San Diego , California , USA ) generating 100 base-pair reads . Sequence reads were mapped using two aligners , Stampy [27] ( with an expected substitution rate of 0 . 01 ) and Burrows-Wheeler Aligner ( BWA , with default settings ) [28] to the C . difficile 630 reference genome ( Genbank:AM180355 ) , CD630 [29] . High quality base counts were extracted from mapped data for variable sites using SAMtools [34] , retaining bases with a base quality score ≥30 and a mapping quality score ≥30 . As the initial algorithm was designed to detect mixed ST infection , the variable sites analysed were first restricted to the 150 single nucleotide variants ( SNPs ) within the 7 MLST loci based on all published alleles [35] . The variable sites studied were subsequently extended to make full use of the whole genome data , see results above . To allow extraction of 16S ribosomal RNA genes , reads were also assembled de novo using Velvet with the Velvet Optimiser [31] . Each sample was assumed to be a mixture of 2 haplotypes , resulting in one dominant and one minor haplotype , with the proportion of the total sequence present made up by the dominant haplotype denoted μ . For each sample analysed we let , N = total number of variable sites considered nj = total number of reads at a variable site j = 1…N bij = an observed nucleotide from a single read i = 1…nj mapped to variable site j , from the set {A , C , G , T} Bj = a vector of the nj nucleotides from the reads mapped to site j , ε = Pr ( sequencing error in a base call ) . Assumed constant across all bases calls , having filtered our data to exclude low quality bases and reads μ = proportion of the sample from the dominant haplotype ( 0 . 5≤μ≤1 ) a1j = nucleotide in the dominant haplotype at site j a2j = nucleotide in the minor haplotype at site j Aj = the combination of nucleotides in the dominant and minor haplotypes respectively , a1j and a2j , one of the set of all 16 possible pairs of nucleotides , A: ( 1 ) We expressed the probability of observing a particular nucleotide in a given read mapped to site j in terms of the underlying dominant and minor haplotypes , the mixture proportion and error probability . We assume the probability of a sequence error , ε , is constant across all variable sites , and if an error occurs it is equally likely to result in any of the three alternative nucleotides ( i . e . if the true nucleotide is A , then a read containing C , G , or T is equally likely ) : ( 2 ) As ε is treated as a known constant , the probability of observing the nj nucleotides mapped to site j , for a given value of Aj is: ( 3 ) Where Aj is unknown , summing over all possible values of Aj gives: ( 4 ) To define Pr ( Aj ) for each possible value of Aj we let d be the proportion of all variable sites included in the analysis that are divergent between the dominant and minor haplotypes . At sites divergent between the haplotypes 12 possible pairs of nucleotides could be present , and at non-divergent sites 4 pairs of nucleotides are possible , such that: ( 5 ) Combining ( 4 ) and ( 5 ) , we then expressed the probability of observing the nj nucleotides mapped to site j , for a given mixture proportion and divergence between the dominant and minor haplotypes: ( 6 ) The values of μ and d were then jointly estimated from their likelihood: ( 7 ) A major question is how to determine whether the data are consistent with a mixed infection . A simple but arbitrary approach would be fix thresholds of μ and d , ( e . g . ) . However , we adopted an alternative approach: comparing the maximum likelihood obtained under the mixed infection model with the likelihood of the data without mixed infection , i . e . μ = 1 , d = 0 . Comparing the log-likelihood ratio statistic to a chi-squared distribution is problematic , as the null hypothesis is on the edge of the parameter space . Therefore we used a calibration set of 100 samples known not to contain mixed infection to determine a deviance ( −2 log likelihood ratio ) threshold for confirming mixed infection . The value of the threshold was chosen to achieve a 5% false positive rate . This empirical approach by using a calibration set of actual sequences also has the advantage that it accounts for low-level sample contamination that may occur during sequencing . This would not be easily accounted for in another alternative for determining mixed infection , simulating under the null hypothesis in a bootstrapping approach . Confidence intervals for μ and d were generated by non-parametric bootstrap sampling . The variable sites were sampled with replacement 1000 times keeping each Bj constant . A database of known sequences was used to estimate the dominant and minor haplotypes present , using the estimate of μ obtained above . From ( 6 ) , for each potential haplotype pair the value of Aj and d are known therefore the probability of observing all the nucleotides mapped to site j is: ( 8 ) Therefore the likelihood of a given pair of dominant and minor haplotypes given the sequence data is: ( 9 ) Finally , the posterior probability of each pair of haplotypes was obtained using the product of the prevalence of the dominant and minor haplotypes as their prior probability and the likelihood of each pair above . For MLST analyses the prevalence of each ST in Oxfordshire from September 2007 to March 2010 was used [20] . STs not found during the study but present in pubMLST [35] were assumed to be as prevalent as the least common ST . For whole genome analyses the prevalence of each unique sequence during the study was used . When using whole genome data to assess whether the dominant and minor pair selected were consistent with the data the estimated divergence and 95% confidence intervals were compared with the actual divergence between the dominant and minor sequences . Where the actual pairwise divergence fell within the estimated 95% confidence interval the pair was considered a good match . However when the divergence fell outside of the confidence interval this was interpreted as evidence that the true dominant or minor sequence was not present in the database . For all analyses the value of ε was set to the sum of the base and error mapping rates , assuming each had a PHRED score of 30 ( the thresholds used for determining high quality bases to retain in the analysis ) , i . e . ε = 2×10−3 . This represents the upper bound on the value of ε after filtering . However results were similar with lower values of ε tested up to ε = 2×10−4 . To generate in silico simulated mixed infections two samples known not to be mixed themselves were mixed in varying proportions . Firstly , read depths were normalised across the two samples by multiplying base counts in the sample with lower coverage to match the coverage in the other sample . Nucleotides were then sampled from reads mapped to each variable site ( with replacement ) . The input sequence each read was sampled from was determined using a binomial distribution with parameters of normalised read depth , and input mixture proportion . All analyses were conducted using R ( http://www . r-project . org ) . The code used can be found in Text S2 . Text S3 contains a short Python ( http://www . python . org ) script that can be used to obtain high quality base counts from mapped BAM files . Text S4 contains an explanation of the required input files for Text S2 and the output generated . Dataset S1 contains an example dataset of the 26 patient samples analysed for the presence of mixed ST infection , and provides an example of the formatting on the input and output files . The sequences reported in this paper have been deposited in the European Nucleotide Archive Sequence Read Archive under study accession number ERP002428 and are available at http://www . ebi . ac . uk/ena/data/view/ERP002428 .
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Traditionally , outbreaks of infectious diseases are investigated by considering contact between cases and their exposure to possible sources of infection . This can be enhanced by using the genetic fingerprint of bacteria to rule out transmission between cases infected with unrelated strains . However , in some cases patients are infected with more than one strain of the same species of bacteria . This is known as mixed infection . Using current methods usually only one strain of bacteria is analysed , so transmission might be ruled out wrongly if there is a mixed infection . We developed a method that exploits new high-resolution genetic fingerprinting in bacteria to detect patients that are infected with multiple strains of the same bacterial species . We investigated the important healthcare-associated infection Clostridium difficile , revealing previously undetected mixed infections , and identifying a previously undetected transmission event . By interrogating a database of bacterial strains , our method deduced the mixed strain types , which we showed were not compatible with direct transmission among the patients under investigation . Our method can improve the sensitivity of outbreak investigation across different types of bacteria , which will ultimately help to reduce transmission in hospitals and the community .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"infectious",
"disease",
"control"
] |
2013
|
Detection of Mixed Infection from Bacterial Whole Genome Sequence Data Allows Assessment of Its Role in Clostridium difficile Transmission
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Honeybees , Apis mellifera , show age-related division of labor in which young adults perform maintenance ( “housekeeping” ) tasks inside the colony before switching to outside foraging at approximately 23 days old . Disease resistance is an important feature of honeybee biology , but little is known about the interaction of pathogens and age-related division of labor . We tested a hypothesis that older forager bees and younger “house” bees differ in susceptibility to infection . We coupled an infection bioassay with a functional analysis of gene expression in individual bees using a whole genome microarray . Forager bees treated with the entomopathogenic fungus Metarhizium anisopliae s . l . survived for significantly longer than house bees . This was concomitant with substantial differences in gene expression including genes associated with immune function . In house bees , infection was associated with differential expression of 35 candidate immune genes contrasted with differential expression of only two candidate immune genes in forager bees . For control bees ( i . e . not treated with M . anisopliae ) the development from the house to the forager stage was associated with differential expression of 49 candidate immune genes , including up-regulation of the antimicrobial peptide gene abaecin , plus major components of the Toll pathway , serine proteases , and serpins . We infer that reduced pathogen susceptibility in forager bees was associated with age-related activation of specific immune system pathways . Our findings contrast with the view that the immunocompetence in social insects declines with the onset of foraging as a result of a trade-off in the allocation of resources for foraging . The up-regulation of immune-related genes in young adult bees in response to M . anisopliae infection was an indicator of disease susceptibility; this also challenges previous research in social insects , in which an elevated immune status has been used as a marker of increased disease resistance and fitness without considering the effects of age-related development .
Declining populations of honeybees , Apis mellifera , have been recorded in many countries , causing widespread concern [1] , [2] . While no single factor has been found to account for all honeybee colony losses in all areas , pathogens ( = parasites that cause disease ) are known to play an important role [3] , [4] . Therefore , detailed understanding of the effects of pathogens on honeybee biology is critical to the development of new ways for improving bee health . Like other eusocial insects , honeybees have a highly developed form of social organization , characterized by the presence of overlapping generations within the colony , cooperative care of offspring , and reproductive division of labor [5] , [6] . Their success can be attributed to living in large , organized colonies which improves their ability to compete for resources against small groups or solitary species [7] . However , the close physical contact within the colonies of eusocial insects enables pathogens to spread rapidly [6] , [8] . As a result , honeybees – like other eusocial insects – invest heavily in pathogen defense [9] . Empirical evidence indicates that selection by pathogens has been a defining feature of the evolution of insect societies [10] . The defenses used by eusocial insects against pathogens include inducible cellular and humoral immunity , antimicrobial defense compounds secreted on the cuticle , as well as group defenses that include hygienic behavior and utilization of antimicrobial compounds acquired from the environment [9] , [11]–[14] . In addition , the genetic diversity within honeybee colonies is increased by polyandry ( mating of the queen with multiple males ) , which is important to help the colony resist disease [15]–[17] . Within eusocial insect societies , functionally sterile adult workers perform most of the tasks of the colony [18] . Some tasks , such as foraging , are done later in life . These tasks are associated with greater risks , and performing them later in life has been shown to increase the average life span of individuals in the colony [19] , [20] . In honeybees , adult workers born in the spring and summer spend the first part of their life inside the colony engaged in housekeeping duties such as food processing and care of brood ( for this reason they are referred to as “house” bees [21]–[23] ) before making a transition to foraging duties outside the colony at an average of 23 days old [24] . Foraging bees senesce rapidly and have a high mortality rate from predation [25] . The average life span of a forager bee is only five days [24] . The exact timing of the onset of foraging is affected by bee genotype [26] and also by the needs of the colony , with house bees switching to foraging duties early if the colony suffers a shortfall in forager numbers [27] . The situation is markedly different for worker bees produced in the late summer and autumn , which remain inside the colony to ensure its survival over the winter and live for approximately six months [24] . An important challenge in the study of eusociality is to understand the relationships between an individual's behavioral role , its age , its ability to withstand infection and the impact on the whole colony . Different hypotheses have been proposed about how honeybee immunity interacts with age-related division of labor . The first hypothesis states that the immunocompetence of adult bees declines markedly when they switch from housekeeping to foraging , driven by natural selection at the colony level , resulting in allocation of resources for foraging rather than immunity , both of which are energetically expensive [28] . This is supported by experiments in which a decrease was observed in the number of functional hemocytes in 26 day old forager bees compared to bees of the same age manipulated to keep them at the housekeeping behavioral stage , alongside an increase in juvenile hormone titer and a decrease in vitellogenin titer [28] . These changes were reversed if foragers were manipulated to revert to housekeeping [28] . Further support for this hypothesis comes from an observation that newly emerged house bees exhibited hemocyte nodulation reactions against bacterial challenge , whereas older , forager bees did not have this ability [29] . Finally , forager bees have a smaller fat body than one day old house bees , which may indicate a reduced ability to produce antimicrobial peptides , as the fat body is the main site of synthesis of these compounds [30] . However , there is also evidence to support a contrasting hypothesis that immunocompetence is enhanced in foragers . Natural selection may act to preserve immunity in foragers , since they are exposed to pathogens at foraging hotspots [31] and thus are a route for bringing new infections into the colony [32] . This is supported by data which showed that: ( i ) foragers had a significantly higher total hemocyte count than one day old house bees; ( ii ) there was no significant difference in the cellular encapsulation response of foragers and one day old house bees; ( iii ) foragers showed significantly greater phenoloxidase activity ( responsible for the melanization of invading pathogen cells ) than one day old house bees [30] . A refinement of this hypothesis , proposed by [33] , states that cellular immunity declines in adult bees as they age , but that other parts of the immune system are maintained . This is based on experimental evidence showing that while the total hemocyte count fell in adult honeybees from one to 24 days old , phenoloxidase activity ( which is involved in the melanisation and encapsulation of invading pathogens in the haemocoel ) increased early in adult life and reached a plateau by the end of the first week [33] . The same patterns were observed in older foragers versus artificially produced younger foragers , and artificially produced older house bees versus younger house bees [33] . Until now , controlled pathogen infection experiments linked to honeybee adult age have not been reported . Moreover , previous research has used a limited number of markers for bee immune response . For this study , we used a laboratory bioassay to quantify the susceptibility of house vs . forager bees from the same cohort to infection with the entomopathogenic fungus Metarhizium anisopliae s . l . At the same time , we quantified changes in global gene expression in individual bees using an oligonucleotide microarray constructed from the official honeybee gene set ( see Figure 1 for a schematic outline of the study ) . We used a balanced statistical design in the microarray experiment with emphasis on maximizing the number of biological replicates per treatment , in order to determine statistically significant changes in gene expression within the experimental population . We used eight biological replicates for each of four treatments hybridized to microarrays . Findings supported our central hypothesis that selection by pathogens would result in foragers being less susceptible to infection than house bees . We went on to quantify our second hypothesis; that this difference is reflected by interpretable differences in gene expression , particularly for immune pathways . This type of combined approach tests whether strong immune responses at the molecular level are a good indicator of resistance to pathogens , and consequently fitness at the level of the whole organism .
House bees ( one day old ) and forager bees ( 26 days old ) showed differences in the rate at which they succumbed to lethal infections of the entomopathogenic fungus M . anisopliae s . l . in a laboratory bioassay . The median ( interquartile range ) observed survival time was 72 ( 24 ) hrs for M . anisopliae-treated house bees and 116 ( 24 ) hrs for M . anisopliae-treated forager bees . The M . anisopliae-treated forager bees survived significantly longer than M . anisopliae-treated house bees ( t149 = 15 . 0 , p<0 . 001 ) ( Figure 2 ) . Random differences within groups of biological replicates did not account for a significant amount of observed deviance ( Δdeviance = 1 . 80 , p = 0 . 097 ) . Quantification of M . anisopliae 18S rRNA by RT-PCR ( see supplementary information Figure S1 ) indicated that the fungus was present at significantly higher levels in M . anisopliae-treated house bees at 48 hrs post inoculation compared to M . anisopliae-treated forager bees ( ΔCt = 2 . 65 , t22 = 14 . 5 , p<0 . 001 ) . The fungus was not detected in control ( = un-inoculated ) bees ( Figure S1 ) . Genome-wide honeybee transcript abundance was quantified using microarrays 48 hrs after bees were treated with M . anisopliae s . l . The transcriptome data was analysed in a mixed effects model , which encompassed experimental sources of variation as structured variance components , and the presence of naturally occurring , asymptomatic honeybee viruses in individual bees as an additional covariate . We observed a significant effect of the virus covariate ( deformed wing virus and/or Varroa destructor virus-1 or their hybrids [34] ) on global gene expression of forager bees ( it was not possible to deduce the effect on house bees because none of the control house bees showed high virus levels ) , where differences in the amount of virus detected with the microarray could account for up to a half of the variation in expression of immunity-related genes in individual forager bees following fungal infection . By comparison of statistical models including or excluding ‘virus level’ , we found that virus level was associated with the differential expression of three honeybee immunity-related genes that were significantly differentially expressed as a result of M . anisopliae infection: Toll-7 ( GB15177 ) , Tube ( GB15684 ) and Tep-B ( thioester containing protein B; GB11563 ) . We then quantified three treatment contrasts relating to transitions between phenotypic states ( younger house bee→older forager bee ) and M . anisopliae disease states ( uninfected→infected ) , summarized as a Venn diagram ( Figure 3 ) . There were marked differences in gene expression depending on treatment . We found that 1109 probes ( representing genes ) showed significant ( p<1/n , where n = 10498 is the number of probes on the array ) differential expression associated with fungal treatment of house bees ( Venn diagram intersections a , d , g , e; Figure 3 ) , while only 73 probes showed significant differential expression associated with fungal treatment of forager bees ( Venn diagram intersections b , d , g , f; Figure 3 ) . In addition , 1989 probes were differentially expressed in forager bees compared to house bees , independent of infection status ( Venn diagram intersections c , e , f , g; Figure 3 ) . Of these , there were 1659 differentially expressed genes that were uniquely associated with ageing in untreated bees ( Venn diagram intersection c; Figure 3 ) . In order to test hypotheses on the role of age-related division of labor in honeybees in response to infection with M . anisopliae , we went on to identify differentially expressed probes associated with M . anisopliae treatment which were either unique or common to house and forager bees . House bees treated with M . anisopliae showed 1088 ( 589 up-regulated , 499 down-regulated , Table 1 ) differentially expressed probes that were not differentially expressed in M . anisopliae-treated forager bees ( Venn diagram intersections a , e; Figure 3 ) . In contrast , there were 52 ( 29 up-regulated , 23 down-regulated , Table 1 ) differentially expressed probes in forager bees that were not found to change in house bees ( Venn diagram intersections b , f; Figure 3 ) . Only 21 probes showed differential expression in response to M . anisopliae treatment that were common to house and forager honeybees ( Venn diagram intersections d , g; Figure 3 ) . Of these , the majority changed expression in the same direction ( either up- or down-regulated ) in both classes of worker bee ( Table 1 ) . We used qRT-PCR to quantify the level of mRNAs for honeybee beta actin and vitellogenin genes . Changes in expression for these genes were in the same direction for the microarray and the qRT-PCR . Levels of vitellogenin mRNA were higher in 26 day old forager bees compared to the one day old house bees ( Figure S1 ) , in accordance with previous studies [35] , [36] . Levels of beta actin mRNA were lower in the forager bees compared to house bees ( Figure S1 ) . Treatment with M . anisopliae had no significant effect on levels of vitellogenin mRNA or beta actin mRNA in house or forager bees . Gene Ontology was used to examine potential biological functions of differentially expressed genes . Information was obtained through comparison with Drosophila melanogaster genome annotation for 6325 out of 10498 bee genes ( 62% ) . To examine functional differences related to the tested phenotypic transition states , we examined sets of genes for over-representation in biological process , molecular function and cellular component GO categories ( Table S1 ) . For the set of genes that were differentially expressed in M . anisopliae-treated house bees but not in forager bees ( Venn diagram intersections a+e ) , there was over-representation ( p<1E-6 ) of GO terms associated with cellular and subcellular organization and regulation . There were no significantly over-represented GO terms associated with responses to fungus that were either unique to forager bees ( Venn diagram intersections b+f; Figure 3 ) or that were common to house and forager bees ( Venn diagram intersections d+g; Figure 3 ) . There were also differences between house and forager bees , independent of M . anisopliae infection ( Venn diagram intersections c , e , f , g; Figure 3 ) associated with ageing , specifically in energy generation and DNA remodelling . We also compared the observed differentially expressed genes in our experiment to a set of 182 previously published homology assignments made for honeybee immune-related genes [37] . A subset of these candidate immune genes showed differential expression in response to infection by M . anisopliae in our experiment , but there was no commonality in the pattern of response between house and forager bees ( Table 2 , Table S2 ) . House bees treated with M . anisopliae showed 35 differentially expressed genes that were associated with immune function ( Venn diagram intersections a , d , g , e; Figure 3 ) . Of these , 20 genes were up-regulated , and 15 down-regulated . In contrast , M . anisopliae-treated forager bees showed only two differentially expressed genes that were associated with immune function ( Venn diagram intersections b , d , g , f; Figure 3 ) ( one up-regulated , one down-regulated ) . One of these two genes ( C-type lectin; GB14265 ) was also differentially expressed in M . anisopliae-treated house bees . However , it was up-regulated in M . anisopliae-treated house bees whereas it was down-regulated in M . anisopliae-treated forager bees . In controls , i . e . bees not treated with M . anisopliae , 49 candidate immune genes showed differential expression associated with honeybee ageing ( i . e . house vs . forager bees; Venn diagram intersections c , e , f , g; Figure 3 ) ( 34 up-regulated , 16 down-regulated ) . Of these , 34 genes were uniquely associated with ageing ( Venn diagram intersection c; Figure 3 ) , i . e . they were not expressed in response to M . anisopliae infection . Of these , 20 were up-regulated and 14 were down regulated . Thirteen differentially expressed candidate immune genes were common to bee ageing and M . anisopliae infection of house bees ( Venn diagram intersection e; Figure 3 ) .
There is an urgent requirement for new knowledge on the molecular mechanisms by which honeybees interact with pathogens in order to better understand honeybee colony losses and to develop new interventions . However , conducting molecular studies with honeybees is not straightforward . Honeybee colonies are semi-wild , outdoor entities and present a number of significant challenges for experimenters . As a result of multiple matings by the queen , the worker bees within a colony are not genetically uniform [17] while background , asymptomatic virus infections are common [38] . In order to understand bee-pathogen interactions in the colony , we need experimental systems that are able to encapsulate the complexity of the bee immune response at the molecular level , ascertain the relationship between immune response and susceptibility to infectious disease , and take into account natural variation between individual bees . Studying whole genome transcriptional responses to infection provides a wider view of the honeybee-induced immune response , for example by enabling different genetic pathways to be studied in parallel . Within the limitations of the financial resources available to us for the microarray study , we designed the experiment to maximize the number of biological replicates using individual bees , as opposed to “pooling” bees into a sample . This enabled us to take into account the level of background asymptomatic virus infection in individual bees as a factor in the data analysis . Measurements of animal immune status are often used as a “short cut” for measuring resistance to infection , based on an assumption that individuals with greater antibody levels , blood cell encapsulation response etc . are fitter and less susceptible to a pathogen [39] . Often , a small number of markers of immune status are employed . This approach has been used widely in studies of honeybee immunity [28]–[30] , [33] , [40] , [41] . In our study , one day old house bees were more susceptible to M . anisopliae infection than 26 day old forager bees ( i . e . they died faster and supported more growth of invading fungus ) but exhibited a greater immune response . Hence , in this case , a strong induced immune response was an indicator of higher susceptibility to a pathogen rather than resistance . In contrast , a lower induced immune response in forager bees was associated with a reduced susceptibility to M . anisopliae , linked to bee ageing ( see below ) . These findings suggest that the underlying assumption behind some previous honeybee studies may be wrong , i . e . the size of the induced immune response is not necessarily related to the ability to withstand infection or with host fitness [39] , [42] . It is also clear from these results that the immunocompetence of foragers bees did not decline compared to house bees , as has been proposed previously [28] . The caveat is that M . anisopliae is a generalist entomopathogen that , although lethal to honeybees and other social insects and provides a very tractable experimental system , does not cause natural honeybee colony-scale outbreaks . There is a requirement to investigate how the immune response of house bees and foragers responds to co-evolved honeybee pathogens , such as Nosema apis and Nosema cerana ( fungal pathogens that infect the midgut epithelium of adult bees and which cause epizootics within colonies ) to compare against the response of M . anisopliae as a baseline , and to determine whether the resource allocation to immune defenses is the same for different types of pathogen . Many of the co-evolved entomopathogens of honeybees , such as the fungus Ascosphaera apis and the bacteria Paenibacillus larvae and Melissicoccus pluton , cause lethal infections only in brood , but their effects on adult bees are unclear [6] . The Gene Ontology analysis provided some useful general information but did not provide the fine level of detail that we needed for new insights on honeybee immune function . This is likely to result from the lack of a genome annotation for A . mellifera and we suggest that this is an important objective for future work . In house bees treated with M . anisopliae , differentially expressed genes were over-represented by GO terms associated with cellular and organelle organization and biochemical regulation . This may reflect the effects of pathogenesis , as entomopathogenic fungi utilize a range of tactics to evade host immune response based on interference with regulatory networks , including suppression of cytoskeleton formation and other features of the subcellular structure of host immune cells [43] , [44] . When forager bees were compared against house bees in the absence of M . anisopliae infection , there was over-representation of terms that highlighted the effects of bee ageing . The transition from house to forager bee is under hormonal control [45] and is accompanied by changes in biochemistry , physiology , neurobiology and metabolism that involve multiple pathways [46]–[48] . In our experiment , over-representation of GO terms associated with the ageing occurred in two areas: firstly in energy generation , with terms such as generation of precursor metabolites and energy , respiratory electron transport chain , and ATP synthesis coupled electron transport being significantly over-represented . Secondly , over-representation of terms such as chromatin assembly or disassembly , and nucleosome suggested DNA re-modelling during the ageing process , with a concomitant impact on DNA transcription , repair , and replication [49] . We went on to look at individual honeybee genes that have been hypothesized to function in bee innate immunity . The honeybee innate immune system is comprised of cellular defenses from specialized blood cells ( granulocytes and plasmatocytes ) within the haemocoel [50] , [51] as well as humoral immunity in the form of Toll , Imd ( immune deficiency ) and Janus kinase/signal transduction and activator of transcription ( JAK/STAT ) pathways for the production of antimicrobial peptides , melanization of invading pathogen cells , and apoptosis [37] . Interpretation of the immune gene expression data in this study has to be done with a certain amount of caution . The current state of knowledge of individual honeybee immune pathways , and the mechanisms by which the different pathways interact , is not fully developed . We can draw on the literature on transcriptomics of the immune response from other insects , particularly Drosophila , but even here very few studies have been done using entomopathogens and natural routes of infection [52]–[54] . House and forager bees were at different physiological stages of M . anisopliae infection at the time of sampling in the bioassay , as shown by significant differences in the amount of fungal biomass detected within infected bees . This raises the question of whether the difference in immune gene expression in forager versus house bees was the cause or the consequence of reduced susceptibility to M . anisopliae in forager bees . Nevertheless , patterns were evident in our data that give insights into the bee innate immune system , including the identification of putative functionally-related components of the immune response . House bees showed significant differential expression of 35 candidate immune genes in response to fungal infection . Fungal infection activated both Imd and Toll signalling pathways in house bees , the major regulators of immune responses in insects [55] . Three out of five honeybee antimicrobial peptide ( AMP ) genes were significantly up-regulated in house bees ( abaecin , GB18323; Defensin-2 , GB10036; Hymenoptaecin , GB17538 ) and showed between 16 and 64 fold increases in expression , which was the highest fold change in expression of all differentially expressed immune genes . Changes in expression levels were observed for several components of the Toll pathway in house bees . The Toll pathway is associated with the immune response to fungi and bacteria in Drosophila and regulates the expression of AMP genes [56] , [57] . Toll pathway genes up-regulated in house bees in our study included those encoding for extracellular components associated with fungal recognition ( PSH-like/cSP14 , GB14044; NEC-like , GB16472 ) and intracellular components including the NF-κB-like transcription factor Dorsal ( GB19066 ) . Only one out of eight genes was up-regulated from the Imd pathway , namely relish ( GB13742 ) , encoding a NF-κB-like transcription factor known to control the expression of abaecin and Hymenoptaecin in honeybees [58] . This was accompanied by down-regulation of two Imd pathway genes , Tab ( GB18650 ) and Tak1 ( GB14664 ) . These genes function in the regulation of the JNK pathway , which is believed to be involved in negative and positive feedback for AMP synthesis [59] . Three out of five genes were up-regulated from the JAK/STAT pathway , which is thought to contribute to immunity by inducing production of hemocytes and induction of complement-like factors [37] . However there was significant down-regulation of NimC2 ( GB13979 ) . In Drosophila , Nimrod C1 ( NimC1 ) is a protein component of the surface of hemocytes and is a determinant of phagocytic activity [60] . There was also significant down-regulation of a gene for an activator of prophenoloxidase , PPOAct/SP8 ( GB18767 ) . The prophenoloxidase cascade is modulated by serine proteases and controls melanin synthesis , which is an important defense mechanism against invading extracellular pathogens including fungi [61] . Pathogens of other insects exhibit adaptations to counteracting phenoloxidase [62] , [63] . Therefore this may be evidence of M . anisopliae-mediated inhibition of part of the honeybee immune response . Our data also indicated that M . anisopliae infection of house bees affected the expression of genes involved in pathogen recognition acting upstream of the antimicrobial effector pathways . There was significant up-regulation of both of the known honeybee fibrinogen-related genes ( Angiopoietin , GB17018; Scabrous , GB11902 ) . In Anopheles and Drosophila , fibrinogen-related proteins function as pattern recognition receptors ( PRRs ) for activation of immune defenses against bacteria [64] , [65] . Infection by M . anisopliae also resulted in significant up-regulation of the Gram-negative binding protein ( GNBP ) gene B-gluc2 ( GB19961 ) . In termites , GNBP-2 functions both as a pattern recognition receptor of Gram-negative bacteria and fungi , including M . anisopliae , and as an antimicrobial effector protein [66] . In Drosophila , the presence of opportunistic fungal pathogens is detected by GNBP-3 operating upstream of the Toll pathway , but infection by entomopathogenic fungi is thought to directly activate Toll by cleavage of the Drosophila serine protease Persephone by the fungal protease Pr1 [67] . Our data also showed significant down-regulation of 4/14 genes encoding scavenger receptor ( SCR ) proteins ( AmSCR-B8 , GB16388; AmSCR-B9 , GB19916; AmSCR-B10 , GB19683; AMSCR-C , GB19925 , ) . There was up-regulation of one C-lectin domain gene ( CTL2 , GB14265 , ) . There was no differential expression of honeybee genes from the PRR immunoglobulin superfamily ( IgSF ) . Insect IgSF proteins are present in the haemolymph and are assocated with binding to bacterial cells in the tobacco hornworm Manduca sexta ( Lepidoptera ) [68] . Probably the most noticeable aspect of the microarray data was the effective absence of differential expression of candidate immune genes after treatment with M . anisopliae in forager bees compared to house bees . Only 2 genes were significantly differentially expressed in forager bees infected with M . anisopliae; down-regulation of CTL2 ( C-type lectin 2; GB14265 ) and up-regulation of IGFn3-2 ( GB11358 ) a member of the immunoglobulin superfamily ( IgSF ) . Can we link this finding with the observation that forager bees were less susceptible than house bees to the pathogen ? Analysis of the microarray data for control bees ( i . e . bees not treated with M . anisopliae ) showed that foragers exhibited significant down-regulation of 6/12 honeybee C-type lectin genes compared to house bees . There was also significant down-regulation of 4/4 honeybee IgSF genes . C-type lectins function in aggregation reactions by binding hemocytes to microbial polysaccharides [69] , while IgSF proteins are also associated with pathogen recognition and cell adhesion [70] . These observations are in keeping with published reports that hemocyte counts fall as honeybees age [28] , [33] . Up-regulation of immunity related genes in foragers compared to house bees occurred in two areas . Firstly , there was significant up-regulation of the AMP gene abaecin ( GB18323 ) alongside significant up-regulation of major gene components of the Toll pathway: NEC-like ( GB16472 , GB19582 ) , PSH–like/cSP14 ( GB14044 ) , PSH-like/SP13 ( GB15640 ) , Toll ( GB18520 ) , pelle ( GB16397 ) , cact-1 ( GB10655 ) and cact-2 ( GB13520 ) . Secondly , there was significant up-regulation of 12 genes encoding clip domain serine proteases ( SPs ) and serine-protease homologues ( SPHs ) . These proteins , which occur in an evolutionarily diverse range of insects [71]–[73] , are secreted into haemolymph as inactive zymogens and are components of cascade reactions that result in rapid activation of the Toll [73] and prophenoloxidase pathways [71] , [74] . There was also significant up-regulation of three of the five honeybee serpin ( Serine Protease Inhibitor ) genes ( serpin-2 , GB16472; serpin-3 . GB12279; and serpin-5 , GB19582 ) which regulate the SP cascade and AMP synthesis [75] . The inference is that parts of the honeybee immune system were activated during the development of adult bees from the house to forager phenotype , resulting in greater resistance in foragers when they were subsequently treated with M . anisopliae . This may also account for the observation that only two immunity-related genes showed statistically significant differential expression in response to M . anisopliae in foragers . It is possible that immune system activation is part of the programmed development of the forager phenotype . This would be in keeping with other aspects of caste development in social insects which are associated with differential expression of shared genes , such as differentiation between honeybee queens and workers [76] . An alternative mechanism could be immune priming , a form of immune memory in which exposure to a pathogen results in reduced susceptibility upon later challenge [77] , [78] . Adult honeybees are naturally exposed to fungal pathogens during their lives which could provide priming opportunities for long term protection . These pathogens include microsporidian fungi ( Nosema apis and Nosema ceranae [3] ) as well as ascomycete fungi , the most common being Ascosphaera apis ( chalkbrood ) and Aspergillus flavus , ( stonebrood ) , although infections by other entomopathogenic ascomycete species including Beauveria and Lecanicillium have also been observed [6] . While it has not been demonstrated in all social insects [79] , immune priming has been observed previously in the bumblebee Bombus terrestris [80] and in the unicolonial ant species Lasius neglectus [81] . Age-dependent effects on immunity have also been observed in Drosophila , with older flies showing increased expression of immune genes , and where variation in gene expression in different in-bred lines is linked to the ability to clear bacterial infection in older flies [82] . Up-regulation of Drosophila immune genes with age may be the result of pathogen exposure earlier in life [83] , although there is also strong evidence of a decline in the ability to terminate AMP gene expression with age , resulting in a net increase in AMP production [84] . Activation of the insect systemic immune response results in a time lag between host detection of pathogen elicitors and synthesis of AMPs . The systemic immune response is part of a complex , integrated system that also contains constitutive defenses to prevent invasion ( for example , antifungal compounds on the cuticle [85] ) as well as haemocytes that are responsible for rapid phagocytosis and nodulation reactions to restrain the development and survival of the pathogen early during invasion . This raises the question of the adaptive significance of AMPs , which come into play later in the infection process . One explanation is that AMPs evolved in insects as a system of clearing low level , persistent pathogens that had evaded constitutive/early acting defenses [86] . Clearly , in our study , strong up regulation of AMP synthesis in house bees failed to prevent lethal infection by M . anisopliae . However , AMP production during a lethal infection could be of adaptive benefit if it delays pathogen growth sufficiently to enable the host to increase its inclusive fitness by , for example , altruistic self-removal from the colony ( = adaptive suicide ) [87] . Sick honeybees are known to engage in suicide behavior and modeling suggests that such self-removal from the colony to prevent transmission of pathogens should be commonplace in social insect species [88] . The information provided in this study is a significant advance in developing our understanding of genome-wide honeybee defenses against pathogens . Experimental validation using loss of function studies will be required to confirm involvement of differentially expressed genes in the immune process . However , the system used here enables testable predictions to be made about the molecular mechanisms underlying the immune response . The study also provides evidence that immune capability does not decline in foragers , commensurate with the idea that bees exposed to pathogens at foraging sites are a route for introducing disease agents into the colony , providing a selection force for the maintenance of immunity [31] , [32] . Our study focused primarily on the expression of genes associated with the honeybee humoral immune response , but it will be important in future to integrate this with information on other forms of defense , particularly the complex social responses of honeybees to pathogens [9] , [11] , [89] . The study of the honeybee immune system is of wide biological and practical interest . Numbers of A . mellifera colonies are declining in many regions of the world and this is causing considerable concern about the impact on crop production and the diversity of wild flowering plants [2] . Recent evidence has shown that pathogens are a key contributor to honeybee colony losses [2] , [3] , [4] . At present , the development of new interventions for disease management for beekeepers is being hampered by a lack of knowledge of the mechanisms of honeybee-pathogen interactions [4] . This is particularly the case at the molecular level . Our findings challenge previous assumptions that a strong innate immune response in honeybees is necessarily an indicator of greater resistance to infection in pathogens . It also provides further evidence of the importance of multi-level immunity operating in invertebrates .
A laboratory bioassay was used to quantify the susceptibility of known-age populations of adult Apis mellifera to Metarhizium anisopliae s . l . ( Ascomycota , Hypocreales ) , a widespread generalist entomopathogenic fungus that has been used in a number of recent studies of host-pathogen interactions in social insects [91] , [90]–[92] and which has also been used to study the molecular basis of the anti-fungal immune defense in Drosophila [67] . Honeybees were collected in summer ( July ) from a single colony , with a naturally mated queen , maintained in the apiary at Rothamsted Research , Harpenden UK . The Rothamsted colonies are typical to the UK in being a mixture of European subspecies and they are maintained according to conventional UK husbandry practice , which includes intensive treatment for varroa mites . None of the bees used in the experiment had symptoms of disease from naturally occurring pathogens , including honeybee viruses ( e . g . physical deformities , unusual movement ) , and none of the bees were observed to harbor phoretic varroa mites . Bees were treated with M . anisopliae s . l . strain 445 . 99 ( = the strain code used in the Warwick University collection of entomopathogenic fungus cultures ) . This strain is used as the active ingredient of the commercial mycoinsecticide Bio-Blast ( Eco-Science Corp . USA ) developed as a biological control agent of termites [93] . Conidia powder was collected from cultures of M . anisopliae 445 . 99 grown on Sabouraud dextrose agar for 10 days at 22°C and was passed through a 250 µm sieve . The bioassay comprised two cohorts of honeybees of known ages . For cohort 1 , brood frames containing pupae were removed from the colony to an observation chamber in an incubator ( 34°C ) 26 days before the bioassay . Approximately 1000 adult worker bees that emerged over a 24 hr period were marked on the thorax using modelling paint , and then returned to the colony . The evening before the bioassay , a mesh field cage ( 3×3×2 m ) was placed over the colony to confine foragers emerging from the colony the next morning . Approximately 200 marked bees were then collected as foragers and placed individually in bijou bottles within an insulated cooler box . Cohort 2 consisted of one day old adult bees collected from a brood frame from the same colony and held in an observation chamber as described above . For each cohort , groups of 15 honeybees were placed in Universal bottles containing 0 . 5 g of M . anisopliae conidia powder . Controls were placed in bottles with no conidia powder . Bottles were rotated gently for 30 s and then left at 30°C in darkness for 30 min to give time for honeybees to shake off excess powder . Each group of 15 honeybees was then transferred to a clear Perspex box ( 13 cm×4 cm×4 cm and drilled with ventilation holes ) lined with a sheet of tissue paper and containing two drip feeders ( one with distilled water and one with 10% sucrose solution ) . Boxes were maintained in darkness at 30°C and 72% RH for 24 hrs before being maintained at ambient humidity for the remainder of the bioassay . Water and sucrose feed solution were changed ad libitum . A census of survivorship was done twice a day for six days . All groups of honeybees were handled in the same way . Dead honeybees were incubated on damp filter paper within Petri dishes and observed for the presence of sporulating mycelium of M . anisopliae in order to confirm fungus-induced mortality . A small number of honeybees found dead less than 12 hrs after treatment were assumed to have died as a result of handling and were removed from the experiment . Controls consisted of two batches of 15 honeybees each ( n = 30 ) , and fungus-treatments consisted of four batches of 15 honeybees each ( n = 60 ) . In addition , one extra bioassay box was set up for each of the four treatments . After 48 hrs , honeybees from these boxes were transferred to liquid nitrogen and then stored at −80°C prior to RNA extraction ( this time was chosen as it takes at c . 48 h for spores of M . anisopliae s . l . to germinate on an insect surface and then penetrate into the body [94] ) . We tested for differences in survival between each of the four experimental treatments , ( house honeybees , forager honeybees ) × ( uninfected , infected ) , using parametric survival regression [95] . Groups of biological replicates were modelled as gamma distributed random effects [96] . Individual honeybees were ground in liquid nitrogen . RNA extraction was done on 50 mg powdered material using TRIzol Reagent ( Invitrogen ) according to the manufacturer's instructions . Total RNA was purified using RNeasy spin columns ( Qiagen RNeasy Plant Mini kit ) and treated with RNAse-free DNAse I ( New England Biolabs ) . RNA concentration and purity was determined by lab-on-chip analysis using a 2100 Bioanalyzer and an RNA 6000 LabChip ( Agilent Technologies ) . 1 µg of total RNA from each total RNA preparation from an individual honeybee was amplified to produce Cy3- or Cy5-labelled aRNA probes using a low input RNA fluorescent linear amplification kit ( Agilent Technologies , Santa Clara USA ) . The eArray platform from Agilent Technologies was used to design 60-mer oligonucleotide probes for a microarray based on the A . mellifera transcriptome , comprising 10498 mRNA sequences from the Official Honeybee Gene Set 1 [97] . In addition , 22 sequences from eight honeybee viruses taken from GenBank were included: deformed wing virus ( DWV ) ; Varroa destructor virus ( VDV-1 ) ; honeybee slow paralysis virus; black queen cell virus; acute bee paralysis virus; Kashmir bee virus; Israeli acute paralysis virus; and sacbrood virus . The microarray slide ( Agilent Design ID: 019875 ) consisted of eight arrays of 15000 elements each including honeybee and virus probes as well as standard internal controls . The microarray experiment used a two-channel ( dye ) system to make direct comparisons between pairs of samples within a customised Agilent 8-pack array ( with each slide containing eight separate arrays , and with each array having two independent samples applied , one labelled with each of the two dyes ) . Four slides were available for the experiment , providing 32 arrays to make comparisons between the 32 samples included in the experiment . These 32 samples comprised eight biological replicates of each of four treatment combinations – forager honeybees treated with M . anisopliae , forager honeybees not treated with M . anisopliae , house honeybees treated with M . anisopliae , house honeybees not treated with M . anisopliae – considered to comprise a 2-by-2 factorial structure for honeybee type ( forager , house ) and infection status ( infected with M . anisopliae , uninfected ) . Each of the 32 samples was hybridised to two different arrays , once with each dye , and was co-hybridised with two different other samples , as follows: Each slide contained two arrays for each of the four possible treatment comparisons , with most comparisons within an array being between samples given the same arbitrary biological replicate labels , but with all direct comparisons between uninfected forager honeybee samples and infected forager honeybee samples being between differently labelled biological replicates ( see Figure S2 for a full diagrammatic representation of this design ) . This linking of the arbitrarily labelled biological replicates ensured that the design was fully connected ( each sample can be indirectly compared with every other sample ) , also providing links between the observations made on different slides . Microarray slide scanning was done using an Agilent Technologies GA2565BA Scanner . Microarray data were processed from raw data image files using feature extraction software ( Agilent Technologies ) . At each probe location , Cy3 and Cy5 intensities were measured as median values of green and red pixels respectively . All probe measurements were corrected for local background intensities . In addition , dark corner corrections were made for each array . Preliminary data inspection supported normalisation by logarithm transformation; base two allowed for intuitive interpretation of changes in gene regulation ( a difference of one equates to a two fold change in expression ) . Spatial bias across arrays was controlled with two-dimensional local smoothing ( ‘loess’ ) separately for each array . This processed dataset was used to test hypotheses on the effects of honeybee role and infection status on whole genome expression . Statistical analyses were conducted using the R statistical programming platform , version 2 . 7 . 1 ( http://www . r-project . org ) . Processed data were modelled in a mixed effects framework using the MAANOVA library from the bioconductor suite of packages ( http://www . bioconductor . org , accessed 03/07/12 ) . Consistent with our bioassay , we used a factorial experimental design , ( house honeybee , forager honeybee ) × ( uninfected , infected with M . anisopliae ) . We were motivated to understand transitions between age-related stages ( house→forager ) and fungal disease states ( uninfected→infected ) , quantifying the appropriate contrasts: uninfected vs . infected house honeybees; uninfected vs . infected forager honeybees; and uninfected house honeybees vs . uninfected forager honeybees . Since fungal infected house honeybees died before developing into forager honeybees , this final contrast was not explicitly quantified . In addition , we modelled the presence of naturally occurring asymptomatic viruses within sampled honeybees as a two level ( ‘low’ , ‘high’ ) categorical covariate ( see supplementary information Figure S3 ) . We modelled other experimental sources of uncertainty as variance components ( ‘slide’ crossed with ‘array’ , and ‘dye’ ) . Two level experimental treatment contrasts were assessed using t-tests . We identified changes in expression with a probability threshold of p< ( 1/number of probes ) , thereby reducing the expected false positives to less than one probe [98] . Changes in expression identified as significant were further categorised as up- or down-regulated . The set of raw microarray data is available via ArrayExpress at the European Bioinformatics Institute ( accession number E-MTAB-1214 ) . The microarray statistical analysis identified sets of genes that were differentially expressed in association with the treatment contrasts used in the bioassay . These sets of differentially expressed genes were subject to Gene Ontology ( GO ) analysis to identify significantly over-represented GO terms . As functional annotation of the bee genome is incomplete , we ascribed putative Gene Ontology classifications to as many genes as possible based on homology to Drosophila melanogaster . Using reciprocal best-BLAST hit ( RBH ) criteria , 6325 ( 62% ) of honeybee genes had an assignable fly ortholog . We were then able to determine which GO categories are statistically over-represented in groups of differentially expressed genes , using Cytoscape ( version 2 . 6 . 0 , Agilent Technologies ) and the BiNGO Plug-in . Over-representation of terms was determined through a Hypergeometric test ( 0 . 05 significance level ) , using the Venn diagram intersection combination genes versus the whole genome annotation as the background ‘universe’ . Benjamini & Hochberg False Discovery Rate correction was applied . FlyBase gene identifiers were converted to EntrezGene IDs using Ensembl Biomart via the webserver ( http://www . ensembl . org ) . The expression of honeybee beta actin and vitellogenin genes , as well as the levels of M . anisopliae rRNA , were analysed using qRT-PCR for each of the 32 biological replicates in the experiment . Superscript II reverse transcriptase ( Invitrogen ) and random hexanucleotides were used to produce cDNA from DNAse I treated total RNA . Real time quantitative PCR was carried out using the Platinum SYBR Green qPCR kit ( Invitrogen ) in triplicates in 20 µL reactions in the ABI PRISM 7900HT system ( Applied Biosystems ) . The amplification program included 2 min at 50°C , 10 min at 95°C , and 40 cycles , 95°C for 15 sec , 60°C for 1 min . Honeybee beta actin mRNA was quantified using primers 5′-AGGAATGGAAGCTTGCGGTA-3′ and 5′-AATTTTCATGGTGGATGGTGC-3′ . Honeybee vitellogenin mRNA was quantified using primers 5′-cggcACGAGTACCTGGACAAGGCcG-3′ and 5′-TCCTTGAAATGTGCATCCATGA -3′ . Finally , M . anisopliae 18 s rRNA was quantified with the primers 5′-CCAACCCCTGTGAATTATACC-3′ and 5′-CGATCCCCAACACCAAGTC-3′ .
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Honeybees have a highly developed form of social biology in which tasks are distributed among workers according to their age , with younger bees performing housekeeping tasks ( “house bees” ) before switching to foraging duties when they grow older . This division of labor is vital to colony function and survival . Pathogens are known to be partly responsible for the current decline in honeybee populations around the world , but we understand little about the responses of different types of worker bee to infection . In this study , we infected house and forager bees with an insect pathogen . We measured bee survival rate and the expression of genes that regulate the immune system . More immune genes were up regulated in house bees than foragers in response to infection , but foragers were more resistant to the pathogen than house bees . We found that development from the house to forager stages resulted in increased expression of genes that regulate the production of antimicrobial proteins . The inference is that parts of the immune system are activated during development , resulting in greater resistance to infectious disease in forager bees . Our study provides new insights into the functioning of the honeybee immune system and its interaction with social organisation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"agroecology",
"ecology",
"immunology",
"biology",
"genomics",
"zoology",
"genetics",
"and",
"genomics",
"agriculture"
] |
2012
|
A Strong Immune Response in Young Adult Honeybees Masks Their Increased Susceptibility to Infection Compared to Older Bees
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Mechanisms involved in severe P . vivax malaria remain unclear . Parasite polymorphisms , parasite load and host cytokine profile may influence the course of infection . In this study , we investigated the influence of circumsporozoite protein ( CSP ) polymorphisms on parasite load and cytokine profile in patients with vivax malaria . A cross-sectional study was carried out in three cities: São Luís , Cedral and Buriticupu , Maranhão state , Brazil , areas of high prevalence of P . vivax . Interleukin ( IL ) -2 , IL-4 , IL-10 , IL-6 , IL-17 , tumor necrosis factor alpha ( TNF-α , interferon gamma ( IFN-γ and transforming growth factor beta ( TGF-β were quantified in blood plasma of patients and in supernatants from peripheral blood mononuclear cell ( PBMC ) cultures . Furthermore , the levels of cytokines and parasite load were correlated with VK210 , VK247 and P . vivax-like CSP variants . Patients infected with P . vivax showed increased IL-10 and IL-6 levels , which correlated with the parasite load , however , in multiple comparisons , only IL-10 kept this association . A regulatory cytokine profile prevailed in plasma , while an inflammatory profile prevailed in PBMC culture supernatants and these patterns were related to CSP polymorphisms . VK247 infected patients showed higher parasitaemia and IL-6 concentrations , which were not associated to IL-10 anti-inflammatory effect . By contrast , in VK210 patients , these two cytokines showed a strong positive correlation and the parasite load was lower . Patients with the VK210 variant showed a regulatory cytokine profile in plasma , while those infected with the VK247 variant have a predominantly inflammatory cytokine profile and higher parasite loads , which altogether may result in more complications in infection . In conclusion , we propose that CSP polymorphisms is associated to the increase of non-regulated inflammatory immune responses , which in turn may be associated with the outcome of infection .
Malaria caused by Plasmodium vivax is responsible for approximately 50% of malaria cases that occur outside Africa , predominantly in countries which are in the disease elimination or pre-elimination phase [1] . Once considered clinically mild when compared with P . falciparum infection , P . vivax malaria cause debilitating effects that affect social and economic indices of the endemic regions and has been associated with the occurrence of severe cases around the world , including Brazil [2–5] . The mechanisms involved in this process are poorly understood [1 , 6] , however evidences of parasite virulence , host inflammation and parasite burden might play a key role in malaria outcome[7] . Parasite virulence can be determined by genetic variations in Plasmodium , particularly on genes encoding immunogenic parasite antigens , such as the repeated portion of the central region of circumsporozoite protein ( CSP ) [8] , which is highly immunogenic , being one of the most studied epitopes of the Plasmodium genus [9] . In P . vivax , CSP exhibits two highly conserved terminal regions ( N- and C-terminal ) and one variable central region composed by two nonapeptides that repeat in tandem , GDRA ( A/D ) GQPA and ANGA ( G/D ) ( N/D ) QPG , characteristic of the VK210 and VK247 molecular variants , respectively [10 , 11] . In addition to these two variants , there is also a third variation , P . vivax-like , whose CSP present the repeated APGANQ ( E/G ) GGAA sequence [12] . In Brazil , serological and molecular studies have shown the prevalence of these genotypes across the country , especially in the Brazilian Amazon region as confirmed by molecular diagnosis , in a study carried out by Machado and Póvoa [13] in the states of Rondônia , Amapá and Pará . Interestingly , in this work the occurrence of VK210 variant was observed in single infections , whereas the VK247 and P . vivax-like variants were demonstrated only in mixed infections . The distribution of these variants was reassessed later in five endemic areas of Brazil , and VK210 still the most prevalent variant . However , these results showed a change in the dynamics of the distribution of the VK247 and P . vivax-like variants because both of them were also observed in single infections [14] . Immunologically , the course of infection by Plasmodium depends on the balance in the production of pro- and anti-inflammatory cytokines . In cases where an inflammatory pattern is prevalent , the disease tends to be more severe . In severe malaria , there is an increased level of inflammatory cytokines , such as interferon gamma ( IFN-γ , tumor necrosis factor alpha ( TNF- α and interleukin ( IL ) -6 [15 , 16] . In the other hand , high levels of IL-10 have also been reported in individuals with severe disease and has been associated with cerebral malaria . Nonetheless , the upregulation of IL-10 appears to occur after the increase in inflammatory cytokines , due to a regulatory mechanism , to prevent the exacerbated of inflammatory response and its deleterious effects [15–17] . There are still many questions concerning which factors inherent to the host and the parasite are responsible for increased systemic inflammation , parasite load and worsening of the disease . CSP variants infection may affect drug response , symptom severity and humoral response patterns in the host [13 , 18 , 19] , however the influence of these variants in the cytokine response and parasite load remains unclear . Thus , the present study aimed to characterize the molecular variants of CSP and to correlate them with the cytokine profile and the parasite load in studied region . This research can demonstrate , even indirectly , differences in the degree of virulence of these P . vivax variants and clarify the immunological aspects of infection by P . vivax variants to understand this new scenario associated with the disease .
A cross-sectional study was conducted from January 2011 to February 2013 with P . vivax-infected patients from São Luís , Cedral and Buriticupu , a pre-Amazon region of the state of Maranhão , which presents a high prevalence of P . vivax infections . Eligible P . vivax-infected patients were selected among people who sought medical care at the Center for Infectious and Parasitic Diseases ( CREDIP ) in São Luís , Unit of Basic Health ( UBS ) in Cedral and Center of Health UFMA in Buriticupu . Epidemiological questionnaires were applied to every patient who freely agreed to participate the study . The patients group included individuals of both genders who were positive for Plasmodium vivax in thick blood smear and negative for Plasmodium falciparum , that had not yet received treatment . A control group included healthy individuals who lived in the same area , that were not infected with either Plasmodium vivax or Plasmodium falciparum . The thick blood smear malaria diagnosis was further confirmed by nested PCR with species-specific primers based on the Plasmodium small subunit ribosomal RNA ( ssrRNA ) genes , as described by Singh and colleagues[20] with modifications ( see S1 Method ) . The treatment recommended by Brazilian Ministry of Health was guaranteed for all patients , including those who did not agree to participate in the study . A total of 8 to 10 mL of blood was collected by standardized venipuncture in ethylenediamine tetraacetic acid ( EDTA ) tubes Vacutainer ( Becton Dickinson , San Jose , CA , USA ) . Then , aliquots obtained from plasma were frozen at -80°C for subsequent evaluation of the cytokines concentration . Approximately 7 mL of the remaining blood was used to the separation process on a Ficoll-Paque PLUS gradient ( GE Healthcare , New Jersey , USA ) to obtain PBMC . The cells were then resuspended in 1 mL of RPMI 1640 medium ( Sigma , St . Louis , USA ) , supplemented with 2 mM L-glutamine ( Sigma , St . Louis , USA ) , 1% streptomycin ( 100 μg/mL , Merck ) , penicillin G ( 100 U/mL , Sigma , St . Louis , USA ) and 10% fetal bovine serum ( Sigma , St . Louis , USA ) . Sample were stained with Trypan Blue and cell counts and viability were accessed using a hemocytometer ( Neubauer ) chamber . Subsequently , the cells were cultured as posteriorly described . Erythrocytes were stored in two aliquots at -20°C to characterize the molecular variants of P . vivax and to quantify the parasite load as described later . PBMCs ( 2 x 106 cells/mL/well ) were plated in duplicates in 48-well flat bottom plates for 48 h at 37°C in a humidified incubator containing 5% CO2 . After this period , the culture supernatants were collected and frozen at -80°C for subsequent analysis of the cytokines . The concentrations of the IL-2 , IL-4 , IL-6 , IL-10 , IFN-γ , TNF-α , IL-17 cytokines were measured using CBA . Th1/Th2/Th17 ( Becton Dickinson Biosciences , San Jose , CA , USA ) . The dosage of TGF-β was carried out using the Single Plex Flex Set ( Becton Dickinson Biosciences , San Jose , CA , USA ) kit . A total of 50 μL of plasma sample and culture supernatants were analyzed in FACSCalibur flow cytometer ( Becton Dickinson , San Jose , CA , USA ) previously calibrated with “setup beads” incubated with fluorescein isothiocyanate ( FITC ) or phycoerythrin ( PE ) according to the manufacturer`s recommendations . A standard curve was performed for each cytokine . The results were analyzed in the FCAP Array Software ( Becton Dickinson , San Jose , CA , USA ) and the values were expressed in pg/mL for each cytokine . Plasmodial DNA was extracted from an aliquot of erythrocytes using the Easy-DNA Kit ( Invitrogen , Carlsbad , CA , USA ) , according to the recommendations of the manufacturer . The amplification was performed according to the description by Cassiano and colleagues , with modifications [21] . A reaction mix with a final volume of 25 μL was obtained: P . vivax DNA , 1 X PCR buffer ( 20 mM Tris–HCl pH 8 . 4 , 50 mM KCl ) , 1 . 6 mM MgCl , 0 . 2 mM of each dNTP , 0 . 2 μM of each primer ( 5’ AGGCAGAGGACTTGGTGAGA 3’ and 5’CCACAGGTTACACTGCATGG 3’ ) and 1 U of Taq Platinum ( Invitrogen , Carlsbad , CA , USA ) . The reaction was performed in a thermocycler ( DNA MasterCycler , Eppendorf , Germany ) as follows: an initial cycle of 94°C for 15 min , followed by 30 cycles of 94°C for 1 min , 58°C for 1 min and 72°C for 1 min , with a final extension at 72°C for 10 min . As a positive control , three plasmids were used , containing a gene insert of the repeat portion of CSP amplified from the VK210 , VK247 and P . vivax-like variants ( BlueScript , Stratagene , La Jolla , USA ) . For the negative control of the reaction , sterile water was used . The P . vivax CSP variants were characterized by the analysis of the PCR-restriction fragment length polymorphism ( PCR-RFLP ) , following the method described by Cassiano and colleagues [21] . The digestion reaction was performed in a final volume of 20 μL: 10 U of AluI ( Invitrogen ) , 2 μL of the enzyme reaction buffer , 10 μL of the PCR product and 7 μL of sterile DNAse-free water . The reactions were performed in a water bath at 37°C overnight . Plasmodial DNA was extracted from an aliquot of the blood using the QIAamp DNA Mini Kit ( QIAGEN , Hilden , Germany ) , according to the recommendations of the manufacturer . The qPCR was developed according to Gonçalves et al . [22] , using the commercial kit Maxima SYBR Green ( Fermentas , Lithuania ) and preparing a reaction mix with a final volume of 15 μL: 7 . 5 μL of Maxima SYBR Green master mixture , 0 . 5 μM of each primer , 4 . 0 μL of DNAse-free water and 2 μL of genomic DNA . The P1 genus-specific primer ( 5’-ACGATCAGATACCGTCGTAATCTT-3’ ) was combined with a species-specific oligonucleotide primer for P . vivax , V1 ( 5’-CAATCTAAGAATAAACTCCGAAGAGAAA-3’ ) . These primers amplified a 100-bp species-specific fragment of the 18S rRNA gene . The assays were performed in triplicate in a Mastercycler Realplex Gradient Thermal Cycler ( Eppendorf , Hamburg , Germany ) and consisted in the denaturation of the template DNA at 95°C for 10 minutes and 40 cycles of 15 seconds at 95°C and 1 minute at 60°C , acquiring fluorescence at the end of each step of the extension . The amplification was immediately followed by a dissociation curve that consisted of 15 seconds at 95°C , 15 seconds at 60°C and one gradual temperature increase of 0 . 2°C . s-1 up to 95°C , acquiring fluorescence at each temperature transition . The result obtained by the software is expressed as the number of copies of the plasmodial genome in a DNA sample , which allows the estimation of the corresponding number of parasites in the sample . The sequence target possessed a length of about 100 base pairs . For quantitation of parasitemia it was used as standard an amplified fragment with oligonucleotide species-specific , purified and cloned into plasmid vector ( pGEM T-Easy , Promega ) , used to transform bacteria DH10B strain . After confirmation of the sequence of cloned recombinant plasmids by sequencing , the solutions with the correct sequence were measured by spectrophotometry and the number of copies per μL of the target sequence was calculated . It was constructed a curve with 10 points from the serial dilution ( dilution factor: 10 ) of the plasmidial solution . The first point contained 1 . 468x109 copies of the target sequence and the qPCR quantification limit was 0 . 2 parasites/μL of blood . The statistical analyses were performed using GraphPad Prism 5 . 0 and Stata 12 softwares . The gender proportion on the variants was compared using Fisher’s exact test . After the normality test of D’Agostino and Pearson , the differences observed in the cytokine profile were evaluated using the two-tailed Mann-Whitney test . For the correlation analyses , the values were log transformed , and the Spearman test was used to evaluate individual cytokines and parasite load associations . To verify the multiple association of cytokines with parasite load the multivariable linear regression model was used . Cytokines and parasite load data were expressed by median ( interquartile range-IQR ) . Differences were considered significant when p values ≤ 0 . 05 . The present study was approved by the Ethics and Research Committee of the Universidade Federal do Maranhão ( Protocol no . 23115 008013/ 2010–07 ) . All the study participants signed a written informed consent or had their legal guardians , if they were underage .
A total of 33 individuals tested positive for P . vivax , however 8 were excluded because they had already started treatment . The 25 remaining patients were from the pre-Amazon region of Maranhão , of both genders ( 24% were women and 76% were men ) with mean of 32 . 9 years old ( range 3 and 58 years ) . All patients were identified infected only with P . vivax . The healthy group consisted of nine individuals from pre-Amazon region , state of Maranhão , Brazil , which had similar profile to patients group , with mean of 29 . 4 years old ( 23% women and 77% men ) . The most prevalent genotype was VK210 ( 56% ) , followed by mixed infection ( VK210/VK247 ) ( 24% ) and VK247 ( 20% ) . None of the patients had the P . vivax-like variant . VK210 , VK247 and mixed infection groups had a similar mean of age ( 38 . 7 , 44 . 4 and 43 . 4 years , respectively ) . The gender difference was not statistically significant among VK210 and VK247 groups ( 85 . 8% and 80% of males , respectively ) ( p = 1 . 0 ) . On the other hand , mixed infection group had a half of male patients ( 50% ) , but also was not statistically different of VK247 ( p = 0 . 54 ) and VK210 ( p = 0 . 13 ) proportion . The plasma levels of IL-2 , IL-4 , TNF-α and IL-17 were very low or undetectable in patients and healthy group . The concentrations of IL-6 ( median of 1 . 75 pg/mL; IQR 0 . 005–18 . 7 pg/mL ) ( Fig 1A ) and IL-10 ( median of 33 . 05 pg/mL; IQR 3 . 9–135 . 4 pg/mL ) ( Fig 1B ) , in turn , were higher in individuals infected by P . vivax ( p = 0 . 0009 and p = 0 . 0002 , respectively ) when compared to healthy group ( median of zero for both IL-6 and IL-10 ) . Conversely , the TGF-β concentration was lower in infected individuals ( median of 22417 pg/mL; IQR 14341–33508 pg/mL ) ( p = 0 . 0009 ) when compared to healthy group ( median of 90507 pg/mL; IQR 56580–107531 pg/mL ) ( Fig 1C ) . Only eight patients had detectable IFN-γ concentrations with a median of approximately zero; however , there was no significant difference compared to the healthy group ( Fig 1D ) . The median parasite load quantified by qPCR was 837 parasites/μL ( IQR 65–2000 parasites/μL ) , with positive correlation both with IL-6 ( r = 0 . 68; p = 0 . 0002 ) and IL-10 ( r = 0 . 64; p = 0 . 0005 ) ( Fig 2A and 2B ) , indicating that these two cytokines tend to increase in response to a raise in parasitaemia . In addition to their correlation with the parasite load , IL-6 and IL-10 also had a positive correlation with each other ( r = 0 . 86 and p<0 . 0001 ) ( Fig 2C ) . The correlation between the remaining cytokines was not performed due to very low or undetectable concentrations in the plasma of patients . However , the multiple linear regression analyses among the IL-10 , IL-6 and parasite load showed that only IL-10 keeps this association with parasite load ( r = 0 . 79 and p = 0 . 005 ) ( Table 1 ) . The parasite loads were higher in individuals infected by VK247 ( median of 3 . 35 log parasites/μL; IQR 3 . 1–3 . 8 log parasites/μL ) compared to patients infected by VK210 ( median of 2 . 5 log parasites/μL; IQR 1 . 8–3 . 1 log parasites/μL ) ( p = 0 . 05 ) and patients with mixed infection ( median of 1 . 8 log parasites/μL; IQR 0 . 52–3 . 1 log parasites/μL ) ( p = 0 . 01 ) ( Fig 3A ) . The same fact was observed regarding IL-6 concentrations , which levels were higher in patients with VK247 ( median of 16 . 7 pg/mL; IQR 13 . 9–82 . 39 pg/mL ) compared to patients with VK210 ( median of 1 . 08 pg/mL; IQR 0 . 0001–25 . 54 pg/mL ) ( p = 0 . 04 ) and to those with mixed infections ( median of 0 . 08 pg/mL; IQR 0 . 0001–11 . 25 pg/mL ) ( p = 0 . 05 ) ( Fig 3B ) . The variants did not influence the IL-10 concentrations ( Fig 3C ) , but in patients infected with VK210 , the IL-10 levels correlated with IL-6 concentrations ( r = 0 . 92 and p<0 . 0001 ) ( Fig 3E ) . However , in patients with VK247 , this was not observed and no correlation between the two cytokines was found ( r = 0 . 3 and p = 0 . 68 ) ( Fig 3D ) , which indicates that the increase in inflammatory cytokines was not followed by the regulatory effect of IL-10 in these patients . In the PBMC culture supernatants , only the IL-10 ( median of 0 . 09 pg/mL; IQR 0 . 0001–2 . 5 pg/mL ) and TNF-α ( median of 10 . 02 pg/mL; IQR 0 . 78–52 . 06 pg/mL ) concentrations were significantly different compared to the healthy group ( median of zero for IL-10 and 54 . 98 pg/mL with IQR 23 . 3–375 . 6 pg/mL for TNF-α; IL-10 was higher ( p = 0 . 03 ) ( Fig 4B ) and TNF-α was lower ( p = 0 . 03 ) ( Fig 4C ) . In contrast to the results observed in the plasma , IL-6 produced by PBMCs of patients ( median of 194 pg/mL; IQR 38 . 3–808 . 3 pg/mL ) did not show a significant difference from the healthy group ( median of 126 pg/mL; IQR 55 . 8–1472 pg/mL ) ( Fig 4A ) . Interestingly , a different cytokine profile was observed between plasma and the PBMC cultures supernatants . The median IL-10 concentration was 367 times higher in the plasma from patients when compared to that of the culture supernatant . The concentration of the inflammatory cytokines TNF-α and IL-6 were higher in the culture supernatant than in the plasma , and IL-6 reached 111 times higher concentrations in the supernatant ( Fig 4D ) . Most patients had an increased IL-6 concentration in the culture supernatant compared to that in the plasma ( Fig 4E ) . Individuals who exhibited a greater IL-10 response in the plasma ( above 30 pg/mL ) had cells that produced lower amounts of the same cytokine in culture ( below 8 pg/mL ) ( Fig 4F ) . In addition , patients with low levels of IL-10 in the plasma maintained this low baseline production in culture supernatant . Altogether , these facts explain the small median value found for this cytokine in the culture supernatant . Little or no TNF-α production was detected in the plasma of infected patients , however an increase in production was observed the culture supernatants ( Fig 4G ) .
P . vivax sporozoites are covered with CSP , a highly immunogenic protein , which is involved in invasion mechanisms into hepatocytes . In the present study , it was shown that the CSP polymorphisms are determinants for both the cytokine balance and the parasite load in vivax malaria . Further , the VK247 variant induced a more prominent inflammatory profile and the highest parasite loads , suggesting that these CSP polymorphisms have systemic effects changing variables that may influence the course of infection . Initially , it was observed that patients infected with P . vivax showed increased plasma IL-10 and IL-6 levels which was associated with the parasite load . This result corroborates with other authors who demonstrated that increased concentrations of IL-6 and IL-10 producing cells , were associated with increased parasitaemia [16 , 23] . Our results support the notion that in the initial moments of the infection , the IL-6 concentration increased as a response to increase in parasite load and is subsequently controlled by IL-10 secretion as has been discussed for other authors [15–17] . This hypothesis was reinforced in this study by the multivariable regression analyses , which showed that , despite the individual correlation of parasite load with IL-6 , only IL-10 is associated to parasite load in this context . Thus , we suggest that the increase of IL-6 induces a counter-regulatory IL-10 production , which , in turns , is crucial to the balance of the parasite-host interaction . It is important to emphasize that the severity of the malaria is intrinsically related to inflammatory reaction and , despite the fact that IL-10 prevents the deleterious effects caused by a exacerbated inflammatory response [23 , 24] , it may contributes to the maintenance of the parasite in the host , in an equilibrated relation . Hence , it is reasonable to think that the malaria outcome depends on the ability of parasite to regulate the inflammatory response by induction of IL-10 production . This hypothesis is reinforced by a murine model where it was demonstrated that IL-10 depleted mice are able to control parasite replication during P . chabaudi AS infection , however , they develop a severe malaria mediated by inflammatory cytokine action [25 , 26] . The cytokines IL-10 , IL-6 and TNF-α were detected in the PBMC culture supernatants , however an inversion in the cytokines pattern was observed in relation to plasma levels ( Fig 4D ) . The IL-10 concentration was higher in plasma than in the supernatant ( Fig 4E ) , in the other hand inflammatory cytokines such IL-6 and TNF-α were higher in the supernatant ( Fig 4E and 4F ) . These results suggest that despite the PBMC from patients produced more IL-10 than healthy individuals ( Fig 4B ) , the IL-10 production was almost abolished in the absence of systemic influence of parasite ( Fig 4D ) . Based on that we suppose that the regulatory pattern observed in vivo is not necessary ex vivo anymore , despite the presence of high levels of IL-6 , differently from that observed in vivo , where the regulatory response was important to control IL-6 . Given this regulatory response that seems to occur in vivo , we investigated the influence of CSP gene polymorphisms on the plasmatic cytokine profile . The present study provided evidences , even that indirectly , of differences in the modulation of host response and malaria outcome among variants of this protein . In this study , there was a prevalence of VK210 in single infections , what corroborate with other studies performed in the same region [14 , 27] . This demonstrates that there was no dominance alternation among the variants along years in this endemic area . It has already been shown that individuals with severe vivax malaria have an imbalance in their cytokine response toward an inflammatory profile and also have higher parasitaemia compared to those with uncomplicated malaria [15] . Based on this fact , we investigated the influence of the variants on the parasite load of the individuals . The results of Fig 3A show that patients infected with VK247 exhibited a significantly higher parasite load than those with VK210 and mixed infections . This result differs from that found by Machado and Póvoa [13] , who observed the greatest parasitaemia in individuals infected by VK210 . This difference may be because the authors not found patients with VK247 in single infections , what impairs to compare the results . A possible hypothesis for VK247 higher parasite loads is the “immune selection” occurrence , since the constant VK210 prevalence over all these years in analyzed region may have generated high anti-VK210 antibody titers , hindering the infection establishment by this variant and facilitating by VK247 . Even though we did not conduct the antibodies research , it is known that in Brazilian Amazon endemic areas there is a smaller antibody response to VK247 in the presence of VK210 [18] . Even though CSP expression occurs only in liver stage [28] the immunological response to this protein reflects in blood stage of the parasite , since it is a target not only to humoral response but also to a protective cellular response [29] . Moreover , in a murine model of natural infection , it was demonstrate that CSP-specific CD8+ T cells were primed by dendritic cells not only locally in liver but also in draining lymph nodes [30] . Beside this , it was found in a murine model that sporozoite antigen persists for over 8 weeks after immunization and remains being presented to naive cells , including those that are recently recruited from thymus [31] . In order to investigate the systemic influence of CSP variants , we performed correlation analysis of these variants with the plasmatic cytokines . A significant increase of IL-6 in patients with VK247 infection was observed when compared to VK210 and mixed infections ( Fig 3B ) . On the other hand , there was no difference in IL-10 production among the groups ( Fig 3C ) . Additionally , in patients with VK210 infection there was a significant positive correlation between IL-6 and IL-10 , which suggests that the increase in inflammatory cytokines was followed by increase in the regulatory response in the same patients ( Fig 3E ) , correlation that was not observed in VK247 infected patients ( Fig 3D ) . These findings demonstrate that VK247 may cause a sustained and stronger inflammatory response than VK210 since this response was not followed by IL-10 . Due to this absence of association , we suggest that the necessary equilibrium to control the infection is not present in the VK247 , since there is a high inflammatory response and parasite load , without association to the counter-regulatory effect of IL-10 cytokine . Based on this context we suggest that VK247 may induce a more severe infection pattern . Despite the apparent association of plasmatic inflammatory pattern to the variant , there was no significant difference in IL-6 and IL-10 in PBMC supernatant . Besides , there was no correlation between these cytokines in each variants groups ( S1 Fig ) . This result from plasma and PBMC cultures demonstrates that the CSP variants may be , at least partially , different in their ability to induce initial inflammatory response . In conclusion , we showed for the first time that P . vivax CSP polymorphisms may have systemic effects that influence the cytokine profile and parasite load . It is important to emphasize that VK247 is not common in single infection in the pre-Amazon region [13 , 14 , 27] . However , in our study , the sample of VK247 in single infection was representative since we had a high percentage ( 20% ) . Our findings represent an important step for better understanding the new scenario related to vivax malaria and shows that polymorphisms in immunogenic proteins of the parasite may be crucial to better understanding this infection and predict malaria outcome .
|
Recent evidences have associated P . vivax infections with clinical complications , previously only attributed to P . falciparum malaria . The interaction between host and parasite may contribute to severity of the disease , however , the specific contribution of each factor remains unclear . Previous studies have shown that polymorphisms in Plasmodium vivax CSP may interfere in systemic reactions , response to drug treatments , leading to different symptoms as well as humoral responses . In this study , we investigate whether these polymorphisms could influence the parasite load and cytokine profile , which altogether may influence the malaria outcome . In this sense , studies have shown that subjects with high parasitic loads , responding with production of pro-inflammatory cytokines develop a more severe disease . The present data indicate that VK247 variant are associated with significant higher parasite loads and pro-inflammatory cytokine profile compared to VK210 variant . In this regard , this study demonstrates that P . vivax CSP polymorphisms have systemic effects in the host immune response , and the investigation of immunogenicity of parasite proteins may provide evidences for a better understanding of this infection .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
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2016
|
Polymorphisms in Plasmodium vivax Circumsporozoite Protein (CSP) Influence Parasite Burden and Cytokine Balance in a Pre-Amazon Endemic Area from Brazil
|
We seek to elucidate the role of macromolecular crowding in transcription and translation . It is well known that stochasticity in gene expression can lead to differential gene expression and heterogeneity in a cell population . Recent experimental observations by Tan et al . have improved our understanding of the functional role of macromolecular crowding . It can be inferred from their observations that macromolecular crowding can lead to robustness in gene expression , resulting in a more homogeneous cell population . We introduce a spatial stochastic model to provide insight into this process . Our results show that macromolecular crowding reduces noise ( as measured by the kurtosis of the mRNA distribution ) in a cell population by limiting the diffusion of transcription factors ( i . e . removing the unstable intermediate states ) , and that crowding by large molecules reduces noise more efficiently than crowding by small molecules . Finally , our simulation results provide evidence that the local variation in chromatin density as well as the total volume exclusion of the chromatin in the nucleus can induce a homogenous cell population .
Even in an isogenic cell population under constant environmental conditions , significant variability in molecular content can be observed . This variability plays an important role in stem cell differentiation [1] , cellular adaptation to a fluctuating environment [2] , variations in cellular response to sudden stress [3] , and evolutionary adaptations [4] . However , it can also be detrimental to cellular function and has been implicated as a factor leading to dangerous diseases such as haploinsufficiency [5] , cancer [6] , age-related cellular degeneration , and death in tissues of multicellular organisms [7] . The variability stems both from stochasticity inherent in the biochemical process of gene expression ( intrinsic noise ) and fluctuations in other cellular components ( extrinsic noise ) , namely , stochastic promoter activation , promoter deactivation , mRNA , and protein production and decay , as well as cell-to-cell differences in , for example , number of ribosomes [8–13] . One consequence of biological noise in gene expression is transcriptional bursting , which is observed in both prokaryotes [14] and eukaryotes [12 , 15] . Transcriptional bursting can bring about a bimodal distribution of mRNA abundance in an isogenic cell population [16–18] . Therefore , understanding critical factors that influence noise in gene expression can provide us with a new tool to tune cellular variability [19–24] . The cellular environment is packed with proteins , RNA , DNA , and other macromolecules . It is estimated that 30–40% of the cell volume is occupied by proteins and RNA [25] . Macromolecular crowding has been studied extensively in the last few decades [26 , 27] and has been ingeniously utilized for numerous medical purposes [28–30] . It is well established that macromolecular crowding can reduce diffusion rates and enhance the binding rates of macromolecules [31] , which can change the optimal number of transcription factors [32] , the nuclear architecture [33] , and the dynamical order of metabolic pathways [34] . It is known that manipulating the binding and unbinding rates ( kon and koff ) can affect the likelihood of observing transcriptional bursting [42 , 43] . Higher values of kon and koff lead to a bimodal distribution and transcriptional bursting , while keeping the basal ( i . e . in the absence of the bursts ) protein abundance constant . It is also known that macromolecular crowding can alter diffusion and reaction rates [44 , 45] . Together , it is implied that macromolecular crowding can have an impact on protein production in a cellular environment . In a previous study [35] , crowding has been modeled by the direct manipulations of reaction rates using experimentally fitted relations . In contrast , we model macromolecular crowding explicitly by altering the effective diffusion rate of transcription factors . This approach is similar to recent studies performed by Isaacson et al . [46] and Cianci et al . [51]; however , we also consider the effects of the artificial crowding agents , in order to capture analogous experimental conditions performed by Tan et al . [35] . It has been observed experimentally [35] that macromolecular crowding can influence cell population homogeneity and gene expression robustness . In this experiment [35] , the influence of the diffusion of macromolecules on transcriptional activity is studied by synthesizing artificial cells in which inert dextran polymers ( Dex ) assume the role of the artificial crowding agent in the system . To capture the impact of the size of the crowding agent , the experiments are performed on two different sizes of Dex molecules , here referred to as Dex-Big and Dex-Small . It can be inferred from this experiment that a highly crowded environment results in a narrow distribution of fold gene-expression perturbation , suggesting that molecular crowding decreases the fluctuation of gene expression rates due to the perturbation of gene environmental factors . However , the mechanism by which cellular crowding can control gene expression has not been elucidated . We demonstrate through modeling that macromolecular crowding reduces the noise ( kurtosis of the mRNA distribution ) in gene expression by limiting the diffusion of the transcription factors . This increases the residence time of the transcription factor on its promoter , thereby reducing the transcriptional noise . As a consequence , unstable intermediate states of gene expression pattern will diminish . Furthermore , our model reveals that small crowding agents reduce noise less than large crowding agents do , which is in agreement with the experimental observations [35] . Finally , our simulation results provide evidence that local variation in the chromatin density , in addition to the total volume exclusion of the chromatin in the nucleus , can alter gene expression patterns .
A simple and well-studied model was employed to simulate transcription and translation . The model includes: a ) one transcription factor ( TF ) placed randomly in the simulation domain , b ) TF diffusion in order to find the gene locus , c ) binding and unbinding of TF to its promoter , d ) mRNA production , and destruction and e ) protein production and destruction . This model and its corresponding parameters were adopted from Kaeren et al . [8] for the sake of comparison . ( The details of the model are available in Materials and Methods ) . We assume that the initial concentrations of mRNA and the target protein are zero , and use spatial stochastic simulation to investigate the gene expression pattern , a model that has been widely used and verified by both theoretical [36–39] and experimental [39 , 40] observations . To account for crowding , we developed a modified next subvolume method ( NSM ) to approximately solve the reaction-diffusion master equation ( RDME ) [41] capable of explicitly treating the crowding agent amount , distribution , and interactions ( Materials and Methods ) . The NSM method was modified so that the mesoscopic diffusion coefficient is linearly dependent on the crowding density in the destination voxel . In our model , the macromolecular crowding stems from two primary sources: chromatin structure and artificial crowding agents , akin to the Tan et al . experiment [35] . We utilized the 3-dimensional structured illumination microscopy data from [50] to model the chromatin structure . To account for chromatin structure , the crowding density in each voxel was assumed to be proportional to DAPI ( 4' , 6-diamidino-2-phenylindole ) intensities in that voxel , similar to the method introduced by Isaacson et al . [46] . To account for different levels of crowding , we added artificial crowding agents distributed randomly in our simulation domain . We define the crowdedness parameter θ as the probability for each voxel to be occupied by an artificial crowding agent . Thus , we are able to explicitly account for different amounts of crowding in our simulation domain by changing θ . To interpret θ correctly , let`s consider the extreme case where θ = 1 . In this case all voxels would be occupied by one and only one crowding agent . Then , crowding reduces the diffusion coefficient depending on the size of the crowding agent ( 90% reduction for a large crowder vs . 40% reduction for a small crowder . ) . Note that under no condition would any voxel be completely blocked ( i . e . 100% crowded ) . For any other θ , approximately θ×N crowding molecules are randomly distributed in θ×N voxels , where N is the total number of the voxels . A convergence study demonstrates that our conclusions are independent of voxel size for a sufficiently small mesh , see ( S4 Fig ) . To validate the model , the simulation was run for 1000 minutes with the same parameters as in [8] while θ = 0 , i . e . with no artificial crowding agent or chromatin present . As in [8] , this resulted in transcriptional bursts . A direct quantitative comparison is not trivial due to the fact that our model is spatially inhomogeneous ( S2 Fig ) . Next , we included the artificial crowding agent and the chromatin in our model and investigated mRNA abundance in our simulation domain for low and high θ values ( θ = 0% vs . θ = 100% ) . It can be seen that the system switches more frequently between active and inactive states for low θ values than it does for high θ values ( Fig 1a and 1b ) . We hypothesized that adding the artificial crowding agent limited the diffusion of the TF . Thus , the TF tends to stay in either of the two stable states ( active or inactive states ) for a longer period of time . This increase in the residence time of the TF on the promoter results in reduced transcriptional bursting . Next we studied the effect of the artificial crowding agent on biochemical rates , by comparing the distributions of active state duration ( ton ) for 3200 trajectories of 1000 min simulations ( Fig 1c and 1d ) . Fig 1c and 1d show a significant ( p-value < 0 . 001 ) decrease in ton for θ = 100% . Likewise , given ton + toff = 1000 min , we observe a significant increase in toff for θ = 100% . Therefore , using gene activation rate constant k+ ~ < toff>-1 ( < . > denotes the mean ) , our simulation results suggest a 12% decrease in k+ ( in agreement with [65] ) and a 23% increase in the deactivating rate constant ( k- ) . Our model predicts a smaller reduction in k- compared to [65] , and thus , predicts a 29% decrease in equilibrium constant ( Keq = k+/k- ) whereas [65] predicts an increase in Keq . This discrepancy might be due to the assumption in [65] that the association rates are always diffusion limited . It would be interesting to repeat similar simulations using particle level methods such as molecular dynamics to obtain a more precise estimate of the change in the equilibrium constant . Our finding is in qualitative agreement with the experimental observation that a crowded condition of heterochromatin can repress gene expression [49] ( Fig 1e ) . Van Paijmans and Ten Wolde [60] showed that in general the abovementioned biochemical system can be reduced to a well-mixed model if there is a clear separation of time scales between rebinding and binding of molecules from the bulk , which can be deduced from the power spectrum of the mRNA expression . Briefly , a characteristic knee in the low-frequency regime ( corresponding to Markovian switching at long times ) , which is well separated from the regime corresponding to the rebindings at higher frequencies renders it possible for a spatially resolved biochemical system to be reduced to a well-mixed system . To explore whether our system can be reduced into a well-mixed system , we used the effective biochemical rate constants obtained by measuring the transcriptional activity ( Fig 1e ) . By comparing the power spectrum of the spatial model for the special case when θ = 100% with the corresponding well-mixed model , we conclude that once the effective biochemical rate constants are measured using our spatial model for a given configuration ( i . e . distinct crowding size and distribution ) , spatial model can be reduced into a well-mixed model ( Fig 1f ) . Note , however , as shown later in this study , these biochemical rate constants depend strongly on the size and the distribution of the crowding agent molecules , and the local chromatin density . Hence , a spatial model is required to measure these constants . To analyze the consequences of macromolecular crowding on a cell population , we simulated 16000 isogenic cells in an analogous situation for different values of θ . We observed ( Fig 2a ) that while low θ values can diversify the cell population and result in intermediate states ( two peaks correspond to two stable states , i . e . active and inactive states ) , with higher values of θ we observed a more homogeneous population ( no intermediate states ) . This observation is in agreement with recent experimental results [35] . In this situation , the average number of mRNA is close to the number of mRNA obtained when noise is removed from gene expression ( deterministic models ) . Our simulation results show that adding the crowding agent to the simulation domain replaces the intermediate states by two more stable states . The two stable modes ( mRNA abundance = 50 and 500 ) are intact after crowding the simulation domain ( Fig 2a ) . It can be inferred from our linear model that there is a statistically significant correlation between kurtosis of the mRNA distribution and the amount of the crowding agent ( p-value < 0 . 01 ) . We should stipulate that the kurtosis values define the noise in our system . Low kurtosis values correspond to a cell population in which mRNA expression in each cell is near either the first or the second peak ( i . e . ~50 and 500 ) . Conversely , high kurtosis corresponds to a cell population in which certain cells have mRNA expression levels that lay between the peaks ( i . e . intermediate states ) . Likewise , a more homogenous cell population can be obtained by removing the intermediate states ( i . e . higher kurtosis value and narrower distributions or lower noise ) . It has been observed experimentally [35] that the larger crowding agents ( Dex-Big ) can contribute robustness to the gene expression pattern more effectively than the smaller crowding agents ( Dex-Small ) . To examine whether our model would reproduce this observation , we repeated the previous simulations using smaller crowding agents ( ~2 times smaller by volume fraction ) . Larger crowding agents occupy more volume in a voxel and reduce the diffusion coefficient more effectively than smaller crowding agents ( 90% reduction in the diffusion coefficient for larger crowding agents compared to 40% reduction for smaller crowding agents ) . However , by occupying more voxels ( ~2 times as many voxels as in the larger crowding agent case ) , a similar level of volume exclusion can be achieved by smaller crowding agents . Note that in order to assess the effect of the artificial crowding agent size , one should compare the kurtosis of mRNA distributions for θ values that correspond to similar total volume exclusion for Dex-Big vs . Dex-Small ( e . g . Dex-Big and θ = 60% vs . Dex-Small and θ = 100% ) . Our diffusion-limited gene expression model is capable of reproducing the same experimental observations ( Fig 2b ) . Our simulation results suggest that the intermediate states do not vanish , despite adding a substantial amount of small crowding agents . Our linear regression model illustrates a small correlation between the kurtosis of the mRNA distribution and the amount of the crowding agent ( p-value > 0 . 01 ) . Therefore we can conclude that , in agreement with experimental observations , our model shows that the smaller crowding agents cannot homogenize the cell population effectively . This is not surprising since small molecules exist in the cellular environment in high concentrations but their impact on gene expression is negligible compared to histones , mRNAs and regulatory proteins . Next , we analyzed the impact of chromatin reorganization , to understand how the local volume exclusion of chromatin can influence the gene expression patterns of specific genes . Three different genes were selected ( Genes 1–3 ) to account for super dense ( Gene 1 ) , dense ( Gene 2 ) and sparse chromatin area ( Gene 3 ) . Identical model and simulation parameters were used for all three genes to control for other effects except the volume exclusion of chromatin . By comparing the mRNA distributions of cell populations consisting of 16000 cells , our simulation results suggest that diffusion-limited gene expression can alter mRNA production in a cell population ( Fig 3 ) . Here , the two-sample ( all compared to Gene3 ) Kolmogorov-Smirnov ( KS ) test ( Bonferroni-adjusted ) was used to compare different mRNA distributions and a statistically significant difference was obtained ( p-value < 0 . 01 ) . To demonstrate that macromolecular crowding reduces the gene expression noise primarily by volume exclusion , thus limiting the diffusion , we repeated the simulations in the absence of the crowding agents but using different diffusion coefficients . This was implemented by replacing the diffusion coefficients ( D ) with the effective diffusion coefficient ( D* ) ( Materials and Methods ) . Each data point ( X , Y ) in Fig 4 was found by running the simulation for different D values ( X ) and evaluating the kurtosis of mRNA distributions . Then the corresponding D* values ( Y ) were obtained by Eq 3 . We hypothesized that if macromolecular crowding is capable of reducing the noise of gene expression primarily by slowing down the diffusion of TF , we should expect to see a linear fit in our data points with the hypothetical line ( Fig 4 , red dotted line ) . As shown in Fig 4 our simulation results support this hypothesis for a physical range of θ values ( 0–100% ) , for a large crowding agent . As previously discussed , the size of the crowding agent plays a vital role in obtaining a homogeneous cell population . By comparing the kurtosis values of the mRNA distributions obtained using a large crowding agent ( θ = 60% ) vs . a small crowding agent from Fig 2 ( θ = 100% ) , where the total volume exclusion is similar , different phenotypes can be observed ( kurtosis value of ~10 vs . ~4 ) . Furthermore , the position of the gene within the chromatin matters . It can be inferred that although the overall volume exclusion effect is similar for all three genes , the local chromatin density can alter the time a TF requires to reach its target . In sum , our study suggests that macromolecular crowding can influence the gene expression noise significantly , both locally and globally ( Fig 3 , yellow curve , p-values < 0 . 01 ) .
A significant portion of cell volume is occupied by proteins , RNAs and other macromolecules . To obtain a complete understanding of the pattern of gene expression , a comprehensive understanding of the impacts of macromolecular crowding is essential . In this study , we have proposed a simple model similar to that of [46] to account for macromolecular crowding in the cellular environment . We utilized the NSM method for simulation of the reaction-diffusion master equation , to include macromolecular crowding . We have avoided any direct manipulation of reaction rates to account for macromolecular crowding [35] . In addition , our method facilitates an explicit treatment of macromolecular crowding , in that geometric dependency of chromatin structure on gene expression is addressed , and interactions between the crowding agent and different molecules can be considered . This provides a platform to assess how the chromatin structure impacts gene expression . Our model accounts for the addition of the artificial crowding agent and its size , and demonstrates that macromolecular crowding can homogenize a cell population by limiting the diffusion of TFs . Therefore , it improves our understanding of the underlying sources of gene expression noise from that of the earlier models [35 , 46] . Our model predicts that a large crowding agent ( Dex-big ) , reduces the diffusion coefficient of TF more effectively than a small crowding agent ( Dex-small ) , in agreement with the experimental observations by Tan et al . Likewise , it can be inferred from other experimental observations by Phillies et al . [69] that the molecular weight and concentration of crowding molecules can change the diffusion coefficient considerably , whereas the size of a TF has insignificant impact . Finally , although Muramatsu and Minton [68] observed an inverse correlation between the size of the crowder and that of the diffusion coefficient , Phillies et al . [69] has shown the opposite ( this controversy is discussed in [68] as well ) . It is worth noting that Isaacson et al . [46] used spatial stochastic simulation to show that the first passage time ( the time required for TF to find the gene locus ) decreases to a minimum at first , and then increases again as the volume exclusion due to chromatin increases further . That study suggests that crowding can accelerate or decelerate the diffusion depending on the density of the crowding agent , leading to faster or slower chemical kinetics , respectively . Our study , on the other hand , demonstrates the mechanism by which crowding can reduce the transcriptional noise of gene expression . For an intuitive understanding of the gene expression noise reduction mechanism , first note that as shown by van Zon et al . [47] , TF diffusion is the dominant source of gene expression noise . Also , macromolecular crowding can effectively partition the available space into smaller compartments , which not only linearizes the input–output relation , but also reduces the noise in the total concentration of the output . In fact , by partitioning the space , macromolecular crowding isolates molecules , as a result of which the molecules in the different compartments are activated independently , thereby reducing the correlations in the gene expression switch . Consequently , this removal of correlations can lower the output noise [48] . We suggest the following function for the macromolecular crowding , by which a uniform cell population can be obtained . By comparing Fig 1a and 1b , it can be inferred that macromolecular crowding can increase the average residence time of TF on its promoter . As a consequence , transcriptional bursts are attenuated which leads to elimination of the intermediate states in the mRNA distributions . Our findings demonstrate the importance of spatial simulations to fully capture several experimental observations . Morelli et al . [65] studied the effect of macromolecular crowding on a gene network by rescaling the association and dissociation constants into a well-mixed model . Here , on the other hand , we provide strong evidence ( Figs 2 and 3 and S4 Fig ) that the impact of crowding structure and distribution cannot be fully understood using well-mixed models . Furthermore , our model sheds light on how to develop engineered cells to achieve advantages in gene expression , cellular computing and metabolic pathways [35] . Investigations of other epigenetic factors show that DNA methylation and chromatin structure may be linked to transcriptional activity , both in single cells and across populations . Gene silencing by histone modification or formation of repressed chromatin states ( heterochromatin ) are good examples of how nature has exploited macromolecular crowding and inherent stochasticity in gene expression to display new traits [49] . Our methodology can be utilized to further assess heterochromatin and euchromatin functional differences at a reasonable resolution .
The inherent stochastic characteristics of gene expression , along with the failure of deterministic models to produce transcriptional bursting , lead us to consider a spatial stochastic model . A modified next subvolume method ( NSM ) [41] was used to simulate the stochastic reaction-diffusion system , using the implementation in PyURDME on the MOLNS software platform [57] . We developed the following modifications to account for crowding ( for access to the software implementation , see URL in [58] ) . Inside the cell , the chromatin , histones , etc . , are crowding the nucleus . Note that we are ignoring dynamic addition and reduction of newly synthetized proteins ( P ) and mRNAs ( M ) since they are negligible when compared to the chromatin . Given a domain which is discretized into N uniform voxels , each voxel is occupied with the artificial crowding agent with a probability θ . The diffusion between two adjacent voxels is linearly dependent on the crowding density of the destination voxel , consisting of the chromatin and the artificial crowding agent . This model assumption is analyzed in detail and compared with the available experimental data in Supporting Information ( S1 Fig ) . This model does not explicitly take into account lock-in effects , that crowding in the origin voxel may affect the diffusion rate to adjacent voxels , or that the effective reaction rate in a voxel may depend on the local crowding and configuration of the chromatin and crowders . For instance , Friedman [66] showed that hydrodynamic effects cause a 15% reduction in the computed rate constant for neutral species or ions in water . The impacts of the electrostatic forces have been widely studied and considered primarily in molecular level simulations [67] . Specific chromatin configurations can affect the hopping rate of particles differently . Namely , even in low chromatin concentrations , distinct configurations might be able to fully trap the particle and reduce the hopping rates significantly . However , we believe that our model is sufficiently accurate to study the qualitative effects of crowding . It is worth mentioning that our model ignores any non-specific interaction between DNA and TF . Paijmans and ten Wolde [60] showed quantitatively that even in the presence of 1D sliding along the DNA , which makes rebinding events not only more frequent but also longer , the effect of diffusion can still be captured in a well-stirred model by renormalizing the rate constants . However , renormalization does not account for the architecture of chromatin and how it can influence the rate constants . Although several studies suggest that such non-specific interactions can help TF to slide on the DNA strand ( facilitated diffusion ) to find the target faster [61 , 62] , recent work by Wang F et al . [63] provides evidence that the promoter-search mechanism of E . coli RNAP is dominated by 3D diffusion . Moreover , in another work [64] the sliding length of TF on DNA is measured to be ~30–900 bps . In our simulation , on the other hand , each voxel contains ~Mbps and therefore , on the length scale of our model , facilitated diffusion is insignificant . The size of the crowding agent is modeled by the parameter δi . We assume that smaller crowders reduce the diffusion less than large crowders . In our simulations we let δi = 0 . 6 for smaller crowding agents , δi = 0 . 1 for larger crowding agents , and δi = 1 when no crowding agent is present . Thus , the diffusion rate into voxel i is computed as D=D0× ( 1−ci ) ×δi ( 1 ) where ci models the crowding due to the chromatin in voxel i . It is unknown exactly how the concentration of chromatin affects the effective diffusion , but as a simple model we assume that ci=DAPI intensity in cell imaxj DAPI intensity in voxel j ( 2 ) The diffusion rate thus depends linearly on the DAPI intensity , and we assume that the voxel with the highest intensity of DAPI is fully blocked . For simplicity we assume that neither the chromatin nor the crowding agent diffuses between voxels . The TF molecule is initially placed randomly in the domain . During the simulation it will diffuse to the gene locus and activate transcription . Recent studies [46 , 51] have proposed more complicated relations to obtain the effective diffusion coefficient in the presence of macromolecular crowding . Here , we use a linear relation to calculate the TF diffusion coefficient as a function of the total crowdedness ( i . e . the effects of both chromatin structure and artificial crowding agents included ) . This simple relation can capture physiologically relevant trends and suffices for the purpose of our simulations . Considering the total effect of the artificial crowding agent as D*=∑iδiND , ( 3 ) where i is the voxel index and N is the total number of voxels in the domain . For a large crowding agent , Eq 3 leads to D* = [θ×0 . 1 + ( 1- θ ) ×1]D = ( 1–0 . 9 θ ) D . Using the linear model presented in Fig 2a ( Kurtosis ( θ ) = 15 θ ) , we obtain ( for D = 1 ) D* =1− 0 . 06 × Kurtosis ( 4 ) to calculate the effective diffusion rate . Each data point ( X , Y ) in Fig 4 is found by running the simulation for different D values ( X ) and evaluating the kurtosis of the mRNA distributions . Then the corresponding D* values ( Y ) are obtained by Eq 4 . In summary , in order to obtain the effective diffusion as illustrated in Fig 4 , the following procedure has been followed: All statistical tests were performed using the ‘R’ statistics package , an open-source software package based on the ‘S’ programming language ( http://www . R-project . org ) . All correlations were calculated using the Pearson’s product-moment correlation coefficient . Comparisons between multiple distributions were undertaken using the two-sample Kolmogorov-Smirnov test corrected for multiple testing with Bonferroni Method . All image analysis tasks were performed using ImageJ . Each of the nine stacks was discretized using a 50 by 50 Cartesian mesh , and the DAPI intensity of each voxel was measured using ImageJ [59] . https://github . com/mgolkaram/pyurdme/tree/crowding
|
The cellular nucleus is packed with macromolecules such as DNAs and proteins , which leaves limited space for other molecules to move around . Recent experimental results by C . Tan et al . have shown that macromolecular crowding can regulate gene expression , resulting in a more homogenous cell population . We introduce a computational model to uncover the mechanism by which macromolecular crowding functions . Our results suggest that macromolecular crowding limits the diffusion of the transcription factors and attenuates the transcriptional bursting , which leads to a more homogenous cell population . Regulation of gene expression noise by macromolecules depends on the size of the crowders , i . e . larger macromolecules can reduce the noise more effectively than smaller macromolecules . We also demonstrate that local variation of chromatin density can affect the noise of gene expression . This shows the importance of the chromatin structure in gene expression regulation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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] |
2016
|
Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion
|
In the last decades , several European countries where arboviral infections are not endemic have faced outbreaks of diseases such as chikungunya and dengue , initially introduced by infectious travellers from tropical endemic areas and then spread locally via mosquito bites . To keep in check the epidemiological risk , interventions targeted to control vector abundance can be implemented by local authorities . We assessed the epidemiological effectiveness and economic costs and benefits of routine larviciding in European towns with temperate climate , using a mathematical model of Aedes albopictus populations and viral transmission , calibrated on entomological surveillance data collected from ten municipalities in Northern Italy during 2014 and 2015 . We found that routine larviciding of public catch basins can limit both the risk of autochthonous transmission and the size of potential epidemics . Ideal larvicide interventions should be timed in such a way to cover the month of July . Optimally timed larviciding can reduce locally transmitted cases of chikungunya by 20% - 33% for a single application ( dengue: 18–22% ) and up to 43% - 65% if treatment is repeated four times throughout the season ( dengue: 31–51% ) . In larger municipalities ( >35 , 000 inhabitants ) , the cost of comprehensive larviciding over the whole urban area overcomes potential health benefits related to preventing cases of disease , suggesting the adoption of more localized interventions . Small/medium sized towns with high mosquito abundance will likely have a positive cost-benefit balance . Involvement of private citizens in routine larviciding activities further reduces transmission risks but with disproportionate costs of intervention . International travels and the incidence of mosquito-borne diseases are increasing worldwide , exposing a growing number of European citizens to higher risks of potential outbreaks . Results from this study may support the planning and timing of interventions aimed to reduce the probability of autochthonous transmission as well as the nuisance for local populations living in temperate areas of Europe .
During the last decade , Europe has faced outbreaks of mosquito-borne diseases ( MBD ) such as dengue and chikungunya , following the continuous importation of human cases in areas with established competent vectors such as the invasive mosquito Aedes ( Stegomyia ) albopictus ( Skuse ) [1] . Vector control interventions can be implemented by local authorities to keep in check mosquito abundance and consequently reduce the epidemiological risk . Adulticide spraying rapidly reduces the number of mosquitoes , but its effect is short-lived [2] . For this reason , it is particularly indicated in situations where the transmission risk needs to be reduced drastically and quickly , such as when an individual is diagnosed with an MBD , to prevent or curtail an outbreak [3] . Since the effectiveness of reactive measures decreases with the delay between outbreak initiation and implementation of control [4] , a better approach may consist in preventive interventions . Treatment of potential breeding sites with larvicide products has a delayed impact in reducing adult populations [3] , but experimental studies show that their effect lasts for several weeks [5] , making them better suited for preventive routine control . The main limit to larviciding as a control option is the proportion of breeding sites that are actually accessible to interventions by public health authorities . To overcome this limit , education campaigns may be carried forward to encourage citizens to remove and treat potential breeding sites from their private premises during the mosquito season [6 , 7] . Mathematical modelling of MBD associated with cost-effectiveness analyses can help optimizing routine vector control interventions [8] with respect to constraints in human and financial resources [9] . With the aim of assisting European municipalities in planning and timing preventive vector control , we assessed the potential epidemiological impact on chikungunya and dengue , and the ensuing economic benefits for the health system , produced by routine larviciding against Ae . albopictus within urban sites in temperate climates .
Mosquito monitoring via adult trapping was carried out in ten municipalities from the Northern Italian provinces of Belluno and Trento , characterized by a temperate climate [10] . Mosquitoes were collected using Biogents ( BG ) Sentinel traps ( Biogents AG , Regensburg , Germany ) baited with lures and CO2 from dry ice . After each trapping session , mosquitoes were killed by freezing at -20°C , identified using taxonomic keys [11 , 12] and confirmed by PCR if found in a location for the first time [12] . We simulated the transmission dynamics associated with chikungunya and dengue using a standard SEI-SEIR approach [13] in which mosquitoes develop lifelong infection after an ( extrinsic ) incubation period since the bite to an infectious human ( SEI sub-model ) , whereas humans develop temporary infection , followed by the development of immunity , after an ( intrinsic ) incubation period since the bite from an infectious mosquito ( SEIR sub-model ) . We considered temperature-dependent extrinsic incubation periods and per-bite transmission probabilities for dengue [14] , whereas only temperature-independent estimates were available for Chikungunya [15 , 16] . The transmission model was initialized with a single infectious human , representing an imported case at a date sampled uniformly between January 1st and December 31st . The population size of female Ae . albopictus mosquitoes over time in the transmission model was estimated by fitting a population model to capture data collected in the absence of larvicidal treatments , following the same approach already adopted in [13 , 17] . The model considers four developmental stages of mosquitoes ( eggs , larvae , pupae and adults ) and reproduces their life cycle by means of temperature-dependent parameters regulating the stage-specific rates of mortality and development . Free model parameters ( i . e . the site- and year- specific habitat suitability and the capture rate of BG traps ) were estimated via a Monte Carlo Markov Chain approach based on a Poisson likelihood [13 , 17] . We then included the effect of routine larviciding in the population model . Experimental studies of several available commercial larvicide products show that 99% of existing larvae and hatching eggs are killed within a given breeding site , with constant efficacy of about 30 days , independently of the specific product used [5 , 18 , 19] . We considered a standard approach targeting breeding sites in publicly accessible spaces ( e . g . , catch basins placed in public parks and along the road system ) , and an additional strategy where public interventions were integrated by the involvement of citizens in treating and removing breeding sites within private premises . The latter was parametrized on results from a pilot project conducted in two municipalities within the same area of this study [7] , in which larvicide products were delivered door-to-door and free of charge to house dwellers , who were sensitized and educated to mosquito control interventions . A key determinant of effectiveness is the fraction of existing breeding sites in a given area that are actually treated ( coverage ) . We adopted a coverage range between 30% and 50% for larviciding of public catch basins only , and between 60% and 75% for interventions that additionally involve citizens . These ranges were computed from available data on the density and proportion of reachable breeding sites in public and private premises [7] . Other strategies aimed at extending the coverage ( e . g . removal of other breeding sites such as water buckets , plant saucers , tarpaulins , etc . ) were not considered . Since the effect of larvicides is transitory , treatment of catch basins may be repeated multiple times within a given season . We considered several different starting dates and from 1 to 4 applications of larvicide treatments within a given mosquito season ( hereafter referred to as “effort level” ) , implemented with monthly frequency . To evaluate the economic acceptability of the two considered strategies , a cost-utility analysis for the prevention of dengue and chikungunya was conducted , taking the number of infections as input from the transmission model . Disability Adjusted Life Years ( DALYs ) averted and net costs were derived comparing an intervention scenario to the case in which no control programs were put in place ( baseline ) . The baseline was set to reflect a municipality where only the monitoring of mosquito presence via ovitraps was performed [7] . The analysis was conducted from a public healthcare system perspective through the maximization of the net health benefit ( NHB ) [20] . This measure is defined as the difference between the DALY averted and the incremental cost due to the intervention , the latter divided by the willingness to pay ( WTP ) by public authorities for each DALY averted . Following WHO recommendations [21] , we assumed such value approximately equal to the Gross Domestic Product ( GDP ) , which is about 35 , 000 euro per capita in our study area [22] . Probabilities of each infected case of being symptomatic , notified , severe , hospitalized and of dying , and the length of stay in hospital , were derived from published studies [23 , 24] and from analyzing data from the Italian Hospital Discharge System ( Schede di Dimissioni Ospedaliere ) , accounting for all hospital admissions for chikungunya and dengue recorded in Italy . The cost of illness was estimated according to expert opinion . The costs of intervention were estimated from actual costs during control activities against Ae . albopictus recently performed in two municipalities from the study area [7] . For all the considered scenarios , the NHB was computed on a set of 100 , 000 stochastic realizations accounting for the uncertainty in both the transmission and the economic model’s parameters . Full details on this analysis are provided in S1 Text . To assess the feasibility and sustainability of public interventions , we used responses from a questionnaire administered in 2013 to municipalities of the province of Trento , aimed at collecting information on the actual public expenditure on vector control activities .
The estimated density of adult female mosquitoes ( averaged between April 10th and September 30th ) was between 4 and 88 per hectare in 2014 and between 9 and 198 in 2015 , depending on the municipality ( see Table 1 ) . The higher abundance in 2015 is mostly due to the much higher temperatures recorded during summer . The initial reproduction numbers and the threshold for autochthonous transmission of chikungunya and dengue over time were estimated in a previous study [17] . Here , for each site and year , we computed the probability of autochthonous transmission of chikungunya and dengue originated by an imported infection in the absence of interventions . Higher vector densities during 2015 resulted in an increased risk of local transmission for both infections , compared to the previous year . The probability of observing at least one secondary case was estimated to be up to 30% for chikungunya and 15% for dengue in highly infested towns in 2015 . Corresponding maximum probabilities in 2014 were around 20% for chikungunya and 5% for dengue . This means that 7 importations of chikungunya and 15 importations of dengue in towns most at risk would have a >90% probability of causing at least one secondary case in 2015 . Sporadic transmission ( less than 10 secondary cases ) is by far the most likely scenario , especially for dengue ( Fig 1 ) . However , we found a low , but non-negligible , probability ( up to 2 . 7% ) that an uncontrolled chikungunya outbreak would produce more than 50 cases in several sites during 2015 . Routine preventive larvicide treatments can reduce significantly mosquito populations and consequently the probability and size of outbreaks triggered by sporadic importation of infected cases . To evaluate the overall effectiveness , we considered the expected number of total secondary infections per imported case . Under the baseline scenario of no control interventions , this index ranged from 0 . 1 to 5 . 2 , depending on the site and year; corresponding numbers for dengue were everywhere below 0 . 5 . Because of the smaller epidemiological risk of dengue , we discuss only the cost-effectiveness analysis on chikungunya , leaving corresponding results for dengue to the S1 Text . For each site and year , and for each timing , effort level and assumed coverage , we evaluated the relative reduction in the expected number of secondary infections per imported case as a measure of effectiveness . Fig 2 and Table 2 show that all interventions with optimal effectiveness covered the month of July , which corresponds to the estimated period of steepest growth of the adult Ae . albopictus population in both years . We selected for further analyses only interventions with optimal timing for each effort level ( Table 2; the reduction in mosquito abundance corresponding to the optimally timed interventions is reported in the S1 Text ) . We found that an increase in the effort level does not proportionally reduce the expected number of cases ( Fig 3 ) . In particular , an expansion in the coverage of breeding sites from 30% to 50% would be more effective than doubling the effort level while keeping the coverage at 30% . In general , interventions are most beneficial when the baseline risk is highest . Towards an optimal allocation of resources , the benefits of reducing the potential number of transmitted cases needs to be compared with the intervention costs . Taking into account all possible clinical outcomes , including the probability of severe illness and of hospitalization , the estimated average cost per infection is 424 . 9 euros ( 95% CI 342–533 ) for chikungunya and 275 . 88 euros ( 95% CI 151–422 ) for each dengue infection . The corresponding average DALY loss per case is higher for chikungunya ( 0 . 45 , 95% CI 0 . 10–1 . 12 ) than for dengue ( 0 . 29 , 95% CI 0 . 15–0 . 44 ) . In Fig 4 , we show the relative probability that each effort level ( including the no-intervention scenario ) will maximize the NHB for each site , year , and coverage . Three main outcomes can be identified . The first is represented by larger cities ( Trento , Belluno and Rovereto , all above 35 , 000 inhabitants ) where non-intervention has the highest likelihood of being optimal . In these sites , the poor economic effectiveness of larviciding depends on the relatively low number of expected secondary cases even in the absence of treatment ( Fig 3 ) , combined with the high intervention costs due to the extent of the area to be covered . The second group consists of smaller towns where intervention is always beneficial ( Povo , Santa Giustina , Tenno and Tezze , all below 10 , 000 inhabitants ) and where higher effort levels have the highest probabilities of being optimal . Strigno ( about 3 , 400 inhabitants ) represents an exception to this rule , where the low intervention costs are counterbalanced by a very small transmission risk in the absence of interventions . Nonetheless , even in Strigno a low-effort intervention ( single treatment ) might be beneficial because of its low cost . The third situation occurs in towns of intermediate size ( Feltre and Riva del Garda , between 20 , 000 and 35 , 000 inhabitants ) where both the intervention costs and the transmission risks are high . In these cases , depending on the larviciding coverage , absence of intervention might be the optimal strategy in seasons of lower mosquito abundance ( 2014 ) while a low-to-moderate effort ( 1 to 3 treatments ) might be the best choice in years of high infestation ( 2015 ) . Overall , the probability that a more intensive intervention will be optimal increases with the coverage and with higher transmission risk ( 2015 vs . 2014 ) . We also tested the cost-effectiveness of expanding the coverage by involving private citizens [7] . We found that this type of intervention might achieve significant additional reductions in the expected number of secondary cases and probability of local transmission ( reported in the S1 Text ) . However , they are rarely optimal from the economic perspective because they require labour-intensive activities . Fig 5 reports results of the NHB analysis for a single larvicide treatment , but qualitative inferences are similar for more intensive efforts ( see S1 Text ) . The only two instances where involvement of citizens was found to be economically beneficial were Povo and Tezze and only during the 2015 mosquito season , i . e . only where the urban size is small enough to keep intervention costs low and where the transmission risk at baseline is sufficiently high . Two municipalities under study , Trento and Riva del Garda , had responded to a previously administered questionnaire on public expenditure on vector control , declaring an overall budget of 0 . 254 euro and 0 . 532 euros per inhabitant , respectively . In Trento , the most cost-effective activity predicted by our model was monitoring by ovitraps ( Fig 4 ) , which has an estimated average cost of 0 . 016 euro per inhabitant; in Riva del Garda , one or two larvicide applications per year would be likely optimal and would cost between 0 . 256 and 0 . 512 euros per inhabitant . Therefore , the most cost-effective strategies are sustainable with respect to the current allocated budget . We provide full details on questionnaires , municipality-specific answers and intervention costs in the S1 Text .
In this work , we evaluated the effect of routine larviciding against dengue and chikungunya , two viruses transmitted by bites of Ae . albopictus mosquitoes . We used data from two seasons of entomological surveillance in multiple sites from northern Italy to parametrize a mathematical model of mosquito population dynamics and control . The population model was coupled with a transmission dynamics model and a cost-effectiveness analysis to identify suitable routine vector control strategies for temperate climate municipalities in Europe . We found that , in the absence of interventions , the risk of autochthonous dengue transmission was low and limited to sporadic transmission in both years , because of the relatively low competence of European strains of Ae . albopictus . On the other hand , the risk of a chikungunya outbreak was estimated to be up to 30% in 2015 , with a non-negligible probability of observing outbreaks larger than 50 cases in most sites . We found that the most effective interventions in reducing the amount of expected locally transmitted cases were those for which the window of larvicide efficacy covered at least the month of July ( Fig 2 , Table 2 ) . Larviciding reduced the probability of secondary cases only moderately , but it had an important impact in avoiding larger outbreaks . Our analysis included two seasons that were representative of a broad range of mosquito abundances , due to the remarkable temperature differences . The cost-effectiveness of larviciding depends on the actual mosquito abundance in a given year; however , general rules could be identified independently of the considered year: small villages ( <10 , 000 inhabitants ) with moderate-to-high mosquito abundances will maximally benefit of intense larviciding efforts made of season-round monthly treatment of public catch basins . For medium-sized towns ( 20–35 , 000 inhabitants ) with high infestation rate , the benefits are partially offset by the higher cost of intervention; in these cases , a moderate larviciding effort ( 1 to 3 treatments within the season ) is recommended . Larger cities in our study ( >35 , 000 inhabitants ) were characterized by a low or intermediate transmission risk , and the high costs of an intervention covering the entire urban area made it economically disadvantageous . In these situations , treating specific neighbourhoods with highest mosquito abundance ( called ‘hot spot' approach [25] ) may be cost-effective . In order to evaluate such a scenario , however , it would be necessary to model the complex effect of the urban layout on the spatial distribution of breeding sites and on the dynamics of mosquito populations [7] , which is out of the scope of our study . Treatment of private breeding sites via the direct involvement of citizens by door-to-door visits was recommended only in small towns with high mosquito infestation . A survey on the allocated budget for mosquito control programs across different municipalities showed that expenses required for the most cost-effective interventions are sustainable for the considered area . These results need to be contextualized with respect to our simplifying assumptions . First , all results are given conditionally on a uniform probability of importation of an infectious individual within a given epidemiological year . For comparison , in the considered provinces of Trento and Belluno , three imported cases of dengue and one imported case of chikungunya were recorded in 2014 ( C . Rizzo , personal communication ) ; however , the actual importation rate may vary significantly by year and time of the year , depending on spatio-temporal patterns of global epidemics and international travel . We did not consider reactive interventions that are implemented when a case of chikungunya or dengue infection is detected or after an outbreak has started ( e . g . , insecticide air spraying in the neighbourhood of the index case [26] ) . In addition , our results are relative to the prevention of arboviral transmission; however , there may be other purposes in vector control activities , such as the reduction of nuisance for citizens , which were not included in our analysis . For what concerns the economic assessment , we did not consider the impact of local transmission detection on the blood supply chain . Upon clinical confirmation of a locally transmitted arboviral infection , restrictions on the usage of blood bags collected in the region are applied to prevent transmission via transfusions , and screening tests on available blood supplies are implemented [26] . These additional interventions are quite expensive , and savings associated to the reduction of transmission risk granted by larvicides may dramatically offset the cost-benefit balance in favour of the intervention . However , these costs are difficult to estimate because of the lack of sufficient data . We did not include other arboviroses transmitted by Ae . albopictus because of their lower epidemiological relevance to the considered area . For example , the risk of Zika virus transmission was found to be close to zero in the study region , even under conservative scenarios [17] . Nonetheless , we note that larvicides produce simultaneous benefits in preventing multiple diseases transmitted not only by Ae . albopictus but also by other affected mosquito species ( e . g . West Nile virus associated to Culex pipiens L . ) . Furthermore , larviciding may assist in limiting the spread of other invasive mosquito species such as Aedes ( Hulecoeteomyia ) japonicus ( Theobald ) and Aedes ( Hulecoeteomyia ) koreicus ( Edwards ) [1 , 27] . An interesting research question is how the balance of ecological interactions between mosquito species [28] may be offset by such interventions . Other studies [2 , 6 , 7] have investigated the effectiveness of vector control in Europe using different approaches . The cost-effectiveness of larvicidal treatment against Ae . albopictus in temperate climates has been evaluated only in combination with other interventions during an ongoing outbreak [29 , 30]; other studies were based on endemic ( extra-European ) settings where transmission is mainly mediated by Aedes ( Stegomyia ) aegypti ( Linneus ) [31 , 32] . Overall , results from different studies and approaches , including our own , are consistent in highlighting the potential of larviciding towards reducing mosquito populations; however , this reduction will not result in a complete elimination of the risk of local chikungunya or dengue transmission . Additional strategies may integrate the control of risks from mosquito-borne diseases , including source reduction methods ( e . g . identification and removal of breeding sites ) , mass trapping ( e . g . via lethal ovitraps ) and approaches leveraging ecological interactions ( such as the use of Wolbachia bacteria or the release of genetically sterilized male mosquitoes ) . A comprehensive review of the potential for these strategies can be found in [9] , but specific cost-effectiveness studies are needed to identify optimal strategies for vector control . European municipalities with temperate climate where Ae . albopictus is established may take advantage of results from this study when planning and timing routine larviciding interventions aimed to prevent or reduce epidemiological risks . Temperate European areas share with our study collection area similar temperature suitability for the transmission of arboviruses [33] and similar abundances of Ae . albopictus [34] , so that results on the epidemiological effectiveness of larviciding should not differ significantly . More caution should be paid when extrapolating cost-effectiveness conclusions to different countries , given potential differences in health and intervention costs and in the choice of the WTP . Finally , we suggest that the proposed methodological approach may also be extended to European areas with different climates , conditional on the availability of local data on mosquito abundances estimated via entomological surveillance activities .
|
Larvicides are a key tool to prevent the growth of mosquito populations and decrease both the risks of outbreaks of mosquito-borne diseases and the nuisance deriving from bites . In order to assist municipalities from temperate areas in Europe in effectively planning vector control programs , we modelled the effect of larviciding in public areas on populations of Aedes albopictus using mosquito collection data from 10 municipalities in Northern Italy , over two years with very different temperature conditions . We then evaluated the resulting probabilities of potential outbreaks of chikungunya and dengue and their expected final sizes , and we compared the intervention costs to the economic and health benefits due to the avoidance of clinical cases . By assessing several different intervention strategies , we found that the optimal timing should be centred on the month of July , corresponding to the period of maximal growth of the mosquito population . Municipality-wide interventions are likely to be cost-effective in small-to-medium towns ( below 35 , 000 inhabitants ) even where mosquito infestation is moderate , whereas for larger cities a neighbourhood-based intervention should be considered . The involvement of citizens to apply larvicides within private premises resulted effective but generally too costly .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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] |
2017
|
Effectiveness and economic assessment of routine larviciding for prevention of chikungunya and dengue in temperate urban settings in Europe
|
Argasid ticks ( soft ticks ) are blood-feeding arthropods that can parasitize rodents , birds , humans , livestock and companion animals . Ticks of the Ornithodoros genus are known to be vectors of relapsing fever borreliosis in humans . In Algeria , little is known about relapsing fever borreliosis and other bacterial pathogens transmitted by argasid ticks . Between May 2013 and October 2015 , we investigated the presence of soft ticks in 20 rodent burrows , 10 yellow-legged gull ( Larus michahellis ) nests and animal shelters in six locations in two different bioclimatic zones in Algeria . Six species of argasid ticks were identified morphologically and through 16S rRNA gene sequencing . The presence and prevalence of Borrelia spp . , Bartonella spp . , Rickettsia spp . and Anaplasmataceae was assessed by qPCR template assays in each specimen . All qPCR-positive samples were confirmed by standard PCR , followed by sequencing the amplified fragments . Two Borrelia species were identified: Borrelia hispanica in Ornithodoros occidentalis in Mostaganem , and Borrelia cf . turicatae in Carios capensis in Algiers . One new Bartonella genotype and one new Anaplasmataceae genotype were also identified in Argas persicus . The present study highlights the presence of relapsing fever borreliosis agents , although this disease is rarely diagnosed in Algeria . Other bacteria of unknown pathogenicity detected in argasid ticks which may bite humans deserve further investigation .
Ticks are obligatory hematophagous ectoparasites that can be vectors of protozoa , viruses and bacteria during their feeding process on animal hosts . They are currently considered to be second only to mosquitoes as vectors of human infectious disease around the world [1] . Two families of ticks are of medical significance: Ixodidae ( hard ticks ) and Argasidae ( soft ticks ) . Hard ticks are the main ticks acting as vectors of human disease , but soft ticks are also known to transmit agents of human infectious diseases which are often neglected [2] . Argasid ticks comprise four genera and about 185 species , including three genera represented by a large range of species: Carios ( 87 species ) , Argas ( 57 species ) and Ornithodoros ( 36 species ) . The fourth genus ( Otobius ) is represented by three species . This classification and the taxonomy of Argasids may evolve with the use of molecular tools [3] . Argasid ticks of the genus Ornithodoros include vectors of relapsing fever caused by Borrelia spp . in humans [4] . All these tick vectors are , for the most part , geographically restricted and are considered to be specific vectors of a given Borrelia spp . As Borrelia spp . may persist for many years in their long-lived vectors , Ornithodoros spp . are considered as both vectors and de facto reservoirs [2] . Vertebrate reservoirs of tick-borne relapsing fever ( TBRF ) , Borrelia spp . include a variety of mammals , mainly rodents and insectivores , which inhabit burrows , dens and caves [5] . In Africa , TBRF Borrelia spp . include neglected vector-borne pathogens responsible for various febrile presentations and are most commonly suspected in malaria-like symptoms [6] . Tick-borne relapsing fever has been recognized as major cause of disease and death in several regions of Africa [7] . Currently , two agents of tick-borne relapsing fever have been detected in North Africa , namely Borrelia crocidurae and Borrelia hispanica [8] . An uncultured bacterium , “Borrelia merionesi , ” has also been detected in Ornithodoros ticks in Morocco [9] , and “Candidatus Borrelia algerica” has been reported in febrile patients in Algeria [10] . In Algeria , since the first human case of TBRF reported by Sergent in 1908 [11] , the disease has been largely neglected , and recent epidemiological data are lacking . The local transmission of TBRF is not known , and cases are not diagnosed . In addition to “Candidatus Borrelia algerica” which has been detected in Oran , Borrelia crocidurae has been detected in O . sonrai ( 2 . 5% prevalence ) [12] . No other bacteria are known to be associated with soft ticks in this area . In this paper , we report a series of entomological investigations that we conducted between 2012 and 2015 . We aimed to describe the distribution of soft ticks in Algeria and the prevalence of associated Borrelia , Bartonella , Rickettsia and Anaplasma .
The study protocol was approved by the Steering Committee of the Algerian Ministry of Health ( Direction Générale de la Prevention ) . Tick collection excluded national parks and protected areas and did not involve endangered or protected species ( CITES , IUCN and national guidelines ) . All tick collections which took place inside homes and on private land were conducted after receiving permission from the owner . This study was carried out between May 2012 and October 2015 in six locations in two different bioclimatic zones in Algeria . We conducted several sampling series in three northern coastal areas with a Mediterranean climate ( Mostaganem , Algiers and El Tarf ) and one in the highlands with a semi-arid climate ( M’sila ) . In Mostaganem , on the western coast of Algeria , three sites were investigated , including the port of Mostaganem ( 35°53’39” N , 0°05’25” E ) , Sidi Ali ( 36° 06’ 00” N , 0°25’00” E ) and Achaacha ( 36° 14’ 47” N , 0°38’03” E ) . In Achaacha in March 2015Achaacha , we also specifically investigated rodent burrows within a farm . The owner of the farm had been hospitalized with an unexplained fever the previous year . Argasid ticks were also collected in Algiers from the central coastal area ( the island of Agueli ) ( 36° 44’ 00” N , 3°21’00” E ) , El Tarf ( 36° 42’ 2” N , 8°18’50” E ) and M’sila in the eastern highlands of Algeria ( 35° 35’ 13” N , 4°40’08” E ) . Ticks were sampled from a variety of natural and human-impacted habitats . In this study , we prospected rodent burrows and yellow-legged gull ( Larus michahellis ) nests , and inspected other animal shelters . We introduced a flexible tube into rodent burrows and aspirated the contents using a portable , petrol-powered aspirator [12] . Ticks from seabird nests were collected after the nesting period; the nests were recovered when the chicks had left their nests . The seabird nests were collected from between the rookeries on the island of Agueli ( Algiers ) . The nests were then placed in individual bags to avoid parasite loss . Ticks were collected by sifting through the contents . Ticks were collected from slits inside animal shelters using entomological forceps . After collection , all biological materials were immediately stored in ethanol or liquid nitrogen ( one tube per positive burrow/nest/animal shelter ) and then forwarded to Marseille , France . Phylogenetic trees were drawn using the neighbor-joining method from an alignment of the different genes used in the experiments . Sequences were aligned using CLUSTALW , and phylogenetic inferences obtained using the ML phylogenetic analysis with the TOPALi 2 . 5 software ( Biomathematics and Statistics Scotland , Edinburgh , UK ) within the integrated ML application , using the K81uf + I + Г substitution model . Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis from 100 replicates to generate a majority consensus tree .
In this study , 20 rodent burrows , 10 yellow-legged gull ( Larus michahellis ) nests and three animal shelters were prospectively sampled . A total of 204 ticks were collected at six sites . The identification of ticks using entomological keys and molecular tools is reported in Table 1 . Six distinct species belonging to three genera were identified , including Carios capensis , Ornithodoros rupestris , Ornithodoros occidentalis , Ornithodoros erraticus , Ornithodoros sonrai and Argas persicus ( Fig 1 ) . In Algiers , 5/48 ( 10 . 4% ) of the C . capensis collected in Larus michahellis nests tested positive for Borrelia spp . by qPCR . Analysis of the sequenced 432 bps-long portion of the flagellin gene ( flaB ) showed 100% identity with several North American genotypes of B . turicatae ( GenBank accession numbers CP015629 , CP000049 , AY604979 etc . ) and 99 . 77% identity with another B . turicatae genotype ( AY934630 ) . The genotype identified here also showed a close identity with non-cultured Borrelia spp . , including Borrelia sp . IA-1 from Carios kelleyi ( EU492387 ) ( 98 . 84% ) , Borrelia sp . “Carios spiro-1” detected in a bat tick ( EF688579 ) ( 98 . 84% ) , Borrelia sp . “Carios spiro-2” detected in a bat tick ( EF688577 ) ( 98 . 56% ) , and Borrelia parkeri ( AY604980 ) ( 98 . 84% ) ( Fig 2 ) . The ticks collected from the farm in Achaacha which were identified as O . occidentalis were 2/6 ( 33 . 3% ) positive for Borrelia spp . by qPCR . DNA sequence analysis of the PCR products targeting the flaB gene showed 100% identity with Borrelia hispanica ( Table 1 ) . All soft ticks which were screened from the port of Mostaganem , Sidi Ali ( Douar Chtaouna ) , El Tarf and M’sila ( 34 O . rupestris , 50 A . persicus , 58 O . erraticus and 8 O . sonrai , respectively ) were negative for Borrelia spp . by qPCR . By qPCR , 31/50 ( 62% ) Argas persicus ticks collected in Sidi Ali ( Mostaganem ) tested positive for Anaplasmataceae bacteria , 3/50 ( 6% ) ticks tested positive for Rickettsia spp . , and 4/50 ( 8% ) ticks tested positive for Bartonella spp . These four ticks tested positive by standard PCR using primers targeting the ftsZ gene , and two were positive by PCR using gltA gene primers . Using DNA sequencing , we identified four ftsZ and two gltA sequences of Bartonella spp . ( Table 1 ) . The closest sequence available in GenBank was that of Bartonella elizabethae ( AF467760; 96% similarity ) from the American Type Culture Collection , Manassas VA , USA ( F9251T/ATCC . 49927 ) . A phylogenetic tree based on concatenated ftsZ ( 292 bps ) and gltA ( 200 bps ) genes showed that our genotype belongs to a cluster of Bartonella spp . including Bartonella bovis , Bartonella capreoli , Bartonella schoenbuchensis and Bartonella chomelii ( Fig 3 ) . Furthermore , standard PCR revealed that 17 samples tested positive for Anaplasmataceae DNA ( 23S rRNA gene ) . We also identified six DNA sequences of Anaplasmataceae bacteria in six Argas persicus ticks from Mostaganem ( Table 1 ) . The closest sequence available in GenBank was the sequence of Anaplasma ovis ( KM021411 . 1; 93% similarity ) , isolated from sheep in Senegal . Although one sample tested positive for Rickettsia DNA ( the gltA gene ) , the quality of the sequence obtained was too poor to be interpreted .
This investigation reports direct evidence of DNA from Borrelia spp . , Bartonella spp . , and an Anaplasmataceae bacterium in argasid ticks in Algeria . In this study , we identified Borrelia DNA that , based on analysis of a portion of the flaB gene , is identical to B . turicatae . Few studies have been performed and little molecular data exists for B . turicatae , related species and isolates [4] . Borrelia turicatae is known as a New World agent of tick-borne relapsing fever , which is prevalent throughout the southern United States and Latin America , and maintained there in enzootic cycles by a soft tick , O . turicata [19] . This tick is present throughout Mexico and Central and South America , where it colonizes peridomestic settings and inhabits burrows , nests , caves , and cavities under rocky outcrops . O . turicata are also promiscuous feeders , and recognized hosts include prairie dogs , ground squirrels , snakes , cattle , pigs , and the gopher tortoise [19] . Borrelia turicatae is genetically closely related to B . parkeri , another New World agent of tick-borne relapsing fever . However , B . parkeri differs from both B . turicatae and B . hermsii , a causative agent of tick-borne relapsing fever in the western United States , by the lack of circular plasmids in its genome [20] . Our results , reporting the infection of C . capensis collected from Algerian seabird nests with Borrelia turicatae , might be considered surprising , as they do not support the tick-spirochete species specificity that has long been highlighted by previous authors . C . capensis are typically nest-associated ticks with a strong specificity for seabirds . They are known to infest the nests of the yellow-legged gull ( Larus michahellis ) along the coasts of Algeria [21] . However , our results are supported by the analysis of a portion of the flaB gene only . Borrelial flagellin is encoded by the flaB gene , which is genus-specific and highly conservative among Borrelia spp . Interestingly , the sequences of the flagellin gene may be identical in Borrelia spp . although they have different tick hosts , different epidemiology , and profound genomic differences ( i . e . , B . recurrentis , the agent of louse-borne relapsing fever , and B . duttonii , the agent of East-African tick-borne relapsing fever ) [6 , 22] . However , due to a lack of DNA , we were unable to analyze more genes of the Borrelia spp . in this study . This is why we reference the genotype identified in this work as originating from Borrelia cf . turicatae . Moreover , several Borrelia sequences which are very closely related to Borrelia turicatae sequences were recently reported from bat ticks in the USA [23] . An almost identical bacterium was also once found in another seagull-associated soft tick , Carios sawaii in Japan [24] , as well as in African penguins [25] . Hence , it would appear that several Borrelia spp . closely related to B . turicatae are associated with seabirds and their ticks [25] . Our results offer additional evidence of this association and the consequences deserve further investigation . Carios capensis may also pose a threat to humans visiting yellow-legged gull rookeries if visitors are exposed to ticks harboring infectious agents . Borrelia hispanica is an agent of tick-borne relapsing fever in the Old World . It is known to be transmitted to humans by infected O . erraticus ticks in Spain , Portugal , and Morocco [26] . In the Iberian Peninsula , this tick is associated with swine and rodents and their surroundings , where ticks remain buried in soil or crevices . Recently , Borrelia hispanica was found in O . marocanus ( 5/43 , 11 . 6% ) and O . occidentalis ( 3/55 , 5 . 5% ) in Morocco , and O . kairouanensis ( 1/5 , 20 . 0% ) in Tunisia [5] . In this study , Borrelia hispanica was detected for the first time in Algeria in O . occidentalis ( 2/6 , 33 . 3% ) collected from a farm owned by a patient with a previously unexplained fever in Achaacha ( Mostaganem ) . Recently , O . occidentalis has been reported to belong , alongside O . marocanus and O . kairouanensis , to a complex of three large species ( female adult length 5 . 5–7 . 5 mm ) distributed in typically Mediterranean areas of Morocco , Algeria , Tunisia and Spain and previously confused with O . erraticus [12] . Interestingly , it has been reported that in northwestern Morocco , 20 . 5% of patients presenting with an unexplained fever had tick-borne relapsing fever related to Borrelia hispanica [27] . However , further epidemiological studies are recommended to screen patients with unexplained fever in Algeria . Human illnesses associated with Bartonella occur worldwide , and they encompass a broad clinical spectrum , including fever , skin lesions , endocarditis , lymphadenopathy , and abnormalities of the central nervous system , eye , liver and bone [28] . Bartonelloses are infectious diseases caused by bacteria from the genus Bartonella which infects erythrocytes and endothelial cells in humans [29] . Biting arthropod vectors transmit these pathogens and infect a wide range of wild and domestic mammals , including rodents , cats , dogs , and cattle . Throughout our investigation , we identified A . persicus Bartonella spp . in 2/50 ( 4% ) fowl ticks by sequence analysis of ftsZ and gltA . The phylogenetic tree demonstrates that our genotype belongs to the cluster of the Bartonella spp . family , including Bartonella bovis transmitted by Haemaphysalis bispinosa [30] , Bartonella capreoli transmitted by Ixodes ricinus [31] , Bartonella schoenbuchensis transmitted by Melophagus ovinus , and Lipoptena cervi for Bartonella chomelii [32] . Argas persicus , currently considered native to Turanian-Central Asia , is a parasite of arboreal nesting birds , and has successfully adapted to coexist with domestic fowl . Probably as the result of human transport , it has spread worldwide , where it survives practically exclusively in association with domestic fowl [33] . Its bite is painful to humans , often with toxic after-effects , but such episodes in humans are rare [34] . The fowl tick has been morphologically described previously in the Mediterranean basin and in Algeria [13] . It may transmit avian encephalomyelitis , leucocytozoonosis [35] , and West Nile virus [36] . A . persicus could harbor different types of bacteria , including those of the genus Salmonella , Aerobacter , Escherichia , Proteus , Staphylococcus , Flavobacterium , Bacillus , Pseudomonas , and Streptococcus [37] . Here , the association between Bartonella sp . and A . persicus is reported for the first time in Algeria . Since ticks have been collected from animals that , in some cases , could be bacteremic , the ticks may be vectors for some pathogens , but also only carriers in other cases . Consequently , we cannot consider the presence of bacteria in an arthropod as proof of vector competence . Anaplasma spp . are globally-distributed tick-transmitted bacteria of importance to veterinary and public health . These pathogens cause anaplasmosis in domestic and wild animal species , as well as in humans . Rhipicephalus , Ixodes , Dermacentor and Amblyomma genera of hard ticks include vectors of Anaplasma spp . [38] . Here , we identified a new genotype of Anaplasmataceae bacteria in 6/50 Argas persicus . In the phylogenetic tree , Anaplasma amplified from ticks collected in this study were closely related to “Candidatus Anaplasma ivoirensis” ( KT364326 ) detected in Amblyomma variegatum ticks in Cote d'Ivoire , Anaplasma ovis strain KMND ( KM021411 ) detected in sheep in Senegal [39] , and Anaplasma marginale ( TCI219 ) detected from Rhipicephalus microplus in Cote d’Ivoire ( Fig 4 ) . The pathogenicity of the Anaplasma genotype detected in our study is still unknown , although the closely related Anaplasma marginale is a globally-distributed tick-borne disease agent with great economic importance in the world’s cattle industry [40] . Interestingly , in vitro cultivation of A . marginale and A . phagocytophilum in argasid ticks cell lines has been achieved [41] . As discussed above , the detection of DNA in a tick does not mean that this tick is an efficient vector of the bacteria . There is a lack of information about Anaplasmataceae infection in argasid ticks , and our findings represent the first report of argasid tick infection with Anaplasma in Algeria . Unfortunately , due to the lack of DNA , we were unable to amplify and sequence other gene fragments in order to better characterize our Anaplasmataceae genotypes as belonging to new species . Finally , for the first time in Algeria , we detected Borrelia cf . turicatae in C . capensis , and Borrelia hispanica in O . occidentalis , in two coastal regions of Algeria . We also detected one genus of Bartonella spp . and one genus of Anaplasmataceae bacteria in A . persicus . We have expanded the understanding of the repertoire of argasid ticks with the first molecular evidence of A . persicus in Mostaganem and tick-borne bacteria present in these arthropods in Algeria . Our findings will help human and veterinary clinicians to enlarge the spectrum of pathogens to be considered in differential diagnosis . Further work is needed to map tick/borrelia distribution in relation to environmental and climatic characteristics .
|
Argasid ticks ( soft ticks ) are obligate blood-feeding arthropods that may bite humans . To increase understanding of the bacteria that are associated with these arthropods , we collected soft ticks from different sites in Algeria and used molecular tools to detect and identify bacteria in these ticks . Molecular tools revealed that some argasid ticks tested positive for agents of relapsing fever in humans , and other bacteria of unknown pathogenicity .
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2017
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Detection of relapsing fever Borrelia spp., Bartonella spp. and Anaplasmataceae bacteria in argasid ticks in Algeria
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Understanding the heterogeneous nature of dengue transmission is important for prioritizing and guiding the implementation of prevention strategies . However , passive surveillance data in endemic countries are rarely adequately informative . We analyzed data from a cluster-sample , cross-sectional seroprevalence study in 1–18 year-olds to investigate geographic differences in dengue seroprevalence and force of infection in Indonesia . We used catalytic models to estimate the force of infection in each of the 30 randomly selected sub-districts . Based on these estimates , we determined the proportion of sub-districts expected to reach seroprevalence levels of 50% , 70% and 90% by year of age . We used population averaged generalized estimating equation models to investigate individual- and cluster-level determinants of dengue seropositivity . Dengue force of infection varied substantially across Indonesia , ranging from 4 . 3% to 30 . 0% between sub-districts . By age nine , 60% of sub-districts are expected to have a seroprevalence ≥70% , rising to 83% by age 11 . Higher odds of seropositivity were associated with higher population density ( OR = 1 . 54 per 10-fold rise in population density , 95% CI: 1 . 03–2 . 32 ) and with City ( relative to Regency ) administrative status ( OR = 1 . 92 , 95% CI: 1 . 32–2 . 79 ) . Our findings highlight the substantial variation in dengue endemicity within Indonesia and the importance of understanding spatial heterogeneity in dengue transmission intensity for optimal dengue prevention strategies including future implementation of dengue vaccination programmes .
Dengue is the most widely distributed mosquito-borne viral infection; 40% of the world’s population is at risk , three-quarters of whom live in the Asia-Pacific region [1–3] . However , the burden of dengue disease remains poorly quantified in many dengue endemic countries in Asia because existing passive surveillance systems capture only a small fraction of all dengue cases , often relying on clinical diagnoses which excludes milder and atypical presentations of disease [4 , 5] . Indonesia is one of the largest countries in the dengue endemic region , with a population of 260 million , more than half of whom live in urban areas . Dengue transmission in Indonesia is hyper-endemic , with co-circulation of all four dengue serotypes . In 2013 , the Ministry of Health of Indonesia reported 112 , 511 cases of dengue ( 41 . 3 per 100 , 000 population ) and 871 deaths , corresponding to a case fatality rate of 0 . 7% [6] . Variable application of surveillance case definitions , health-seeking behaviour and lack of laboratory confirmation means that the rates of dengue infection and disease are likely to be heavily underestimated [7 , 8] . In a longitudinal study of dengue burden in high-incidence populations within five Southeast Asian countries ( Indonesia , Malaysia , Thailand , the Philippines and Vietnam ) , the rate of virologically-confirmed dengue in healthy Indonesian children aged 2–14 years was 3 . 6 cases per 100 person-years , more than 10 times that detected by national surveillance data . The sensitivity of clinical diagnosis in this research environment in Indonesia was 59% [9 , 10] . Of the five countries , the Indonesian cohort experienced the highest rate of virologically-confirmed dengue hospitalizations ( 1 . 6 hospitalizations per 100 person-years ) and dengue haemorrhagic fever ( 0 . 6 episodes per 100 person-years ) [9] . Dengue transmission can exhibit significant temporal and geographical variability even at small spatial scales , with large variations in dengue incidence sometimes observed in neighbouring administrative units [11 , 12] . Drivers of such differences in dengue transmission may be multifactorial , with climatic variables , level of urbanization , socioeconomic factors and vector ecology likely to be playing significant roles . Determining the roles of these factors in local dengue transmission can help inform decisions about where prevention and control strategies may be most needed . In September 2016 , Indonesia approved Dengvaxia ( Sanofi Pasteur ) , a live-attenuated , chimeric , tetravalent dengue vaccine . The vaccine is recommended for use in individuals who have already experienced dengue infection [13] . The World Health Organization ( WHO ) Strategic Advisory Group of Experts on Immunization ( SAGE ) previously issued guidelines for implementation of the vaccine based on local transmission intensity , recommending countries consider introducing the vaccine according to seroprevalence thresholds of approximately 70% or greater in the age group targeted for vaccination [14] . These age-related serological thresholds remain important to consider when assessing population-level impacts and the cost-effectiveness of dengue vaccination strategies [14–16] . Knowledge of sub-national dengue transmission intensity levels can be extremely valuable to policy makers when considering the geographic regions and population age-groups that may benefit most from prevention and control interventions . Only one nationwide , age-stratified dengue seroprevalence survey has been conducted in Indonesia , in the 2014 urban paediatric population , the results of which are reported by Prayitno et al . [17] . In this paper , we use the geographically-stratified design of this survey to describe the variation in dengue seroprevalence and force of infection across Indonesia , with a view to inform public health policy decisions surrounding dengue vaccination and other prevention strategies .
Ethical approval for the original survey was obtained from the Health Research Ethics Committee of Faculty of Medicine of University of Indonesia . Signed , informed consent was obtained from a parent or legal guardian of each child for participation in the study as well as from participants aged 13–18 years . In addition , signed assent was obtained from participants aged 8–12 years old . Additional approval for this secondary analysis was obtained from the Ethical Review Committee of the London School of Hygiene and Tropical Medicine . A cross-sectional seroprevalence survey , nationally-representative of the urban population , was carried out between 30th October and 27th November 2014 . The cluster sampling design was self-weighted and adapted from the WHO Expanded Program on Immunization ( EPI ) cluster survey method [18] . The survey design and characteristics of the survey population have been described in detail by Prayitno et al . [17] . Briefly , 30 urban sub-districts in Indonesia were randomly selected for the study based on probability proportional to population size . Only sub-districts with a minimum population of 1000 were included in the sampling list to ensure the desired sample size was obtained . Within each sub-district , 107 healthy children aged 1–18 years old were enrolled , stratified by age-group . The study aimed to enrol 660 children aged 1–4 years ( 22 per cluster ) , 870 aged 5–9 years ( 29 per cluster ) , 870 aged 10–14 years ( 29 per cluster ) and 810 aged 15–18 years ( 27 per cluster ) , giving a total sample size of 3 , 210 children . Children were included in the study if they were aged 1–18 years on the day of enrolment , were reported as healthy by a parent or legal guardian , and had been resident in the selected study site for more than one year . Children were excluded if they had fever ( axillary temperature ≥37 . 5°C ) at the time of recruitment or on the day of blood sampling , if any other child from the household was already included in the study , or if they were unable to report to the local puskesmas ( health centre ) for blood drawing within 7 days of the household visit . Each 2ml sample of venous blood was centrifuged , aliquoted and the serum frozen and stored at -20°C . All samples were transported to and processed at the Eijkman Institute for Molecular Biology in Jakarta , Indonesia . The dengue sero-status of each sample was determined using the commercial Panbio Dengue IgG enzyme-linked immunosorbent assay ( ELISA ) as per the manufacturer’s instructions ( Panbio units <9 is negative; 9–11 is equivocal; and >11 is positive ) [17] .
A total of 3 , 194 children were enrolled in the survey , of whom 2 , 216 ( 69% , 95% CI: 65–74% ) tested positive for dengue IgG antibodies . Seroprevalence ranged from 26 . 5% ( 95% CI: 17 . 2–38 . 4% ) amongst one year-olds to 95 . 3% ( 95% CI: 85 . 7–98 . 6% ) amongst 18 year-olds . Assuming an age-constant FOI , we estimated that 14 . 0% ( 95% CI: 11 . 8–16 . 6% ) of seronegative children in Indonesia experience their first infection each year , shown in Fig 1 . Models allowing for age-varying forces of infection did not provide a better fit to the data than the age-constant model . There was substantial variation in seroprevalence between clusters , ranging from 34 . 6% to 87 . 9% . Fig 2 shows the variation between clusters in the predicted FOI , ranging from 4 . 3% ( 95% CI: 3 . 1–6 . 0% ) per year in cluster 22 , to 30 . 0% ( 95% CI: 22 . 4–40 . 2% ) per year in cluster 30 , see S1 Table for full details . The relationship between force of infection estimates and the observed and predicted total seroprevalence amongst 1–18 year olds is shown in Fig 3 , where predicted seroprevalence is calculated assuming an age-constant force of infection and a uniform age-distribution . Fig 4 shows the percentage of sub-districts that reach a seroprevalence of 50% , 70% and 90% by year of age , based on the predicted force of infection estimates . Our models show that by age nine 87% ( 26/30 ) , 60% ( 18/30 ) and 10% ( 3/30 ) of sub-districts will have reached a seroprevalence level ≥50% , ≥70% and ≥90% , respectively . By age eleven 97% ( 29/30 ) , 83% ( 25/30 ) and 20% ( 6/30 ) have respectively reached these levels of seroprevalence . Initial assessment of correlations between independent variables indicated a strong correlation between district-level household per capita expenditure and the proportion of households with access to safe water ( Spearman's rho = 0 . 77 ) , and only the former was included in multivariable analyses . Human Development Index , immunization coverage and geographic coordinates showed no association with dengue seropositivity and were not included in multivariable analysis . In the final multivariable GEE model controlling for age , sex and the self-reported number of dengue cases in the household since the participant's birth , cluster-level variables associated with dengue seropositivity included district-level administrative status , household per capita expenditure , and sub-district level population density ( Table 1 ) . The odds of seropositivity were 92% higher among individuals residing in districts with City as opposed Regency administrative status ( OR = 1 . 92 , 95% CI: 1 . 32–2 . 80 ) , and increased by 54% on average per 10-fold increase in population density ( OR = 1 . 54 , 95% CI: 1 . 03–1 . 71 ) between the range of 241–38 , 182 inhabitants per km2 . The backward stepwise variable selection procedure yielded the same final model .
To our knowledge , this is the first detailed analysis of within-country variation in dengue seroprevalence and force of transmission . Financial and logistical challenges in conducting large-scale , geographically widespread serological studies mean that such surveys are often restricted to a very limited number of sites . Our data , from a cluster-sample survey representative of urban areas in Indonesia , provided a unique opportunity to study geographic variation in dengue transmission . Our results indicate a high dengue transmission intensity in the paediatric population of Indonesia , with 14 . 0% seroconverting per year , similar to estimates found in Colombo , Sri Lanka ( 14 . 1% ) in children aged 0–12 years [14] . The survey was not designed to estimate force of infection at sub-national level and further work , for example supplementing seroprevalence information with sub-national dengue notification data , could help to determine how representative the estimates are of different administrative levels . However , our analyses highlight the highly heterogeneous nature of dengue transmission in Indonesia , with sub-district seroprevalence ranging from 35% to 88% . These findings are relevant for informing dengue vaccination strategies , as they highlight the within-country variation in force of infection and therefore the variability in the expected proportion of seropositive individuals in any one age group targeted for vaccination . Importantly , based on the predicted FOI estimates , 60% of clusters reached a seroprevalence of ≥70% by age nine years , rising to 83% of clusters by age 11 years . We found higher population density and City status to be associated with elevated odds of dengue seropositivity . This is not surprising given the epidemiology of dengue virus , which is primarily transmitted by the mainly urban Aedes aegypti mosquito . Higher population density is likely to be an indicator of increasing urbanization and higher transmission risk . A number of limitations should be considered when interpreting our analysis . The original survey was designed to be nationally representative rather than provide estimates of seroprevalence by geographic region . Consequently , the sample size in each cluster was modest and the self-weighted survey design meant that clusters were sampled favourably from areas with higher population size . As a result , more sparsely populated areas were under-represented in our analysis . The survey was also confined to urban areas . Nevertheless , the availability of data from 30 locations presented a unique opportunity to investigate geographic variation in dengue transmission . We assumed a constant force of infection over time in our analyses . Whilst our age-varying model for the total study population showed no evidence of variation in FOI by age , past epidemics could have resulted in specific age groups having disproportionately high seroconversion rates , potentially inflating force of infection estimates , particularly when analysing cluster-specific data . However , this is unlikely to explain our observation that certain clusters had considerably lower seroprevalence and force of infection than others . The clustered , age-stratified sampling design enabled assessment of the relationship between cluster-level seroprevalence and force of infection in 1–18 year olds . Our catalytic models estimating cluster-level force of infection fit the data well and could largely reproduce the total seroprevalence in individual clusters assuming a uniform age-distribution ( Fig 3 ) . As the sampling strategy of individual clusters was approximately evenly distributed by age-group ( 21% 1–4 , 27% 5–9 , 27% 10–14 , and 25% 15–19 ) we expect these results to be largely generalizable to similarly age-stratified surveys in paediatric populations . Force of infection estimates from age-constant catalytic models are invaluable for estimating the long-term average infection rates required for assessment of vaccine suitability . In our survey , dengue seropositivity was determined using a commercial IgG ELISA . IgG seropositivity measures past exposure to at least one dengue serotype , but provides no information about exposure to individual serotypes , precluding estimation of serotype-specific forces of infection . However , force of infection estimates from IgG seroprevalence data have been shown to be comparable to the sum of serotype-specific forces of infection derived from plaque reduction neutralization tests ( PRNT ) [25] . A further complication of IgG ELISA results is the possibility of cross-reactivity with antibodies against other flaviviruses , including those raised following vaccination against Japanese encephalitis and yellow fever viruses . Previous studies in US travellers have shown high levels of false positive results when using manufacturer recommended diagnostic thresholds , suggesting a need to optimise cut-offs by validating ELISA tests against PRNT results [26] . We expect this to be much less of a problem in Indonesia , where dengue virus is the predominant circulating flavivirus and where there was no Japanese encephalitis immunization programme at the time of the survey [27 , 28] . Indeed , PRNTs in a third of IgG positive samples from our survey indicated that 98% were positive for at least one dengue serotype [29] . Our analysis of geographic determinants of seroprevalence was limited by the availability of data with adequate geographic resolution . Data on drinking water infrastructure , vector densities and weather factors were not available at the sub-district level and therefore not accounted for in this analysis . Further research to establish the role of climate and other determinants in dengue transmission across Indonesia could help to predict areas or periods of high dengue transmission intensity , in order to effectively target control measures or further characterize geographic regions that could benefit from a dengue vaccination campaign . This study highlights the value of geographically-stratified dengue seroprevalence data to understand the variation in dengue epidemiology , and inform prevention and control strategies , including dengue vaccination . Our study describes the variability in dengue endemicity which is observed between individual sub-districts within urban areas of a dengue-endemic country . Largely urbanized areas with high population densities may be where implementation of dengue control and prevention strategies would be of most value .
|
Understanding the geographic distribution of dengue transmission intensity is of key importance for guiding dengue prevention strategies , including vaccination . We analyzed age-stratified data from a cross-sectional survey of 30 randomly selected urban sub-districts in Indonesia and estimated the force of infection ( FOI ) in each . Substantial variation in FOI estimates were observed , ranging from 4% to 30% between sub-districts . Heterogeneity which will be important to understand when considering future vaccine introduction in Indonesia . Higher odds of dengue seropositivity were associated with increasing levels of urbanization , which may represent areas where more people could benefit from dengue vaccination or should otherwise be prioritized for dengue control .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"urban",
"geography",
"medicine",
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"enzyme-linked",
"immunoassays",
"immunology",
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"locations",
"spatial",
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"indonesia",
"pediatrics",
"vaccines",
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"medicine",
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"life",
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"population",
"density"
] |
2018
|
Geographic variation in dengue seroprevalence and force of infection in the urban paediatric population of Indonesia
|
Nontyphoidal Salmonellae commonly cause invasive disease in African children that is often fatal . The clinical diagnosis of these infections is hampered by the absence of a clear clinical syndrome . Drug resistance means that empirical antibiotic therapy is often ineffective and currently no vaccine is available . The study objective was to identify risk factors for mortality among children presenting to hospital with invasive Salmonella disease in Africa . We conducted a prospective study enrolling consecutive children with microbiologically-confirmed invasive Salmonella disease admitted to Queen Elizabeth Central Hospital , Blantyre , in 2006 . Data on clinical presentation , co-morbidities and outcome were used to identify children at risk of inpatient mortality through logistic-regression modeling . Over one calendar year , 263 consecutive children presented with invasive Salmonella disease . Median age was 16 months ( range 0–15 years ) and 52/256 children ( 20%; 95%CI 15–25% ) died . Nontyphoidal serovars caused 248/263 ( 94% ) of cases . 211/259 ( 81% ) of isolates were multi-drug resistant . 251/263 children presented with bacteremia , 6 with meningitis and 6 with both . Respiratory symptoms were present in 184/240 ( 77%; 95%CI 71–82% ) , 123/240 ( 51%; 95%CI 45–58% ) had gastrointestinal symptoms and 101/240 ( 42%; 95%CI 36–49% ) had an overlapping clinical syndrome . Presentation at <7 months ( OR 10 . 0; 95%CI 2 . 8–35 . 1 ) , dyspnea ( OR 4 . 2; 95%CI 1 . 5–12 . 0 ) and HIV infection ( OR 3 . 3; 95%CI 1 . 1–10 . 2 ) were independent risk factors for inpatient mortality . Invasive Salmonella disease in Malawi is characterized by high mortality and prevalence of multi-drug resistant isolates , along with non-specific presentation . Young infants , children with dyspnea and HIV-infected children bear a disproportionate burden of the Salmonella-associated mortality in Malawi . Strategies to improve prevention , diagnosis and management of invasive Salmonella disease should be targeted at these children .
Invasive bacterial disease is a major cause of mortality among African children [1–4] . Prospective studies from rural-based district hospitals in Kenya and Mozambique have found community-acquired bacteremia to be responsible for 26% [1] and 21% [3] of pediatric inpatient deaths . Pneumococcus and nontyphoidal Salmonellae ( NTS ) are the most common causes of bacteremia among African children [1–5] . While the burden of disease due to pneumococcus should fall with implementation of pneumococcal conjugate vaccines , no vaccine is available or in clinical trials against invasive nontyphoidal Salmonella ( iNTS ) disease [6 , 7] . iNTS disease among African children is caused primarily by Salmonella enterica serovars Typhimurium and Enteritidis [8–12] , and has an associated case fatality of around 20–25% for bacteremia [8 , 9 , 11 , 13 , 14] and 52% for meningitis [15] . These findings contrast with Salmonella disease in industrialized nations which typically presents as self-limiting enterocolitis , rarely requiring hospitalization [16] . Despite reports of declining iNTS disease with falling prevalence of malaria from specific African sites [17–19] , recently published data from multiple countries across sub-Saharan Africa indicate that NTS continues to be a major cause of childhood bacteremia in west [20 , 21] , central [22] , east [23 , 24] and southern Africa [25 , 26] . The Phase 3 RTS , S/AS01 malaria vaccine trial conducted in eleven sites across sub-Saharan Africa , agnostic to levels of Salmonella bacteremia , found an incidence of approximately 500 cases of Salmonella sepsis/100 , 000 children/year among children under two years [27] . Recent data from the Typhoid Surveillance in Africa Program across a further seven sub-Saharan African sites confirm that iNTS disease continues to be a major public health problem in the region [28] . The clinical presentation of iNTS disease is often a non-specific febrile illness [7–10 , 14] . Further diagnostic challenges result from the association of iNTS disease with other co-morbidities [7–10 , 14] . Among African children , iNTS disease often occurs with malaria [29–31] , HIV [26 , 31] , malnutrition [32] and anemia [33] . In adults , there is a strong association with HIV infection [26 , 34] . Increasing multi-drug resistance among iNTS isolates in Africa adds to the high attrition from iNTS disease [8 , 10–12] . For Malawian isolates , 90% have been reported as resistant to ampicillin , chloramphenicol and cotrimoxazole [35] . We set out to provide data to guide improved management of children with iNTS disease . Consecutive microbiologically-confirmed cases admitted to a large government hospital in Malawi were studied for the relationship between clinical presentation and outcome .
Queen Elizabeth Central Hospital ( QECH ) is the largest hospital in Malawi . It is situated in Blantyre , one of the two main cities in Malawi , and serves a combined urban and peri-urban rural population of around 1 million . The hospital has approximately 1000 beds and admits around 10 , 000 adults and 30 , 000 children ( <16 years ) annually . Since 1996 , the Malawi-Liverpool-Wellcome Trust Clinical Research Programme has performed blood and cerebrospinal fluid ( CSF ) cultures from patients admitted with suspected sepsis and meningitis . Eligible participants were children age ≤ 14 years admitted to the Pediatric Department of QECH with isolation of Salmonella from blood and/or CSF between January 1st and December 31st 2006 . Subjects not meeting all three eligibility criteria were excluded . In order to reduce bias due to early death following admission , children were approached for recruitment on the day that Gram-negative bacteria were first detected in the blood or CSF , either on initial Gram stain or following positive culture . First-line antimicrobial therapy for suspected sepsis in children at the time of the study was intramuscular chloramphenicol and gentamicin , or penicillin and gentamicin . The great majority of Gram-negative bacteraemias detected were due to NTS , and these bacteria were almost all resistant to chloramphenicol and penicillin . Therefore , children were started on oral ciprofloxacin and/or intravenous ceftriaxone as soon as Gram-negative bacteria were first detected . Once the organism and antibiotic sensitivity profile were known , treatment would be amended as necessary . First line antimicrobial therapy for pediatric meningitis was intravenous ceftriaxone . Demographic and clinical data collected on admission were recorded on standard forms , along with weight and height or length . Clinical progress was recorded daily through to discharge from hospital or inpatient death . Respiratory distress was defined as the presence of a tracheal tug , intercostal or subcostal recession , head-bobbing or nasal flaring . Tachypnea and tachycardia on admission were defined as rates greater than the 90th centile for age [36] . Children with an admission axillary temperature >37 . 5°C were classified as febrile . Children were considered to have gastroenteritis if diarrhea ( ≥3 loose stools per day ) or vomiting were present , and respiratory disease if there was shortness of breath or respiratory distress . Fever without focus was defined as fever in the absence of localizing clinical signs . Severe malnutrition in children 6–60 months was defined as a weight-for-height greater than 3 z-scores below the WHO median , or bilateral pedal edema ( kwashiorkor ) . In children over 60 months , severe malnutrition was defined as a body mass index ( BMI ) -for-age greater than 3 z-scores below the WHO median . Severe anemia was defined as a packed cell volume ( PCV ) <15% or a hemoglobin concentration <5 . 0 g/dl . Hypoglycemia was defined as a glucose concentration on admission <3 . 0 mmol/L . Blood and CSF cultures were performed using a standard aerobic bottle ( BacT/Alert , bioMérieux , France ) using 1–2 ml of blood and 0 . 5–1 ml of CSF . Following detection of Gram-negative bacteria , Salmonellae were confirmed by biochemical testing with API 20E kits ( bioMérieux ) and serovar was determined using agglutinating antisera ( Prolab Diagnostics ) . Antimicrobial susceptibility testing was performed by disc diffusion using ampicillin , chloramphenicol , cotrimoxazole , gentamicin , ceftriaxone and ciprofloxacin discs according to British Society of Antimicrobial Chemotherapy methods and breakpoints . The laboratory participates in the UK National External Quality Assurance Scheme . All children admitted to QECH have a malaria parasite slide and PCV performed . Full blood counts were determined using a HMX ( Becton Coulter ) . Tests for HIV infection were with Determine ( Abbot Laboratories ) and UniGold ( Trinity Biotech ) rapid tests , following national Voluntary Counseling and Testing guidelines . For discordant results and children under 18 months with positive results , the diagnosis was confirmed by polymerase chain reaction for proviral DNA . Monthly rainfall data through 2006 were collected from the meteorological station at Chileka Airport , Blantyre . Data were entered into a Microsoft Access database and analysis was performed using R ( http://www . R-project . org/ ) . Variation with age and season , of clinical indices , co-morbidity and outcome , were calculated as Mantel-Haenszel odds ratios . To facilitate this , age was converted into four ordinal categories ( 0–6 months , 7–12 months , 1–2 years , >2 years ) . Presentation season was grouped as January-March , April-June , July-September and October-December and ranked by mean rainfall . Odds ratios of case fatality given the presence of clinical indices or co-morbidity adjusted for age and season of presentation were calculated by logistic regression . Finally , a model of case fatality was constructed and tested by multivariate logistic regression . Covariates were chosen for the first fit of the model if they predicted mortality with a p value <0 . 1 along with age and sex . To minimize the number of covariate patterns , age was collapsed into a binary variable ( children 0–6 months and children 7 months and over ) for the purposes of the model . Covariates with the largest remaining p value ( but retaining age and sex ) were sequentially removed from the model and the model re-evaluated for goodness-of-fit and distribution of residuals at each step . The model was also evaluated for two-way interactions between significantly-associated terms . Missing data were addressed by providing denominator information where applicable . After ensuring that each child was on appropriate antimicrobial therapy , the study was explained to the parents or guardians and informed written consent obtained . Ethical approval for the study was granted by the College of Medicine Research and Ethics Committee , College of Medicine , University of Malawi .
Clinical features and comorbidities of children with invasive Salmonella disease are shown in Table 2 and Fig 2 . Most children presented with a history of fever and were febrile on admission . Cough was significantly more common as a presenting symptom than either diarrhea ( p<0 . 001 ) or vomiting ( p<0 . 001 ) , with signs of respiratory distress in 72/166 ( 43% ) of children at admission . Older children ( ≥7 months ) tended to present more often with fever in isolation than younger children ( <7 months; OR = 1 . 26 , 95% CI 1 . 02–2 . 20 ) . Neither the proportions of respiratory nor gastrointestinal presentations varied with age ( Fig 2E ) , and clinical presentation did not vary significantly with season of presentation ( Fig 2F ) . 45/180 ( 25% ) of patients were severely malnourished . Median PCV on admission was 25% , and 47/244 ( 19% ) of children were severely anemic . 38/249 ( 15% ) had concurrent Plasmodium falciparum parasitemia . Of children tested , 70/162 ( 43% ) were HIV-infected . Significantly more older children ( ≥7 months ) were HIV-infected than younger children ( <7 months , OR = 1 . 55; 95% CI 1 . 15–2 . 09 ) . Malaria and malnutrition did not vary significantly with age ( Fig 2C ) . Children diagnosed with iNTS disease during the months with high rainfall were less likely to be HIV-infected than those admitted in the dry season ( OR = 0 . 67; 95% CI 0 . 49–0 . 89 ) . The rates of malaria and malnutrition did not vary significantly with season ( Fig 2D ) . Most strains were resistant to ampicillin , cotrimoxazole and chloramphenicol ( multi-drug resistant; 211/259 , 81 . 5% ) . None were resistant to ciprofloxacin or ceftriaxone . Only 15/244 ( 6% ) of children received first-line antimicrobials on admission to which their Salmonella isolate was susceptible in vitro . Conversion to either ceftriaxone or ciprofloxacin occurred with a median delay of 3 days ( range 1–12 days ) . Inpatient mortality was 52/256 ( 20% ) ( Fig 1C ) , highest among infants of 0 to 6 months inclusive ( 53% ) ( Fig 2A ) , and declined with increasing age ( OR = 0 . 56 , 95%CI 0 . 42–0 . 75; age in this model was divided into four ordinal categories , children 0–6 months , 7–12 years , 13–24 months , and >24 months ) . Mortality did not vary significantly with season ( Fig 2B ) . Death occurred on the calendar day of admission in 8/52 ( 15% ) of children who died , with a median time from admission to death of 3 days ( range 0–18 days ) . Adjusting for age and season , significant risk factors for mortality were: a history of dyspnea , absence of fever , presence of respiratory distress or hypoglycemia at presentation , and HIV infection ( Table 2 ) . Cotrimoxazole preventive therapy ( CPT ) was not associated with mortality in HIV-infected children ( OR = 2 . 17 , 95%CI = 0 . 54–10 . 88; p = 0 . 30 ) . 147/204 ( 72% ) of children surviving their acute admission were followed up four to six weeks post-discharge from hospital ( median time to follow up: 43 days ) , with no reported deaths in the intervening time . Only one out of the nine children with recurrent NTS bacteremia died . A logistic regression model predicting mortality including age , sex , HIV status and a history of dyspnea was shown to be statistically significant ( likelihood ratio test χ2 = 21 . 69 , p = 0 . 0002 ) with a pseudo-R2 of 0 . 179 . The Hosmer-Lemeshow goodness-of-fit test for the model provides no evidence to reject the model ( p = 0 . 73 ) and the distribution and influence of the residuals ( S1 Fig ) demonstrate that the model is consistent with the data . In this model , age less than 7 months ( OR = 10 . 0; 95% CI 2 . 8–35 . 1 ) , HIV infection ( OR = 3 . 3 , 95% CI 1 . 1–10 . 2 ) and a history of dyspnea ( OR = 4 . 2; 95% CI 1 . 5–12 . 0 ) were significant independent risk factors for death . Inpatient survival in children with and without each of these risk factors for mortality is presented in Fig 3 .
The main limitation of the study is that these data were collected from one site during one period in time , the calendar year 2006 . New prospective studies from other sites in sub-Saharan Africa would be valuable for confirming the reproducibility of our findings across the region and their ongoing applicability to iNTS disease in Africa . In Blantyre , the epidemiology of invasive Salmonella disease changed markedly in 2011 with the start of an epidemic of typhoid fever . By 2012 S . Typhi was a more common blood culture isolate than NTS [39] , indicating the fluidity of the epidemiology of invasive Salmonella disease in one site . Additionally , iNTS disease has declined in Blantyre , accompanied by reductions in malaria , HIV ( following the successful implementation of an antiretroviral therapy program beginning in 2005 ) and acute malnutrition [31] . Nevertheless , across sub-Saharan Africa , iNTS disease remains the commonest presentation of invasive Salmonella disease and a major cause of pediatric bacteremia [2 , 8 , 27] .
This prospective study provides additional evidence that iNTS disease is common and frequently fatal among children in sub-Saharan Africa , and particularly highlights the major diagnostic and management challenges associated with iNTS disease [7] . iNTS disease does not present with a readily-identifiable clinical syndrome and current empirical antimicrobials do not provide effective treatment for Salmonella infections . Empirical antimicrobial treatment for potential Salmonella infection should be considered for children presenting with a clinical syndrome compatible with systemic infection . This is especially true for children with HIV infection and young infants , and particularly applies to presentations suggestive of community-acquired pneumonia in regions where invasive Salmonella disease is common . The development of cheap rapid diagnostic tests for iNTS disease in children should be a priority . The poor effectiveness of antimicrobials means that other approaches to treatment are required . Epidemiological studies provide strong evidence that antibodies bactericidal to iNTS in the presence of complement protect against these infections [45 , 46] . The development and use of a vaccine that induces protective antibodies in young infants may provide effective protection [6 , 7] .
|
Nontyphoidal Salmonellae are a major , yet often neglected , cause of fatal invasive disease among young African children , responsible for over 100 , 000 deaths a year . There is currently a lack of prospective studies to understand the clinical presentation and course of this disease . Diagnosis can be confusing , antibiotic resistance is an increasing problem and no vaccine is available . Studying 263 consecutive cases of invasive Salmonella disease over a one year period , we observed disparate clinical presentations , precluding the possibility of making a reliable clinical diagnosis in cases of this disease . We confirmed high mortality rates , finding that young age , dyspnea and HIV infection are independent risk factors for death . These findings support both the need for improved measures for prevention , diagnosis and management of invasive nontyphoidal Salmonella disease and indicate which children with this disease are at most need of intensive clinical care .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusions"
] |
[
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"infections",
"medicine",
"and",
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"body",
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"pathology",
"and",
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"pathogens",
"tropical",
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"microbiology",
"geographical",
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"pulmonology",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"bacterial",
"diseases",
"rna",
"viruses",
"enterobacteriaceae",
"pharmacology",
"bacteria",
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"africa",
"infectious",
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"pathogens",
"salmonella",
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"and",
"life",
"sciences",
"viral",
"diseases",
"malaria",
"lentivirus",
"organisms"
] |
2017
|
Presentation of life-threatening invasive nontyphoidal Salmonella disease in Malawian children: A prospective observational study
|
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